chore: 添加Stock-Prediction-Models项目文件
添加了Stock-Prediction-Models项目的多个文件，包括数据集、模型代码、README文档和CSS样式文件。这些文件用于股票预测模型的训练和展示，涵盖了LSTM、GRU等深度学习模型的应用。
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"sns.set()"
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" <th>Low</th>\n",
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" <th>Close</th>\n",
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" Date Open High Low Close Adj Close \\\n",
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"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
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"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
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"\n",
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" Volume \n",
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"source": [
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"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
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"df.head()"
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
" <td>913.809998</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>229</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>953.270020</td>\n",
|
||||
" <td>959.109985</td>\n",
|
||||
" <td>913.809998</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>230</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>957.789978</td>\n",
|
||||
" <td>959.109985</td>\n",
|
||||
" <td>913.809998</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>231</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>951.679993</td>\n",
|
||||
" <td>959.109985</td>\n",
|
||||
" <td>913.809998</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>232</th>\n",
|
||||
" <td>-1.0</td>\n",
|
||||
" <td>969.960022</td>\n",
|
||||
" <td>959.109985</td>\n",
|
||||
" <td>915.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>233</th>\n",
|
||||
" <td>-1.0</td>\n",
|
||||
" <td>978.890015</td>\n",
|
||||
" <td>969.960022</td>\n",
|
||||
" <td>915.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>234</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>977.000000</td>\n",
|
||||
" <td>978.890015</td>\n",
|
||||
" <td>915.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>235</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>972.599976</td>\n",
|
||||
" <td>978.890015</td>\n",
|
||||
" <td>915.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>236</th>\n",
|
||||
" <td>-1.0</td>\n",
|
||||
" <td>989.250000</td>\n",
|
||||
" <td>978.890015</td>\n",
|
||||
" <td>915.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>237</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>987.830017</td>\n",
|
||||
" <td>989.250000</td>\n",
|
||||
" <td>915.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>238</th>\n",
|
||||
" <td>-1.0</td>\n",
|
||||
" <td>989.679993</td>\n",
|
||||
" <td>989.250000</td>\n",
|
||||
" <td>915.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>239</th>\n",
|
||||
" <td>-1.0</td>\n",
|
||||
" <td>992.000000</td>\n",
|
||||
" <td>989.679993</td>\n",
|
||||
" <td>915.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>240</th>\n",
|
||||
" <td>-1.0</td>\n",
|
||||
" <td>992.179993</td>\n",
|
||||
" <td>992.000000</td>\n",
|
||||
" <td>915.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>241</th>\n",
|
||||
" <td>-1.0</td>\n",
|
||||
" <td>992.809998</td>\n",
|
||||
" <td>992.179993</td>\n",
|
||||
" <td>915.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>242</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>984.450012</td>\n",
|
||||
" <td>992.809998</td>\n",
|
||||
" <td>915.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>243</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>988.200012</td>\n",
|
||||
" <td>992.809998</td>\n",
|
||||
" <td>915.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>244</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>968.450012</td>\n",
|
||||
" <td>992.809998</td>\n",
|
||||
" <td>915.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>245</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>970.539978</td>\n",
|
||||
" <td>992.809998</td>\n",
|
||||
" <td>915.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>246</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>973.330017</td>\n",
|
||||
" <td>992.809998</td>\n",
|
||||
" <td>920.969971</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>247</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>972.559998</td>\n",
|
||||
" <td>992.809998</td>\n",
|
||||
" <td>920.969971</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>248</th>\n",
|
||||
" <td>-1.0</td>\n",
|
||||
" <td>1019.270020</td>\n",
|
||||
" <td>992.809998</td>\n",
|
||||
" <td>920.969971</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>249</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>1017.109985</td>\n",
|
||||
" <td>1019.270020</td>\n",
|
||||
" <td>920.969971</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>250</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>1016.640015</td>\n",
|
||||
" <td>1019.270020</td>\n",
|
||||
" <td>920.969971</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>251</th>\n",
|
||||
" <td>-1.0</td>\n",
|
||||
" <td>1025.500000</td>\n",
|
||||
" <td>1019.270020</td>\n",
|
||||
" <td>924.859985</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>252 rows × 4 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" signal trend RollingMax RollingMin\n",
|
||||
"0 0.0 768.700012 NaN NaN\n",
|
||||
"1 0.0 762.130005 NaN NaN\n",
|
||||
"2 0.0 762.020020 NaN NaN\n",
|
||||
"3 0.0 782.520020 NaN NaN\n",
|
||||
"4 0.0 790.510010 NaN NaN\n",
|
||||
"5 0.0 785.309998 NaN NaN\n",
|
||||
"6 0.0 762.559998 NaN NaN\n",
|
||||
"7 0.0 754.020020 NaN NaN\n",
|
||||
"8 0.0 736.080017 NaN NaN\n",
|
||||
"9 0.0 758.489990 NaN NaN\n",
|
||||
"10 0.0 764.479980 NaN NaN\n",
|
||||
"11 0.0 771.229980 NaN NaN\n",
|
||||
"12 0.0 760.539978 NaN NaN\n",
|
||||
"13 0.0 769.200012 NaN NaN\n",
|
||||
"14 0.0 768.270020 NaN NaN\n",
|
||||
"15 0.0 760.989990 NaN NaN\n",
|
||||
"16 0.0 761.679993 NaN NaN\n",
|
||||
"17 0.0 768.239990 NaN NaN\n",
|
||||
"18 0.0 770.840027 NaN NaN\n",
|
||||
"19 0.0 758.039978 NaN NaN\n",
|
||||
"20 0.0 747.919983 NaN NaN\n",
|
||||
"21 0.0 750.500000 NaN NaN\n",
|
||||
"22 0.0 762.520020 NaN NaN\n",
|
||||
"23 0.0 759.109985 NaN NaN\n",
|
||||
"24 0.0 771.190002 NaN NaN\n",
|
||||
"25 0.0 776.419983 NaN NaN\n",
|
||||
"26 0.0 789.289978 790.510010 736.080017\n",
|
||||
"27 0.0 789.270020 790.510010 736.080017\n",
|
||||
"28 -1.0 796.099976 790.510010 736.080017\n",
|
||||
"29 -1.0 797.070007 796.099976 736.080017\n",
|
||||
".. ... ... ... ...\n",
|
||||
"222 0.0 932.450012 939.330017 906.659973\n",
|
||||
"223 0.0 928.530029 939.330017 906.659973\n",
|
||||
"224 0.0 920.969971 939.330017 906.659973\n",
|
||||
"225 0.0 924.859985 939.330017 906.659973\n",
|
||||
"226 -1.0 944.489990 939.330017 906.659973\n",
|
||||
"227 -1.0 949.500000 944.489990 913.809998\n",
|
||||
"228 -1.0 959.109985 949.500000 913.809998\n",
|
||||
"229 0.0 953.270020 959.109985 913.809998\n",
|
||||
"230 0.0 957.789978 959.109985 913.809998\n",
|
||||
"231 0.0 951.679993 959.109985 913.809998\n",
|
||||
"232 -1.0 969.960022 959.109985 915.000000\n",
|
||||
"233 -1.0 978.890015 969.960022 915.000000\n",
|
||||
"234 0.0 977.000000 978.890015 915.000000\n",
|
||||
"235 0.0 972.599976 978.890015 915.000000\n",
|
||||
"236 -1.0 989.250000 978.890015 915.000000\n",
|
||||
"237 0.0 987.830017 989.250000 915.000000\n",
|
||||
"238 -1.0 989.679993 989.250000 915.000000\n",
|
||||
"239 -1.0 992.000000 989.679993 915.000000\n",
|
||||
"240 -1.0 992.179993 992.000000 915.000000\n",
|
||||
"241 -1.0 992.809998 992.179993 915.000000\n",
|
||||
"242 0.0 984.450012 992.809998 915.000000\n",
|
||||
"243 0.0 988.200012 992.809998 915.000000\n",
|
||||
"244 0.0 968.450012 992.809998 915.000000\n",
|
||||
"245 0.0 970.539978 992.809998 915.000000\n",
|
||||
"246 0.0 973.330017 992.809998 920.969971\n",
|
||||
"247 0.0 972.559998 992.809998 920.969971\n",
|
||||
"248 -1.0 1019.270020 992.809998 920.969971\n",
|
||||
"249 0.0 1017.109985 1019.270020 920.969971\n",
|
||||
"250 0.0 1016.640015 1019.270020 920.969971\n",
|
||||
"251 -1.0 1025.500000 1019.270020 924.859985\n",
|
||||
"\n",
|
||||
"[252 rows x 4 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"count = int(np.ceil(len(df) * 0.1))\n",
|
||||
"signals = pd.DataFrame(index=df.index)\n",
|
||||
"signals['signal'] = 0.0\n",
|
||||
"signals['trend'] = df['Close']\n",
|
||||
"signals['RollingMax'] = (signals.trend.shift(1).rolling(count).max())\n",
|
||||
"signals['RollingMin'] = (signals.trend.shift(1).rolling(count).min())\n",
|
||||
"signals.loc[signals['RollingMax'] < signals.trend, 'signal'] = -1\n",
|
||||
"signals.loc[signals['RollingMin'] > signals.trend, 'signal'] = 1\n",
|
||||
"signals"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def buy_stock(\n",
|
||||
" real_movement,\n",
|
||||
" signal,\n",
|
||||
" initial_money = 10000,\n",
|
||||
" max_buy = 1,\n",
|
||||
" max_sell = 1,\n",
|
||||
"):\n",
|
||||
" \"\"\"\n",
|
||||
" real_movement = actual movement in the real world\n",
|
||||
" delay = how much interval you want to delay to change our decision from buy to sell, vice versa\n",
|
||||
" initial_state = 1 is buy, 0 is sell\n",
|
||||
" initial_money = 1000, ignore what kind of currency\n",
|
||||
" max_buy = max quantity for share to buy\n",
|
||||
" max_sell = max quantity for share to sell\n",
|
||||
" \"\"\"\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" current_inventory = 0\n",
|
||||
"\n",
|
||||
" def buy(i, initial_money, current_inventory):\n",
|
||||
" shares = initial_money // real_movement[i]\n",
|
||||
" if shares < 1:\n",
|
||||
" print(\n",
|
||||
" 'day %d: total balances %f, not enough money to buy a unit price %f'\n",
|
||||
" % (i, initial_money, real_movement[i])\n",
|
||||
" )\n",
|
||||
" else:\n",
|
||||
" if shares > max_buy:\n",
|
||||
" buy_units = max_buy\n",
|
||||
" else:\n",
|
||||
" buy_units = shares\n",
|
||||
" initial_money -= buy_units * real_movement[i]\n",
|
||||
" current_inventory += buy_units\n",
|
||||
" print(\n",
|
||||
" 'day %d: buy %d units at price %f, total balance %f'\n",
|
||||
" % (i, buy_units, buy_units * real_movement[i], initial_money)\n",
|
||||
" )\n",
|
||||
" states_buy.append(0)\n",
|
||||
" return initial_money, current_inventory\n",
|
||||
"\n",
|
||||
" for i in range(real_movement.shape[0] - int(0.025 * len(df))):\n",
|
||||
" state = signal[i]\n",
|
||||
" if state == 1:\n",
|
||||
" initial_money, current_inventory = buy(\n",
|
||||
" i, initial_money, current_inventory\n",
|
||||
" )\n",
|
||||
" states_buy.append(i)\n",
|
||||
" elif state == -1:\n",
|
||||
" if current_inventory == 0:\n",
|
||||
" print('day %d: cannot sell anything, inventory 0' % (i))\n",
|
||||
" else:\n",
|
||||
" if current_inventory > max_sell:\n",
|
||||
" sell_units = max_sell\n",
|
||||
" else:\n",
|
||||
" sell_units = current_inventory\n",
|
||||
" current_inventory -= sell_units\n",
|
||||
" total_sell = sell_units * real_movement[i]\n",
|
||||
" initial_money += total_sell\n",
|
||||
" try:\n",
|
||||
" invest = (\n",
|
||||
" (real_movement[i] - real_movement[states_buy[-1]])\n",
|
||||
" / real_movement[states_buy[-1]]\n",
|
||||
" ) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell %d units at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (i, sell_units, total_sell, invest, initial_money)\n",
|
||||
" )\n",
|
||||
" states_sell.append(i)\n",
|
||||
" \n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 28: cannot sell anything, inventory 0\n",
|
||||
"day 29: cannot sell anything, inventory 0\n",
|
||||
"day 30: cannot sell anything, inventory 0\n",
|
||||
"day 44: cannot sell anything, inventory 0\n",
|
||||
"day 45: cannot sell anything, inventory 0\n",
|
||||
"day 47: cannot sell anything, inventory 0\n",
|
||||
"day 54: cannot sell anything, inventory 0\n",
|
||||
"day 55: cannot sell anything, inventory 0\n",
|
||||
"day 56: cannot sell anything, inventory 0\n",
|
||||
"day 85: cannot sell anything, inventory 0\n",
|
||||
"day 86: cannot sell anything, inventory 0\n",
|
||||
"day 87: cannot sell anything, inventory 0\n",
|
||||
"day 88: cannot sell anything, inventory 0\n",
|
||||
"day 89: cannot sell anything, inventory 0\n",
|
||||
"day 90: cannot sell anything, inventory 0\n",
|
||||
"day 91: cannot sell anything, inventory 0\n",
|
||||
"day 92: cannot sell anything, inventory 0\n",
|
||||
"day 96: buy 1 units at price 817.580017, total balance 9182.419983\n",
|
||||
"day 97: buy 1 units at price 814.429993, total balance 8367.989990\n",
|
||||
"day 117, sell 1 units at price 862.760010, investment 5.934214 %, total balance 9230.750000,\n",
|
||||
"day 118, sell 1 units at price 872.299988, investment 7.105582 %, total balance 10103.049988,\n",
|
||||
"day 120: cannot sell anything, inventory 0\n",
|
||||
"day 121: cannot sell anything, inventory 0\n",
|
||||
"day 122: cannot sell anything, inventory 0\n",
|
||||
"day 123: cannot sell anything, inventory 0\n",
|
||||
"day 124: cannot sell anything, inventory 0\n",
|
||||
"day 125: cannot sell anything, inventory 0\n",
|
||||
"day 127: cannot sell anything, inventory 0\n",
|
||||
"day 132: cannot sell anything, inventory 0\n",
|
||||
"day 133: cannot sell anything, inventory 0\n",
|
||||
"day 138: cannot sell anything, inventory 0\n",
|
||||
"day 139: cannot sell anything, inventory 0\n",
|
||||
"day 140: cannot sell anything, inventory 0\n",
|
||||
"day 141: cannot sell anything, inventory 0\n",
|
||||
"day 142: cannot sell anything, inventory 0\n",
|
||||
"day 146: cannot sell anything, inventory 0\n",
|
||||
"day 162: buy 1 units at price 927.330017, total balance 9175.719971\n",
|
||||
"day 164: buy 1 units at price 917.789978, total balance 8257.929993\n",
|
||||
"day 165: buy 1 units at price 908.729980, total balance 7349.200013\n",
|
||||
"day 166: buy 1 units at price 898.700012, total balance 6450.500001\n",
|
||||
"day 177, sell 1 units at price 970.890015, investment 8.032714 %, total balance 7421.390016,\n",
|
||||
"day 179, sell 1 units at price 972.919983, investment 8.258592 %, total balance 8394.309999,\n",
|
||||
"day 180, sell 1 units at price 980.340027, investment 9.084234 %, total balance 9374.650026,\n",
|
||||
"day 200: buy 1 units at price 906.659973, total balance 8467.990053\n",
|
||||
"day 226, sell 1 units at price 944.489990, investment 4.172459 %, total balance 9412.480043,\n",
|
||||
"day 227, sell 1 units at price 949.500000, investment 4.725038 %, total balance 10361.980043,\n",
|
||||
"day 228: cannot sell anything, inventory 0\n",
|
||||
"day 232: cannot sell anything, inventory 0\n",
|
||||
"day 233: cannot sell anything, inventory 0\n",
|
||||
"day 236: cannot sell anything, inventory 0\n",
|
||||
"day 238: cannot sell anything, inventory 0\n",
|
||||
"day 239: cannot sell anything, inventory 0\n",
|
||||
"day 240: cannot sell anything, inventory 0\n",
|
||||
"day 241: cannot sell anything, inventory 0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = buy_stock(df.Close, signals['signal'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df['Close']\n",
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
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|
||||
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|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+509
@@ -0,0 +1,509 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
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|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
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|
||||
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|
||||
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|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from collections import deque\n",
|
||||
"import random\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class Agent:\n",
|
||||
" def __init__(self, state_size, window_size, trend, skip, batch_size):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" self.action_size = 3\n",
|
||||
" self.batch_size = batch_size\n",
|
||||
" self.memory = deque(maxlen = 1000)\n",
|
||||
" self.inventory = []\n",
|
||||
"\n",
|
||||
" self.gamma = 0.95\n",
|
||||
" self.epsilon = 0.5\n",
|
||||
" self.epsilon_min = 0.01\n",
|
||||
" self.epsilon_decay = 0.999\n",
|
||||
"\n",
|
||||
" tf.reset_default_graph()\n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.X = tf.placeholder(tf.float32, [None, self.state_size])\n",
|
||||
" self.Y = tf.placeholder(tf.float32, [None, self.action_size])\n",
|
||||
" feed = tf.layers.dense(self.X, 512, activation = tf.nn.relu)\n",
|
||||
" tensor_action, tensor_validation = tf.split(feed,2,1)\n",
|
||||
" feed_action = tf.layers.dense(tensor_action, self.action_size)\n",
|
||||
" feed_validation = tf.layers.dense(tensor_validation, 1)\n",
|
||||
" self.logits = feed_validation + tf.subtract(feed_action,tf.reduce_mean(feed_action,axis=1,keep_dims=True))\n",
|
||||
" self.cost = tf.reduce_mean(tf.square(self.Y - self.logits))\n",
|
||||
" self.optimizer = tf.train.GradientDescentOptimizer(1e-5).minimize(\n",
|
||||
" self.cost\n",
|
||||
" )\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
"\n",
|
||||
" def act(self, state):\n",
|
||||
" if random.random() <= self.epsilon:\n",
|
||||
" return random.randrange(self.action_size)\n",
|
||||
" return np.argmax(\n",
|
||||
" self.sess.run(self.logits, feed_dict = {self.X: state})[0]\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array([res])\n",
|
||||
"\n",
|
||||
" def replay(self, batch_size):\n",
|
||||
" mini_batch = []\n",
|
||||
" l = len(self.memory)\n",
|
||||
" for i in range(l - batch_size, l):\n",
|
||||
" mini_batch.append(self.memory[i])\n",
|
||||
" replay_size = len(mini_batch)\n",
|
||||
" X = np.empty((replay_size, self.state_size))\n",
|
||||
" Y = np.empty((replay_size, self.action_size))\n",
|
||||
" states = np.array([a[0][0] for a in mini_batch])\n",
|
||||
" new_states = np.array([a[3][0] for a in mini_batch])\n",
|
||||
" Q = self.sess.run(self.logits, feed_dict = {self.X: states})\n",
|
||||
" Q_new = self.sess.run(self.logits, feed_dict = {self.X: new_states})\n",
|
||||
" for i in range(len(mini_batch)):\n",
|
||||
" state, action, reward, next_state, done = mini_batch[i]\n",
|
||||
" target = Q[i]\n",
|
||||
" target[action] = reward\n",
|
||||
" if not done:\n",
|
||||
" target[action] += self.gamma * np.amax(Q_new[i])\n",
|
||||
" X[i] = state\n",
|
||||
" Y[i] = target\n",
|
||||
" cost, _ = self.sess.run(\n",
|
||||
" [self.cost, self.optimizer], feed_dict = {self.X: X, self.Y: Y}\n",
|
||||
" )\n",
|
||||
" if self.epsilon > self.epsilon_min:\n",
|
||||
" self.epsilon *= self.epsilon_decay\n",
|
||||
" return cost\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self.act(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t] and t < (len(self.trend) - self.half_window):\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" state = next_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self.act(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t] and t < (len(self.trend) - self.half_window):\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" \n",
|
||||
" invest = ((starting_money - initial_money) / initial_money)\n",
|
||||
" self.memory.append((state, action, invest, \n",
|
||||
" next_state, starting_money < initial_money))\n",
|
||||
" state = next_state\n",
|
||||
" batch_size = min(self.batch_size, len(self.memory))\n",
|
||||
" cost = self.replay(batch_size)\n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING:tensorflow:From <ipython-input-3-28bed545c0f8>:30: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
|
||||
"Instructions for updating:\n",
|
||||
"keep_dims is deprecated, use keepdims instead\n",
|
||||
"epoch: 10, total rewards: 231.100222.3, cost: 0.499693, total money: 10231.100222\n",
|
||||
"epoch: 20, total rewards: 195.875063.3, cost: 0.324152, total money: 10195.875063\n",
|
||||
"epoch: 30, total rewards: 219.615054.3, cost: 0.237771, total money: 10219.615054\n",
|
||||
"epoch: 40, total rewards: 56.505131.3, cost: 0.183305, total money: 10056.505131\n",
|
||||
"epoch: 50, total rewards: 190.745120.3, cost: 0.129967, total money: 10190.745120\n",
|
||||
"epoch: 60, total rewards: 165.275088.3, cost: 0.134246, total money: 10165.275088\n",
|
||||
"epoch: 70, total rewards: 201.795107.3, cost: 0.075016, total money: 10201.795107\n",
|
||||
"epoch: 80, total rewards: 187.545045.3, cost: 0.062454, total money: 10187.545045\n",
|
||||
"epoch: 90, total rewards: 206.835023.3, cost: 0.050687, total money: 10206.835023\n",
|
||||
"epoch: 100, total rewards: 199.895082.3, cost: 0.041359, total money: 10199.895082\n",
|
||||
"epoch: 110, total rewards: 184.405092.3, cost: 0.035289, total money: 10184.405092\n",
|
||||
"epoch: 120, total rewards: 242.405092.3, cost: 0.047248, total money: 10242.405092\n",
|
||||
"epoch: 130, total rewards: 148.405032.3, cost: 0.050786, total money: 10148.405032\n",
|
||||
"epoch: 140, total rewards: 225.724978.3, cost: 0.021171, total money: 10225.724978\n",
|
||||
"epoch: 150, total rewards: 168.344972.3, cost: 0.018388, total money: 10168.344972\n",
|
||||
"epoch: 160, total rewards: 230.095034.3, cost: 0.199324, total money: 10230.095034\n",
|
||||
"epoch: 170, total rewards: 206.275026.3, cost: 0.044696, total money: 10206.275026\n",
|
||||
"epoch: 180, total rewards: 364.895023.3, cost: 0.016494, total money: 10364.895023\n",
|
||||
"epoch: 190, total rewards: 220.664980.3, cost: 0.014381, total money: 10220.664980\n",
|
||||
"epoch: 200, total rewards: 175.284975.3, cost: 0.010883, total money: 10175.284975\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"batch_size = 32\n",
|
||||
"agent = Agent(state_size = window_size, \n",
|
||||
" window_size = window_size, \n",
|
||||
" trend = close, \n",
|
||||
" skip = skip, \n",
|
||||
" batch_size = batch_size)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 7: buy 1 unit at price 754.020020, total balance 9245.979980\n",
|
||||
"day 8: buy 1 unit at price 736.080017, total balance 8509.899963\n",
|
||||
"day 9, sell 1 unit at price 758.489990, investment 0.592818 %, total balance 9268.389953,\n",
|
||||
"day 11: buy 1 unit at price 771.229980, total balance 8497.159973\n",
|
||||
"day 12, sell 1 unit at price 760.539978, investment 3.323003 %, total balance 9257.699951,\n",
|
||||
"day 14, sell 1 unit at price 768.270020, investment -0.383797 %, total balance 10025.969971,\n",
|
||||
"day 22: buy 1 unit at price 762.520020, total balance 9263.449951\n",
|
||||
"day 24, sell 1 unit at price 771.190002, investment 1.137017 %, total balance 10034.639953,\n",
|
||||
"day 28: buy 1 unit at price 796.099976, total balance 9238.539977\n",
|
||||
"day 29: buy 1 unit at price 797.070007, total balance 8441.469970\n",
|
||||
"day 31, sell 1 unit at price 790.799988, investment -0.665744 %, total balance 9232.269958,\n",
|
||||
"day 32: buy 1 unit at price 794.200012, total balance 8438.069946\n",
|
||||
"day 33: buy 1 unit at price 796.419983, total balance 7641.649963\n",
|
||||
"day 37, sell 1 unit at price 791.549988, investment -0.692539 %, total balance 8433.199951,\n",
|
||||
"day 39, sell 1 unit at price 782.789978, investment -1.436670 %, total balance 9215.989929,\n",
|
||||
"day 41, sell 1 unit at price 786.140015, investment -1.290772 %, total balance 10002.129944,\n",
|
||||
"day 48: buy 1 unit at price 806.359985, total balance 9195.769959\n",
|
||||
"day 49: buy 1 unit at price 807.880005, total balance 8387.889954\n",
|
||||
"day 50, sell 1 unit at price 804.609985, investment -0.217025 %, total balance 9192.499939,\n",
|
||||
"day 52: buy 1 unit at price 802.174988, total balance 8390.324951\n",
|
||||
"day 53, sell 1 unit at price 805.020020, investment -0.354011 %, total balance 9195.344971,\n",
|
||||
"day 56, sell 1 unit at price 835.669983, investment 4.175522 %, total balance 10031.014954,\n",
|
||||
"day 67: buy 1 unit at price 809.559998, total balance 9221.454956\n",
|
||||
"day 68: buy 1 unit at price 813.669983, total balance 8407.784973\n",
|
||||
"day 69, sell 1 unit at price 819.239990, investment 1.195710 %, total balance 9227.024963,\n",
|
||||
"day 71: buy 1 unit at price 818.979980, total balance 8408.044983\n",
|
||||
"day 72, sell 1 unit at price 824.159973, investment 1.289219 %, total balance 9232.204956,\n",
|
||||
"day 76: buy 1 unit at price 831.330017, total balance 8400.874939\n",
|
||||
"day 77, sell 1 unit at price 828.640015, investment 1.179520 %, total balance 9229.514954,\n",
|
||||
"day 78: buy 1 unit at price 829.280029, total balance 8400.234925\n",
|
||||
"day 79, sell 1 unit at price 823.210022, investment -0.976747 %, total balance 9223.444947,\n",
|
||||
"day 80: buy 1 unit at price 835.239990, total balance 8388.204957\n",
|
||||
"day 81, sell 1 unit at price 830.630005, investment 0.162789 %, total balance 9218.834962,\n",
|
||||
"day 83, sell 1 unit at price 827.780029, investment -0.893152 %, total balance 10046.614991,\n",
|
||||
"day 88: buy 1 unit at price 845.539978, total balance 9201.075013\n",
|
||||
"day 89, sell 1 unit at price 845.619995, investment 0.009463 %, total balance 10046.695008,\n",
|
||||
"day 91: buy 1 unit at price 848.780029, total balance 9197.914979\n",
|
||||
"day 92: buy 1 unit at price 852.119995, total balance 8345.794984\n",
|
||||
"day 93: buy 1 unit at price 848.400024, total balance 7497.394960\n",
|
||||
"day 96, sell 1 unit at price 817.580017, investment -3.675865 %, total balance 8314.974977,\n",
|
||||
"day 97: buy 1 unit at price 814.429993, total balance 7500.544984\n",
|
||||
"day 100, sell 1 unit at price 831.409973, investment -2.430411 %, total balance 8331.954957,\n",
|
||||
"day 101, sell 1 unit at price 831.500000, investment -1.991988 %, total balance 9163.454957,\n",
|
||||
"day 103, sell 1 unit at price 838.549988, investment 2.961580 %, total balance 10002.004945,\n",
|
||||
"day 104: buy 1 unit at price 834.570007, total balance 9167.434938\n",
|
||||
"day 105: buy 1 unit at price 831.409973, total balance 8336.024965\n",
|
||||
"day 106, sell 1 unit at price 827.880005, investment -0.801611 %, total balance 9163.904970,\n",
|
||||
"day 107: buy 1 unit at price 824.669983, total balance 8339.234987\n",
|
||||
"day 108: buy 1 unit at price 824.729980, total balance 7514.505007\n",
|
||||
"day 110: buy 1 unit at price 824.320007, total balance 6690.185000\n",
|
||||
"day 111: buy 1 unit at price 823.559998, total balance 5866.625002\n",
|
||||
"day 112, sell 1 unit at price 837.169983, investment 0.692800 %, total balance 6703.794985,\n",
|
||||
"day 113, sell 1 unit at price 836.820007, investment 1.473320 %, total balance 7540.614992,\n",
|
||||
"day 114, sell 1 unit at price 838.210022, investment 1.634479 %, total balance 8378.825014,\n",
|
||||
"day 115, sell 1 unit at price 841.650024, investment 2.102341 %, total balance 9220.475038,\n",
|
||||
"day 116, sell 1 unit at price 843.190002, investment 2.383555 %, total balance 10063.665040,\n",
|
||||
"day 122: buy 1 unit at price 912.570007, total balance 9151.095033\n",
|
||||
"day 123, sell 1 unit at price 916.440002, investment 0.424077 %, total balance 10067.535035,\n",
|
||||
"day 125: buy 1 unit at price 931.659973, total balance 9135.875062\n",
|
||||
"day 126, sell 1 unit at price 927.130005, investment -0.486225 %, total balance 10063.005067,\n",
|
||||
"day 144: buy 1 unit at price 966.950012, total balance 9096.055055\n",
|
||||
"day 145, sell 1 unit at price 975.599976, investment 0.894562 %, total balance 10071.655031,\n",
|
||||
"day 146: buy 1 unit at price 983.679993, total balance 9087.975038\n",
|
||||
"day 148: buy 1 unit at price 980.940002, total balance 8107.035036\n",
|
||||
"day 149, sell 1 unit at price 983.409973, investment -0.027450 %, total balance 9090.445009,\n",
|
||||
"day 151, sell 1 unit at price 942.900024, investment -3.877911 %, total balance 10033.345033,\n",
|
||||
"day 155: buy 1 unit at price 939.780029, total balance 9093.565004\n",
|
||||
"day 157, sell 1 unit at price 950.630005, investment 1.154523 %, total balance 10044.195009,\n",
|
||||
"day 160: buy 1 unit at price 965.590027, total balance 9078.604982\n",
|
||||
"day 161, sell 1 unit at price 952.270020, investment -1.379468 %, total balance 10030.875002,\n",
|
||||
"day 164: buy 1 unit at price 917.789978, total balance 9113.085024\n",
|
||||
"day 166, sell 1 unit at price 898.700012, investment -2.079993 %, total balance 10011.785036,\n",
|
||||
"day 168: buy 1 unit at price 906.690002, total balance 9105.095034\n",
|
||||
"day 171: buy 1 unit at price 930.090027, total balance 8175.005007\n",
|
||||
"day 172, sell 1 unit at price 943.830017, investment 4.096220 %, total balance 9118.835024,\n",
|
||||
"day 173, sell 1 unit at price 947.159973, investment 1.835300 %, total balance 10065.994997,\n",
|
||||
"day 176: buy 1 unit at price 965.400024, total balance 9100.594973\n",
|
||||
"day 179, sell 1 unit at price 972.919983, investment 0.778947 %, total balance 10073.514956,\n",
|
||||
"day 180: buy 1 unit at price 980.340027, total balance 9093.174929\n",
|
||||
"day 181, sell 1 unit at price 950.700012, investment -3.023442 %, total balance 10043.874941,\n",
|
||||
"day 194: buy 1 unit at price 914.390015, total balance 9129.484926\n",
|
||||
"day 195, sell 1 unit at price 922.669983, investment 0.905518 %, total balance 10052.154909,\n",
|
||||
"day 197: buy 1 unit at price 926.960022, total balance 9125.194887\n",
|
||||
"day 199: buy 1 unit at price 910.669983, total balance 8214.524904\n",
|
||||
"day 201, sell 1 unit at price 924.690002, investment -0.244889 %, total balance 9139.214906,\n",
|
||||
"day 202, sell 1 unit at price 927.000000, investment 1.793187 %, total balance 10066.214906,\n",
|
||||
"day 208: buy 1 unit at price 939.330017, total balance 9126.884889\n",
|
||||
"day 211, sell 1 unit at price 927.809998, investment -1.226408 %, total balance 10054.694887,\n",
|
||||
"day 214: buy 1 unit at price 929.080017, total balance 9125.614870\n",
|
||||
"day 216, sell 1 unit at price 935.090027, investment 0.646878 %, total balance 10060.704897,\n",
|
||||
"day 219: buy 1 unit at price 915.000000, total balance 9145.704897\n",
|
||||
"day 221: buy 1 unit at price 931.580017, total balance 8214.124880\n",
|
||||
"day 222, sell 1 unit at price 932.450012, investment 1.907105 %, total balance 9146.574892,\n",
|
||||
"day 223, sell 1 unit at price 928.530029, investment -0.327399 %, total balance 10075.104921,\n",
|
||||
"day 224: buy 1 unit at price 920.969971, total balance 9154.134950\n",
|
||||
"day 226: buy 1 unit at price 944.489990, total balance 8209.644960\n",
|
||||
"day 227: buy 1 unit at price 949.500000, total balance 7260.144960\n",
|
||||
"day 228, sell 1 unit at price 959.109985, investment 4.141287 %, total balance 8219.254945,\n",
|
||||
"day 229, sell 1 unit at price 953.270020, investment 0.929605 %, total balance 9172.524965,\n",
|
||||
"day 230: buy 1 unit at price 957.789978, total balance 8214.734987\n",
|
||||
"day 231: buy 1 unit at price 951.679993, total balance 7263.054994\n",
|
||||
"day 232, sell 1 unit at price 969.960022, investment 2.154821 %, total balance 8233.015016,\n",
|
||||
"day 233, sell 1 unit at price 978.890015, investment 2.202992 %, total balance 9211.905031,\n",
|
||||
"day 234, sell 1 unit at price 977.000000, investment 2.660559 %, total balance 10188.905031,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+495
@@ -0,0 +1,495 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
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|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from collections import deque\n",
|
||||
"import random\n",
|
||||
"\n",
|
||||
"class Model:\n",
|
||||
" def __init__(self, input_size, output_size, layer_size, learning_rate):\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, input_size))\n",
|
||||
" self.Y = tf.placeholder(tf.float32, (None, output_size))\n",
|
||||
" feed = tf.layers.dense(self.X, layer_size, activation = tf.nn.relu)\n",
|
||||
" tensor_action, tensor_validation = tf.split(feed,2,1)\n",
|
||||
" feed_action = tf.layers.dense(tensor_action, output_size)\n",
|
||||
" feed_validation = tf.layers.dense(tensor_validation, 1)\n",
|
||||
" self.logits = feed_validation + tf.subtract(feed_action,tf.reduce_mean(feed_action,axis=1,keep_dims=True))\n",
|
||||
" self.cost = tf.reduce_sum(tf.square(self.Y - self.logits))\n",
|
||||
" self.optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(self.cost)\n",
|
||||
" \n",
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" LEARNING_RATE = 0.003\n",
|
||||
" BATCH_SIZE = 32\n",
|
||||
" LAYER_SIZE = 500\n",
|
||||
" OUTPUT_SIZE = 3\n",
|
||||
" EPSILON = 0.5\n",
|
||||
" DECAY_RATE = 0.005\n",
|
||||
" MIN_EPSILON = 0.1\n",
|
||||
" GAMMA = 0.99\n",
|
||||
" MEMORIES = deque()\n",
|
||||
" COPY = 1000\n",
|
||||
" T_COPY = 0\n",
|
||||
" MEMORY_SIZE = 300\n",
|
||||
" \n",
|
||||
" def __init__(self, state_size, window_size, trend, skip):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" tf.reset_default_graph()\n",
|
||||
" self.model = Model(self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE, self.LEARNING_RATE)\n",
|
||||
" self.model_negative = Model(self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE, self.LEARNING_RATE)\n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
" self.trainable = tf.trainable_variables()\n",
|
||||
" \n",
|
||||
" def _assign(self):\n",
|
||||
" for i in range(len(self.trainable)//2):\n",
|
||||
" assign_op = self.trainable[i+len(self.trainable)//2].assign(self.trainable[i])\n",
|
||||
" self.sess.run(assign_op)\n",
|
||||
"\n",
|
||||
" def _memorize(self, state, action, reward, new_state, done):\n",
|
||||
" self.MEMORIES.append((state, action, reward, new_state, done))\n",
|
||||
" if len(self.MEMORIES) > self.MEMORY_SIZE:\n",
|
||||
" self.MEMORIES.popleft()\n",
|
||||
"\n",
|
||||
" def _select_action(self, state):\n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action = self.get_predicted_action([state])\n",
|
||||
" return action\n",
|
||||
"\n",
|
||||
" def _construct_memories(self, replay):\n",
|
||||
" states = np.array([a[0] for a in replay])\n",
|
||||
" new_states = np.array([a[3] for a in replay])\n",
|
||||
" Q = self.predict(states)\n",
|
||||
" Q_new = self.predict(new_states)\n",
|
||||
" Q_new_negative = self.sess.run(self.model_negative.logits, feed_dict={self.model_negative.X:new_states})\n",
|
||||
" replay_size = len(replay)\n",
|
||||
" X = np.empty((replay_size, self.state_size))\n",
|
||||
" Y = np.empty((replay_size, self.OUTPUT_SIZE))\n",
|
||||
" for i in range(replay_size):\n",
|
||||
" state_r, action_r, reward_r, new_state_r, done_r = replay[i]\n",
|
||||
" target = Q[i]\n",
|
||||
" target[action_r] = reward_r\n",
|
||||
" if not done_r:\n",
|
||||
" target[action_r] += self.GAMMA * Q_new_negative[i, np.argmax(Q_new[i])]\n",
|
||||
" X[i] = state_r\n",
|
||||
" Y[i] = target\n",
|
||||
" return X, Y\n",
|
||||
"\n",
|
||||
" def predict(self, inputs):\n",
|
||||
" return self.sess.run(self.model.logits, feed_dict={self.model.X:inputs})\n",
|
||||
" \n",
|
||||
" def get_predicted_action(self, sequence):\n",
|
||||
" prediction = self.predict(np.array(sequence))[0]\n",
|
||||
" return np.argmax(prediction)\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array(res)\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self._select_action(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" state = next_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" if (self.T_COPY + 1) % self.COPY == 0:\n",
|
||||
" self._assign()\n",
|
||||
" \n",
|
||||
" action = self._select_action(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" \n",
|
||||
" invest = ((starting_money - initial_money) / initial_money)\n",
|
||||
" \n",
|
||||
" self._memorize(state, action, invest, next_state, starting_money < initial_money)\n",
|
||||
" batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)\n",
|
||||
" state = next_state\n",
|
||||
" replay = random.sample(self.MEMORIES, batch_size)\n",
|
||||
" X, Y = self._construct_memories(replay)\n",
|
||||
" \n",
|
||||
" cost, _ = self.sess.run([self.model.cost, self.model.optimizer], \n",
|
||||
" feed_dict={self.model.X: X, self.model.Y:Y})\n",
|
||||
" self.T_COPY += 1\n",
|
||||
" self.EPSILON = self.MIN_EPSILON + (1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)\n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING:tensorflow:From <ipython-input-3-42f2d1e26a9d>:12: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
|
||||
"Instructions for updating:\n",
|
||||
"keep_dims is deprecated, use keepdims instead\n",
|
||||
"epoch: 10, total rewards: 1486.684997.3, cost: 0.694152, total money: 10514.124999\n",
|
||||
"epoch: 20, total rewards: 313.279660.3, cost: 0.878157, total money: 8354.909665\n",
|
||||
"epoch: 30, total rewards: 752.595089.3, cost: 0.320037, total money: 10752.595089\n",
|
||||
"epoch: 40, total rewards: 1159.299987.3, cost: 0.318166, total money: 10186.739989\n",
|
||||
"epoch: 50, total rewards: 993.220279.3, cost: 0.391151, total money: 4149.310245\n",
|
||||
"epoch: 60, total rewards: 1616.499880.3, cost: 0.307440, total money: 9630.939883\n",
|
||||
"epoch: 70, total rewards: 941.484560.3, cost: 0.332979, total money: 6969.054506\n",
|
||||
"epoch: 80, total rewards: 904.899903.3, cost: 0.718111, total money: 1132.559876\n",
|
||||
"epoch: 90, total rewards: 346.619873.3, cost: 0.482044, total money: 542.599852\n",
|
||||
"epoch: 100, total rewards: 141.554626.3, cost: 0.238426, total money: 6115.974608\n",
|
||||
"epoch: 110, total rewards: -159.529845.3, cost: 0.202412, total money: 8852.270143\n",
|
||||
"epoch: 120, total rewards: -37.579779.3, cost: 0.433529, total money: 8945.780206\n",
|
||||
"epoch: 130, total rewards: 1049.544800.3, cost: 0.408910, total money: 8099.664795\n",
|
||||
"epoch: 140, total rewards: 59.114809.3, cost: 0.028664, total money: 7098.904848\n",
|
||||
"epoch: 150, total rewards: 96.424866.3, cost: 0.070552, total money: 9079.784851\n",
|
||||
"epoch: 160, total rewards: 74.179754.3, cost: 0.044092, total money: 10074.179754\n",
|
||||
"epoch: 170, total rewards: 80.999883.3, cost: 0.018813, total money: 8047.249883\n",
|
||||
"epoch: 180, total rewards: 62.700011.3, cost: 0.083292, total money: 10062.700011\n",
|
||||
"epoch: 190, total rewards: 70.424991.3, cost: 0.013884, total money: 9053.315006\n",
|
||||
"epoch: 200, total rewards: 10.620115.3, cost: 0.030838, total money: 10010.620115\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"batch_size = 32\n",
|
||||
"agent = Agent(state_size = window_size, \n",
|
||||
" window_size = window_size, \n",
|
||||
" trend = close, \n",
|
||||
" skip = skip)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 1: buy 1 unit at price 762.130005, total balance 9237.869995\n",
|
||||
"day 2, sell 1 unit at price 762.020020, investment -0.014431 %, total balance 9999.890015,\n",
|
||||
"day 11: buy 1 unit at price 771.229980, total balance 9228.660035\n",
|
||||
"day 12: buy 1 unit at price 760.539978, total balance 8468.120057\n",
|
||||
"day 13, sell 1 unit at price 769.200012, investment -0.263212 %, total balance 9237.320069,\n",
|
||||
"day 15, sell 1 unit at price 760.989990, investment 0.059170 %, total balance 9998.310059,\n",
|
||||
"day 34: buy 1 unit at price 794.559998, total balance 9203.750061\n",
|
||||
"day 35, sell 1 unit at price 791.260010, investment -0.415323 %, total balance 9995.010071,\n",
|
||||
"day 36: buy 1 unit at price 789.909973, total balance 9205.100098\n",
|
||||
"day 37, sell 1 unit at price 791.549988, investment 0.207620 %, total balance 9996.650086,\n",
|
||||
"day 38: buy 1 unit at price 785.049988, total balance 9211.600098\n",
|
||||
"day 40, sell 1 unit at price 771.820007, investment -1.685241 %, total balance 9983.420105,\n",
|
||||
"day 54: buy 1 unit at price 819.309998, total balance 9164.110107\n",
|
||||
"day 55, sell 1 unit at price 823.869995, investment 0.556566 %, total balance 9987.980102,\n",
|
||||
"day 62: buy 1 unit at price 798.530029, total balance 9189.450073\n",
|
||||
"day 64, sell 1 unit at price 801.340027, investment 0.351896 %, total balance 9990.790100,\n",
|
||||
"day 68: buy 1 unit at price 813.669983, total balance 9177.120117\n",
|
||||
"day 69, sell 1 unit at price 819.239990, investment 0.684554 %, total balance 9996.360107,\n",
|
||||
"day 72: buy 1 unit at price 824.159973, total balance 9172.200134\n",
|
||||
"day 73, sell 1 unit at price 828.070007, investment 0.474427 %, total balance 10000.270141,\n",
|
||||
"day 74: buy 1 unit at price 831.659973, total balance 9168.610168\n",
|
||||
"day 75, sell 1 unit at price 830.760010, investment -0.108213 %, total balance 9999.370178,\n",
|
||||
"day 79: buy 1 unit at price 823.210022, total balance 9176.160156\n",
|
||||
"day 80, sell 1 unit at price 835.239990, investment 1.461349 %, total balance 10011.400146,\n",
|
||||
"day 90: buy 1 unit at price 847.200012, total balance 9164.200134\n",
|
||||
"day 91, sell 1 unit at price 848.780029, investment 0.186499 %, total balance 10012.980163,\n",
|
||||
"day 93: buy 1 unit at price 848.400024, total balance 9164.580139\n",
|
||||
"day 94: buy 1 unit at price 830.460022, total balance 8334.120117\n",
|
||||
"day 95, sell 1 unit at price 829.590027, investment -2.217114 %, total balance 9163.710144,\n",
|
||||
"day 96, sell 1 unit at price 817.580017, investment -1.550948 %, total balance 9981.290161,\n",
|
||||
"day 100: buy 1 unit at price 831.409973, total balance 9149.880188\n",
|
||||
"day 101, sell 1 unit at price 831.500000, investment 0.010828 %, total balance 9981.380188,\n",
|
||||
"day 104: buy 1 unit at price 834.570007, total balance 9146.810181\n",
|
||||
"day 106: buy 1 unit at price 827.880005, total balance 8318.930176\n",
|
||||
"day 107, sell 1 unit at price 824.669983, investment -1.186242 %, total balance 9143.600159,\n",
|
||||
"day 108, sell 1 unit at price 824.729980, investment -0.380493 %, total balance 9968.330139,\n",
|
||||
"day 110: buy 1 unit at price 824.320007, total balance 9144.010132\n",
|
||||
"day 111, sell 1 unit at price 823.559998, investment -0.092198 %, total balance 9967.570130,\n",
|
||||
"day 115: buy 1 unit at price 841.650024, total balance 9125.920106\n",
|
||||
"day 116, sell 1 unit at price 843.190002, investment 0.182971 %, total balance 9969.110108,\n",
|
||||
"day 125: buy 1 unit at price 931.659973, total balance 9037.450135\n",
|
||||
"day 126, sell 1 unit at price 927.130005, investment -0.486225 %, total balance 9964.580140,\n",
|
||||
"day 127: buy 1 unit at price 934.299988, total balance 9030.280152\n",
|
||||
"day 128, sell 1 unit at price 932.169983, investment -0.227979 %, total balance 9962.450135,\n",
|
||||
"day 141: buy 1 unit at price 971.469971, total balance 8990.980164\n",
|
||||
"day 142, sell 1 unit at price 975.880005, investment 0.453955 %, total balance 9966.860169,\n",
|
||||
"day 152: buy 1 unit at price 953.400024, total balance 9013.460145\n",
|
||||
"day 153, sell 1 unit at price 950.760010, investment -0.276905 %, total balance 9964.220155,\n",
|
||||
"day 156: buy 1 unit at price 957.369995, total balance 9006.850160\n",
|
||||
"day 157, sell 1 unit at price 950.630005, investment -0.704011 %, total balance 9957.480165,\n",
|
||||
"day 166: buy 1 unit at price 898.700012, total balance 9058.780153\n",
|
||||
"day 167, sell 1 unit at price 911.710022, investment 1.447648 %, total balance 9970.490175,\n",
|
||||
"day 172: buy 1 unit at price 943.830017, total balance 9026.660158\n",
|
||||
"day 173, sell 1 unit at price 947.159973, investment 0.352813 %, total balance 9973.820131,\n",
|
||||
"day 185: buy 1 unit at price 930.500000, total balance 9043.320131\n",
|
||||
"day 186: buy 1 unit at price 930.830017, total balance 8112.490114\n",
|
||||
"day 187, sell 1 unit at price 930.390015, investment -0.011820 %, total balance 9042.880129,\n",
|
||||
"day 188, sell 1 unit at price 923.650024, investment -0.771354 %, total balance 9966.530153,\n",
|
||||
"day 193: buy 1 unit at price 907.239990, total balance 9059.290163\n",
|
||||
"day 194, sell 1 unit at price 914.390015, investment 0.788107 %, total balance 9973.680178,\n",
|
||||
"day 197: buy 1 unit at price 926.960022, total balance 9046.720156\n",
|
||||
"day 199, sell 1 unit at price 910.669983, investment -1.757362 %, total balance 9957.390139,\n",
|
||||
"day 211: buy 1 unit at price 927.809998, total balance 9029.580141\n",
|
||||
"day 212, sell 1 unit at price 935.950012, investment 0.877336 %, total balance 9965.530153,\n",
|
||||
"day 213: buy 1 unit at price 926.500000, total balance 9039.030153\n",
|
||||
"day 214, sell 1 unit at price 929.080017, investment 0.278469 %, total balance 9968.110170,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+448
@@ -0,0 +1,448 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from collections import deque\n",
|
||||
"import random\n",
|
||||
" \n",
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" LEARNING_RATE = 0.003\n",
|
||||
" BATCH_SIZE = 32\n",
|
||||
" LAYER_SIZE = 256\n",
|
||||
" OUTPUT_SIZE = 3\n",
|
||||
" EPSILON = 0.5\n",
|
||||
" DECAY_RATE = 0.005\n",
|
||||
" MIN_EPSILON = 0.1\n",
|
||||
" GAMMA = 0.99\n",
|
||||
" MEMORIES = deque()\n",
|
||||
" MEMORY_SIZE = 300\n",
|
||||
" \n",
|
||||
" def __init__(self, state_size, window_size, trend, skip):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" tf.reset_default_graph()\n",
|
||||
" self.INITIAL_FEATURES = np.zeros((4, self.state_size))\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, None, self.state_size))\n",
|
||||
" self.Y = tf.placeholder(tf.float32, (None, self.OUTPUT_SIZE))\n",
|
||||
" cell = tf.nn.rnn_cell.LSTMCell(self.LAYER_SIZE, state_is_tuple = False)\n",
|
||||
" self.hidden_layer = tf.placeholder(tf.float32, (None, 2 * self.LAYER_SIZE))\n",
|
||||
" self.rnn,self.last_state = tf.nn.dynamic_rnn(inputs=self.X,cell=cell,\n",
|
||||
" dtype=tf.float32,\n",
|
||||
" initial_state=self.hidden_layer)\n",
|
||||
" tensor_action, tensor_validation = tf.split(self.rnn[:,-1],2,1)\n",
|
||||
" feed_action = tf.layers.dense(tensor_action, self.OUTPUT_SIZE)\n",
|
||||
" feed_validation = tf.layers.dense(tensor_validation, 1)\n",
|
||||
" self.logits = feed_validation + tf.subtract(feed_action,tf.reduce_mean(feed_action,axis=1,keep_dims=True))\n",
|
||||
" self.cost = tf.reduce_sum(tf.square(self.Y - self.logits))\n",
|
||||
" self.optimizer = tf.train.AdamOptimizer(learning_rate = self.LEARNING_RATE).minimize(self.cost)\n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
" \n",
|
||||
" def _memorize(self, state, action, reward, new_state, dead, rnn_state):\n",
|
||||
" self.MEMORIES.append((state, action, reward, new_state, dead, rnn_state))\n",
|
||||
" if len(self.MEMORIES) > self.MEMORY_SIZE:\n",
|
||||
" self.MEMORIES.popleft()\n",
|
||||
"\n",
|
||||
" def _construct_memories(self, replay):\n",
|
||||
" states = np.array([a[0] for a in replay])\n",
|
||||
" new_states = np.array([a[3] for a in replay])\n",
|
||||
" init_values = np.array([a[-1] for a in replay])\n",
|
||||
" Q = self.sess.run(self.logits, feed_dict={self.X:states, self.hidden_layer:init_values})\n",
|
||||
" Q_new = self.sess.run(self.logits, feed_dict={self.X:new_states, self.hidden_layer:init_values})\n",
|
||||
" replay_size = len(replay)\n",
|
||||
" X = np.empty((replay_size, 4, self.state_size))\n",
|
||||
" Y = np.empty((replay_size, self.OUTPUT_SIZE))\n",
|
||||
" INIT_VAL = np.empty((replay_size, 2 * self.LAYER_SIZE))\n",
|
||||
" for i in range(replay_size):\n",
|
||||
" state_r, action_r, reward_r, new_state_r, dead_r, rnn_memory = replay[i]\n",
|
||||
" target = Q[i]\n",
|
||||
" target[action_r] = reward_r\n",
|
||||
" if not dead_r:\n",
|
||||
" target[action_r] += self.GAMMA * np.amax(Q_new[i])\n",
|
||||
" X[i] = state_r\n",
|
||||
" Y[i] = target\n",
|
||||
" INIT_VAL[i] = rnn_memory\n",
|
||||
" return X, Y, INIT_VAL\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array(res)\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" init_value = np.zeros((1, 2 * self.LAYER_SIZE))\n",
|
||||
" for k in range(self.INITIAL_FEATURES.shape[0]):\n",
|
||||
" self.INITIAL_FEATURES[k,:] = state\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action, last_state = self.sess.run([self.logits,self.last_state],\n",
|
||||
" feed_dict={self.X:[self.INITIAL_FEATURES],\n",
|
||||
" self.hidden_layer:init_value})\n",
|
||||
" action, init_value = np.argmax(action[0]), last_state\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" new_state = np.append([self.get_state(t + 1)], self.INITIAL_FEATURES[:3, :], axis = 0)\n",
|
||||
" self.INITIAL_FEATURES = new_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" init_value = np.zeros((1, 2 * self.LAYER_SIZE))\n",
|
||||
" for k in range(self.INITIAL_FEATURES.shape[0]):\n",
|
||||
" self.INITIAL_FEATURES[k,:] = state\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" \n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action, last_state = self.sess.run([self.logits,\n",
|
||||
" self.last_state],\n",
|
||||
" feed_dict={self.X:[self.INITIAL_FEATURES],\n",
|
||||
" self.hidden_layer:init_value})\n",
|
||||
" action, init_value = np.argmax(action[0]), last_state\n",
|
||||
" \n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" \n",
|
||||
" invest = ((starting_money - initial_money) / initial_money)\n",
|
||||
" new_state = np.append([self.get_state(t + 1)], self.INITIAL_FEATURES[:3, :], axis = 0)\n",
|
||||
" self._memorize(self.INITIAL_FEATURES, action, invest, new_state, \n",
|
||||
" starting_money < initial_money, init_value[0])\n",
|
||||
" self.INITIAL_FEATURES = new_state\n",
|
||||
" batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)\n",
|
||||
" replay = random.sample(self.MEMORIES, batch_size)\n",
|
||||
" X, Y, INIT_VAL = self._construct_memories(replay)\n",
|
||||
" \n",
|
||||
" cost, _ = self.sess.run([self.cost, self.optimizer], \n",
|
||||
" feed_dict={self.X: X, self.Y:Y,\n",
|
||||
" self.hidden_layer: INIT_VAL})\n",
|
||||
" self.EPSILON = self.MIN_EPSILON + (1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)\n",
|
||||
" \n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f2873435940>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"WARNING:tensorflow:From <ipython-input-3-976c717fc00c>:35: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
|
||||
"Instructions for updating:\n",
|
||||
"keep_dims is deprecated, use keepdims instead\n",
|
||||
"epoch: 10, total rewards: 1303.755127.3, cost: 0.204159, total money: 2622.175109\n",
|
||||
"epoch: 20, total rewards: 1332.510133.3, cost: 2.512769, total money: 11332.510133\n",
|
||||
"epoch: 30, total rewards: 167.034789.3, cost: 0.204751, total money: 10167.034789\n",
|
||||
"epoch: 40, total rewards: 885.269897.3, cost: 0.095390, total money: 8848.889892\n",
|
||||
"epoch: 50, total rewards: 312.624996.3, cost: 0.415782, total money: 10312.624996\n",
|
||||
"epoch: 60, total rewards: 220.209960.3, cost: 0.119438, total money: 10220.209960\n",
|
||||
"epoch: 70, total rewards: 407.794859.3, cost: 0.983801, total money: 8417.984861\n",
|
||||
"epoch: 80, total rewards: 200.149718.3, cost: 0.235913, total money: 9226.819701\n",
|
||||
"epoch: 90, total rewards: 87.564821.3, cost: 0.034903, total money: 8097.894838\n",
|
||||
"epoch: 100, total rewards: 1056.600041.3, cost: 0.286240, total money: 11056.600041\n",
|
||||
"epoch: 110, total rewards: 537.204957.3, cost: 0.140037, total money: 7610.014955\n",
|
||||
"epoch: 120, total rewards: 263.944828.3, cost: 0.535866, total money: 9247.304813\n",
|
||||
"epoch: 130, total rewards: 387.030092.3, cost: 0.352989, total money: 8396.590090\n",
|
||||
"epoch: 140, total rewards: 207.069887.3, cost: 0.474047, total money: 10207.069887\n",
|
||||
"epoch: 150, total rewards: -119.230104.3, cost: 0.301262, total money: 9880.769896\n",
|
||||
"epoch: 160, total rewards: 21.299804.3, cost: 0.709494, total money: 10021.299804\n",
|
||||
"epoch: 170, total rewards: 241.145077.3, cost: 0.486697, total money: 10241.145077\n",
|
||||
"epoch: 180, total rewards: 5.329770.3, cost: 0.447255, total money: 7042.329770\n",
|
||||
"epoch: 190, total rewards: 126.395198.3, cost: 0.240739, total money: 9107.125178\n",
|
||||
"epoch: 200, total rewards: 91.499876.3, cost: 0.259028, total money: 8055.119871\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"batch_size = 32\n",
|
||||
"agent = Agent(state_size = window_size, \n",
|
||||
" window_size = window_size, \n",
|
||||
" trend = close, \n",
|
||||
" skip = skip)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 53: buy 1 unit at price 805.020020, total balance 9194.979980\n",
|
||||
"day 54, sell 1 unit at price 819.309998, investment 1.775108 %, total balance 10014.289978,\n",
|
||||
"day 64: buy 1 unit at price 801.340027, total balance 9212.949951\n",
|
||||
"day 68, sell 1 unit at price 813.669983, investment 1.538667 %, total balance 10026.619934,\n",
|
||||
"day 79: buy 1 unit at price 823.210022, total balance 9203.409912\n",
|
||||
"day 82: buy 1 unit at price 829.080017, total balance 8374.329895\n",
|
||||
"day 84, sell 1 unit at price 831.909973, investment 1.056832 %, total balance 9206.239868,\n",
|
||||
"day 86, sell 1 unit at price 838.679993, investment 1.157907 %, total balance 10044.919861,\n",
|
||||
"day 111: buy 1 unit at price 823.559998, total balance 9221.359863\n",
|
||||
"day 116, sell 1 unit at price 843.190002, investment 2.383555 %, total balance 10064.549865,\n",
|
||||
"day 167: buy 1 unit at price 911.710022, total balance 9152.839843\n",
|
||||
"day 169, sell 1 unit at price 918.590027, investment 0.754626 %, total balance 10071.429870,\n",
|
||||
"day 182: buy 1 unit at price 947.799988, total balance 9123.629882\n",
|
||||
"day 183, sell 1 unit at price 934.090027, investment -1.446504 %, total balance 10057.719909,\n",
|
||||
"day 185: buy 1 unit at price 930.500000, total balance 9127.219909\n",
|
||||
"day 187: buy 1 unit at price 930.390015, total balance 8196.829894\n",
|
||||
"day 188, sell 1 unit at price 923.650024, investment -0.736161 %, total balance 9120.479918,\n",
|
||||
"day 190, sell 1 unit at price 929.359985, investment -0.110709 %, total balance 10049.839903,\n",
|
||||
"day 206: buy 1 unit at price 921.289978, total balance 9128.549925\n",
|
||||
"day 207, sell 1 unit at price 929.570007, investment 0.898743 %, total balance 10058.119932,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+593
@@ -0,0 +1,593 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from collections import deque\n",
|
||||
"import random\n",
|
||||
"\n",
|
||||
"class Model:\n",
|
||||
" def __init__(self, input_size, output_size, layer_size, learning_rate, name):\n",
|
||||
" with tf.variable_scope(name):\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, None, input_size))\n",
|
||||
" self.Y = tf.placeholder(tf.float32, (None, output_size))\n",
|
||||
" cell = tf.nn.rnn_cell.LSTMCell(layer_size, state_is_tuple = False)\n",
|
||||
" self.hidden_layer = tf.placeholder(tf.float32, (None, 2 * layer_size))\n",
|
||||
" self.rnn,self.last_state = tf.nn.dynamic_rnn(inputs=self.X,cell=cell,\n",
|
||||
" dtype=tf.float32,\n",
|
||||
" initial_state=self.hidden_layer)\n",
|
||||
" tensor_action, tensor_validation = tf.split(self.rnn[:,-1],2,1)\n",
|
||||
" feed_action = tf.layers.dense(tensor_action, output_size)\n",
|
||||
" feed_validation = tf.layers.dense(tensor_validation, 1)\n",
|
||||
" self.logits = feed_validation + tf.subtract(feed_action,tf.reduce_mean(feed_action,axis=1,keep_dims=True))\n",
|
||||
" self.cost = tf.reduce_sum(tf.square(self.Y - self.logits))\n",
|
||||
" self.optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(self.cost)\n",
|
||||
" \n",
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" LEARNING_RATE = 0.003\n",
|
||||
" BATCH_SIZE = 32\n",
|
||||
" LAYER_SIZE = 256\n",
|
||||
" OUTPUT_SIZE = 3\n",
|
||||
" EPSILON = 0.5\n",
|
||||
" DECAY_RATE = 0.005\n",
|
||||
" MIN_EPSILON = 0.1\n",
|
||||
" GAMMA = 0.99\n",
|
||||
" MEMORIES = deque()\n",
|
||||
" COPY = 1000\n",
|
||||
" T_COPY = 0\n",
|
||||
" MEMORY_SIZE = 300\n",
|
||||
" \n",
|
||||
" def __init__(self, state_size, window_size, trend, skip):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" tf.reset_default_graph()\n",
|
||||
" self.INITIAL_FEATURES = np.zeros((4, self.state_size))\n",
|
||||
" self.model = Model(self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE, self.LEARNING_RATE,\n",
|
||||
" 'real_model')\n",
|
||||
" self.model_negative = Model(self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE, self.LEARNING_RATE,\n",
|
||||
" 'negative_model')\n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
" self.trainable = tf.trainable_variables()\n",
|
||||
" \n",
|
||||
" def _assign(self, from_name, to_name):\n",
|
||||
" from_w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=from_name)\n",
|
||||
" to_w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=to_name)\n",
|
||||
" for i in range(len(from_w)):\n",
|
||||
" assign_op = to_w[i].assign(from_w[i])\n",
|
||||
" self.sess.run(assign_op)\n",
|
||||
"\n",
|
||||
" def _memorize(self, state, action, reward, new_state, dead, rnn_state):\n",
|
||||
" self.MEMORIES.append((state, action, reward, new_state, dead, rnn_state))\n",
|
||||
" if len(self.MEMORIES) > self.MEMORY_SIZE:\n",
|
||||
" self.MEMORIES.popleft()\n",
|
||||
"\n",
|
||||
" def _select_action(self, state):\n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action = self.get_predicted_action([state])\n",
|
||||
" return action\n",
|
||||
"\n",
|
||||
" def _construct_memories(self, replay):\n",
|
||||
" states = np.array([a[0] for a in replay])\n",
|
||||
" new_states = np.array([a[3] for a in replay])\n",
|
||||
" init_values = np.array([a[-1] for a in replay])\n",
|
||||
" Q = self.sess.run(self.model.logits, feed_dict={self.model.X:states, \n",
|
||||
" self.model.hidden_layer:init_values})\n",
|
||||
" Q_new = self.sess.run(self.model.logits, feed_dict={self.model.X:new_states, \n",
|
||||
" self.model.hidden_layer:init_values})\n",
|
||||
" Q_new_negative = self.sess.run(self.model_negative.logits, \n",
|
||||
" feed_dict={self.model_negative.X:new_states, \n",
|
||||
" self.model_negative.hidden_layer:init_values})\n",
|
||||
" replay_size = len(replay)\n",
|
||||
" X = np.empty((replay_size, 4, self.state_size))\n",
|
||||
" Y = np.empty((replay_size, self.OUTPUT_SIZE))\n",
|
||||
" INIT_VAL = np.empty((replay_size, 2 * self.LAYER_SIZE))\n",
|
||||
" for i in range(replay_size):\n",
|
||||
" state_r, action_r, reward_r, new_state_r, dead_r, rnn_memory = replay[i]\n",
|
||||
" target = Q[i]\n",
|
||||
" target[action_r] = reward_r\n",
|
||||
" if not dead_r:\n",
|
||||
" target[action_r] += self.GAMMA * Q_new_negative[i, np.argmax(Q_new[i])]\n",
|
||||
" X[i] = state_r\n",
|
||||
" Y[i] = target\n",
|
||||
" INIT_VAL[i] = rnn_memory\n",
|
||||
" return X, Y, INIT_VAL\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array(res)\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" init_value = np.zeros((1, 2 * self.LAYER_SIZE))\n",
|
||||
" for k in range(self.INITIAL_FEATURES.shape[0]):\n",
|
||||
" self.INITIAL_FEATURES[k,:] = state\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action, last_state = self.sess.run([self.model.logits,self.model.last_state],\n",
|
||||
" feed_dict={self.model.X:[self.INITIAL_FEATURES],\n",
|
||||
" self.model.hidden_layer:init_value})\n",
|
||||
" action, init_value = np.argmax(action[0]), last_state\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" new_state = np.append([self.get_state(t + 1)], self.INITIAL_FEATURES[:3, :], axis = 0)\n",
|
||||
" self.INITIAL_FEATURES = new_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" init_value = np.zeros((1, 2 * self.LAYER_SIZE))\n",
|
||||
" for k in range(self.INITIAL_FEATURES.shape[0]):\n",
|
||||
" self.INITIAL_FEATURES[k,:] = state\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" if (self.T_COPY + 1) % self.COPY == 0:\n",
|
||||
" self._assign('real_model', 'negative_model')\n",
|
||||
" \n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action, last_state = self.sess.run([self.model.logits,\n",
|
||||
" self.model.last_state],\n",
|
||||
" feed_dict={self.model.X:[self.INITIAL_FEATURES],\n",
|
||||
" self.model.hidden_layer:init_value})\n",
|
||||
" action, init_value = np.argmax(action[0]), last_state\n",
|
||||
" \n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" \n",
|
||||
" invest = ((starting_money - initial_money) / initial_money)\n",
|
||||
" new_state = np.append([self.get_state(t + 1)], self.INITIAL_FEATURES[:3, :], axis = 0)\n",
|
||||
" \n",
|
||||
" self._memorize(self.INITIAL_FEATURES, action, invest, new_state, \n",
|
||||
" starting_money < initial_money, init_value[0])\n",
|
||||
" self.INITIAL_FEATURES = new_state\n",
|
||||
" batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)\n",
|
||||
" replay = random.sample(self.MEMORIES, batch_size)\n",
|
||||
" X, Y, INIT_VAL = self._construct_memories(replay)\n",
|
||||
" \n",
|
||||
" cost, _ = self.sess.run([self.model.cost, self.model.optimizer], \n",
|
||||
" feed_dict={self.model.X: X, self.model.Y:Y,\n",
|
||||
" self.model.hidden_layer: INIT_VAL})\n",
|
||||
" self.T_COPY += 1\n",
|
||||
" self.EPSILON = self.MIN_EPSILON + (1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)\n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f39ffaed7b8>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"WARNING:tensorflow:From <ipython-input-3-401815182242>:17: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
|
||||
"Instructions for updating:\n",
|
||||
"keep_dims is deprecated, use keepdims instead\n",
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f39ffaede80>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"epoch: 10, total rewards: 328.014401.3, cost: 0.233912, total money: 2446.714413\n",
|
||||
"epoch: 20, total rewards: 629.485052.3, cost: 0.592428, total money: 5723.605047\n",
|
||||
"epoch: 30, total rewards: 1222.065245.3, cost: 0.182284, total money: 7288.965209\n",
|
||||
"epoch: 40, total rewards: 719.309753.3, cost: 0.690094, total money: 3739.159728\n",
|
||||
"epoch: 50, total rewards: 328.994876.3, cost: 0.918951, total money: 2756.724856\n",
|
||||
"epoch: 60, total rewards: 1518.540281.3, cost: 0.226017, total money: 10545.210264\n",
|
||||
"epoch: 70, total rewards: 440.315127.3, cost: 0.145386, total money: 7494.335086\n",
|
||||
"epoch: 80, total rewards: 656.779966.3, cost: 0.113699, total money: 6666.949948\n",
|
||||
"epoch: 90, total rewards: 846.820129.3, cost: 0.444679, total money: 6860.080139\n",
|
||||
"epoch: 100, total rewards: 1044.679930.3, cost: 0.240218, total money: 9067.419920\n",
|
||||
"epoch: 110, total rewards: 207.934935.3, cost: 0.236219, total money: 10207.934935\n",
|
||||
"epoch: 120, total rewards: 6.745002.3, cost: 1.133358, total money: 10006.745002\n",
|
||||
"epoch: 130, total rewards: 586.910091.3, cost: 0.162622, total money: 4665.650081\n",
|
||||
"epoch: 140, total rewards: 1084.244877.3, cost: 0.630996, total money: 6178.484867\n",
|
||||
"epoch: 150, total rewards: 991.774842.3, cost: 1.439193, total money: 420.904786\n",
|
||||
"epoch: 160, total rewards: 714.735100.3, cost: 0.337296, total money: 5744.735038\n",
|
||||
"epoch: 170, total rewards: 1158.574706.3, cost: 0.186633, total money: 10185.244689\n",
|
||||
"epoch: 180, total rewards: 1120.314817.3, cost: 0.539594, total money: 7186.704770\n",
|
||||
"epoch: 190, total rewards: 230.760193.3, cost: 0.110742, total money: 4290.020202\n",
|
||||
"epoch: 200, total rewards: 218.420047.3, cost: 0.125164, total money: 10218.420047\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"batch_size = 32\n",
|
||||
"agent = Agent(state_size = window_size, \n",
|
||||
" window_size = window_size, \n",
|
||||
" trend = close, \n",
|
||||
" skip = skip)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 17: buy 1 unit at price 768.239990, total balance 9231.760010\n",
|
||||
"day 18, sell 1 unit at price 770.840027, investment 0.338441 %, total balance 10002.600037,\n",
|
||||
"day 20: buy 1 unit at price 747.919983, total balance 9254.680054\n",
|
||||
"day 21: buy 1 unit at price 750.500000, total balance 8504.180054\n",
|
||||
"day 23, sell 1 unit at price 759.109985, investment 1.496150 %, total balance 9263.290039,\n",
|
||||
"day 24, sell 1 unit at price 771.190002, investment 2.756829 %, total balance 10034.480041,\n",
|
||||
"day 27: buy 1 unit at price 789.270020, total balance 9245.210021\n",
|
||||
"day 28, sell 1 unit at price 796.099976, investment 0.865351 %, total balance 10041.309997,\n",
|
||||
"day 34: buy 1 unit at price 794.559998, total balance 9246.749999\n",
|
||||
"day 35, sell 1 unit at price 791.260010, investment -0.415323 %, total balance 10038.010009,\n",
|
||||
"day 36: buy 1 unit at price 789.909973, total balance 9248.100036\n",
|
||||
"day 38: buy 1 unit at price 785.049988, total balance 8463.050048\n",
|
||||
"day 40: buy 1 unit at price 771.820007, total balance 7691.230041\n",
|
||||
"day 41, sell 1 unit at price 786.140015, investment -0.477264 %, total balance 8477.370056,\n",
|
||||
"day 44: buy 1 unit at price 806.150024, total balance 7671.220032\n",
|
||||
"day 45, sell 1 unit at price 806.650024, investment 2.751422 %, total balance 8477.870056,\n",
|
||||
"day 48, sell 1 unit at price 806.359985, investment 4.475134 %, total balance 9284.230041,\n",
|
||||
"day 49, sell 1 unit at price 807.880005, investment 0.214598 %, total balance 10092.110046,\n",
|
||||
"day 51: buy 1 unit at price 806.070007, total balance 9286.040039\n",
|
||||
"day 52, sell 1 unit at price 802.174988, investment -0.483211 %, total balance 10088.215027,\n",
|
||||
"day 57: buy 1 unit at price 832.150024, total balance 9256.065003\n",
|
||||
"day 58: buy 1 unit at price 823.309998, total balance 8432.755005\n",
|
||||
"day 61: buy 1 unit at price 795.695007, total balance 7637.059998\n",
|
||||
"day 63: buy 1 unit at price 801.489990, total balance 6835.570008\n",
|
||||
"day 64, sell 1 unit at price 801.340027, investment -3.702457 %, total balance 7636.910035,\n",
|
||||
"day 66, sell 1 unit at price 808.380005, investment -1.813411 %, total balance 8445.290040,\n",
|
||||
"day 67, sell 1 unit at price 809.559998, investment 1.742501 %, total balance 9254.850038,\n",
|
||||
"day 68: buy 1 unit at price 813.669983, total balance 8441.180055\n",
|
||||
"day 70, sell 1 unit at price 820.450012, investment 2.365597 %, total balance 9261.630067,\n",
|
||||
"day 71: buy 1 unit at price 818.979980, total balance 8442.650087\n",
|
||||
"day 73: buy 1 unit at price 828.070007, total balance 7614.580080\n",
|
||||
"day 76, sell 1 unit at price 831.330017, investment 2.170417 %, total balance 8445.910097,\n",
|
||||
"day 77, sell 1 unit at price 828.640015, investment 1.179520 %, total balance 9274.550112,\n",
|
||||
"day 78: buy 1 unit at price 829.280029, total balance 8445.270083\n",
|
||||
"day 82: buy 1 unit at price 829.080017, total balance 7616.190066\n",
|
||||
"day 83: buy 1 unit at price 827.780029, total balance 6788.410037\n",
|
||||
"day 84: buy 1 unit at price 831.909973, total balance 5956.500064\n",
|
||||
"day 87: buy 1 unit at price 843.250000, total balance 5113.250064\n",
|
||||
"day 88: buy 1 unit at price 845.539978, total balance 4267.710086\n",
|
||||
"day 90: buy 1 unit at price 847.200012, total balance 3420.510074\n",
|
||||
"day 91, sell 1 unit at price 848.780029, investment 2.500999 %, total balance 4269.290103,\n",
|
||||
"day 98: buy 1 unit at price 819.510010, total balance 3449.780093\n",
|
||||
"day 99, sell 1 unit at price 820.919983, investment -1.008109 %, total balance 4270.700076,\n",
|
||||
"day 100: buy 1 unit at price 831.409973, total balance 3439.290103\n",
|
||||
"day 103, sell 1 unit at price 838.549988, investment 1.142226 %, total balance 4277.840091,\n",
|
||||
"day 104: buy 1 unit at price 834.570007, total balance 3443.270084\n",
|
||||
"day 106: buy 1 unit at price 827.880005, total balance 2615.390079\n",
|
||||
"day 107, sell 1 unit at price 824.669983, investment -0.375709 %, total balance 3440.060062,\n",
|
||||
"day 108, sell 1 unit at price 824.729980, investment -0.863073 %, total balance 4264.790042,\n",
|
||||
"day 109, sell 1 unit at price 823.349976, investment -2.359920 %, total balance 5088.140018,\n",
|
||||
"day 110: buy 1 unit at price 824.320007, total balance 4263.820011\n",
|
||||
"day 111, sell 1 unit at price 823.559998, investment -2.599520 %, total balance 5087.380009,\n",
|
||||
"day 114: buy 1 unit at price 838.210022, total balance 4249.169987\n",
|
||||
"day 115, sell 1 unit at price 841.650024, investment -0.655098 %, total balance 5090.820011,\n",
|
||||
"day 117, sell 1 unit at price 862.760010, investment 5.277544 %, total balance 5953.580021,\n",
|
||||
"day 118, sell 1 unit at price 872.299988, investment 4.918153 %, total balance 6825.880009,\n",
|
||||
"day 119: buy 1 unit at price 871.729980, total balance 5954.150029\n",
|
||||
"day 121, sell 1 unit at price 905.960022, investment 8.554107 %, total balance 6860.110051,\n",
|
||||
"day 122, sell 1 unit at price 912.570007, investment 10.229744 %, total balance 7772.680058,\n",
|
||||
"day 123: buy 1 unit at price 916.440002, total balance 6856.240056\n",
|
||||
"day 124, sell 1 unit at price 927.039978, investment 12.461177 %, total balance 7783.280034,\n",
|
||||
"day 125, sell 1 unit at price 931.659973, investment 11.148751 %, total balance 8714.940007,\n",
|
||||
"day 126, sell 1 unit at price 927.130005, investment 6.355182 %, total balance 9642.070012,\n",
|
||||
"day 129: buy 1 unit at price 928.780029, total balance 8713.289983\n",
|
||||
"day 131: buy 1 unit at price 932.219971, total balance 7781.070012\n",
|
||||
"day 134, sell 1 unit at price 919.619995, investment 0.346994 %, total balance 8700.690007,\n",
|
||||
"day 136: buy 1 unit at price 934.010010, total balance 7766.679997\n",
|
||||
"day 138, sell 1 unit at price 948.820007, investment 2.157667 %, total balance 8715.500004,\n",
|
||||
"day 139, sell 1 unit at price 954.960022, investment 2.439344 %, total balance 9670.460026,\n",
|
||||
"day 140, sell 1 unit at price 969.539978, investment 3.804024 %, total balance 10640.000004,\n",
|
||||
"day 141: buy 1 unit at price 971.469971, total balance 9668.530033\n",
|
||||
"day 142, sell 1 unit at price 975.880005, investment 0.453955 %, total balance 10644.410038,\n",
|
||||
"day 143: buy 1 unit at price 964.859985, total balance 9679.550053\n",
|
||||
"day 144, sell 1 unit at price 966.950012, investment 0.216615 %, total balance 10646.500065,\n",
|
||||
"day 145: buy 1 unit at price 975.599976, total balance 9670.900089\n",
|
||||
"day 146: buy 1 unit at price 983.679993, total balance 8687.220096\n",
|
||||
"day 148, sell 1 unit at price 980.940002, investment 0.547358 %, total balance 9668.160098,\n",
|
||||
"day 150, sell 1 unit at price 949.830017, investment -3.441157 %, total balance 10617.990115,\n",
|
||||
"day 152: buy 1 unit at price 953.400024, total balance 9664.590091\n",
|
||||
"day 154, sell 1 unit at price 942.309998, investment -1.163208 %, total balance 10606.900089,\n",
|
||||
"day 162: buy 1 unit at price 927.330017, total balance 9679.570072\n",
|
||||
"day 163: buy 1 unit at price 940.489990, total balance 8739.080082\n",
|
||||
"day 170: buy 1 unit at price 928.799988, total balance 7810.280094\n",
|
||||
"day 173, sell 1 unit at price 947.159973, investment 2.138393 %, total balance 8757.440067,\n",
|
||||
"day 175: buy 1 unit at price 953.419983, total balance 7804.020084\n",
|
||||
"day 176, sell 1 unit at price 965.400024, investment 2.648623 %, total balance 8769.420108,\n",
|
||||
"day 177, sell 1 unit at price 970.890015, investment 4.531657 %, total balance 9740.310123,\n",
|
||||
"day 178: buy 1 unit at price 968.150024, total balance 8772.160099\n",
|
||||
"day 179, sell 1 unit at price 972.919983, investment 2.045269 %, total balance 9745.080082,\n",
|
||||
"day 180, sell 1 unit at price 980.340027, investment 1.259103 %, total balance 10725.420109,\n",
|
||||
"day 185: buy 1 unit at price 930.500000, total balance 9794.920109\n",
|
||||
"day 186: buy 1 unit at price 930.830017, total balance 8864.090092\n",
|
||||
"day 187: buy 1 unit at price 930.390015, total balance 7933.700077\n",
|
||||
"day 188: buy 1 unit at price 923.650024, total balance 7010.050053\n",
|
||||
"day 191, sell 1 unit at price 926.789978, investment -0.398713 %, total balance 7936.840031,\n",
|
||||
"day 192, sell 1 unit at price 922.900024, investment -0.851927 %, total balance 8859.740055,\n",
|
||||
"day 195, sell 1 unit at price 922.669983, investment -0.829763 %, total balance 9782.410038,\n",
|
||||
"day 198: buy 1 unit at price 910.979980, total balance 8871.430058\n",
|
||||
"day 202: buy 1 unit at price 927.000000, total balance 7944.430058\n",
|
||||
"day 203, sell 1 unit at price 921.280029, investment -0.256590 %, total balance 8865.710087,\n",
|
||||
"day 205, sell 1 unit at price 913.809998, investment 0.310656 %, total balance 9779.520085,\n",
|
||||
"day 206, sell 1 unit at price 921.289978, investment -0.615968 %, total balance 10700.810063,\n",
|
||||
"day 207: buy 1 unit at price 929.570007, total balance 9771.240056\n",
|
||||
"day 209, sell 1 unit at price 937.340027, investment 0.835872 %, total balance 10708.580083,\n",
|
||||
"day 212: buy 1 unit at price 935.950012, total balance 9772.630071\n",
|
||||
"day 213, sell 1 unit at price 926.500000, investment -1.009671 %, total balance 10699.130071,\n",
|
||||
"day 216: buy 1 unit at price 935.090027, total balance 9764.040044\n",
|
||||
"day 217: buy 1 unit at price 925.109985, total balance 8838.930059\n",
|
||||
"day 219: buy 1 unit at price 915.000000, total balance 7923.930059\n",
|
||||
"day 221: buy 1 unit at price 931.580017, total balance 6992.350042\n",
|
||||
"day 222: buy 1 unit at price 932.450012, total balance 6059.900030\n",
|
||||
"day 223, sell 1 unit at price 928.530029, investment -0.701537 %, total balance 6988.430059,\n",
|
||||
"day 224, sell 1 unit at price 920.969971, investment -0.447516 %, total balance 7909.400030,\n",
|
||||
"day 225: buy 1 unit at price 924.859985, total balance 6984.540045\n",
|
||||
"day 226: buy 1 unit at price 944.489990, total balance 6040.050055\n",
|
||||
"day 227: buy 1 unit at price 949.500000, total balance 5090.550055\n",
|
||||
"day 228, sell 1 unit at price 959.109985, investment 4.820763 %, total balance 6049.660040,\n",
|
||||
"day 229, sell 1 unit at price 953.270020, investment 2.328303 %, total balance 7002.930060,\n",
|
||||
"day 230: buy 1 unit at price 957.789978, total balance 6045.140082\n",
|
||||
"day 235, sell 1 unit at price 972.599976, investment 4.305857 %, total balance 7017.740058,\n",
|
||||
"day 236, sell 1 unit at price 989.250000, investment 6.962137 %, total balance 8006.990058,\n",
|
||||
"day 238: buy 1 unit at price 989.679993, total balance 7017.310065\n",
|
||||
"day 239, sell 1 unit at price 992.000000, investment 5.030229 %, total balance 8009.310065,\n",
|
||||
"day 240: buy 1 unit at price 992.179993, total balance 7017.130072\n",
|
||||
"day 241, sell 1 unit at price 992.809998, investment 4.561348 %, total balance 8009.940070,\n",
|
||||
"day 242, sell 1 unit at price 984.450012, investment 2.783495 %, total balance 8994.390082,\n",
|
||||
"day 244, sell 1 unit at price 968.450012, investment -2.145136 %, total balance 9962.840094,\n",
|
||||
"day 245, sell 1 unit at price 970.539978, investment -2.181057 %, total balance 10933.380072,\n",
|
||||
"day 248: buy 1 unit at price 1019.270020, total balance 9914.110052\n",
|
||||
"day 249, sell 1 unit at price 1017.109985, investment -0.211920 %, total balance 10931.220037,\n",
|
||||
"day 250: buy 1 unit at price 1016.640015, total balance 9914.580022\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
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|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
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|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+568
@@ -0,0 +1,568 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
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|
||||
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|
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|
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|
||||
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||||
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|
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from collections import deque\n",
|
||||
"import random\n",
|
||||
"\n",
|
||||
"class Actor:\n",
|
||||
" def __init__(self, name, input_size, output_size, size_layer):\n",
|
||||
" with tf.variable_scope(name):\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, input_size))\n",
|
||||
" feed_actor = tf.layers.dense(self.X, size_layer, activation = tf.nn.relu)\n",
|
||||
" self.logits = tf.layers.dense(feed_actor, output_size)\n",
|
||||
"\n",
|
||||
"class Critic:\n",
|
||||
" def __init__(self, name, input_size, output_size, size_layer, learning_rate):\n",
|
||||
" with tf.variable_scope(name):\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, input_size))\n",
|
||||
" self.Y = tf.placeholder(tf.float32, (None, output_size))\n",
|
||||
" self.REWARD = tf.placeholder(tf.float32, (None, 1))\n",
|
||||
" feed_critic = tf.layers.dense(self.X, size_layer, activation = tf.nn.relu)\n",
|
||||
" feed_critic = tf.layers.dense(feed_critic, output_size, activation = tf.nn.relu) + self.Y\n",
|
||||
" feed_critic = tf.layers.dense(feed_critic, size_layer//2, activation = tf.nn.relu)\n",
|
||||
" self.logits = tf.layers.dense(feed_critic, 1)\n",
|
||||
" self.cost = tf.reduce_mean(tf.square(self.REWARD - self.logits))\n",
|
||||
" self.optimizer = tf.train.AdamOptimizer(learning_rate).minimize(self.cost)\n",
|
||||
" \n",
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" LEARNING_RATE = 0.001\n",
|
||||
" BATCH_SIZE = 32\n",
|
||||
" LAYER_SIZE = 256\n",
|
||||
" OUTPUT_SIZE = 3\n",
|
||||
" EPSILON = 0.5\n",
|
||||
" DECAY_RATE = 0.005\n",
|
||||
" MIN_EPSILON = 0.1\n",
|
||||
" GAMMA = 0.99\n",
|
||||
" MEMORIES = deque()\n",
|
||||
" MEMORY_SIZE = 300\n",
|
||||
" COPY = 1000\n",
|
||||
" T_COPY = 0\n",
|
||||
"\n",
|
||||
" def __init__(self, state_size, window_size, trend, skip):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" tf.reset_default_graph()\n",
|
||||
" self.actor = Actor('actor-original', self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE)\n",
|
||||
" self.actor_target = Actor('actor-target', self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE)\n",
|
||||
" self.critic = Critic('critic-original', self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE, self.LEARNING_RATE)\n",
|
||||
" self.critic_target = Critic('critic-target', self.state_size, self.OUTPUT_SIZE, \n",
|
||||
" self.LAYER_SIZE, self.LEARNING_RATE)\n",
|
||||
" self.grad_critic = tf.gradients(self.critic.logits, self.critic.Y)\n",
|
||||
" self.actor_critic_grad = tf.placeholder(tf.float32, [None, self.OUTPUT_SIZE])\n",
|
||||
" weights_actor = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='actor')\n",
|
||||
" self.grad_actor = tf.gradients(self.actor.logits, weights_actor, -self.actor_critic_grad)\n",
|
||||
" grads = zip(self.grad_actor, weights_actor)\n",
|
||||
" self.optimizer = tf.train.AdamOptimizer(self.LEARNING_RATE).apply_gradients(grads)\n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
" \n",
|
||||
" def _assign(self, from_name, to_name):\n",
|
||||
" from_w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=from_name)\n",
|
||||
" to_w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=to_name)\n",
|
||||
" for i in range(len(from_w)):\n",
|
||||
" assign_op = to_w[i].assign(from_w[i])\n",
|
||||
" self.sess.run(assign_op)\n",
|
||||
" \n",
|
||||
" def _memorize(self, state, action, reward, new_state, dead):\n",
|
||||
" self.MEMORIES.append((state, action, reward, new_state, dead))\n",
|
||||
" if len(self.MEMORIES) > self.MEMORY_SIZE:\n",
|
||||
" self.MEMORIES.popleft()\n",
|
||||
" \n",
|
||||
" def _select_action(self, state):\n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" prediction = self.sess.run(self.actor.logits, feed_dict={self.actor.X:[state]})[0]\n",
|
||||
" action = np.argmax(prediction)\n",
|
||||
" return action\n",
|
||||
" \n",
|
||||
" def _construct_memories_and_train(self, replay):\n",
|
||||
" states = np.array([a[0] for a in replay])\n",
|
||||
" new_states = np.array([a[3] for a in replay])\n",
|
||||
" Q = self.sess.run(self.actor.logits, feed_dict={self.actor.X: states})\n",
|
||||
" Q_target = self.sess.run(self.actor_target.logits, feed_dict={self.actor_target.X: states})\n",
|
||||
" grads = self.sess.run(self.grad_critic, feed_dict={self.critic.X:states, self.critic.Y:Q})[0]\n",
|
||||
" self.sess.run(self.optimizer, feed_dict={self.actor.X:states, self.actor_critic_grad:grads})\n",
|
||||
" \n",
|
||||
" rewards = np.array([a[2] for a in replay]).reshape((-1, 1))\n",
|
||||
" rewards_target = self.sess.run(self.critic_target.logits, \n",
|
||||
" feed_dict={self.critic_target.X:new_states,self.critic_target.Y:Q_target})\n",
|
||||
" for i in range(len(replay)):\n",
|
||||
" if not replay[0][-1]:\n",
|
||||
" rewards[i] += self.GAMMA * rewards_target[i]\n",
|
||||
" cost, _ = self.sess.run([self.critic.cost, self.critic.optimizer], \n",
|
||||
" feed_dict={self.critic.X:states, self.critic.Y:Q, self.critic.REWARD:rewards})\n",
|
||||
" return cost\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array(res)\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self._select_action(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" state = next_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" if (self.T_COPY + 1) % self.COPY == 0:\n",
|
||||
" self._assign('actor-original', 'actor-target')\n",
|
||||
" self._assign('critic-original', 'critic-target')\n",
|
||||
" \n",
|
||||
" action = self._select_action(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" \n",
|
||||
" invest = ((starting_money - initial_money) / initial_money)\n",
|
||||
" \n",
|
||||
" self._memorize(state, action, invest, next_state, starting_money < initial_money)\n",
|
||||
" state = next_state\n",
|
||||
" batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)\n",
|
||||
" replay = random.sample(self.MEMORIES, batch_size)\n",
|
||||
" cost = self._construct_memories_and_train(replay)\n",
|
||||
" self.T_COPY += 1\n",
|
||||
" self.EPSILON = self.MIN_EPSILON + (1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)\n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"epoch: 10, total rewards: 1539.185237.3, cost: 2.181347, total money: 1684.395196\n",
|
||||
"epoch: 20, total rewards: 1308.335026.3, cost: 658.992737, total money: 11308.335026\n",
|
||||
"epoch: 30, total rewards: 810.315002.3, cost: 19406.357422, total money: 5871.594971\n",
|
||||
"epoch: 40, total rewards: 380.889899.3, cost: 436790400.000000, total money: 7327.869879\n",
|
||||
"epoch: 50, total rewards: 676.170224.3, cost: 27570524160.000000, total money: 10676.170224\n",
|
||||
"epoch: 60, total rewards: 796.770199.3, cost: 935274741760.000000, total money: 10796.770199\n",
|
||||
"epoch: 70, total rewards: 47.440366.3, cost: 8344191369216.000000, total money: 7043.150388\n",
|
||||
"epoch: 80, total rewards: 450.169980.3, cost: 88121093914624.000000, total money: 6472.479916\n",
|
||||
"epoch: 90, total rewards: 443.664980.3, cost: 675454474256384.000000, total money: 9427.024965\n",
|
||||
"epoch: 100, total rewards: 350.460142.3, cost: 1153362061950976.000000, total money: 10350.460142\n",
|
||||
"epoch: 110, total rewards: 247.584961.3, cost: 6317238688677888.000000, total money: 9230.944946\n",
|
||||
"epoch: 120, total rewards: 138.510132.3, cost: 3956869119726321664.000000, total money: 8102.600097\n",
|
||||
"epoch: 130, total rewards: 410.025086.3, cost: 2205253088434978816.000000, total money: 10410.025086\n",
|
||||
"epoch: 140, total rewards: 513.814999.3, cost: 5849743807884558336.000000, total money: 9497.174984\n",
|
||||
"epoch: 150, total rewards: 876.734991.3, cost: 25442419893862400.000000, total money: 9860.094976\n",
|
||||
"epoch: 160, total rewards: 216.929627.3, cost: 73146239398445056.000000, total money: 9244.369629\n",
|
||||
"epoch: 170, total rewards: 26.000066.3, cost: 210379489706770432.000000, total money: 7992.250066\n",
|
||||
"epoch: 180, total rewards: 230.090269.3, cost: 378469838063927296.000000, total money: 8194.180234\n",
|
||||
"epoch: 190, total rewards: 31.099796.3, cost: 1333389845631860736.000000, total money: 6978.079776\n",
|
||||
"epoch: 200, total rewards: 158.599487.3, cost: 459357028892629008384.000000, total money: 10158.599487\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"batch_size = 32\n",
|
||||
"agent = Agent(state_size = window_size, \n",
|
||||
" window_size = window_size, \n",
|
||||
" trend = close, \n",
|
||||
" skip = skip)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 3: buy 1 unit at price 782.520020, total balance 9217.479980\n",
|
||||
"day 4: buy 1 unit at price 790.510010, total balance 8426.969970\n",
|
||||
"day 5, sell 1 unit at price 785.309998, investment 0.356538 %, total balance 9212.279968,\n",
|
||||
"day 6, sell 1 unit at price 762.559998, investment -3.535694 %, total balance 9974.839966,\n",
|
||||
"day 16: buy 1 unit at price 761.679993, total balance 9213.159973\n",
|
||||
"day 17: buy 1 unit at price 768.239990, total balance 8444.919983\n",
|
||||
"day 18, sell 1 unit at price 770.840027, investment 1.202609 %, total balance 9215.760010,\n",
|
||||
"day 19, sell 1 unit at price 758.039978, investment -1.327712 %, total balance 9973.799988,\n",
|
||||
"day 20: buy 1 unit at price 747.919983, total balance 9225.880005\n",
|
||||
"day 21: buy 1 unit at price 750.500000, total balance 8475.380005\n",
|
||||
"day 22, sell 1 unit at price 762.520020, investment 1.952085 %, total balance 9237.900025,\n",
|
||||
"day 24, sell 1 unit at price 771.190002, investment 2.756829 %, total balance 10009.090027,\n",
|
||||
"day 25: buy 1 unit at price 776.419983, total balance 9232.670044\n",
|
||||
"day 26, sell 1 unit at price 789.289978, investment 1.657607 %, total balance 10021.960022,\n",
|
||||
"day 27: buy 1 unit at price 789.270020, total balance 9232.690002\n",
|
||||
"day 28: buy 1 unit at price 796.099976, total balance 8436.590026\n",
|
||||
"day 31, sell 1 unit at price 790.799988, investment 0.193846 %, total balance 9227.390014,\n",
|
||||
"day 32, sell 1 unit at price 794.200012, investment -0.238659 %, total balance 10021.590026,\n",
|
||||
"day 33: buy 1 unit at price 796.419983, total balance 9225.170043\n",
|
||||
"day 34, sell 1 unit at price 794.559998, investment -0.233543 %, total balance 10019.730041,\n",
|
||||
"day 36: buy 1 unit at price 789.909973, total balance 9229.820068\n",
|
||||
"day 37, sell 1 unit at price 791.549988, investment 0.207620 %, total balance 10021.370056,\n",
|
||||
"day 49: buy 1 unit at price 807.880005, total balance 9213.490051\n",
|
||||
"day 51: buy 1 unit at price 806.070007, total balance 8407.420044\n",
|
||||
"day 52, sell 1 unit at price 802.174988, investment -0.706171 %, total balance 9209.595032,\n",
|
||||
"day 53, sell 1 unit at price 805.020020, investment -0.130260 %, total balance 10014.615052,\n",
|
||||
"day 59: buy 1 unit at price 802.320007, total balance 9212.295045\n",
|
||||
"day 62, sell 1 unit at price 798.530029, investment -0.472377 %, total balance 10010.825074,\n",
|
||||
"day 63: buy 1 unit at price 801.489990, total balance 9209.335084\n",
|
||||
"day 64, sell 1 unit at price 801.340027, investment -0.018711 %, total balance 10010.675111,\n",
|
||||
"day 67: buy 1 unit at price 809.559998, total balance 9201.115113\n",
|
||||
"day 68, sell 1 unit at price 813.669983, investment 0.507681 %, total balance 10014.785096,\n",
|
||||
"day 73: buy 1 unit at price 828.070007, total balance 9186.715089\n",
|
||||
"day 74, sell 1 unit at price 831.659973, investment 0.433534 %, total balance 10018.375062,\n",
|
||||
"day 81: buy 1 unit at price 830.630005, total balance 9187.745057\n",
|
||||
"day 82, sell 1 unit at price 829.080017, investment -0.186604 %, total balance 10016.825074,\n",
|
||||
"day 87: buy 1 unit at price 843.250000, total balance 9173.575074\n",
|
||||
"day 88, sell 1 unit at price 845.539978, investment 0.271566 %, total balance 10019.115052,\n",
|
||||
"day 92: buy 1 unit at price 852.119995, total balance 9166.995057\n",
|
||||
"day 93, sell 1 unit at price 848.400024, investment -0.436555 %, total balance 10015.395081,\n",
|
||||
"day 97: buy 1 unit at price 814.429993, total balance 9200.965088\n",
|
||||
"day 98: buy 1 unit at price 819.510010, total balance 8381.455078\n",
|
||||
"day 99, sell 1 unit at price 820.919983, investment 0.796875 %, total balance 9202.375061,\n",
|
||||
"day 100: buy 1 unit at price 831.409973, total balance 8370.965088\n",
|
||||
"day 101, sell 1 unit at price 831.500000, investment 1.463068 %, total balance 9202.465088,\n",
|
||||
"day 102, sell 1 unit at price 829.559998, investment -0.222511 %, total balance 10032.025086,\n",
|
||||
"day 107: buy 1 unit at price 824.669983, total balance 9207.355103\n",
|
||||
"day 109, sell 1 unit at price 823.349976, investment -0.160065 %, total balance 10030.705079,\n",
|
||||
"day 110: buy 1 unit at price 824.320007, total balance 9206.385072\n",
|
||||
"day 111, sell 1 unit at price 823.559998, investment -0.092198 %, total balance 10029.945070,\n",
|
||||
"day 115: buy 1 unit at price 841.650024, total balance 9188.295046\n",
|
||||
"day 116, sell 1 unit at price 843.190002, investment 0.182971 %, total balance 10031.485048,\n",
|
||||
"day 119: buy 1 unit at price 871.729980, total balance 9159.755068\n",
|
||||
"day 120, sell 1 unit at price 874.250000, investment 0.289083 %, total balance 10034.005068,\n",
|
||||
"day 129: buy 1 unit at price 928.780029, total balance 9105.225039\n",
|
||||
"day 130, sell 1 unit at price 930.599976, investment 0.195950 %, total balance 10035.825015,\n",
|
||||
"day 137: buy 1 unit at price 941.859985, total balance 9093.965030\n",
|
||||
"day 138, sell 1 unit at price 948.820007, investment 0.738966 %, total balance 10042.785037,\n",
|
||||
"day 140: buy 1 unit at price 969.539978, total balance 9073.245059\n",
|
||||
"day 141, sell 1 unit at price 971.469971, investment 0.199063 %, total balance 10044.715030,\n",
|
||||
"day 144: buy 1 unit at price 966.950012, total balance 9077.765018\n",
|
||||
"day 145, sell 1 unit at price 975.599976, investment 0.894562 %, total balance 10053.364994,\n",
|
||||
"day 147: buy 1 unit at price 976.570007, total balance 9076.794987\n",
|
||||
"day 148, sell 1 unit at price 980.940002, investment 0.447484 %, total balance 10057.734989,\n",
|
||||
"day 156: buy 1 unit at price 957.369995, total balance 9100.364994\n",
|
||||
"day 157, sell 1 unit at price 950.630005, investment -0.704011 %, total balance 10050.994999,\n",
|
||||
"day 160: buy 1 unit at price 965.590027, total balance 9085.404972\n",
|
||||
"day 161, sell 1 unit at price 952.270020, investment -1.379468 %, total balance 10037.674992,\n",
|
||||
"day 165: buy 1 unit at price 908.729980, total balance 9128.945012\n",
|
||||
"day 166: buy 1 unit at price 898.700012, total balance 8230.245000\n",
|
||||
"day 167, sell 1 unit at price 911.710022, investment 0.327935 %, total balance 9141.955022,\n",
|
||||
"day 168, sell 1 unit at price 906.690002, investment 0.889061 %, total balance 10048.645024,\n",
|
||||
"day 169: buy 1 unit at price 918.590027, total balance 9130.054997\n",
|
||||
"day 170, sell 1 unit at price 928.799988, investment 1.111482 %, total balance 10058.854985,\n",
|
||||
"day 171: buy 1 unit at price 930.090027, total balance 9128.764958\n",
|
||||
"day 172: buy 1 unit at price 943.830017, total balance 8184.934941\n",
|
||||
"day 173, sell 1 unit at price 947.159973, investment 1.835300 %, total balance 9132.094914,\n",
|
||||
"day 174: buy 1 unit at price 955.989990, total balance 8176.104924\n",
|
||||
"day 176: buy 1 unit at price 965.400024, total balance 7210.704900\n",
|
||||
"day 177, sell 1 unit at price 970.890015, investment 2.867041 %, total balance 8181.594915,\n",
|
||||
"day 178, sell 1 unit at price 968.150024, investment 1.271983 %, total balance 9149.744939,\n",
|
||||
"day 179, sell 1 unit at price 972.919983, investment 0.778947 %, total balance 10122.664922,\n",
|
||||
"day 182: buy 1 unit at price 947.799988, total balance 9174.864934\n",
|
||||
"day 183, sell 1 unit at price 934.090027, investment -1.446504 %, total balance 10108.954961,\n",
|
||||
"day 184: buy 1 unit at price 941.530029, total balance 9167.424932\n",
|
||||
"day 185: buy 1 unit at price 930.500000, total balance 8236.924932\n",
|
||||
"day 186, sell 1 unit at price 930.830017, investment -1.136449 %, total balance 9167.754949,\n",
|
||||
"day 187, sell 1 unit at price 930.390015, investment -0.011820 %, total balance 10098.144964,\n",
|
||||
"day 189: buy 1 unit at price 927.960022, total balance 9170.184942\n",
|
||||
"day 190, sell 1 unit at price 929.359985, investment 0.150865 %, total balance 10099.544927,\n",
|
||||
"day 192: buy 1 unit at price 922.900024, total balance 9176.644903\n",
|
||||
"day 193, sell 1 unit at price 907.239990, investment -1.696829 %, total balance 10083.884893,\n",
|
||||
"day 197: buy 1 unit at price 926.960022, total balance 9156.924871\n",
|
||||
"day 198, sell 1 unit at price 910.979980, investment -1.723919 %, total balance 10067.904851,\n",
|
||||
"day 199: buy 1 unit at price 910.669983, total balance 9157.234868\n",
|
||||
"day 200, sell 1 unit at price 906.659973, investment -0.440336 %, total balance 10063.894841,\n",
|
||||
"day 202: buy 1 unit at price 927.000000, total balance 9136.894841\n",
|
||||
"day 203, sell 1 unit at price 921.280029, investment -0.617041 %, total balance 10058.174870,\n",
|
||||
"day 204: buy 1 unit at price 915.890015, total balance 9142.284855\n",
|
||||
"day 205: buy 1 unit at price 913.809998, total balance 8228.474857\n",
|
||||
"day 206, sell 1 unit at price 921.289978, investment 0.589586 %, total balance 9149.764835,\n",
|
||||
"day 207, sell 1 unit at price 929.570007, investment 1.724648 %, total balance 10079.334842,\n",
|
||||
"day 209: buy 1 unit at price 937.340027, total balance 9141.994815\n",
|
||||
"day 210, sell 1 unit at price 928.450012, investment -0.948430 %, total balance 10070.444827,\n",
|
||||
"day 211: buy 1 unit at price 927.809998, total balance 9142.634829\n",
|
||||
"day 212: buy 1 unit at price 935.950012, total balance 8206.684817\n",
|
||||
"day 213: buy 1 unit at price 926.500000, total balance 7280.184817\n",
|
||||
"day 214, sell 1 unit at price 929.080017, investment 0.136884 %, total balance 8209.264834,\n",
|
||||
"day 215: buy 1 unit at price 932.070007, total balance 7277.194827\n",
|
||||
"day 216: buy 1 unit at price 935.090027, total balance 6342.104800\n",
|
||||
"day 217, sell 1 unit at price 925.109985, investment -1.158184 %, total balance 7267.214785,\n",
|
||||
"day 218, sell 1 unit at price 920.289978, investment -0.670267 %, total balance 8187.504763,\n",
|
||||
"day 219, sell 1 unit at price 915.000000, investment -1.831408 %, total balance 9102.504763,\n",
|
||||
"day 221, sell 1 unit at price 931.580017, investment -0.375366 %, total balance 10034.084780,\n",
|
||||
"day 223: buy 1 unit at price 928.530029, total balance 9105.554751\n",
|
||||
"day 224: buy 1 unit at price 920.969971, total balance 8184.584780\n",
|
||||
"day 225, sell 1 unit at price 924.859985, investment -0.395253 %, total balance 9109.444765,\n",
|
||||
"day 226, sell 1 unit at price 944.489990, investment 2.553831 %, total balance 10053.934755,\n",
|
||||
"day 227: buy 1 unit at price 949.500000, total balance 9104.434755\n",
|
||||
"day 228, sell 1 unit at price 959.109985, investment 1.012110 %, total balance 10063.544740,\n",
|
||||
"day 231: buy 1 unit at price 951.679993, total balance 9111.864747\n",
|
||||
"day 232: buy 1 unit at price 969.960022, total balance 8141.904725\n",
|
||||
"day 233: buy 1 unit at price 978.890015, total balance 7163.014710\n",
|
||||
"day 235, sell 1 unit at price 972.599976, investment 2.198216 %, total balance 8135.614686,\n",
|
||||
"day 236, sell 1 unit at price 989.250000, investment 1.988739 %, total balance 9124.864686,\n",
|
||||
"day 238, sell 1 unit at price 989.679993, investment 1.102267 %, total balance 10114.544679,\n",
|
||||
"day 243: buy 1 unit at price 988.200012, total balance 9126.344667\n",
|
||||
"day 244: buy 1 unit at price 968.450012, total balance 8157.894655\n",
|
||||
"day 245: buy 1 unit at price 970.539978, total balance 7187.354677\n",
|
||||
"day 246, sell 1 unit at price 973.330017, investment -1.504756 %, total balance 8160.684694,\n",
|
||||
"day 247: buy 1 unit at price 972.559998, total balance 7188.124696\n",
|
||||
"day 248, sell 1 unit at price 1019.270020, investment 5.247561 %, total balance 8207.394716,\n",
|
||||
"day 249, sell 1 unit at price 1017.109985, investment 4.798361 %, total balance 9224.504701,\n",
|
||||
"day 250: buy 1 unit at price 1016.640015, total balance 8207.864686\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+571
@@ -0,0 +1,571 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from collections import deque\n",
|
||||
"import random\n",
|
||||
"\n",
|
||||
"class Actor:\n",
|
||||
" def __init__(self, name, input_size, output_size, size_layer):\n",
|
||||
" with tf.variable_scope(name):\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, input_size))\n",
|
||||
" feed_actor = tf.layers.dense(self.X, size_layer, activation = tf.nn.relu)\n",
|
||||
" tensor_action, tensor_validation = tf.split(feed_actor,2,1)\n",
|
||||
" feed_action = tf.layers.dense(tensor_action, output_size)\n",
|
||||
" feed_validation = tf.layers.dense(tensor_validation, 1)\n",
|
||||
" self.logits = feed_validation + tf.subtract(feed_action,\n",
|
||||
" tf.reduce_mean(feed_action,axis=1,keep_dims=True))\n",
|
||||
"\n",
|
||||
"class Critic:\n",
|
||||
" def __init__(self, name, input_size, output_size, size_layer, learning_rate):\n",
|
||||
" with tf.variable_scope(name):\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, input_size))\n",
|
||||
" self.Y = tf.placeholder(tf.float32, (None, output_size))\n",
|
||||
" self.REWARD = tf.placeholder(tf.float32, (None, 1))\n",
|
||||
" feed_critic = tf.layers.dense(self.X, size_layer, activation = tf.nn.relu)\n",
|
||||
" tensor_action, tensor_validation = tf.split(feed_critic,2,1)\n",
|
||||
" feed_action = tf.layers.dense(tensor_action, output_size)\n",
|
||||
" feed_validation = tf.layers.dense(tensor_validation, 1)\n",
|
||||
" feed_critic = feed_validation + tf.subtract(feed_action,tf.reduce_mean(feed_action,axis=1,keep_dims=True))\n",
|
||||
" feed_critic = tf.nn.relu(feed_critic) + self.Y\n",
|
||||
" feed_critic = tf.layers.dense(feed_critic, size_layer//2, activation = tf.nn.relu)\n",
|
||||
" self.logits = tf.layers.dense(feed_critic, 1)\n",
|
||||
" self.cost = tf.reduce_mean(tf.square(self.REWARD - self.logits))\n",
|
||||
" self.optimizer = tf.train.AdamOptimizer(learning_rate).minimize(self.cost)\n",
|
||||
" \n",
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" LEARNING_RATE = 0.001\n",
|
||||
" BATCH_SIZE = 32\n",
|
||||
" LAYER_SIZE = 256\n",
|
||||
" OUTPUT_SIZE = 3\n",
|
||||
" EPSILON = 0.5\n",
|
||||
" DECAY_RATE = 0.005\n",
|
||||
" MIN_EPSILON = 0.1\n",
|
||||
" GAMMA = 0.99\n",
|
||||
" MEMORIES = deque()\n",
|
||||
" MEMORY_SIZE = 300\n",
|
||||
" COPY = 1000\n",
|
||||
" T_COPY = 0\n",
|
||||
"\n",
|
||||
" def __init__(self, state_size, window_size, trend, skip):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" tf.reset_default_graph()\n",
|
||||
" self.actor = Actor('actor-original', self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE)\n",
|
||||
" self.actor_target = Actor('actor-target', self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE)\n",
|
||||
" self.critic = Critic('critic-original', self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE, self.LEARNING_RATE)\n",
|
||||
" self.critic_target = Critic('critic-target', self.state_size, self.OUTPUT_SIZE, \n",
|
||||
" self.LAYER_SIZE, self.LEARNING_RATE)\n",
|
||||
" self.grad_critic = tf.gradients(self.critic.logits, self.critic.Y)\n",
|
||||
" self.actor_critic_grad = tf.placeholder(tf.float32, [None, self.OUTPUT_SIZE])\n",
|
||||
" weights_actor = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='actor')\n",
|
||||
" self.grad_actor = tf.gradients(self.actor.logits, weights_actor, -self.actor_critic_grad)\n",
|
||||
" grads = zip(self.grad_actor, weights_actor)\n",
|
||||
" self.optimizer = tf.train.AdamOptimizer(self.LEARNING_RATE).apply_gradients(grads)\n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
" \n",
|
||||
" def _assign(self, from_name, to_name):\n",
|
||||
" from_w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=from_name)\n",
|
||||
" to_w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=to_name)\n",
|
||||
" for i in range(len(from_w)):\n",
|
||||
" assign_op = to_w[i].assign(from_w[i])\n",
|
||||
" self.sess.run(assign_op)\n",
|
||||
" \n",
|
||||
" def _memorize(self, state, action, reward, new_state, dead):\n",
|
||||
" self.MEMORIES.append((state, action, reward, new_state, dead))\n",
|
||||
" if len(self.MEMORIES) > self.MEMORY_SIZE:\n",
|
||||
" self.MEMORIES.popleft()\n",
|
||||
" \n",
|
||||
" def _select_action(self, state):\n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" prediction = self.sess.run(self.actor.logits, feed_dict={self.actor.X:[state]})[0]\n",
|
||||
" action = np.argmax(prediction)\n",
|
||||
" return action\n",
|
||||
" \n",
|
||||
" def _construct_memories_and_train(self, replay):\n",
|
||||
" states = np.array([a[0] for a in replay])\n",
|
||||
" new_states = np.array([a[3] for a in replay])\n",
|
||||
" Q = self.sess.run(self.actor.logits, feed_dict={self.actor.X: states})\n",
|
||||
" Q_target = self.sess.run(self.actor_target.logits, feed_dict={self.actor_target.X: states})\n",
|
||||
" grads = self.sess.run(self.grad_critic, feed_dict={self.critic.X:states, self.critic.Y:Q})[0]\n",
|
||||
" self.sess.run(self.optimizer, feed_dict={self.actor.X:states, self.actor_critic_grad:grads})\n",
|
||||
" \n",
|
||||
" rewards = np.array([a[2] for a in replay]).reshape((-1, 1))\n",
|
||||
" rewards_target = self.sess.run(self.critic_target.logits, \n",
|
||||
" feed_dict={self.critic_target.X:new_states,self.critic_target.Y:Q_target})\n",
|
||||
" for i in range(len(replay)):\n",
|
||||
" if not replay[0][-1]:\n",
|
||||
" rewards[i] += self.GAMMA * rewards_target[i]\n",
|
||||
" cost, _ = self.sess.run([self.critic.cost, self.critic.optimizer], \n",
|
||||
" feed_dict={self.critic.X:states, self.critic.Y:Q, self.critic.REWARD:rewards})\n",
|
||||
" return cost\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array(res)\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self._select_action(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" state = next_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" if (self.T_COPY + 1) % self.COPY == 0:\n",
|
||||
" self._assign('actor-original', 'actor-target')\n",
|
||||
" self._assign('critic-original', 'critic-target')\n",
|
||||
" \n",
|
||||
" action = self._select_action(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" \n",
|
||||
" invest = ((starting_money - initial_money) / initial_money)\n",
|
||||
" \n",
|
||||
" self._memorize(state, action, invest, next_state, starting_money < initial_money)\n",
|
||||
" batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)\n",
|
||||
" state = next_state\n",
|
||||
" replay = random.sample(self.MEMORIES, batch_size)\n",
|
||||
" cost = self._construct_memories_and_train(replay)\n",
|
||||
" self.T_COPY += 1\n",
|
||||
" self.EPSILON = self.MIN_EPSILON + (1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)\n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING:tensorflow:From <ipython-input-3-a50a3d0b4e36>:13: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
|
||||
"Instructions for updating:\n",
|
||||
"keep_dims is deprecated, use keepdims instead\n",
|
||||
"epoch: 10, total rewards: 707.200200.3, cost: 0.405626, total money: 9715.020207\n",
|
||||
"epoch: 20, total rewards: 1598.640143.3, cost: 30.734631, total money: 10581.530158\n",
|
||||
"epoch: 30, total rewards: 1271.279733.3, cost: 465.966644, total money: 10254.169748\n",
|
||||
"epoch: 40, total rewards: 611.054993.3, cost: 38.079464, total money: 2818.014953\n",
|
||||
"epoch: 50, total rewards: 1098.115172.3, cost: 71481.406250, total money: 1453.295102\n",
|
||||
"epoch: 60, total rewards: 575.370237.3, cost: 45955692.000000, total money: 9558.260252\n",
|
||||
"epoch: 70, total rewards: 1020.545110.3, cost: 244974075904.000000, total money: 10003.435125\n",
|
||||
"epoch: 80, total rewards: 824.555359.3, cost: 62751015698432.000000, total money: 4025.125366\n",
|
||||
"epoch: 90, total rewards: 182.215205.3, cost: 3949580517376.000000, total money: 10182.215205\n",
|
||||
"epoch: 100, total rewards: 861.215276.3, cost: 7310792458240.000000, total money: 7918.025274\n",
|
||||
"epoch: 110, total rewards: 68.690005.3, cost: 3184271573385216.000000, total money: 10068.690005\n",
|
||||
"epoch: 120, total rewards: 205.980352.3, cost: 224217291292672.000000, total money: 10205.980352\n",
|
||||
"epoch: 130, total rewards: 256.794983.3, cost: 363017178972160.000000, total money: 8275.784973\n",
|
||||
"epoch: 140, total rewards: 1586.720156.3, cost: 530019768074240.000000, total money: 11586.720156\n",
|
||||
"epoch: 150, total rewards: 824.849978.3, cost: 3151772092727296.000000, total money: 8881.750002\n",
|
||||
"epoch: 160, total rewards: 222.490291.3, cost: 6080023886823424.000000, total money: 9205.850276\n",
|
||||
"epoch: 170, total rewards: 37.630069.3, cost: 9586346603577344.000000, total money: 9020.990054\n",
|
||||
"epoch: 180, total rewards: 510.125126.3, cost: 22490134536519680.000000, total money: 5604.765140\n",
|
||||
"epoch: 190, total rewards: 639.559874.3, cost: 106721235701858304.000000, total money: 9669.019896\n",
|
||||
"epoch: 200, total rewards: 945.395079.3, cost: 31826508674760704.000000, total money: 384.445006\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"batch_size = 32\n",
|
||||
"agent = Agent(state_size = window_size, \n",
|
||||
" window_size = window_size, \n",
|
||||
" trend = close, \n",
|
||||
" skip = skip)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 0: buy 1 unit at price 768.700012, total balance 9231.299988\n",
|
||||
"day 2: buy 1 unit at price 762.020020, total balance 8469.279968\n",
|
||||
"day 3, sell 1 unit at price 782.520020, investment 1.797842 %, total balance 9251.799988,\n",
|
||||
"day 4, sell 1 unit at price 790.510010, investment 3.738746 %, total balance 10042.309998,\n",
|
||||
"day 5: buy 1 unit at price 785.309998, total balance 9257.000000\n",
|
||||
"day 8: buy 1 unit at price 736.080017, total balance 8520.919983\n",
|
||||
"day 11, sell 1 unit at price 771.229980, investment -1.792925 %, total balance 9292.149963,\n",
|
||||
"day 12: buy 1 unit at price 760.539978, total balance 8531.609985\n",
|
||||
"day 14: buy 1 unit at price 768.270020, total balance 7763.339965\n",
|
||||
"day 15: buy 1 unit at price 760.989990, total balance 7002.349975\n",
|
||||
"day 17, sell 1 unit at price 768.239990, investment 4.369087 %, total balance 7770.589965,\n",
|
||||
"day 20, sell 1 unit at price 747.919983, investment -1.659347 %, total balance 8518.509948,\n",
|
||||
"day 21, sell 1 unit at price 750.500000, investment -2.312991 %, total balance 9269.009948,\n",
|
||||
"day 22, sell 1 unit at price 762.520020, investment 0.201058 %, total balance 10031.529968,\n",
|
||||
"day 27: buy 1 unit at price 789.270020, total balance 9242.259948\n",
|
||||
"day 33: buy 1 unit at price 796.419983, total balance 8445.839965\n",
|
||||
"day 34, sell 1 unit at price 794.559998, investment 0.670237 %, total balance 9240.399963,\n",
|
||||
"day 35, sell 1 unit at price 791.260010, investment -0.647896 %, total balance 10031.659973,\n",
|
||||
"day 36: buy 1 unit at price 789.909973, total balance 9241.750000\n",
|
||||
"day 37, sell 1 unit at price 791.549988, investment 0.207620 %, total balance 10033.299988,\n",
|
||||
"day 41: buy 1 unit at price 786.140015, total balance 9247.159973\n",
|
||||
"day 43: buy 1 unit at price 794.020020, total balance 8453.139953\n",
|
||||
"day 44: buy 1 unit at price 806.150024, total balance 7646.989929\n",
|
||||
"day 45, sell 1 unit at price 806.650024, investment 2.608951 %, total balance 8453.639953,\n",
|
||||
"day 47: buy 1 unit at price 807.909973, total balance 7645.729980\n",
|
||||
"day 48: buy 1 unit at price 806.359985, total balance 6839.369995\n",
|
||||
"day 49, sell 1 unit at price 807.880005, investment 1.745546 %, total balance 7647.250000,\n",
|
||||
"day 55: buy 1 unit at price 823.869995, total balance 6823.380005\n",
|
||||
"day 56, sell 1 unit at price 835.669983, investment 3.661844 %, total balance 7659.049988,\n",
|
||||
"day 57, sell 1 unit at price 832.150024, investment 3.000341 %, total balance 8491.200012,\n",
|
||||
"day 58, sell 1 unit at price 823.309998, investment 2.102040 %, total balance 9314.510010,\n",
|
||||
"day 59: buy 1 unit at price 802.320007, total balance 8512.190003\n",
|
||||
"day 60, sell 1 unit at price 796.789978, investment -3.286928 %, total balance 9308.979981,\n",
|
||||
"day 62: buy 1 unit at price 798.530029, total balance 8510.449952\n",
|
||||
"day 63, sell 1 unit at price 801.489990, investment -0.103452 %, total balance 9311.939942,\n",
|
||||
"day 69, sell 1 unit at price 819.239990, investment 2.593511 %, total balance 10131.179932,\n",
|
||||
"day 75: buy 1 unit at price 830.760010, total balance 9300.419922\n",
|
||||
"day 76, sell 1 unit at price 831.330017, investment 0.068613 %, total balance 10131.749939,\n",
|
||||
"day 77: buy 1 unit at price 828.640015, total balance 9303.109924\n",
|
||||
"day 78: buy 1 unit at price 829.280029, total balance 8473.829895\n",
|
||||
"day 80, sell 1 unit at price 835.239990, investment 0.796483 %, total balance 9309.069885,\n",
|
||||
"day 83: buy 1 unit at price 827.780029, total balance 8481.289856\n",
|
||||
"day 84, sell 1 unit at price 831.909973, investment 0.317136 %, total balance 9313.199829,\n",
|
||||
"day 86: buy 1 unit at price 838.679993, total balance 8474.519836\n",
|
||||
"day 87: buy 1 unit at price 843.250000, total balance 7631.269836\n",
|
||||
"day 88: buy 1 unit at price 845.539978, total balance 6785.729858\n",
|
||||
"day 89, sell 1 unit at price 845.619995, investment 2.155158 %, total balance 7631.349853,\n",
|
||||
"day 91: buy 1 unit at price 848.780029, total balance 6782.569824\n",
|
||||
"day 92: buy 1 unit at price 852.119995, total balance 5930.449829\n",
|
||||
"day 94, sell 1 unit at price 830.460022, investment -0.980108 %, total balance 6760.909851,\n",
|
||||
"day 95: buy 1 unit at price 829.590027, total balance 5931.319824\n",
|
||||
"day 96: buy 1 unit at price 817.580017, total balance 5113.739807\n",
|
||||
"day 97: buy 1 unit at price 814.429993, total balance 4299.309814\n",
|
||||
"day 98: buy 1 unit at price 819.510010, total balance 3479.799804\n",
|
||||
"day 104, sell 1 unit at price 834.570007, investment -1.029350 %, total balance 4314.369811,\n",
|
||||
"day 105: buy 1 unit at price 831.409973, total balance 3482.959838\n",
|
||||
"day 106: buy 1 unit at price 827.880005, total balance 2655.079833\n",
|
||||
"day 107, sell 1 unit at price 824.669983, investment -2.468245 %, total balance 3479.749816,\n",
|
||||
"day 108: buy 1 unit at price 824.729980, total balance 2655.019836\n",
|
||||
"day 110, sell 1 unit at price 824.320007, investment -2.881786 %, total balance 3479.339843,\n",
|
||||
"day 111, sell 1 unit at price 823.559998, investment -3.351640 %, total balance 4302.899841,\n",
|
||||
"day 112: buy 1 unit at price 837.169983, total balance 3465.729858\n",
|
||||
"day 118, sell 1 unit at price 872.299988, investment 5.148321 %, total balance 4338.029846,\n",
|
||||
"day 121, sell 1 unit at price 905.960022, investment 10.809952 %, total balance 5243.989868,\n",
|
||||
"day 122, sell 1 unit at price 912.570007, investment 12.050147 %, total balance 6156.559875,\n",
|
||||
"day 123, sell 1 unit at price 916.440002, investment 11.827798 %, total balance 7072.999877,\n",
|
||||
"day 125: buy 1 unit at price 931.659973, total balance 6141.339904\n",
|
||||
"day 128: buy 1 unit at price 932.169983, total balance 5209.169921\n",
|
||||
"day 132, sell 1 unit at price 937.080017, investment 12.709740 %, total balance 6146.249938,\n",
|
||||
"day 133, sell 1 unit at price 943.000000, investment 13.905396 %, total balance 7089.249938,\n",
|
||||
"day 134: buy 1 unit at price 919.619995, total balance 6169.629943\n",
|
||||
"day 136, sell 1 unit at price 934.010010, investment 13.250401 %, total balance 7103.639953,\n",
|
||||
"day 137, sell 1 unit at price 941.859985, investment 12.505226 %, total balance 8045.499938,\n",
|
||||
"day 139, sell 1 unit at price 954.960022, investment 2.500918 %, total balance 9000.459960,\n",
|
||||
"day 140: buy 1 unit at price 969.539978, total balance 8030.919982\n",
|
||||
"day 143: buy 1 unit at price 964.859985, total balance 7066.059997\n",
|
||||
"day 149, sell 1 unit at price 983.409973, investment 5.496850 %, total balance 8049.469970,\n",
|
||||
"day 150: buy 1 unit at price 949.830017, total balance 7099.639953\n",
|
||||
"day 152, sell 1 unit at price 953.400024, investment 3.673260 %, total balance 8053.039977,\n",
|
||||
"day 153, sell 1 unit at price 950.760010, investment -1.936998 %, total balance 9003.799987,\n",
|
||||
"day 154, sell 1 unit at price 942.309998, investment -2.337125 %, total balance 9946.109985,\n",
|
||||
"day 156: buy 1 unit at price 957.369995, total balance 8988.739990\n",
|
||||
"day 157, sell 1 unit at price 950.630005, investment 0.084224 %, total balance 9939.369995,\n",
|
||||
"day 159, sell 1 unit at price 957.090027, investment -0.029243 %, total balance 10896.460022,\n",
|
||||
"day 161: buy 1 unit at price 952.270020, total balance 9944.190002\n",
|
||||
"day 163: buy 1 unit at price 940.489990, total balance 9003.700012\n",
|
||||
"day 167, sell 1 unit at price 911.710022, investment -4.259296 %, total balance 9915.410034,\n",
|
||||
"day 168: buy 1 unit at price 906.690002, total balance 9008.720032\n",
|
||||
"day 170, sell 1 unit at price 928.799988, investment -1.242969 %, total balance 9937.520020,\n",
|
||||
"day 171, sell 1 unit at price 930.090027, investment 2.580819 %, total balance 10867.610047,\n",
|
||||
"day 188: buy 1 unit at price 923.650024, total balance 9943.960023\n",
|
||||
"day 191, sell 1 unit at price 926.789978, investment 0.339951 %, total balance 10870.750001,\n",
|
||||
"day 196: buy 1 unit at price 922.219971, total balance 9948.530030\n",
|
||||
"day 197: buy 1 unit at price 926.960022, total balance 9021.570008\n",
|
||||
"day 198, sell 1 unit at price 910.979980, investment -1.218797 %, total balance 9932.549988,\n",
|
||||
"day 199: buy 1 unit at price 910.669983, total balance 9021.880005\n",
|
||||
"day 202, sell 1 unit at price 927.000000, investment 0.004313 %, total balance 9948.880005,\n",
|
||||
"day 203: buy 1 unit at price 921.280029, total balance 9027.599976\n",
|
||||
"day 204, sell 1 unit at price 915.890015, investment 0.573208 %, total balance 9943.489991,\n",
|
||||
"day 205: buy 1 unit at price 913.809998, total balance 9029.679993\n",
|
||||
"day 206, sell 1 unit at price 921.289978, investment 0.001080 %, total balance 9950.969971,\n",
|
||||
"day 207: buy 1 unit at price 929.570007, total balance 9021.399964\n",
|
||||
"day 208, sell 1 unit at price 939.330017, investment 2.792705 %, total balance 9960.729981,\n",
|
||||
"day 209: buy 1 unit at price 937.340027, total balance 9023.389954\n",
|
||||
"day 210: buy 1 unit at price 928.450012, total balance 8094.939942\n",
|
||||
"day 212: buy 1 unit at price 935.950012, total balance 7158.989930\n",
|
||||
"day 213, sell 1 unit at price 926.500000, investment -0.330261 %, total balance 8085.489930,\n",
|
||||
"day 219: buy 1 unit at price 915.000000, total balance 7170.489930\n",
|
||||
"day 220, sell 1 unit at price 921.809998, investment -1.656819 %, total balance 8092.299928,\n",
|
||||
"day 221, sell 1 unit at price 931.580017, investment 0.337122 %, total balance 9023.879945,\n",
|
||||
"day 222, sell 1 unit at price 932.450012, investment -0.373952 %, total balance 9956.329957,\n",
|
||||
"day 223: buy 1 unit at price 928.530029, total balance 9027.799928\n",
|
||||
"day 226: buy 1 unit at price 944.489990, total balance 8083.309938\n",
|
||||
"day 227, sell 1 unit at price 949.500000, investment 3.770492 %, total balance 9032.809938,\n",
|
||||
"day 228: buy 1 unit at price 959.109985, total balance 8073.699953\n",
|
||||
"day 229, sell 1 unit at price 953.270020, investment 2.664426 %, total balance 9026.969973,\n",
|
||||
"day 231: buy 1 unit at price 951.679993, total balance 8075.289980\n",
|
||||
"day 232, sell 1 unit at price 969.960022, investment 2.696697 %, total balance 9045.250002,\n",
|
||||
"day 233, sell 1 unit at price 978.890015, investment 2.062332 %, total balance 10024.140017,\n",
|
||||
"day 234, sell 1 unit at price 977.000000, investment 2.660559 %, total balance 11001.140017,\n",
|
||||
"day 235: buy 1 unit at price 972.599976, total balance 10028.540041\n",
|
||||
"day 238: buy 1 unit at price 989.679993, total balance 9038.860048\n",
|
||||
"day 240, sell 1 unit at price 992.179993, investment 2.013162 %, total balance 10031.040041,\n",
|
||||
"day 241, sell 1 unit at price 992.809998, investment 0.316264 %, total balance 11023.850039,\n",
|
||||
"day 247: buy 1 unit at price 972.559998, total balance 10051.290041\n",
|
||||
"day 248, sell 1 unit at price 1019.270020, investment 4.802791 %, total balance 11070.560061,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
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|
||||
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|
||||
"name": "python3"
|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+544
@@ -0,0 +1,544 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
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|
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|
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||||
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|
||||
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|
||||
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|
||||
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|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from collections import deque\n",
|
||||
"import random\n",
|
||||
"\n",
|
||||
"class Actor:\n",
|
||||
" def __init__(self, name, input_size, output_size, size_layer):\n",
|
||||
" with tf.variable_scope(name):\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, None, input_size))\n",
|
||||
" self.hidden_layer = tf.placeholder(tf.float32, (None, 2 * size_layer))\n",
|
||||
" cell = tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
|
||||
" self.rnn,self.last_state = tf.nn.dynamic_rnn(inputs=self.X, cell=cell,\n",
|
||||
" dtype=tf.float32,\n",
|
||||
" initial_state=self.hidden_layer)\n",
|
||||
" self.logits = tf.layers.dense(self.rnn[:,-1], output_size)\n",
|
||||
"\n",
|
||||
"class Critic:\n",
|
||||
" def __init__(self, name, input_size, output_size, size_layer, learning_rate):\n",
|
||||
" with tf.variable_scope(name):\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, None, input_size))\n",
|
||||
" self.Y = tf.placeholder(tf.float32, (None, output_size))\n",
|
||||
" self.hidden_layer = tf.placeholder(tf.float32, (None, 2 * size_layer))\n",
|
||||
" self.REWARD = tf.placeholder(tf.float32, (None, 1))\n",
|
||||
" feed_critic = tf.layers.dense(self.X, size_layer, activation = tf.nn.relu)\n",
|
||||
" cell = tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
|
||||
" self.rnn,self.last_state = tf.nn.dynamic_rnn(inputs=self.X, cell=cell,\n",
|
||||
" dtype=tf.float32,\n",
|
||||
" initial_state=self.hidden_layer)\n",
|
||||
" feed_critic = tf.layers.dense(self.rnn[:,-1], output_size, activation = tf.nn.relu) + self.Y\n",
|
||||
" feed_critic = tf.layers.dense(feed_critic, size_layer//2, activation = tf.nn.relu)\n",
|
||||
" self.logits = tf.layers.dense(feed_critic, 1)\n",
|
||||
" self.cost = tf.reduce_mean(tf.square(self.REWARD - self.logits))\n",
|
||||
" self.optimizer = tf.train.AdamOptimizer(learning_rate).minimize(self.cost)\n",
|
||||
" \n",
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" LEARNING_RATE = 0.001\n",
|
||||
" BATCH_SIZE = 32\n",
|
||||
" LAYER_SIZE = 256\n",
|
||||
" OUTPUT_SIZE = 3\n",
|
||||
" EPSILON = 0.5\n",
|
||||
" DECAY_RATE = 0.005\n",
|
||||
" MIN_EPSILON = 0.1\n",
|
||||
" GAMMA = 0.99\n",
|
||||
" MEMORIES = deque()\n",
|
||||
" MEMORY_SIZE = 300\n",
|
||||
" COPY = 1000\n",
|
||||
" T_COPY = 0\n",
|
||||
"\n",
|
||||
" def __init__(self, state_size, window_size, trend, skip):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.INITIAL_FEATURES = np.zeros((4, self.state_size))\n",
|
||||
" self.skip = skip\n",
|
||||
" tf.reset_default_graph()\n",
|
||||
" self.actor = Actor('actor-original', self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE)\n",
|
||||
" self.actor_target = Actor('actor-target', self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE)\n",
|
||||
" self.critic = Critic('critic-original', self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE, self.LEARNING_RATE)\n",
|
||||
" self.critic_target = Critic('critic-target', self.state_size, self.OUTPUT_SIZE, \n",
|
||||
" self.LAYER_SIZE, self.LEARNING_RATE)\n",
|
||||
" self.grad_critic = tf.gradients(self.critic.logits, self.critic.Y)\n",
|
||||
" self.actor_critic_grad = tf.placeholder(tf.float32, [None, self.OUTPUT_SIZE])\n",
|
||||
" weights_actor = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='actor')\n",
|
||||
" self.grad_actor = tf.gradients(self.actor.logits, weights_actor, -self.actor_critic_grad)\n",
|
||||
" grads = zip(self.grad_actor, weights_actor)\n",
|
||||
" self.optimizer = tf.train.AdamOptimizer(self.LEARNING_RATE).apply_gradients(grads)\n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
" \n",
|
||||
" def _assign(self, from_name, to_name):\n",
|
||||
" from_w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=from_name)\n",
|
||||
" to_w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=to_name)\n",
|
||||
" for i in range(len(from_w)):\n",
|
||||
" assign_op = to_w[i].assign(from_w[i])\n",
|
||||
" self.sess.run(assign_op)\n",
|
||||
" \n",
|
||||
" def _memorize(self, state, action, reward, new_state, dead, rnn_state):\n",
|
||||
" self.MEMORIES.append((state, action, reward, new_state, dead, rnn_state))\n",
|
||||
" if len(self.MEMORIES) > self.MEMORY_SIZE:\n",
|
||||
" self.MEMORIES.popleft()\n",
|
||||
" \n",
|
||||
" def _select_action(self, state):\n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" prediction = self.sess.run(self.actor.logits, feed_dict={self.actor.X:[state]})[0]\n",
|
||||
" action = np.argmax(prediction)\n",
|
||||
" return action\n",
|
||||
" \n",
|
||||
" def _construct_memories_and_train(self, replay):\n",
|
||||
" states = np.array([a[0] for a in replay])\n",
|
||||
" new_states = np.array([a[3] for a in replay])\n",
|
||||
" init_values = np.array([a[-1] for a in replay])\n",
|
||||
" Q = self.sess.run(self.actor.logits, feed_dict={self.actor.X: states,\n",
|
||||
" self.actor.hidden_layer: init_values})\n",
|
||||
" Q_target = self.sess.run(self.actor_target.logits, feed_dict={self.actor_target.X: states,\n",
|
||||
" self.actor_target.hidden_layer: init_values})\n",
|
||||
" grads = self.sess.run(self.grad_critic, feed_dict={self.critic.X:states, self.critic.Y:Q,\n",
|
||||
" self.critic.hidden_layer: init_values})[0]\n",
|
||||
" self.sess.run(self.optimizer, feed_dict={self.actor.X:states, self.actor_critic_grad:grads,\n",
|
||||
" self.actor.hidden_layer: init_values})\n",
|
||||
" \n",
|
||||
" rewards = np.array([a[2] for a in replay]).reshape((-1, 1))\n",
|
||||
" rewards_target = self.sess.run(self.critic_target.logits, \n",
|
||||
" feed_dict={self.critic_target.X:new_states,self.critic_target.Y:Q_target,\n",
|
||||
" self.critic_target.hidden_layer: init_values})\n",
|
||||
" for i in range(len(replay)):\n",
|
||||
" if not replay[0][-2]:\n",
|
||||
" rewards[i] += self.GAMMA * rewards_target[i]\n",
|
||||
" cost, _ = self.sess.run([self.critic.cost, self.critic.optimizer], \n",
|
||||
" feed_dict={self.critic.X:states, self.critic.Y:Q, self.critic.REWARD:rewards,\n",
|
||||
" self.critic.hidden_layer: init_values})\n",
|
||||
" return cost\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array(res)\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" init_value = np.zeros((1, 2 * self.LAYER_SIZE))\n",
|
||||
" for k in range(self.INITIAL_FEATURES.shape[0]):\n",
|
||||
" self.INITIAL_FEATURES[k,:] = state\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" \n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action, last_state = self.sess.run([self.actor.logits,\n",
|
||||
" self.actor.last_state],\n",
|
||||
" feed_dict={self.actor.X:[self.INITIAL_FEATURES],\n",
|
||||
" self.actor.hidden_layer:init_value})\n",
|
||||
" action, init_value = np.argmax(action[0]), last_state\n",
|
||||
" \n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" new_state = np.append([self.get_state(t + 1)], self.INITIAL_FEATURES[:3, :], axis = 0)\n",
|
||||
" self.INITIAL_FEATURES = new_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" init_value = np.zeros((1, 2 * self.LAYER_SIZE))\n",
|
||||
" for k in range(self.INITIAL_FEATURES.shape[0]):\n",
|
||||
" self.INITIAL_FEATURES[k,:] = state\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" if (self.T_COPY + 1) % self.COPY == 0:\n",
|
||||
" self._assign('actor-original', 'actor-target')\n",
|
||||
" self._assign('critic-original', 'critic-target')\n",
|
||||
" \n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action, last_state = self.sess.run([self.actor.logits,\n",
|
||||
" self.actor.last_state],\n",
|
||||
" feed_dict={self.actor.X:[self.INITIAL_FEATURES],\n",
|
||||
" self.actor.hidden_layer:init_value})\n",
|
||||
" action, init_value = np.argmax(action[0]), last_state\n",
|
||||
" \n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" \n",
|
||||
" invest = ((starting_money - initial_money) / initial_money)\n",
|
||||
" new_state = np.append([self.get_state(t + 1)], self.INITIAL_FEATURES[:3, :], axis = 0)\n",
|
||||
" self._memorize(self.INITIAL_FEATURES, action, invest, new_state, \n",
|
||||
" starting_money < initial_money, init_value[0])\n",
|
||||
" batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)\n",
|
||||
" self.INITIAL_FEATURES = new_state\n",
|
||||
" replay = random.sample(self.MEMORIES, batch_size)\n",
|
||||
" cost = self._construct_memories_and_train(replay)\n",
|
||||
" self.T_COPY += 1\n",
|
||||
" self.EPSILON = self.MIN_EPSILON + (1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)\n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f46cd19b6d8>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f46cd102ef0>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f46ccc7ce10>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f46cc5685f8>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"epoch: 10, total rewards: 1158.549991.3, cost: 0.046632, total money: 4247.099979\n",
|
||||
"epoch: 20, total rewards: 466.185119.3, cost: 0.035100, total money: 5537.135131\n",
|
||||
"epoch: 30, total rewards: 477.615173.3, cost: 0.330107, total money: 975.775206\n",
|
||||
"epoch: 40, total rewards: 1200.205012.3, cost: 0.215860, total money: 10180.934992\n",
|
||||
"epoch: 50, total rewards: 283.615237.3, cost: 0.116108, total money: 3314.845217\n",
|
||||
"epoch: 60, total rewards: 324.265078.3, cost: 0.435482, total money: 9334.585085\n",
|
||||
"epoch: 70, total rewards: 587.429873.3, cost: 0.749076, total money: 4785.129884\n",
|
||||
"epoch: 80, total rewards: 1248.729918.3, cost: 0.167420, total money: 663.739866\n",
|
||||
"epoch: 90, total rewards: 520.270204.3, cost: 0.006982, total money: 9503.630189\n",
|
||||
"epoch: 100, total rewards: 195.270142.3, cost: 0.153058, total money: 10195.270142\n",
|
||||
"epoch: 110, total rewards: 74.399840.3, cost: 0.350105, total money: 10074.399840\n",
|
||||
"epoch: 120, total rewards: 2842.805359.3, cost: 0.074852, total money: 7832.085327\n",
|
||||
"epoch: 130, total rewards: 509.049985.3, cost: 0.053447, total money: 8518.609983\n",
|
||||
"epoch: 140, total rewards: -2.900205.3, cost: 0.015182, total money: 8979.989810\n",
|
||||
"epoch: 150, total rewards: 93.080022.3, cost: 0.008775, total money: 10093.080022\n",
|
||||
"epoch: 160, total rewards: 89.794983.3, cost: 0.107893, total money: 10089.794983\n",
|
||||
"epoch: 170, total rewards: 222.045106.3, cost: 0.189179, total money: 10222.045106\n",
|
||||
"epoch: 180, total rewards: -57.619995.3, cost: 0.002425, total money: 8925.739990\n",
|
||||
"epoch: 190, total rewards: 21.009889.3, cost: 0.005919, total money: 10021.009889\n",
|
||||
"epoch: 200, total rewards: 201.354980.3, cost: 0.002352, total money: 10201.354980\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"batch_size = 32\n",
|
||||
"agent = Agent(state_size = window_size, \n",
|
||||
" window_size = window_size, \n",
|
||||
" trend = close, \n",
|
||||
" skip = skip)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 0: buy 1 unit at price 768.700012, total balance 9231.299988\n",
|
||||
"day 1, sell 1 unit at price 762.130005, investment -0.854691 %, total balance 9993.429993,\n",
|
||||
"day 3: buy 1 unit at price 782.520020, total balance 9210.909973\n",
|
||||
"day 4, sell 1 unit at price 790.510010, investment 1.021059 %, total balance 10001.419983,\n",
|
||||
"day 22: buy 1 unit at price 762.520020, total balance 9238.899963\n",
|
||||
"day 23: buy 1 unit at price 759.109985, total balance 8479.789978\n",
|
||||
"day 24, sell 1 unit at price 771.190002, investment 1.137017 %, total balance 9250.979980,\n",
|
||||
"day 26, sell 1 unit at price 789.289978, investment 3.975708 %, total balance 10040.269958,\n",
|
||||
"day 31: buy 1 unit at price 790.799988, total balance 9249.469970\n",
|
||||
"day 32, sell 1 unit at price 794.200012, investment 0.429947 %, total balance 10043.669982,\n",
|
||||
"day 33: buy 1 unit at price 796.419983, total balance 9247.249999\n",
|
||||
"day 34, sell 1 unit at price 794.559998, investment -0.233543 %, total balance 10041.809997,\n",
|
||||
"day 39: buy 1 unit at price 782.789978, total balance 9259.020019\n",
|
||||
"day 40: buy 1 unit at price 771.820007, total balance 8487.200012\n",
|
||||
"day 42, sell 1 unit at price 786.900024, investment 0.525051 %, total balance 9274.100036,\n",
|
||||
"day 45, sell 1 unit at price 806.650024, investment 4.512712 %, total balance 10080.750060,\n",
|
||||
"day 64: buy 1 unit at price 801.340027, total balance 9279.410033\n",
|
||||
"day 65, sell 1 unit at price 806.969971, investment 0.702566 %, total balance 10086.380004,\n",
|
||||
"day 68: buy 1 unit at price 813.669983, total balance 9272.710021\n",
|
||||
"day 70, sell 1 unit at price 820.450012, investment 0.833265 %, total balance 10093.160033,\n",
|
||||
"day 103: buy 1 unit at price 838.549988, total balance 9254.610045\n",
|
||||
"day 104, sell 1 unit at price 834.570007, investment -0.474627 %, total balance 10089.180052,\n",
|
||||
"day 110: buy 1 unit at price 824.320007, total balance 9264.860045\n",
|
||||
"day 111, sell 1 unit at price 823.559998, investment -0.092198 %, total balance 10088.420043,\n",
|
||||
"day 114: buy 1 unit at price 838.210022, total balance 9250.210021\n",
|
||||
"day 115, sell 1 unit at price 841.650024, investment 0.410399 %, total balance 10091.860045,\n",
|
||||
"day 128: buy 1 unit at price 932.169983, total balance 9159.690062\n",
|
||||
"day 129: buy 1 unit at price 928.780029, total balance 8230.910033\n",
|
||||
"day 131, sell 1 unit at price 932.219971, investment 0.005363 %, total balance 9163.130004,\n",
|
||||
"day 132, sell 1 unit at price 937.080017, investment 0.893644 %, total balance 10100.210021,\n",
|
||||
"day 144: buy 1 unit at price 966.950012, total balance 9133.260009\n",
|
||||
"day 145, sell 1 unit at price 975.599976, investment 0.894562 %, total balance 10108.859985,\n",
|
||||
"day 148: buy 1 unit at price 980.940002, total balance 9127.919983\n",
|
||||
"day 149, sell 1 unit at price 983.409973, investment 0.251796 %, total balance 10111.329956,\n",
|
||||
"day 151: buy 1 unit at price 942.900024, total balance 9168.429932\n",
|
||||
"day 153, sell 1 unit at price 950.760010, investment 0.833597 %, total balance 10119.189942,\n",
|
||||
"day 168: buy 1 unit at price 906.690002, total balance 9212.499940\n",
|
||||
"day 169, sell 1 unit at price 918.590027, investment 1.312469 %, total balance 10131.089967,\n",
|
||||
"day 171: buy 1 unit at price 930.090027, total balance 9200.999940\n",
|
||||
"day 172, sell 1 unit at price 943.830017, investment 1.477275 %, total balance 10144.829957,\n",
|
||||
"day 175: buy 1 unit at price 953.419983, total balance 9191.409974\n",
|
||||
"day 176, sell 1 unit at price 965.400024, investment 1.256533 %, total balance 10156.809998,\n",
|
||||
"day 178: buy 1 unit at price 968.150024, total balance 9188.659974\n",
|
||||
"day 179, sell 1 unit at price 972.919983, investment 0.492688 %, total balance 10161.579957,\n",
|
||||
"day 192: buy 1 unit at price 922.900024, total balance 9238.679933\n",
|
||||
"day 193, sell 1 unit at price 907.239990, investment -1.696829 %, total balance 10145.919923,\n",
|
||||
"day 194: buy 1 unit at price 914.390015, total balance 9231.529908\n",
|
||||
"day 196: buy 1 unit at price 922.219971, total balance 8309.309937\n",
|
||||
"day 197, sell 1 unit at price 926.960022, investment 1.374688 %, total balance 9236.269959,\n",
|
||||
"day 198, sell 1 unit at price 910.979980, investment -1.218797 %, total balance 10147.249939,\n",
|
||||
"day 207: buy 1 unit at price 929.570007, total balance 9217.679932\n",
|
||||
"day 208: buy 1 unit at price 939.330017, total balance 8278.349915\n",
|
||||
"day 209, sell 1 unit at price 937.340027, investment 0.835872 %, total balance 9215.689942,\n",
|
||||
"day 210, sell 1 unit at price 928.450012, investment -1.158273 %, total balance 10144.139954,\n",
|
||||
"day 211: buy 1 unit at price 927.809998, total balance 9216.329956\n",
|
||||
"day 212, sell 1 unit at price 935.950012, investment 0.877336 %, total balance 10152.279968,\n",
|
||||
"day 214: buy 1 unit at price 929.080017, total balance 9223.199951\n",
|
||||
"day 215, sell 1 unit at price 932.070007, investment 0.321823 %, total balance 10155.269958,\n",
|
||||
"day 226: buy 1 unit at price 944.489990, total balance 9210.779968\n",
|
||||
"day 227, sell 1 unit at price 949.500000, investment 0.530446 %, total balance 10160.279968,\n",
|
||||
"day 233: buy 1 unit at price 978.890015, total balance 9181.389953\n",
|
||||
"day 234, sell 1 unit at price 977.000000, investment -0.193077 %, total balance 10158.389953,\n",
|
||||
"day 243: buy 1 unit at price 988.200012, total balance 9170.189941\n",
|
||||
"day 244, sell 1 unit at price 968.450012, investment -1.998583 %, total balance 10138.639953,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
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truncated
|
||||
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},
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||||
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|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
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||||
"cell_type": "code",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from collections import deque\n",
|
||||
"import random\n",
|
||||
"\n",
|
||||
"class Actor:\n",
|
||||
" def __init__(self, name, input_size, output_size, size_layer):\n",
|
||||
" with tf.variable_scope(name):\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, None, input_size))\n",
|
||||
" self.hidden_layer = tf.placeholder(tf.float32, (None, 2 * size_layer))\n",
|
||||
" cell = tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
|
||||
" self.rnn,self.last_state = tf.nn.dynamic_rnn(inputs=self.X, cell=cell,\n",
|
||||
" dtype=tf.float32,\n",
|
||||
" initial_state=self.hidden_layer)\n",
|
||||
" tensor_action, tensor_validation = tf.split(self.rnn[:,-1],2,1)\n",
|
||||
" feed_action = tf.layers.dense(tensor_action, output_size)\n",
|
||||
" feed_validation = tf.layers.dense(tensor_validation, 1)\n",
|
||||
" self.logits = feed_validation + tf.subtract(feed_action,\n",
|
||||
" tf.reduce_mean(feed_action,axis=1,keep_dims=True))\n",
|
||||
"\n",
|
||||
"class Critic:\n",
|
||||
" def __init__(self, name, input_size, output_size, size_layer, learning_rate):\n",
|
||||
" with tf.variable_scope(name):\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, None, input_size))\n",
|
||||
" self.Y = tf.placeholder(tf.float32, (None, output_size))\n",
|
||||
" self.hidden_layer = tf.placeholder(tf.float32, (None, 2 * size_layer))\n",
|
||||
" self.REWARD = tf.placeholder(tf.float32, (None, 1))\n",
|
||||
" feed_critic = tf.layers.dense(self.X, size_layer, activation = tf.nn.relu)\n",
|
||||
" cell = tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
|
||||
" self.rnn,self.last_state = tf.nn.dynamic_rnn(inputs=self.X, cell=cell,\n",
|
||||
" dtype=tf.float32,\n",
|
||||
" initial_state=self.hidden_layer)\n",
|
||||
" tensor_action, tensor_validation = tf.split(self.rnn[:,-1],2,1)\n",
|
||||
" feed_action = tf.layers.dense(tensor_action, output_size)\n",
|
||||
" feed_validation = tf.layers.dense(tensor_validation, 1)\n",
|
||||
" feed_critic = feed_validation + tf.subtract(feed_action,tf.reduce_mean(feed_action,axis=1,keep_dims=True))\n",
|
||||
" feed_critic = tf.nn.relu(feed_critic) + self.Y\n",
|
||||
" feed_critic = tf.layers.dense(feed_critic, size_layer//2, activation = tf.nn.relu)\n",
|
||||
" self.logits = tf.layers.dense(feed_critic, 1)\n",
|
||||
" self.cost = tf.reduce_mean(tf.square(self.REWARD - self.logits))\n",
|
||||
" self.optimizer = tf.train.AdamOptimizer(learning_rate).minimize(self.cost)\n",
|
||||
" \n",
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" LEARNING_RATE = 0.001\n",
|
||||
" BATCH_SIZE = 32\n",
|
||||
" LAYER_SIZE = 256\n",
|
||||
" OUTPUT_SIZE = 3\n",
|
||||
" EPSILON = 0.5\n",
|
||||
" DECAY_RATE = 0.005\n",
|
||||
" MIN_EPSILON = 0.1\n",
|
||||
" GAMMA = 0.99\n",
|
||||
" MEMORIES = deque()\n",
|
||||
" MEMORY_SIZE = 300\n",
|
||||
" COPY = 1000\n",
|
||||
" T_COPY = 0\n",
|
||||
"\n",
|
||||
" def __init__(self, state_size, window_size, trend, skip):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.INITIAL_FEATURES = np.zeros((4, self.state_size))\n",
|
||||
" self.skip = skip\n",
|
||||
" tf.reset_default_graph()\n",
|
||||
" self.actor = Actor('actor-original', self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE)\n",
|
||||
" self.actor_target = Actor('actor-target', self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE)\n",
|
||||
" self.critic = Critic('critic-original', self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE, self.LEARNING_RATE)\n",
|
||||
" self.critic_target = Critic('critic-target', self.state_size, self.OUTPUT_SIZE, \n",
|
||||
" self.LAYER_SIZE, self.LEARNING_RATE)\n",
|
||||
" self.grad_critic = tf.gradients(self.critic.logits, self.critic.Y)\n",
|
||||
" self.actor_critic_grad = tf.placeholder(tf.float32, [None, self.OUTPUT_SIZE])\n",
|
||||
" weights_actor = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='actor')\n",
|
||||
" self.grad_actor = tf.gradients(self.actor.logits, weights_actor, -self.actor_critic_grad)\n",
|
||||
" grads = zip(self.grad_actor, weights_actor)\n",
|
||||
" self.optimizer = tf.train.AdamOptimizer(self.LEARNING_RATE).apply_gradients(grads)\n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
" \n",
|
||||
" def _assign(self, from_name, to_name):\n",
|
||||
" from_w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=from_name)\n",
|
||||
" to_w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=to_name)\n",
|
||||
" for i in range(len(from_w)):\n",
|
||||
" assign_op = to_w[i].assign(from_w[i])\n",
|
||||
" self.sess.run(assign_op)\n",
|
||||
" \n",
|
||||
" def _memorize(self, state, action, reward, new_state, dead, rnn_state):\n",
|
||||
" self.MEMORIES.append((state, action, reward, new_state, dead, rnn_state))\n",
|
||||
" if len(self.MEMORIES) > self.MEMORY_SIZE:\n",
|
||||
" self.MEMORIES.popleft()\n",
|
||||
" \n",
|
||||
" def _select_action(self, state):\n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" prediction = self.sess.run(self.actor.logits, feed_dict={self.actor.X:[state]})[0]\n",
|
||||
" action = np.argmax(prediction)\n",
|
||||
" return action\n",
|
||||
" \n",
|
||||
" def _construct_memories_and_train(self, replay):\n",
|
||||
" states = np.array([a[0] for a in replay])\n",
|
||||
" new_states = np.array([a[3] for a in replay])\n",
|
||||
" init_values = np.array([a[-1] for a in replay])\n",
|
||||
" Q = self.sess.run(self.actor.logits, feed_dict={self.actor.X: states,\n",
|
||||
" self.actor.hidden_layer: init_values})\n",
|
||||
" Q_target = self.sess.run(self.actor_target.logits, feed_dict={self.actor_target.X: states,\n",
|
||||
" self.actor_target.hidden_layer: init_values})\n",
|
||||
" grads = self.sess.run(self.grad_critic, feed_dict={self.critic.X:states, self.critic.Y:Q,\n",
|
||||
" self.critic.hidden_layer: init_values})[0]\n",
|
||||
" self.sess.run(self.optimizer, feed_dict={self.actor.X:states, self.actor_critic_grad:grads,\n",
|
||||
" self.actor.hidden_layer: init_values})\n",
|
||||
" \n",
|
||||
" rewards = np.array([a[2] for a in replay]).reshape((-1, 1))\n",
|
||||
" rewards_target = self.sess.run(self.critic_target.logits, \n",
|
||||
" feed_dict={self.critic_target.X:new_states,self.critic_target.Y:Q_target,\n",
|
||||
" self.critic_target.hidden_layer: init_values})\n",
|
||||
" for i in range(len(replay)):\n",
|
||||
" if not replay[0][-2]:\n",
|
||||
" rewards[i] += self.GAMMA * rewards_target[i]\n",
|
||||
" cost, _ = self.sess.run([self.critic.cost, self.critic.optimizer], \n",
|
||||
" feed_dict={self.critic.X:states, self.critic.Y:Q, self.critic.REWARD:rewards,\n",
|
||||
" self.critic.hidden_layer: init_values})\n",
|
||||
" return cost\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array(res)\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" init_value = np.zeros((1, 2 * self.LAYER_SIZE))\n",
|
||||
" for k in range(self.INITIAL_FEATURES.shape[0]):\n",
|
||||
" self.INITIAL_FEATURES[k,:] = state\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" \n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action, last_state = self.sess.run([self.actor.logits,\n",
|
||||
" self.actor.last_state],\n",
|
||||
" feed_dict={self.actor.X:[self.INITIAL_FEATURES],\n",
|
||||
" self.actor.hidden_layer:init_value})\n",
|
||||
" action, init_value = np.argmax(action[0]), last_state\n",
|
||||
" \n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" new_state = np.append([self.get_state(t + 1)], self.INITIAL_FEATURES[:3, :], axis = 0)\n",
|
||||
" self.INITIAL_FEATURES = new_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" init_value = np.zeros((1, 2 * self.LAYER_SIZE))\n",
|
||||
" for k in range(self.INITIAL_FEATURES.shape[0]):\n",
|
||||
" self.INITIAL_FEATURES[k,:] = state\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" if (self.T_COPY + 1) % self.COPY == 0:\n",
|
||||
" self._assign('actor-original', 'actor-target')\n",
|
||||
" self._assign('critic-original', 'critic-target')\n",
|
||||
" \n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action, last_state = self.sess.run([self.actor.logits,\n",
|
||||
" self.actor.last_state],\n",
|
||||
" feed_dict={self.actor.X:[self.INITIAL_FEATURES],\n",
|
||||
" self.actor.hidden_layer:init_value})\n",
|
||||
" action, init_value = np.argmax(action[0]), last_state\n",
|
||||
" \n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" \n",
|
||||
" invest = ((starting_money - initial_money) / initial_money)\n",
|
||||
" new_state = np.append([self.get_state(t + 1)], self.INITIAL_FEATURES[:3, :], axis = 0)\n",
|
||||
" self._memorize(self.INITIAL_FEATURES, action, invest, new_state, \n",
|
||||
" starting_money < initial_money, init_value[0])\n",
|
||||
" batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)\n",
|
||||
" self.INITIAL_FEATURES = new_state\n",
|
||||
" replay = random.sample(self.MEMORIES, batch_size)\n",
|
||||
" cost = self._construct_memories_and_train(replay)\n",
|
||||
" self.T_COPY += 1\n",
|
||||
" self.EPSILON = self.MIN_EPSILON + (1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)\n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f8ac3f890b8>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"WARNING:tensorflow:From <ipython-input-3-b82c6dfdfdbf>:17: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
|
||||
"Instructions for updating:\n",
|
||||
"keep_dims is deprecated, use keepdims instead\n",
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f8a4343d2b0>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f8a42e484e0>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f8a42670c50>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"epoch: 10, total rewards: 1217.199710.3, cost: 0.428947, total money: 9258.459720\n",
|
||||
"epoch: 20, total rewards: 154.669988.3, cost: 0.205311, total money: 8167.020025\n",
|
||||
"epoch: 30, total rewards: 225.259892.3, cost: 0.080974, total money: 10225.259892\n",
|
||||
"epoch: 40, total rewards: 1857.994754.3, cost: 0.147440, total money: 7906.464724\n",
|
||||
"epoch: 50, total rewards: 864.365355.3, cost: 0.133079, total money: 3145.525327\n",
|
||||
"epoch: 60, total rewards: 252.179754.3, cost: 0.349886, total money: 10252.179754\n",
|
||||
"epoch: 70, total rewards: 2285.265256.3, cost: 0.122869, total money: 841.845272\n",
|
||||
"epoch: 80, total rewards: 2273.160095.3, cost: 0.042144, total money: 1779.580078\n",
|
||||
"epoch: 90, total rewards: 695.794921.3, cost: 0.652829, total money: 10695.794921\n",
|
||||
"epoch: 100, total rewards: -63.870359.3, cost: 0.026901, total money: 9936.129641\n",
|
||||
"epoch: 110, total rewards: 1660.049986.3, cost: 0.050525, total money: 236.529905\n",
|
||||
"epoch: 120, total rewards: 2137.930355.3, cost: 0.019048, total money: 635.270319\n",
|
||||
"epoch: 130, total rewards: 1263.700071.3, cost: 0.105621, total money: 836.610044\n",
|
||||
"epoch: 140, total rewards: 2582.234985.3, cost: 0.026973, total money: 1985.844970\n",
|
||||
"epoch: 150, total rewards: 1342.129822.3, cost: 0.045669, total money: 1933.479859\n",
|
||||
"epoch: 160, total rewards: 171.394838.3, cost: 0.186082, total money: 9198.064821\n",
|
||||
"epoch: 170, total rewards: 581.185307.3, cost: 0.243257, total money: 26.655338\n",
|
||||
"epoch: 180, total rewards: 109.954956.3, cost: 0.001933, total money: 9092.844971\n",
|
||||
"epoch: 190, total rewards: -85.549868.3, cost: 0.004746, total money: 9914.450132\n",
|
||||
"epoch: 200, total rewards: 94.994872.3, cost: 0.006849, total money: 10094.994872\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"batch_size = 32\n",
|
||||
"agent = Agent(state_size = window_size, \n",
|
||||
" window_size = window_size, \n",
|
||||
" trend = close, \n",
|
||||
" skip = skip)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 0: buy 1 unit at price 768.700012, total balance 9231.299988\n",
|
||||
"day 1, sell 1 unit at price 762.130005, investment -0.854691 %, total balance 9993.429993,\n",
|
||||
"day 3: buy 1 unit at price 782.520020, total balance 9210.909973\n",
|
||||
"day 4, sell 1 unit at price 790.510010, investment 1.021059 %, total balance 10001.419983,\n",
|
||||
"day 22: buy 1 unit at price 762.520020, total balance 9238.899963\n",
|
||||
"day 23: buy 1 unit at price 759.109985, total balance 8479.789978\n",
|
||||
"day 24, sell 1 unit at price 771.190002, investment 1.137017 %, total balance 9250.979980,\n",
|
||||
"day 26, sell 1 unit at price 789.289978, investment 3.975708 %, total balance 10040.269958,\n",
|
||||
"day 31: buy 1 unit at price 790.799988, total balance 9249.469970\n",
|
||||
"day 32, sell 1 unit at price 794.200012, investment 0.429947 %, total balance 10043.669982,\n",
|
||||
"day 33: buy 1 unit at price 796.419983, total balance 9247.249999\n",
|
||||
"day 34, sell 1 unit at price 794.559998, investment -0.233543 %, total balance 10041.809997,\n",
|
||||
"day 39: buy 1 unit at price 782.789978, total balance 9259.020019\n",
|
||||
"day 40: buy 1 unit at price 771.820007, total balance 8487.200012\n",
|
||||
"day 42, sell 1 unit at price 786.900024, investment 0.525051 %, total balance 9274.100036,\n",
|
||||
"day 45, sell 1 unit at price 806.650024, investment 4.512712 %, total balance 10080.750060,\n",
|
||||
"day 64: buy 1 unit at price 801.340027, total balance 9279.410033\n",
|
||||
"day 65, sell 1 unit at price 806.969971, investment 0.702566 %, total balance 10086.380004,\n",
|
||||
"day 68: buy 1 unit at price 813.669983, total balance 9272.710021\n",
|
||||
"day 70, sell 1 unit at price 820.450012, investment 0.833265 %, total balance 10093.160033,\n",
|
||||
"day 103: buy 1 unit at price 838.549988, total balance 9254.610045\n",
|
||||
"day 104, sell 1 unit at price 834.570007, investment -0.474627 %, total balance 10089.180052,\n",
|
||||
"day 110: buy 1 unit at price 824.320007, total balance 9264.860045\n",
|
||||
"day 111, sell 1 unit at price 823.559998, investment -0.092198 %, total balance 10088.420043,\n",
|
||||
"day 114: buy 1 unit at price 838.210022, total balance 9250.210021\n",
|
||||
"day 115, sell 1 unit at price 841.650024, investment 0.410399 %, total balance 10091.860045,\n",
|
||||
"day 128: buy 1 unit at price 932.169983, total balance 9159.690062\n",
|
||||
"day 129: buy 1 unit at price 928.780029, total balance 8230.910033\n",
|
||||
"day 131, sell 1 unit at price 932.219971, investment 0.005363 %, total balance 9163.130004,\n",
|
||||
"day 132, sell 1 unit at price 937.080017, investment 0.893644 %, total balance 10100.210021,\n",
|
||||
"day 144: buy 1 unit at price 966.950012, total balance 9133.260009\n",
|
||||
"day 145, sell 1 unit at price 975.599976, investment 0.894562 %, total balance 10108.859985,\n",
|
||||
"day 148: buy 1 unit at price 980.940002, total balance 9127.919983\n",
|
||||
"day 149, sell 1 unit at price 983.409973, investment 0.251796 %, total balance 10111.329956,\n",
|
||||
"day 151: buy 1 unit at price 942.900024, total balance 9168.429932\n",
|
||||
"day 153, sell 1 unit at price 950.760010, investment 0.833597 %, total balance 10119.189942,\n",
|
||||
"day 168: buy 1 unit at price 906.690002, total balance 9212.499940\n",
|
||||
"day 169, sell 1 unit at price 918.590027, investment 1.312469 %, total balance 10131.089967,\n",
|
||||
"day 171: buy 1 unit at price 930.090027, total balance 9200.999940\n",
|
||||
"day 172, sell 1 unit at price 943.830017, investment 1.477275 %, total balance 10144.829957,\n",
|
||||
"day 175: buy 1 unit at price 953.419983, total balance 9191.409974\n",
|
||||
"day 176, sell 1 unit at price 965.400024, investment 1.256533 %, total balance 10156.809998,\n",
|
||||
"day 178: buy 1 unit at price 968.150024, total balance 9188.659974\n",
|
||||
"day 179, sell 1 unit at price 972.919983, investment 0.492688 %, total balance 10161.579957,\n",
|
||||
"day 192: buy 1 unit at price 922.900024, total balance 9238.679933\n",
|
||||
"day 193, sell 1 unit at price 907.239990, investment -1.696829 %, total balance 10145.919923,\n",
|
||||
"day 194: buy 1 unit at price 914.390015, total balance 9231.529908\n",
|
||||
"day 196: buy 1 unit at price 922.219971, total balance 8309.309937\n",
|
||||
"day 197, sell 1 unit at price 926.960022, investment 1.374688 %, total balance 9236.269959,\n",
|
||||
"day 198, sell 1 unit at price 910.979980, investment -1.218797 %, total balance 10147.249939,\n",
|
||||
"day 207: buy 1 unit at price 929.570007, total balance 9217.679932\n",
|
||||
"day 208: buy 1 unit at price 939.330017, total balance 8278.349915\n",
|
||||
"day 209, sell 1 unit at price 937.340027, investment 0.835872 %, total balance 9215.689942,\n",
|
||||
"day 210, sell 1 unit at price 928.450012, investment -1.158273 %, total balance 10144.139954,\n",
|
||||
"day 211: buy 1 unit at price 927.809998, total balance 9216.329956\n",
|
||||
"day 212, sell 1 unit at price 935.950012, investment 0.877336 %, total balance 10152.279968,\n",
|
||||
"day 214: buy 1 unit at price 929.080017, total balance 9223.199951\n",
|
||||
"day 215, sell 1 unit at price 932.070007, investment 0.321823 %, total balance 10155.269958,\n",
|
||||
"day 226: buy 1 unit at price 944.489990, total balance 9210.779968\n",
|
||||
"day 227, sell 1 unit at price 949.500000, investment 0.530446 %, total balance 10160.279968,\n",
|
||||
"day 233: buy 1 unit at price 978.890015, total balance 9181.389953\n",
|
||||
"day 234, sell 1 unit at price 977.000000, investment -0.193077 %, total balance 10158.389953,\n",
|
||||
"day 243: buy 1 unit at price 988.200012, total balance 9170.189941\n",
|
||||
"day 244, sell 1 unit at price 968.450012, investment -1.998583 %, total balance 10138.639953,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+587
@@ -0,0 +1,587 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from collections import deque\n",
|
||||
"import random\n",
|
||||
"\n",
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" LEARNING_RATE = 0.003\n",
|
||||
" BATCH_SIZE = 32\n",
|
||||
" LAYER_SIZE = 500\n",
|
||||
" OUTPUT_SIZE = 3\n",
|
||||
" EPSILON = 0.5\n",
|
||||
" DECAY_RATE = 0.005\n",
|
||||
" MIN_EPSILON = 0.1\n",
|
||||
" GAMMA = 0.99\n",
|
||||
" MEMORIES = deque()\n",
|
||||
" COPY = 1000\n",
|
||||
" T_COPY = 0\n",
|
||||
" MEMORY_SIZE = 300\n",
|
||||
" \n",
|
||||
" def __init__(self, state_size, window_size, trend, skip):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" tf.reset_default_graph()\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, self.state_size))\n",
|
||||
" self.Y = tf.placeholder(tf.float32, (None, self.state_size))\n",
|
||||
" self.ACTION = tf.placeholder(tf.float32, (None))\n",
|
||||
" self.REWARD = tf.placeholder(tf.float32, (None))\n",
|
||||
" self.batch_size = tf.shape(self.ACTION)[0]\n",
|
||||
" \n",
|
||||
" with tf.variable_scope('curiosity_model'):\n",
|
||||
" action = tf.reshape(self.ACTION, (-1,1))\n",
|
||||
" state_action = tf.concat([self.X, action], axis=1)\n",
|
||||
" save_state = tf.identity(self.Y)\n",
|
||||
" \n",
|
||||
" feed = tf.layers.dense(state_action, 32, activation=tf.nn.relu)\n",
|
||||
" self.curiosity_logits = tf.layers.dense(feed, self.state_size)\n",
|
||||
" self.curiosity_cost = tf.reduce_sum(tf.square(save_state - self.curiosity_logits), axis=1)\n",
|
||||
" \n",
|
||||
" self.curiosity_optimizer = tf.train.RMSPropOptimizer(self.LEARNING_RATE)\\\n",
|
||||
" .minimize(tf.reduce_mean(self.curiosity_cost))\n",
|
||||
" \n",
|
||||
" total_reward = tf.add(self.curiosity_cost, self.REWARD)\n",
|
||||
" \n",
|
||||
" with tf.variable_scope(\"q_model\"):\n",
|
||||
" with tf.variable_scope(\"eval_net\"):\n",
|
||||
" x_action = tf.layers.dense(self.X, 128, tf.nn.relu)\n",
|
||||
" self.logits = tf.layers.dense(x_action, self.OUTPUT_SIZE)\n",
|
||||
" \n",
|
||||
" with tf.variable_scope(\"target_net\"):\n",
|
||||
" y_action = tf.layers.dense(self.Y, 128, tf.nn.relu)\n",
|
||||
" y_q = tf.layers.dense(y_action, self.OUTPUT_SIZE)\n",
|
||||
" \n",
|
||||
" q_target = total_reward + self.GAMMA * tf.reduce_max(y_q, axis=1)\n",
|
||||
" action = tf.cast(self.ACTION, tf.int32)\n",
|
||||
" action_indices = tf.stack([tf.range(self.batch_size, dtype=tf.int32), action], axis=1)\n",
|
||||
" q = tf.gather_nd(params=self.logits, indices=action_indices)\n",
|
||||
" self.cost = tf.losses.mean_squared_error(labels=q_target, predictions=q)\n",
|
||||
" self.optimizer = tf.train.RMSPropOptimizer(self.LEARNING_RATE).minimize(\n",
|
||||
" self.cost, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, \"q_model/eval_net\"))\n",
|
||||
" \n",
|
||||
" t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_model/target_net')\n",
|
||||
" e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_model/eval_net')\n",
|
||||
" self.target_replace_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]\n",
|
||||
" \n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
" \n",
|
||||
" def _memorize(self, state, action, reward, new_state, done):\n",
|
||||
" self.MEMORIES.append((state, action, reward, new_state, done))\n",
|
||||
" if len(self.MEMORIES) > self.MEMORY_SIZE:\n",
|
||||
" self.MEMORIES.popleft()\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array(res)\n",
|
||||
" \n",
|
||||
" def predict(self, inputs):\n",
|
||||
" return self.sess.run(self.logits, feed_dict={self.X:inputs})\n",
|
||||
" \n",
|
||||
" def get_predicted_action(self, sequence):\n",
|
||||
" prediction = self.predict(np.array(sequence))[0]\n",
|
||||
" return np.argmax(prediction)\n",
|
||||
" \n",
|
||||
" def _select_action(self, state):\n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action = self.get_predicted_action([state])\n",
|
||||
" return action\n",
|
||||
" \n",
|
||||
" def _construct_memories(self, replay):\n",
|
||||
" states = np.array([a[0] for a in replay])\n",
|
||||
" actions = np.array([a[1] for a in replay])\n",
|
||||
" rewards = np.array([a[2] for a in replay])\n",
|
||||
" new_states = np.array([a[3] for a in replay])\n",
|
||||
" if (self.T_COPY + 1) % self.COPY == 0:\n",
|
||||
" self.sess.run(self.target_replace_op)\n",
|
||||
" \n",
|
||||
" cost, _ = self.sess.run([self.cost, self.optimizer], feed_dict = {\n",
|
||||
" self.X: states, self.Y: new_states, self.ACTION: actions, self.REWARD: rewards\n",
|
||||
" })\n",
|
||||
" \n",
|
||||
" if (self.T_COPY + 1) % self.COPY == 0:\n",
|
||||
" self.sess.run(self.curiosity_optimizer, feed_dict = {\n",
|
||||
" self.X: states, self.Y: new_states, self.ACTION: actions, self.REWARD: rewards\n",
|
||||
" })\n",
|
||||
" return cost\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self._select_action(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" state = next_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" \n",
|
||||
" action = self._select_action(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" \n",
|
||||
" invest = ((starting_money - initial_money) / initial_money)\n",
|
||||
" \n",
|
||||
" self._memorize(state, action, invest, next_state, starting_money < initial_money)\n",
|
||||
" batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)\n",
|
||||
" state = next_state\n",
|
||||
" replay = random.sample(self.MEMORIES, batch_size)\n",
|
||||
" cost = self._construct_memories(replay)\n",
|
||||
" self.T_COPY += 1\n",
|
||||
" self.EPSILON = self.MIN_EPSILON + (1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)\n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"epoch: 10, total rewards: 2349.819823.3, cost: 69092.625000, total money: 12349.819823\n",
|
||||
"epoch: 20, total rewards: 648.444882.3, cost: 4775652.000000, total money: 6742.654903\n",
|
||||
"epoch: 30, total rewards: 1543.784977.3, cost: 26533.583984, total money: 7642.034916\n",
|
||||
"epoch: 40, total rewards: 1360.930418.3, cost: 871420.750000, total money: 695.580380\n",
|
||||
"epoch: 50, total rewards: 2233.069826.3, cost: 228718.296875, total money: 6354.209779\n",
|
||||
"epoch: 60, total rewards: 1573.414983.3, cost: 407432.843750, total money: 8625.614995\n",
|
||||
"epoch: 70, total rewards: -7.114931.3, cost: 32132.660156, total money: 5021.405088\n",
|
||||
"epoch: 80, total rewards: 798.045042.3, cost: 435778.562500, total money: 9780.935057\n",
|
||||
"epoch: 90, total rewards: 575.719967.3, cost: 72847.468750, total money: 9559.079952\n",
|
||||
"epoch: 100, total rewards: 338.655157.3, cost: 379671.968750, total money: 820.245184\n",
|
||||
"epoch: 110, total rewards: 277.220155.3, cost: 391019.375000, total money: 3452.330140\n",
|
||||
"epoch: 120, total rewards: 370.379826.3, cost: 429969.843750, total money: 7361.909793\n",
|
||||
"epoch: 130, total rewards: 441.860107.3, cost: 2082513.625000, total money: 2538.970093\n",
|
||||
"epoch: 140, total rewards: 709.099850.3, cost: 558315.562500, total money: 130.919796\n",
|
||||
"epoch: 150, total rewards: 159.675106.3, cost: 2904243.000000, total money: 481.725093\n",
|
||||
"epoch: 160, total rewards: 581.489981.3, cost: 1408646.250000, total money: 5631.309988\n",
|
||||
"epoch: 170, total rewards: 1768.579776.3, cost: 1693698.250000, total money: 15.189760\n",
|
||||
"epoch: 180, total rewards: 952.280210.3, cost: 1472623.250000, total money: 8990.750181\n",
|
||||
"epoch: 190, total rewards: 1418.655145.3, cost: 25627934.000000, total money: 3706.275139\n",
|
||||
"epoch: 200, total rewards: 272.595214.3, cost: 922414.500000, total money: 9255.485229\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"batch_size = 32\n",
|
||||
"agent = Agent(state_size = window_size, \n",
|
||||
" window_size = window_size, \n",
|
||||
" trend = close, \n",
|
||||
" skip = skip)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 7: buy 1 unit at price 754.020020, total balance 9245.979980\n",
|
||||
"day 8: buy 1 unit at price 736.080017, total balance 8509.899963\n",
|
||||
"day 9: buy 1 unit at price 758.489990, total balance 7751.409973\n",
|
||||
"day 10: buy 1 unit at price 764.479980, total balance 6986.929993\n",
|
||||
"day 11: buy 1 unit at price 771.229980, total balance 6215.700013\n",
|
||||
"day 15, sell 1 unit at price 760.989990, investment 0.924375 %, total balance 6976.690003,\n",
|
||||
"day 18: buy 1 unit at price 770.840027, total balance 6205.849976\n",
|
||||
"day 19: buy 1 unit at price 758.039978, total balance 5447.809998\n",
|
||||
"day 22: buy 1 unit at price 762.520020, total balance 4685.289978\n",
|
||||
"day 23: buy 1 unit at price 759.109985, total balance 3926.179993\n",
|
||||
"day 24, sell 1 unit at price 771.190002, investment 4.769860 %, total balance 4697.369995,\n",
|
||||
"day 27: buy 1 unit at price 789.270020, total balance 3908.099975\n",
|
||||
"day 28, sell 1 unit at price 796.099976, investment 4.958534 %, total balance 4704.199951,\n",
|
||||
"day 29: buy 1 unit at price 797.070007, total balance 3907.129944\n",
|
||||
"day 30, sell 1 unit at price 797.849976, investment 4.365058 %, total balance 4704.979920,\n",
|
||||
"day 31: buy 1 unit at price 790.799988, total balance 3914.179932\n",
|
||||
"day 32, sell 1 unit at price 794.200012, investment 2.978363 %, total balance 4708.379944,\n",
|
||||
"day 33: buy 1 unit at price 796.419983, total balance 3911.959961\n",
|
||||
"day 35: buy 1 unit at price 791.260010, total balance 3120.699951\n",
|
||||
"day 36, sell 1 unit at price 789.909973, investment 2.473917 %, total balance 3910.609924,\n",
|
||||
"day 37: buy 1 unit at price 791.549988, total balance 3119.059936\n",
|
||||
"day 40, sell 1 unit at price 771.820007, investment 1.817850 %, total balance 3890.879943,\n",
|
||||
"day 41, sell 1 unit at price 786.140015, investment 3.097623 %, total balance 4677.019958,\n",
|
||||
"day 42, sell 1 unit at price 786.900024, investment 3.660871 %, total balance 5463.919982,\n",
|
||||
"day 43, sell 1 unit at price 794.020020, investment 0.601822 %, total balance 6257.940002,\n",
|
||||
"day 44: buy 1 unit at price 806.150024, total balance 5451.789978\n",
|
||||
"day 45: buy 1 unit at price 806.650024, total balance 4645.139954\n",
|
||||
"day 47: buy 1 unit at price 807.909973, total balance 3837.229981\n",
|
||||
"day 48, sell 1 unit at price 806.359985, investment 1.165516 %, total balance 4643.589966,\n",
|
||||
"day 50, sell 1 unit at price 804.609985, investment 1.746332 %, total balance 5448.199951,\n",
|
||||
"day 51: buy 1 unit at price 806.070007, total balance 4642.129944\n",
|
||||
"day 52: buy 1 unit at price 802.174988, total balance 3839.954956\n",
|
||||
"day 54: buy 1 unit at price 819.309998, total balance 3020.644958\n",
|
||||
"day 55, sell 1 unit at price 823.869995, investment 3.446675 %, total balance 3844.514953,\n",
|
||||
"day 56: buy 1 unit at price 835.669983, total balance 3008.844970\n",
|
||||
"day 57: buy 1 unit at price 832.150024, total balance 2176.694946\n",
|
||||
"day 58: buy 1 unit at price 823.309998, total balance 1353.384948\n",
|
||||
"day 60, sell 1 unit at price 796.789978, investment 0.698881 %, total balance 2150.174926,\n",
|
||||
"day 61: buy 1 unit at price 795.695007, total balance 1354.479919\n",
|
||||
"day 62: buy 1 unit at price 798.530029, total balance 555.949890\n",
|
||||
"day 63, sell 1 unit at price 801.489990, investment 1.255764 %, total balance 1357.439880,\n",
|
||||
"day 64, sell 1 unit at price 801.340027, investment -0.596663 %, total balance 2158.779907,\n",
|
||||
"day 65, sell 1 unit at price 806.969971, investment 0.039664 %, total balance 2965.749878,\n",
|
||||
"day 66, sell 1 unit at price 808.380005, investment 0.058179 %, total balance 3774.129883,\n",
|
||||
"day 67: buy 1 unit at price 809.559998, total balance 2964.569885\n",
|
||||
"day 68, sell 1 unit at price 813.669983, investment 0.942843 %, total balance 3778.239868,\n",
|
||||
"day 69, sell 1 unit at price 819.239990, investment 2.127342 %, total balance 4597.479858,\n",
|
||||
"day 70, sell 1 unit at price 820.450012, investment 0.139143 %, total balance 5417.929870,\n",
|
||||
"day 73: buy 1 unit at price 828.070007, total balance 4589.859863\n",
|
||||
"day 75: buy 1 unit at price 830.760010, total balance 3759.099853\n",
|
||||
"day 76, sell 1 unit at price 831.330017, investment -0.519340 %, total balance 4590.429870,\n",
|
||||
"day 81, sell 1 unit at price 830.630005, investment -0.182662 %, total balance 5421.059875,\n",
|
||||
"day 82, sell 1 unit at price 829.080017, investment 0.700832 %, total balance 6250.139892,\n",
|
||||
"day 83, sell 1 unit at price 827.780029, investment 4.032327 %, total balance 7077.919921,\n",
|
||||
"day 84: buy 1 unit at price 831.909973, total balance 6246.009948\n",
|
||||
"day 87: buy 1 unit at price 843.250000, total balance 5402.759948\n",
|
||||
"day 88: buy 1 unit at price 845.539978, total balance 4557.219970\n",
|
||||
"day 89, sell 1 unit at price 845.619995, investment 5.897081 %, total balance 5402.839965,\n",
|
||||
"day 92, sell 1 unit at price 852.119995, investment 5.257176 %, total balance 6254.959960,\n",
|
||||
"day 93: buy 1 unit at price 848.400024, total balance 5406.559936\n",
|
||||
"day 94: buy 1 unit at price 830.460022, total balance 4576.099914\n",
|
||||
"day 95, sell 1 unit at price 829.590027, investment 0.183562 %, total balance 5405.689941,\n",
|
||||
"day 99, sell 1 unit at price 820.919983, investment -1.184461 %, total balance 6226.609924,\n",
|
||||
"day 101, sell 1 unit at price 831.500000, investment -0.049281 %, total balance 7058.109924,\n",
|
||||
"day 103, sell 1 unit at price 838.549988, investment -0.557369 %, total balance 7896.659912,\n",
|
||||
"day 104: buy 1 unit at price 834.570007, total balance 7062.089905\n",
|
||||
"day 107, sell 1 unit at price 824.669983, investment -2.468245 %, total balance 7886.759888,\n",
|
||||
"day 109, sell 1 unit at price 823.349976, investment -2.952622 %, total balance 8710.109864,\n",
|
||||
"day 110, sell 1 unit at price 824.320007, investment -0.739351 %, total balance 9534.429871,\n",
|
||||
"day 111: buy 1 unit at price 823.559998, total balance 8710.869873\n",
|
||||
"day 112, sell 1 unit at price 837.169983, investment 0.311535 %, total balance 9548.039856,\n",
|
||||
"day 113, sell 1 unit at price 836.820007, investment 1.610084 %, total balance 10384.859863,\n",
|
||||
"day 122: buy 1 unit at price 912.570007, total balance 9472.289856\n",
|
||||
"day 123, sell 1 unit at price 916.440002, investment 0.424077 %, total balance 10388.729858,\n",
|
||||
"day 128: buy 1 unit at price 932.169983, total balance 9456.559875\n",
|
||||
"day 129, sell 1 unit at price 928.780029, investment -0.363663 %, total balance 10385.339904,\n",
|
||||
"day 130: buy 1 unit at price 930.599976, total balance 9454.739928\n",
|
||||
"day 132: buy 1 unit at price 937.080017, total balance 8517.659911\n",
|
||||
"day 133, sell 1 unit at price 943.000000, investment 1.332476 %, total balance 9460.659911,\n",
|
||||
"day 134, sell 1 unit at price 919.619995, investment -1.863237 %, total balance 10380.279906,\n",
|
||||
"day 140: buy 1 unit at price 969.539978, total balance 9410.739928\n",
|
||||
"day 141, sell 1 unit at price 971.469971, investment 0.199063 %, total balance 10382.209899,\n",
|
||||
"day 145: buy 1 unit at price 975.599976, total balance 9406.609923\n",
|
||||
"day 146: buy 1 unit at price 983.679993, total balance 8422.929930\n",
|
||||
"day 149, sell 1 unit at price 983.409973, investment 0.800533 %, total balance 9406.339903,\n",
|
||||
"day 150: buy 1 unit at price 949.830017, total balance 8456.509886\n",
|
||||
"day 151, sell 1 unit at price 942.900024, investment -4.145654 %, total balance 9399.409910,\n",
|
||||
"day 152, sell 1 unit at price 953.400024, investment 0.375857 %, total balance 10352.809934,\n",
|
||||
"day 159: buy 1 unit at price 957.090027, total balance 9395.719907\n",
|
||||
"day 160: buy 1 unit at price 965.590027, total balance 8430.129880\n",
|
||||
"day 161: buy 1 unit at price 952.270020, total balance 7477.859860\n",
|
||||
"day 164: buy 1 unit at price 917.789978, total balance 6560.069882\n",
|
||||
"day 167: buy 1 unit at price 911.710022, total balance 5648.359860\n",
|
||||
"day 170: buy 1 unit at price 928.799988, total balance 4719.559872\n",
|
||||
"day 171, sell 1 unit at price 930.090027, investment -2.821051 %, total balance 5649.649899,\n",
|
||||
"day 173: buy 1 unit at price 947.159973, total balance 4702.489926\n",
|
||||
"day 174: buy 1 unit at price 955.989990, total balance 3746.499936\n",
|
||||
"day 175: buy 1 unit at price 953.419983, total balance 2793.079953\n",
|
||||
"day 176, sell 1 unit at price 965.400024, investment -0.019677 %, total balance 3758.479977,\n",
|
||||
"day 177: buy 1 unit at price 970.890015, total balance 2787.589962\n",
|
||||
"day 178, sell 1 unit at price 968.150024, investment 1.667595 %, total balance 3755.739986,\n",
|
||||
"day 179: buy 1 unit at price 972.919983, total balance 2782.820003\n",
|
||||
"day 180: buy 1 unit at price 980.340027, total balance 1802.479976\n",
|
||||
"day 181: buy 1 unit at price 950.700012, total balance 851.779964\n",
|
||||
"day 183, sell 1 unit at price 934.090027, investment 1.776011 %, total balance 1785.869991,\n",
|
||||
"day 184, sell 1 unit at price 941.530029, investment 3.270778 %, total balance 2727.400020,\n",
|
||||
"day 185, sell 1 unit at price 930.500000, investment 0.183033 %, total balance 3657.900020,\n",
|
||||
"day 186: buy 1 unit at price 930.830017, total balance 2727.070003\n",
|
||||
"day 190: buy 1 unit at price 929.359985, total balance 1797.710018\n",
|
||||
"day 191: buy 1 unit at price 926.789978, total balance 870.920040\n",
|
||||
"day 192, sell 1 unit at price 922.900024, investment -2.561336 %, total balance 1793.820064,\n",
|
||||
"day 193, sell 1 unit at price 907.239990, investment -5.099426 %, total balance 2701.060054,\n",
|
||||
"day 194, sell 1 unit at price 914.390015, investment -4.093681 %, total balance 3615.450069,\n",
|
||||
"day 195: buy 1 unit at price 922.669983, total balance 2692.780086\n",
|
||||
"day 196, sell 1 unit at price 922.219971, investment -5.012931 %, total balance 3615.000057,\n",
|
||||
"day 197: buy 1 unit at price 926.960022, total balance 2688.040035\n",
|
||||
"day 198: buy 1 unit at price 910.979980, total balance 1777.060055\n",
|
||||
"day 199: buy 1 unit at price 910.669983, total balance 866.390072\n",
|
||||
"day 200, sell 1 unit at price 906.659973, investment -6.810427 %, total balance 1773.050045,\n",
|
||||
"day 201, sell 1 unit at price 924.690002, investment -5.676604 %, total balance 2697.740047,\n",
|
||||
"day 202: buy 1 unit at price 927.000000, total balance 1770.740047\n",
|
||||
"day 204, sell 1 unit at price 915.890015, investment -3.661512 %, total balance 2686.630062,\n",
|
||||
"day 210, sell 1 unit at price 928.450012, investment -0.255686 %, total balance 3615.080074,\n",
|
||||
"day 214: buy 1 unit at price 929.080017, total balance 2686.000057\n",
|
||||
"day 215: buy 1 unit at price 932.070007, total balance 1753.930050\n",
|
||||
"day 216: buy 1 unit at price 935.090027, total balance 818.840023\n",
|
||||
"day 220, sell 1 unit at price 921.809998, investment -0.812386 %, total balance 1740.650021,\n",
|
||||
"day 221, sell 1 unit at price 931.580017, investment 0.516842 %, total balance 2672.230038,\n",
|
||||
"day 222, sell 1 unit at price 932.450012, investment 1.059970 %, total balance 3604.680050,\n",
|
||||
"day 224, sell 1 unit at price 920.969971, investment -0.646204 %, total balance 4525.650021,\n",
|
||||
"day 226, sell 1 unit at price 944.489990, investment 3.678457 %, total balance 5470.140011,\n",
|
||||
"day 227, sell 1 unit at price 949.500000, investment 4.263896 %, total balance 6419.640011,\n",
|
||||
"day 228, sell 1 unit at price 959.109985, investment 3.463860 %, total balance 7378.749996,\n",
|
||||
"day 229, sell 1 unit at price 953.270020, investment 2.603651 %, total balance 8332.020016,\n",
|
||||
"day 230, sell 1 unit at price 957.789978, investment 2.759446 %, total balance 9289.809994,\n",
|
||||
"day 232, sell 1 unit at price 969.960022, investment 3.729052 %, total balance 10259.770016,\n",
|
||||
"day 235: buy 1 unit at price 972.599976, total balance 9287.170040\n",
|
||||
"day 236, sell 1 unit at price 989.250000, investment 1.711909 %, total balance 10276.420040,\n",
|
||||
"day 237: buy 1 unit at price 987.830017, total balance 9288.590023\n",
|
||||
"day 238, sell 1 unit at price 989.679993, investment 0.187277 %, total balance 10278.270016,\n",
|
||||
"day 241: buy 1 unit at price 992.809998, total balance 9285.460018\n",
|
||||
"day 242, sell 1 unit at price 984.450012, investment -0.842053 %, total balance 10269.910030,\n",
|
||||
"day 245: buy 1 unit at price 970.539978, total balance 9299.370052\n",
|
||||
"day 246: buy 1 unit at price 973.330017, total balance 8326.040035\n",
|
||||
"day 247, sell 1 unit at price 972.559998, investment 0.208134 %, total balance 9298.600033,\n",
|
||||
"day 249: buy 1 unit at price 1017.109985, total balance 8281.490048\n",
|
||||
"day 250, sell 1 unit at price 1016.640015, investment 4.449672 %, total balance 9298.130063,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+527
@@ -0,0 +1,527 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from collections import deque\n",
|
||||
"import random\n",
|
||||
"\n",
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" LEARNING_RATE = 0.003\n",
|
||||
" BATCH_SIZE = 32\n",
|
||||
" LAYER_SIZE = 128\n",
|
||||
" OUTPUT_SIZE = 3\n",
|
||||
" EPSILON = 0.5\n",
|
||||
" DECAY_RATE = 0.005\n",
|
||||
" MIN_EPSILON = 0.1\n",
|
||||
" GAMMA = 0.99\n",
|
||||
" MEMORIES = deque()\n",
|
||||
" COPY = 1000\n",
|
||||
" T_COPY = 0\n",
|
||||
" MEMORY_SIZE = 300\n",
|
||||
" \n",
|
||||
" def __init__(self, state_size, window_size, trend, skip):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" tf.reset_default_graph()\n",
|
||||
" self.INITIAL_FEATURES = np.zeros((4, self.state_size))\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, None, self.state_size))\n",
|
||||
" self.Y = tf.placeholder(tf.float32, (None, None, self.state_size))\n",
|
||||
" self.hidden_layer = tf.placeholder(tf.float32, (None, 2 * self.LAYER_SIZE))\n",
|
||||
" self.ACTION = tf.placeholder(tf.float32, (None))\n",
|
||||
" self.REWARD = tf.placeholder(tf.float32, (None))\n",
|
||||
" self.batch_size = tf.shape(self.ACTION)[0]\n",
|
||||
" self.seq_len = tf.shape(self.X)[1]\n",
|
||||
" \n",
|
||||
" with tf.variable_scope('curiosity_model'):\n",
|
||||
" action = tf.reshape(self.ACTION, (-1,1,1))\n",
|
||||
" repeat_action = tf.tile(action, [1,self.seq_len,1])\n",
|
||||
" state_action = tf.concat([self.X, repeat_action], axis=-1)\n",
|
||||
" save_state = tf.identity(self.Y)\n",
|
||||
" cell = tf.nn.rnn_cell.LSTMCell(self.LAYER_SIZE, state_is_tuple = False)\n",
|
||||
" self.rnn,last_state = tf.nn.dynamic_rnn(inputs=state_action,cell=cell,\n",
|
||||
" dtype=tf.float32,\n",
|
||||
" initial_state=self.hidden_layer)\n",
|
||||
" self.curiosity_logits = tf.layers.dense(self.rnn[:,-1], self.state_size)\n",
|
||||
" self.curiosity_cost = tf.reduce_sum(tf.square(save_state[:,-1] - self.curiosity_logits), axis=1)\n",
|
||||
" \n",
|
||||
" self.curiosity_optimizer = tf.train.RMSPropOptimizer(self.LEARNING_RATE)\\\n",
|
||||
" .minimize(tf.reduce_mean(self.curiosity_cost))\n",
|
||||
" \n",
|
||||
" total_reward = tf.add(self.curiosity_cost, self.REWARD)\n",
|
||||
" \n",
|
||||
" with tf.variable_scope(\"q_model\"):\n",
|
||||
" with tf.variable_scope(\"eval_net\"):\n",
|
||||
" cell = tf.nn.rnn_cell.LSTMCell(self.LAYER_SIZE, state_is_tuple = False)\n",
|
||||
" rnn,self.last_state = tf.nn.dynamic_rnn(inputs=self.X,cell=cell,\n",
|
||||
" dtype=tf.float32,\n",
|
||||
" initial_state=self.hidden_layer)\n",
|
||||
" self.logits = tf.layers.dense(rnn[:,-1], self.OUTPUT_SIZE)\n",
|
||||
" \n",
|
||||
" with tf.variable_scope(\"target_net\"):\n",
|
||||
" cell = tf.nn.rnn_cell.LSTMCell(self.LAYER_SIZE, state_is_tuple = False)\n",
|
||||
" rnn,last_state = tf.nn.dynamic_rnn(inputs=self.Y,cell=cell,\n",
|
||||
" dtype=tf.float32,\n",
|
||||
" initial_state=self.hidden_layer)\n",
|
||||
" y_q = tf.layers.dense(rnn[:,-1], self.OUTPUT_SIZE)\n",
|
||||
" \n",
|
||||
" q_target = total_reward + self.GAMMA * tf.reduce_max(y_q, axis=1)\n",
|
||||
" action = tf.cast(self.ACTION, tf.int32)\n",
|
||||
" action_indices = tf.stack([tf.range(self.batch_size, dtype=tf.int32), action], axis=1)\n",
|
||||
" q = tf.gather_nd(params=self.logits, indices=action_indices)\n",
|
||||
" self.cost = tf.losses.mean_squared_error(labels=q_target, predictions=q)\n",
|
||||
" self.optimizer = tf.train.RMSPropOptimizer(self.LEARNING_RATE).minimize(\n",
|
||||
" self.cost, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, \"q_model/eval_net\"))\n",
|
||||
" \n",
|
||||
" t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_model/target_net')\n",
|
||||
" e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_model/eval_net')\n",
|
||||
" self.target_replace_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]\n",
|
||||
" \n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
" \n",
|
||||
" def _memorize(self, state, action, reward, new_state, done, rnn_state):\n",
|
||||
" self.MEMORIES.append((state, action, reward, new_state, done, rnn_state))\n",
|
||||
" if len(self.MEMORIES) > self.MEMORY_SIZE:\n",
|
||||
" self.MEMORIES.popleft()\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array(res)\n",
|
||||
" \n",
|
||||
" def _construct_memories(self, replay):\n",
|
||||
" states = np.array([a[0] for a in replay])\n",
|
||||
" actions = np.array([a[1] for a in replay])\n",
|
||||
" rewards = np.array([a[2] for a in replay])\n",
|
||||
" new_states = np.array([a[3] for a in replay])\n",
|
||||
" init_values = np.array([a[-1] for a in replay])\n",
|
||||
" if (self.T_COPY + 1) % self.COPY == 0:\n",
|
||||
" self.sess.run(self.target_replace_op)\n",
|
||||
" \n",
|
||||
" cost, _ = self.sess.run([self.cost, self.optimizer], feed_dict = {\n",
|
||||
" self.X: states, self.Y: new_states, self.ACTION: actions, self.REWARD: rewards,\n",
|
||||
" self.hidden_layer: init_values\n",
|
||||
" })\n",
|
||||
" \n",
|
||||
" if (self.T_COPY + 1) % self.COPY == 0:\n",
|
||||
" self.sess.run(self.curiosity_optimizer, feed_dict = {\n",
|
||||
" self.X: states, self.Y: new_states, self.ACTION: actions, self.REWARD: rewards,\n",
|
||||
" self.hidden_layer: init_values\n",
|
||||
" })\n",
|
||||
" return cost\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" init_value = np.zeros((1, 2 * self.LAYER_SIZE))\n",
|
||||
" for k in range(self.INITIAL_FEATURES.shape[0]):\n",
|
||||
" self.INITIAL_FEATURES[k,:] = state\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" \n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action, last_state = self.sess.run([self.logits,\n",
|
||||
" self.last_state],\n",
|
||||
" feed_dict={self.X:[self.INITIAL_FEATURES],\n",
|
||||
" self.hidden_layer:init_value})\n",
|
||||
" action, init_value = np.argmax(action[0]), last_state\n",
|
||||
" \n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" new_state = np.append([self.get_state(t + 1)], self.INITIAL_FEATURES[:3, :], axis = 0)\n",
|
||||
" self.INITIAL_FEATURES = new_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" init_value = np.zeros((1, 2 * self.LAYER_SIZE))\n",
|
||||
" for k in range(self.INITIAL_FEATURES.shape[0]):\n",
|
||||
" self.INITIAL_FEATURES[k,:] = state\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action, last_state = self.sess.run([self.logits,\n",
|
||||
" self.last_state],\n",
|
||||
" feed_dict={self.X:[self.INITIAL_FEATURES],\n",
|
||||
" self.hidden_layer:init_value})\n",
|
||||
" action, init_value = np.argmax(action[0]), last_state\n",
|
||||
" \n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" \n",
|
||||
" invest = ((starting_money - initial_money) / initial_money)\n",
|
||||
" new_state = np.append([self.get_state(t + 1)], self.INITIAL_FEATURES[:3, :], axis = 0)\n",
|
||||
" self._memorize(self.INITIAL_FEATURES, action, invest, new_state, \n",
|
||||
" starting_money < initial_money, init_value[0])\n",
|
||||
" self.INITIAL_FEATURES = new_state\n",
|
||||
" batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)\n",
|
||||
" replay = random.sample(self.MEMORIES, batch_size)\n",
|
||||
" cost = self._construct_memories(replay)\n",
|
||||
" self.T_COPY += 1\n",
|
||||
" self.EPSILON = self.MIN_EPSILON + (1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)\n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7ff38845bba8>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7ff2f112ed68>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7ff2f112eac8>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"epoch: 10, total rewards: 685.860168.3, cost: 4139534.500000, total money: 977.580137\n",
|
||||
"epoch: 20, total rewards: 1724.255003.3, cost: 5132677.500000, total money: 5851.904966\n",
|
||||
"epoch: 30, total rewards: 493.970035.3, cost: 3979546.750000, total money: 8528.600039\n",
|
||||
"epoch: 40, total rewards: 1580.255128.3, cost: 5099559.000000, total money: 4018.855103\n",
|
||||
"epoch: 50, total rewards: 1467.990231.3, cost: 4410721.500000, total money: 8490.720211\n",
|
||||
"epoch: 60, total rewards: 1285.420161.3, cost: 3993190.000000, total money: 2688.440118\n",
|
||||
"epoch: 70, total rewards: 391.130068.3, cost: 3420379.000000, total money: 6491.710085\n",
|
||||
"epoch: 80, total rewards: 1276.110108.3, cost: 3443612.750000, total money: 3698.110047\n",
|
||||
"epoch: 90, total rewards: 672.475340.3, cost: 2882908.000000, total money: 208.605285\n",
|
||||
"epoch: 100, total rewards: 706.604982.3, cost: 3108476.500000, total money: 1169.724916\n",
|
||||
"epoch: 110, total rewards: 979.940367.3, cost: 2024909.750000, total money: 3200.720335\n",
|
||||
"epoch: 120, total rewards: 853.199893.3, cost: 4572564.500000, total money: 6070.309879\n",
|
||||
"epoch: 130, total rewards: 1339.975223.3, cost: 3904469.500000, total money: 7475.465274\n",
|
||||
"epoch: 140, total rewards: 1136.924864.3, cost: 4352429.000000, total money: 4448.164854\n",
|
||||
"epoch: 150, total rewards: 1499.745116.3, cost: 2398584.500000, total money: 3999.355042\n",
|
||||
"epoch: 160, total rewards: 481.755190.3, cost: 3168836.250000, total money: 7573.215212\n",
|
||||
"epoch: 170, total rewards: 1733.610290.3, cost: 1907320.875000, total money: 6940.950254\n",
|
||||
"epoch: 180, total rewards: 390.074828.3, cost: 2862924.000000, total money: 5516.364805\n",
|
||||
"epoch: 190, total rewards: 714.815121.3, cost: 2666878.750000, total money: 9726.615109\n",
|
||||
"epoch: 200, total rewards: 1474.129822.3, cost: 3016419.000000, total money: 1901.589906\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"batch_size = 32\n",
|
||||
"agent = Agent(state_size = window_size, \n",
|
||||
" window_size = window_size, \n",
|
||||
" trend = close, \n",
|
||||
" skip = skip)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 0: buy 1 unit at price 768.700012, total balance 9231.299988\n",
|
||||
"day 1, sell 1 unit at price 762.130005, investment -0.854691 %, total balance 9993.429993,\n",
|
||||
"day 4: buy 1 unit at price 790.510010, total balance 9202.919983\n",
|
||||
"day 5: buy 1 unit at price 785.309998, total balance 8417.609985\n",
|
||||
"day 8: buy 1 unit at price 736.080017, total balance 7681.529968\n",
|
||||
"day 9: buy 1 unit at price 758.489990, total balance 6923.039978\n",
|
||||
"day 11, sell 1 unit at price 771.229980, investment -2.438936 %, total balance 7694.269958,\n",
|
||||
"day 13: buy 1 unit at price 769.200012, total balance 6925.069946\n",
|
||||
"day 17: buy 1 unit at price 768.239990, total balance 6156.829956\n",
|
||||
"day 19, sell 1 unit at price 758.039978, investment -3.472517 %, total balance 6914.869934,\n",
|
||||
"day 25, sell 1 unit at price 776.419983, investment 5.480378 %, total balance 7691.289917,\n",
|
||||
"day 26: buy 1 unit at price 789.289978, total balance 6901.999939\n",
|
||||
"day 28: buy 1 unit at price 796.099976, total balance 6105.899963\n",
|
||||
"day 31: buy 1 unit at price 790.799988, total balance 5315.099975\n",
|
||||
"day 40, sell 1 unit at price 771.820007, investment 1.757441 %, total balance 6086.919982,\n",
|
||||
"day 46, sell 1 unit at price 804.789978, investment 4.626881 %, total balance 6891.709960,\n",
|
||||
"day 47, sell 1 unit at price 807.909973, investment 5.163749 %, total balance 7699.619933,\n",
|
||||
"day 50: buy 1 unit at price 804.609985, total balance 6895.009948\n",
|
||||
"day 57: buy 1 unit at price 832.150024, total balance 6062.859924\n",
|
||||
"day 58, sell 1 unit at price 823.309998, investment 4.310205 %, total balance 6886.169922,\n",
|
||||
"day 61: buy 1 unit at price 795.695007, total balance 6090.474915\n",
|
||||
"day 62: buy 1 unit at price 798.530029, total balance 5291.944886\n",
|
||||
"day 70: buy 1 unit at price 820.450012, total balance 4471.494874\n",
|
||||
"day 73: buy 1 unit at price 828.070007, total balance 3643.424867\n",
|
||||
"day 76: buy 1 unit at price 831.330017, total balance 2812.094850\n",
|
||||
"day 85: buy 1 unit at price 835.369995, total balance 1976.724855\n",
|
||||
"day 89, sell 1 unit at price 845.619995, investment 6.220327 %, total balance 2822.344850,\n",
|
||||
"day 91: buy 1 unit at price 848.780029, total balance 1973.564821\n",
|
||||
"day 98: buy 1 unit at price 819.510010, total balance 1154.054811\n",
|
||||
"day 100: buy 1 unit at price 831.409973, total balance 322.644838\n",
|
||||
"day 102, sell 1 unit at price 829.559998, investment 4.901367 %, total balance 1152.204836,\n",
|
||||
"day 111, sell 1 unit at price 823.559998, investment 2.355180 %, total balance 1975.764834,\n",
|
||||
"day 113: buy 1 unit at price 836.820007, total balance 1138.944827\n",
|
||||
"day 114, sell 1 unit at price 838.210022, investment 0.728234 %, total balance 1977.154849,\n",
|
||||
"day 117: buy 1 unit at price 862.760010, total balance 1114.394839\n",
|
||||
"day 118: buy 1 unit at price 872.299988, total balance 242.094851\n",
|
||||
"day 132, sell 1 unit at price 937.080017, investment 17.768744 %, total balance 1179.174868,\n",
|
||||
"day 138: buy 1 unit at price 948.820007, total balance 230.354861\n",
|
||||
"day 139, sell 1 unit at price 954.960022, investment 19.589745 %, total balance 1185.314883,\n",
|
||||
"day 140: buy 1 unit at price 969.539978, total balance 215.774905\n",
|
||||
"day 154, sell 1 unit at price 942.309998, investment 14.852823 %, total balance 1158.084903,\n",
|
||||
"day 158, sell 1 unit at price 959.450012, investment 15.865809 %, total balance 2117.534915,\n",
|
||||
"day 160: buy 1 unit at price 965.590027, total balance 1151.944888\n",
|
||||
"day 168: buy 1 unit at price 906.690002, total balance 245.254886\n",
|
||||
"day 169, sell 1 unit at price 918.590027, investment 10.496434 %, total balance 1163.844913,\n",
|
||||
"day 176: buy 1 unit at price 965.400024, total balance 198.444889\n",
|
||||
"day 189, sell 1 unit at price 927.960022, investment 11.083715 %, total balance 1126.404911,\n",
|
||||
"day 191: buy 1 unit at price 926.789978, total balance 199.614933\n",
|
||||
"day 195, sell 1 unit at price 922.669983, investment 8.705430 %, total balance 1122.284916,\n",
|
||||
"day 200, sell 1 unit at price 906.659973, investment 10.634399 %, total balance 2028.944889,\n",
|
||||
"day 201: buy 1 unit at price 924.690002, total balance 1104.254887\n",
|
||||
"day 202, sell 1 unit at price 927.000000, investment 11.497339 %, total balance 2031.254887,\n",
|
||||
"day 206: buy 1 unit at price 921.289978, total balance 1109.964909\n",
|
||||
"day 211: buy 1 unit at price 927.809998, total balance 182.154911\n",
|
||||
"day 220, sell 1 unit at price 921.809998, investment 10.156305 %, total balance 1103.964909,\n",
|
||||
"day 226, sell 1 unit at price 944.489990, investment 9.473084 %, total balance 2048.454899,\n",
|
||||
"day 228, sell 1 unit at price 959.109985, investment 9.951851 %, total balance 3007.564884,\n",
|
||||
"day 230: buy 1 unit at price 957.789978, total balance 2049.774906\n",
|
||||
"day 231, sell 1 unit at price 951.679993, investment 0.301426 %, total balance 3001.454899,\n",
|
||||
"day 234: buy 1 unit at price 977.000000, total balance 2024.454899\n",
|
||||
"day 237: buy 1 unit at price 987.830017, total balance 1036.624882\n",
|
||||
"day 238, sell 1 unit at price 989.679993, investment 2.077275 %, total balance 2026.304875,\n",
|
||||
"day 240: buy 1 unit at price 992.179993, total balance 1034.124882\n",
|
||||
"day 241: buy 1 unit at price 992.809998, total balance 41.314884\n",
|
||||
"day 242, sell 1 unit at price 984.450012, investment 1.953208 %, total balance 1025.764896,\n",
|
||||
"day 248, sell 1 unit at price 1019.270020, investment 12.416594 %, total balance 2045.034916,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
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||||
},
|
||||
"metadata": {
|
||||
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|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+866
@@ -0,0 +1,866 @@
|
||||
{
|
||||
"cells": [
|
||||
{
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||||
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|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
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|
||||
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|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
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||||
" <td>768.700012</td>\n",
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||||
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||||
" <td>1872400</td>\n",
|
||||
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|
||||
" <tr>\n",
|
||||
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|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
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||||
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||||
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|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
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||||
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||||
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|
||||
" <td>2134800</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
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||||
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|
||||
" <td>1350800</td>\n",
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||||
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|
||||
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|
||||
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|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
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||||
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|
||||
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|
||||
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|
||||
" <th>long_ma</th>\n",
|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
" <td>0.0</td>\n",
|
||||
" <td>764.283346</td>\n",
|
||||
" <td>764.283346</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>768.842514</td>\n",
|
||||
" <td>768.842514</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>773.176013</td>\n",
|
||||
" <td>773.176013</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>775.198344</td>\n",
|
||||
" <td>775.198344</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>6</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>774.175008</td>\n",
|
||||
" <td>773.392866</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>7</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>772.823344</td>\n",
|
||||
" <td>770.971260</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>8</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>768.500010</td>\n",
|
||||
" <td>767.094456</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>764.495005</td>\n",
|
||||
" <td>766.234009</td>\n",
|
||||
" <td>-1.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>10</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>760.156667</td>\n",
|
||||
" <td>766.074552</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>11</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>757.809998</td>\n",
|
||||
" <td>766.504171</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>12</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>757.473327</td>\n",
|
||||
" <td>765.824168</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>13</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>760.003326</td>\n",
|
||||
" <td>766.413335</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>14</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>765.368327</td>\n",
|
||||
" <td>766.934169</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>15</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>765.784993</td>\n",
|
||||
" <td>765.139999</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>16</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>765.318329</td>\n",
|
||||
" <td>762.737498</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>17</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>764.819997</td>\n",
|
||||
" <td>761.314997</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>18</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>766.536672</td>\n",
|
||||
" <td>762.005000</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>19</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>764.676666</td>\n",
|
||||
" <td>762.339996</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>20</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>761.284993</td>\n",
|
||||
" <td>763.326660</td>\n",
|
||||
" <td>-1.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>21</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>759.536662</td>\n",
|
||||
" <td>762.660828</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>22</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>759.676666</td>\n",
|
||||
" <td>762.497498</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>23</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>758.154999</td>\n",
|
||||
" <td>761.487498</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>24</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>758.213328</td>\n",
|
||||
" <td>762.375000</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>25</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>761.276662</td>\n",
|
||||
" <td>762.976664</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>26</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>768.171661</td>\n",
|
||||
" <td>764.728327</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>27</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>774.633331</td>\n",
|
||||
" <td>767.084997</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>28</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>780.229991</td>\n",
|
||||
" <td>769.953328</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>29</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>786.556661</td>\n",
|
||||
" <td>772.355830</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>222</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>924.373332</td>\n",
|
||||
" <td>927.728338</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>223</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>924.943339</td>\n",
|
||||
" <td>927.788340</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>224</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>925.056671</td>\n",
|
||||
" <td>926.540003</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>225</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>926.700002</td>\n",
|
||||
" <td>926.403335</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>226</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>930.480001</td>\n",
|
||||
" <td>927.687500</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>227</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>933.466664</td>\n",
|
||||
" <td>929.139999</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>228</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>937.909993</td>\n",
|
||||
" <td>931.141662</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>229</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>942.033325</td>\n",
|
||||
" <td>933.488332</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>230</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>948.169993</td>\n",
|
||||
" <td>936.613332</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>231</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>952.639994</td>\n",
|
||||
" <td>939.669998</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>232</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>956.885000</td>\n",
|
||||
" <td>943.682500</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>233</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>961.783335</td>\n",
|
||||
" <td>947.625000</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>234</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>964.765005</td>\n",
|
||||
" <td>951.337499</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>235</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>967.986664</td>\n",
|
||||
" <td>955.009995</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>236</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>973.230001</td>\n",
|
||||
" <td>960.699997</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>237</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>979.255005</td>\n",
|
||||
" <td>965.947500</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>238</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>982.541667</td>\n",
|
||||
" <td>969.713333</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>239</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>984.726664</td>\n",
|
||||
" <td>973.255000</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>240</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>987.256663</td>\n",
|
||||
" <td>976.010834</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>241</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>990.625000</td>\n",
|
||||
" <td>979.305832</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>242</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>989.825002</td>\n",
|
||||
" <td>981.527502</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>243</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>989.886668</td>\n",
|
||||
" <td>984.570836</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>244</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>986.348338</td>\n",
|
||||
" <td>984.445002</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>245</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>982.771667</td>\n",
|
||||
" <td>983.749166</td>\n",
|
||||
" <td>-1.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>246</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>979.630005</td>\n",
|
||||
" <td>983.443334</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>247</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>976.255005</td>\n",
|
||||
" <td>983.440002</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>248</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>982.058339</td>\n",
|
||||
" <td>985.941671</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>249</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>986.876668</td>\n",
|
||||
" <td>988.381668</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>250</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>994.908335</td>\n",
|
||||
" <td>990.628337</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>251</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1004.068339</td>\n",
|
||||
" <td>993.420003</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>252 rows × 4 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" signal short_ma long_ma positions\n",
|
||||
"0 0.0 768.700012 768.700012 NaN\n",
|
||||
"1 0.0 765.415008 765.415008 0.0\n",
|
||||
"2 0.0 764.283346 764.283346 0.0\n",
|
||||
"3 0.0 768.842514 768.842514 0.0\n",
|
||||
"4 0.0 773.176013 773.176013 0.0\n",
|
||||
"5 0.0 775.198344 775.198344 0.0\n",
|
||||
"6 1.0 774.175008 773.392866 1.0\n",
|
||||
"7 1.0 772.823344 770.971260 0.0\n",
|
||||
"8 1.0 768.500010 767.094456 0.0\n",
|
||||
"9 0.0 764.495005 766.234009 -1.0\n",
|
||||
"10 0.0 760.156667 766.074552 0.0\n",
|
||||
"11 0.0 757.809998 766.504171 0.0\n",
|
||||
"12 0.0 757.473327 765.824168 0.0\n",
|
||||
"13 0.0 760.003326 766.413335 0.0\n",
|
||||
"14 0.0 765.368327 766.934169 0.0\n",
|
||||
"15 1.0 765.784993 765.139999 1.0\n",
|
||||
"16 1.0 765.318329 762.737498 0.0\n",
|
||||
"17 1.0 764.819997 761.314997 0.0\n",
|
||||
"18 1.0 766.536672 762.005000 0.0\n",
|
||||
"19 1.0 764.676666 762.339996 0.0\n",
|
||||
"20 0.0 761.284993 763.326660 -1.0\n",
|
||||
"21 0.0 759.536662 762.660828 0.0\n",
|
||||
"22 0.0 759.676666 762.497498 0.0\n",
|
||||
"23 0.0 758.154999 761.487498 0.0\n",
|
||||
"24 0.0 758.213328 762.375000 0.0\n",
|
||||
"25 0.0 761.276662 762.976664 0.0\n",
|
||||
"26 1.0 768.171661 764.728327 1.0\n",
|
||||
"27 1.0 774.633331 767.084997 0.0\n",
|
||||
"28 1.0 780.229991 769.953328 0.0\n",
|
||||
"29 1.0 786.556661 772.355830 0.0\n",
|
||||
".. ... ... ... ...\n",
|
||||
"222 0.0 924.373332 927.728338 0.0\n",
|
||||
"223 0.0 924.943339 927.788340 0.0\n",
|
||||
"224 0.0 925.056671 926.540003 0.0\n",
|
||||
"225 1.0 926.700002 926.403335 1.0\n",
|
||||
"226 1.0 930.480001 927.687500 0.0\n",
|
||||
"227 1.0 933.466664 929.139999 0.0\n",
|
||||
"228 1.0 937.909993 931.141662 0.0\n",
|
||||
"229 1.0 942.033325 933.488332 0.0\n",
|
||||
"230 1.0 948.169993 936.613332 0.0\n",
|
||||
"231 1.0 952.639994 939.669998 0.0\n",
|
||||
"232 1.0 956.885000 943.682500 0.0\n",
|
||||
"233 1.0 961.783335 947.625000 0.0\n",
|
||||
"234 1.0 964.765005 951.337499 0.0\n",
|
||||
"235 1.0 967.986664 955.009995 0.0\n",
|
||||
"236 1.0 973.230001 960.699997 0.0\n",
|
||||
"237 1.0 979.255005 965.947500 0.0\n",
|
||||
"238 1.0 982.541667 969.713333 0.0\n",
|
||||
"239 1.0 984.726664 973.255000 0.0\n",
|
||||
"240 1.0 987.256663 976.010834 0.0\n",
|
||||
"241 1.0 990.625000 979.305832 0.0\n",
|
||||
"242 1.0 989.825002 981.527502 0.0\n",
|
||||
"243 1.0 989.886668 984.570836 0.0\n",
|
||||
"244 1.0 986.348338 984.445002 0.0\n",
|
||||
"245 0.0 982.771667 983.749166 -1.0\n",
|
||||
"246 0.0 979.630005 983.443334 0.0\n",
|
||||
"247 0.0 976.255005 983.440002 0.0\n",
|
||||
"248 0.0 982.058339 985.941671 0.0\n",
|
||||
"249 0.0 986.876668 988.381668 0.0\n",
|
||||
"250 1.0 994.908335 990.628337 1.0\n",
|
||||
"251 1.0 1004.068339 993.420003 0.0\n",
|
||||
"\n",
|
||||
"[252 rows x 4 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"short_window = int(0.025 * len(df))\n",
|
||||
"long_window = int(0.05 * len(df))\n",
|
||||
"\n",
|
||||
"signals = pd.DataFrame(index=df.index)\n",
|
||||
"signals['signal'] = 0.0\n",
|
||||
"\n",
|
||||
"signals['short_ma'] = df['Close'].rolling(window=short_window, min_periods=1, center=False).mean()\n",
|
||||
"signals['long_ma'] = df['Close'].rolling(window=long_window, min_periods=1, center=False).mean()\n",
|
||||
"\n",
|
||||
"signals['signal'][short_window:] = np.where(signals['short_ma'][short_window:] \n",
|
||||
" > signals['long_ma'][short_window:], 1.0, 0.0) \n",
|
||||
"signals['positions'] = signals['signal'].diff()\n",
|
||||
"\n",
|
||||
"signals"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def buy_stock(\n",
|
||||
" real_movement,\n",
|
||||
" signal,\n",
|
||||
" initial_money = 10000,\n",
|
||||
" max_buy = 1,\n",
|
||||
" max_sell = 1,\n",
|
||||
"):\n",
|
||||
" \"\"\"\n",
|
||||
" real_movement = actual movement in the real world\n",
|
||||
" delay = how much interval you want to delay to change our decision from buy to sell, vice versa\n",
|
||||
" initial_state = 1 is buy, 0 is sell\n",
|
||||
" initial_money = 1000, ignore what kind of currency\n",
|
||||
" max_buy = max quantity for share to buy\n",
|
||||
" max_sell = max quantity for share to sell\n",
|
||||
" \"\"\"\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" current_inventory = 0\n",
|
||||
"\n",
|
||||
" def buy(i, initial_money, current_inventory):\n",
|
||||
" shares = initial_money // real_movement[i]\n",
|
||||
" if shares < 1:\n",
|
||||
" print(\n",
|
||||
" 'day %d: total balances %f, not enough money to buy a unit price %f'\n",
|
||||
" % (i, initial_money, real_movement[i])\n",
|
||||
" )\n",
|
||||
" else:\n",
|
||||
" if shares > max_buy:\n",
|
||||
" buy_units = max_buy\n",
|
||||
" else:\n",
|
||||
" buy_units = shares\n",
|
||||
" initial_money -= buy_units * real_movement[i]\n",
|
||||
" current_inventory += buy_units\n",
|
||||
" print(\n",
|
||||
" 'day %d: buy %d units at price %f, total balance %f'\n",
|
||||
" % (i, buy_units, buy_units * real_movement[i], initial_money)\n",
|
||||
" )\n",
|
||||
" states_buy.append(0)\n",
|
||||
" return initial_money, current_inventory\n",
|
||||
"\n",
|
||||
" for i in range(real_movement.shape[0] - int(0.025 * len(df))):\n",
|
||||
" state = signal[i]\n",
|
||||
" if state == 1:\n",
|
||||
" initial_money, current_inventory = buy(\n",
|
||||
" i, initial_money, current_inventory\n",
|
||||
" )\n",
|
||||
" states_buy.append(i)\n",
|
||||
" elif state == -1:\n",
|
||||
" if current_inventory == 0:\n",
|
||||
" print('day %d: cannot sell anything, inventory 0' % (i))\n",
|
||||
" else:\n",
|
||||
" if current_inventory > max_sell:\n",
|
||||
" sell_units = max_sell\n",
|
||||
" else:\n",
|
||||
" sell_units = current_inventory\n",
|
||||
" current_inventory -= sell_units\n",
|
||||
" total_sell = sell_units * real_movement[i]\n",
|
||||
" initial_money += total_sell\n",
|
||||
" try:\n",
|
||||
" invest = (\n",
|
||||
" (real_movement[i] - real_movement[states_buy[-1]])\n",
|
||||
" / real_movement[states_buy[-1]]\n",
|
||||
" ) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell %d units at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (i, sell_units, total_sell, invest, initial_money)\n",
|
||||
" )\n",
|
||||
" states_sell.append(i)\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 6: buy 1 units at price 762.559998, total balance 9237.440002\n",
|
||||
"day 9, sell 1 units at price 758.489990, investment -0.533730 %, total balance 9995.929992,\n",
|
||||
"day 15: buy 1 units at price 760.989990, total balance 9234.940002\n",
|
||||
"day 20, sell 1 units at price 747.919983, investment -1.717501 %, total balance 9982.859985,\n",
|
||||
"day 26: buy 1 units at price 789.289978, total balance 9193.570007\n",
|
||||
"day 37, sell 1 units at price 791.549988, investment 0.286335 %, total balance 9985.119995,\n",
|
||||
"day 45: buy 1 units at price 806.650024, total balance 9178.469971\n",
|
||||
"day 62, sell 1 units at price 798.530029, investment -1.006632 %, total balance 9977.000000,\n",
|
||||
"day 69: buy 1 units at price 819.239990, total balance 9157.760010\n",
|
||||
"day 84, sell 1 units at price 831.909973, investment 1.546553 %, total balance 9989.669983,\n",
|
||||
"day 85: buy 1 units at price 835.369995, total balance 9154.299988\n",
|
||||
"day 96, sell 1 units at price 817.580017, investment -2.129593 %, total balance 9971.880005,\n",
|
||||
"day 104: buy 1 units at price 834.570007, total balance 9137.309998\n",
|
||||
"day 109, sell 1 units at price 823.349976, investment -1.344409 %, total balance 9960.659974,\n",
|
||||
"day 114: buy 1 units at price 838.210022, total balance 9122.449952\n",
|
||||
"day 151, sell 1 units at price 942.900024, investment 12.489710 %, total balance 10065.349976,\n",
|
||||
"day 160: buy 1 units at price 965.590027, total balance 9099.759949\n",
|
||||
"day 164, sell 1 units at price 917.789978, investment -4.950346 %, total balance 10017.549927,\n",
|
||||
"day 173: buy 1 units at price 947.159973, total balance 9070.389954\n",
|
||||
"day 184, sell 1 units at price 941.530029, investment -0.594403 %, total balance 10011.919983,\n",
|
||||
"day 204: buy 1 units at price 915.890015, total balance 9096.029968\n",
|
||||
"day 218, sell 1 units at price 920.289978, investment 0.480403 %, total balance 10016.319946,\n",
|
||||
"day 225: buy 1 units at price 924.859985, total balance 9091.459961\n",
|
||||
"day 245, sell 1 units at price 970.539978, investment 4.939125 %, total balance 10061.999939,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = buy_stock(df.Close, signals['positions'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df['Close']\n",
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+623
@@ -0,0 +1,623 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from collections import deque\n",
|
||||
"import random\n",
|
||||
"\n",
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" LEARNING_RATE = 0.003\n",
|
||||
" BATCH_SIZE = 32\n",
|
||||
" LAYER_SIZE = 500\n",
|
||||
" OUTPUT_SIZE = 3\n",
|
||||
" EPSILON = 0.5\n",
|
||||
" DECAY_RATE = 0.005\n",
|
||||
" MIN_EPSILON = 0.1\n",
|
||||
" GAMMA = 0.99\n",
|
||||
" MEMORIES = deque()\n",
|
||||
" COPY = 1000\n",
|
||||
" T_COPY = 0\n",
|
||||
" MEMORY_SIZE = 300\n",
|
||||
" \n",
|
||||
" def __init__(self, state_size, window_size, trend, skip):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" tf.reset_default_graph()\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, self.state_size))\n",
|
||||
" self.Y = tf.placeholder(tf.float32, (None, self.state_size))\n",
|
||||
" self.ACTION = tf.placeholder(tf.float32, (None))\n",
|
||||
" self.REWARD = tf.placeholder(tf.float32, (None))\n",
|
||||
" self.batch_size = tf.shape(self.ACTION)[0]\n",
|
||||
" \n",
|
||||
" with tf.variable_scope('curiosity_model'):\n",
|
||||
" action = tf.reshape(self.ACTION, (-1,1))\n",
|
||||
" state_action = tf.concat([self.X, action], axis=1)\n",
|
||||
" save_state = tf.identity(self.Y)\n",
|
||||
" \n",
|
||||
" feed = tf.layers.dense(state_action, 32, activation=tf.nn.relu)\n",
|
||||
" self.curiosity_logits = tf.layers.dense(feed, self.state_size)\n",
|
||||
" self.curiosity_cost = tf.reduce_sum(tf.square(save_state - self.curiosity_logits), axis=1)\n",
|
||||
" \n",
|
||||
" self.curiosity_optimizer = tf.train.RMSPropOptimizer(self.LEARNING_RATE)\\\n",
|
||||
" .minimize(tf.reduce_mean(self.curiosity_cost))\n",
|
||||
" \n",
|
||||
" total_reward = tf.add(self.curiosity_cost, self.REWARD)\n",
|
||||
" \n",
|
||||
" with tf.variable_scope(\"q_model\"):\n",
|
||||
" with tf.variable_scope(\"eval_net\"):\n",
|
||||
" x_action = tf.layers.dense(self.X, 128, tf.nn.relu)\n",
|
||||
" tensor_action, tensor_validation = tf.split(x_action,2,1)\n",
|
||||
" feed_action = tf.layers.dense(tensor_action, self.OUTPUT_SIZE)\n",
|
||||
" feed_validation = tf.layers.dense(tensor_validation, 1)\n",
|
||||
" self.logits = feed_validation + \\\n",
|
||||
" tf.subtract(feed_action,tf.reduce_mean(feed_action,axis=1,keep_dims=True))\n",
|
||||
" \n",
|
||||
" with tf.variable_scope(\"target_net\"):\n",
|
||||
" y_action = tf.layers.dense(self.Y, 128, tf.nn.relu)\n",
|
||||
" tensor_action, tensor_validation = tf.split(y_action,2,1)\n",
|
||||
" feed_action = tf.layers.dense(tensor_action, self.OUTPUT_SIZE)\n",
|
||||
" feed_validation = tf.layers.dense(tensor_validation, 1)\n",
|
||||
" y_q = feed_validation + \\\n",
|
||||
" tf.subtract(feed_action,tf.reduce_mean(feed_action,axis=1,keep_dims=True))\n",
|
||||
" \n",
|
||||
" q_target = total_reward + self.GAMMA * tf.reduce_max(y_q, axis=1)\n",
|
||||
" action = tf.cast(self.ACTION, tf.int32)\n",
|
||||
" action_indices = tf.stack([tf.range(self.batch_size, dtype=tf.int32), action], axis=1)\n",
|
||||
" q = tf.gather_nd(params=self.logits, indices=action_indices)\n",
|
||||
" self.cost = tf.losses.mean_squared_error(labels=q_target, predictions=q)\n",
|
||||
" self.optimizer = tf.train.RMSPropOptimizer(self.LEARNING_RATE).minimize(\n",
|
||||
" self.cost, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, \"q_model/eval_net\"))\n",
|
||||
" \n",
|
||||
" t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_model/target_net')\n",
|
||||
" e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_model/eval_net')\n",
|
||||
" self.target_replace_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]\n",
|
||||
" \n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
" \n",
|
||||
" def _memorize(self, state, action, reward, new_state, done):\n",
|
||||
" self.MEMORIES.append((state, action, reward, new_state, done))\n",
|
||||
" if len(self.MEMORIES) > self.MEMORY_SIZE:\n",
|
||||
" self.MEMORIES.popleft()\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array(res)\n",
|
||||
" \n",
|
||||
" def predict(self, inputs):\n",
|
||||
" return self.sess.run(self.logits, feed_dict={self.X:inputs})\n",
|
||||
" \n",
|
||||
" def get_predicted_action(self, sequence):\n",
|
||||
" prediction = self.predict(np.array(sequence))[0]\n",
|
||||
" return np.argmax(prediction)\n",
|
||||
" \n",
|
||||
" def _select_action(self, state):\n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action = self.get_predicted_action([state])\n",
|
||||
" return action\n",
|
||||
" \n",
|
||||
" def _construct_memories(self, replay):\n",
|
||||
" states = np.array([a[0] for a in replay])\n",
|
||||
" actions = np.array([a[1] for a in replay])\n",
|
||||
" rewards = np.array([a[2] for a in replay])\n",
|
||||
" new_states = np.array([a[3] for a in replay])\n",
|
||||
" if (self.T_COPY + 1) % self.COPY == 0:\n",
|
||||
" self.sess.run(self.target_replace_op)\n",
|
||||
" \n",
|
||||
" cost, _ = self.sess.run([self.cost, self.optimizer], feed_dict = {\n",
|
||||
" self.X: states, self.Y: new_states, self.ACTION: actions, self.REWARD: rewards\n",
|
||||
" })\n",
|
||||
" \n",
|
||||
" if (self.T_COPY + 1) % self.COPY == 0:\n",
|
||||
" self.sess.run(self.curiosity_optimizer, feed_dict = {\n",
|
||||
" self.X: states, self.Y: new_states, self.ACTION: actions, self.REWARD: rewards\n",
|
||||
" })\n",
|
||||
" return cost\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self._select_action(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" state = next_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" \n",
|
||||
" action = self._select_action(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" \n",
|
||||
" invest = ((starting_money - initial_money) / initial_money)\n",
|
||||
" \n",
|
||||
" self._memorize(state, action, invest, next_state, starting_money < initial_money)\n",
|
||||
" batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)\n",
|
||||
" state = next_state\n",
|
||||
" replay = random.sample(self.MEMORIES, batch_size)\n",
|
||||
" cost = self._construct_memories(replay)\n",
|
||||
" self.T_COPY += 1\n",
|
||||
" self.EPSILON = self.MIN_EPSILON + (1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)\n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING:tensorflow:From <ipython-input-3-e49b5b607a66>:53: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
|
||||
"Instructions for updating:\n",
|
||||
"keep_dims is deprecated, use keepdims instead\n",
|
||||
"epoch: 10, total rewards: 698.460085.3, cost: 596251.000000, total money: 10698.460085\n",
|
||||
"epoch: 20, total rewards: 1683.164917.3, cost: 5890915.500000, total money: 6720.204895\n",
|
||||
"epoch: 30, total rewards: 1686.875004.3, cost: 75077.554688, total money: 6721.424992\n",
|
||||
"epoch: 40, total rewards: 541.999876.3, cost: 2707843.750000, total money: 9525.359861\n",
|
||||
"epoch: 50, total rewards: 1668.824950.3, cost: 32719.388672, total money: 7666.774900\n",
|
||||
"epoch: 60, total rewards: 751.654909.3, cost: 1165742.750000, total money: 8743.134889\n",
|
||||
"epoch: 70, total rewards: 1637.889772.3, cost: 325201.937500, total money: 6669.909730\n",
|
||||
"epoch: 80, total rewards: 587.055053.3, cost: 1527037.250000, total money: 892.705077\n",
|
||||
"epoch: 90, total rewards: 2170.969727.3, cost: 122936.546875, total money: 8204.549683\n",
|
||||
"epoch: 100, total rewards: 1565.850155.3, cost: 844705.187500, total money: 19.270138\n",
|
||||
"epoch: 110, total rewards: 1733.244930.3, cost: 557043.125000, total money: 6744.174861\n",
|
||||
"epoch: 120, total rewards: 1282.489866.3, cost: 3785043.750000, total money: 8328.149839\n",
|
||||
"epoch: 130, total rewards: 1260.559873.3, cost: 596946.312500, total money: 6319.639890\n",
|
||||
"epoch: 140, total rewards: 1346.769778.3, cost: 26543662.000000, total money: 10330.129763\n",
|
||||
"epoch: 150, total rewards: 2415.594848.3, cost: 851761.625000, total money: 9467.174865\n",
|
||||
"epoch: 160, total rewards: 1033.800112.3, cost: 3596937.500000, total money: 9044.600099\n",
|
||||
"epoch: 170, total rewards: 1597.439823.3, cost: 511038.375000, total money: 93.789859\n",
|
||||
"epoch: 180, total rewards: 1736.860354.3, cost: 3795484.000000, total money: 1011.990359\n",
|
||||
"epoch: 190, total rewards: 1682.540215.3, cost: 657330.250000, total money: 8675.460198\n",
|
||||
"epoch: 200, total rewards: 875.094668.3, cost: 30907612.000000, total money: 10875.094668\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"batch_size = 32\n",
|
||||
"agent = Agent(state_size = window_size, \n",
|
||||
" window_size = window_size, \n",
|
||||
" trend = close, \n",
|
||||
" skip = skip)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 3: buy 1 unit at price 782.520020, total balance 9217.479980\n",
|
||||
"day 4: buy 1 unit at price 790.510010, total balance 8426.969970\n",
|
||||
"day 5: buy 1 unit at price 785.309998, total balance 7641.659972\n",
|
||||
"day 6: buy 1 unit at price 762.559998, total balance 6879.099974\n",
|
||||
"day 7: buy 1 unit at price 754.020020, total balance 6125.079954\n",
|
||||
"day 9: buy 1 unit at price 758.489990, total balance 5366.589964\n",
|
||||
"day 10, sell 1 unit at price 764.479980, investment -2.305377 %, total balance 6131.069944,\n",
|
||||
"day 11: buy 1 unit at price 771.229980, total balance 5359.839964\n",
|
||||
"day 13: buy 1 unit at price 769.200012, total balance 4590.639952\n",
|
||||
"day 15: buy 1 unit at price 760.989990, total balance 3829.649962\n",
|
||||
"day 16: buy 1 unit at price 761.679993, total balance 3067.969969\n",
|
||||
"day 17: buy 1 unit at price 768.239990, total balance 2299.729979\n",
|
||||
"day 19: buy 1 unit at price 758.039978, total balance 1541.690001\n",
|
||||
"day 21: buy 1 unit at price 750.500000, total balance 791.190001\n",
|
||||
"day 22, sell 1 unit at price 762.520020, investment -3.540751 %, total balance 1553.710021,\n",
|
||||
"day 23, sell 1 unit at price 759.109985, investment -3.336264 %, total balance 2312.820006,\n",
|
||||
"day 24: buy 1 unit at price 771.190002, total balance 1541.630004\n",
|
||||
"day 25, sell 1 unit at price 776.419983, investment 1.817560 %, total balance 2318.049987,\n",
|
||||
"day 27, sell 1 unit at price 789.270020, investment 4.674942 %, total balance 3107.320007,\n",
|
||||
"day 29, sell 1 unit at price 797.070007, investment 5.086424 %, total balance 3904.390014,\n",
|
||||
"day 30, sell 1 unit at price 797.849976, investment 3.451629 %, total balance 4702.239990,\n",
|
||||
"day 32: buy 1 unit at price 794.200012, total balance 3908.039978\n",
|
||||
"day 33, sell 1 unit at price 796.419983, investment 3.538738 %, total balance 4704.459961,\n",
|
||||
"day 34, sell 1 unit at price 794.559998, investment 4.411360 %, total balance 5499.019959,\n",
|
||||
"day 38: buy 1 unit at price 785.049988, total balance 4713.969971\n",
|
||||
"day 39, sell 1 unit at price 782.789978, investment 2.771503 %, total balance 5496.759949,\n",
|
||||
"day 40, sell 1 unit at price 771.820007, investment 0.466002 %, total balance 6268.579956,\n",
|
||||
"day 41: buy 1 unit at price 786.140015, total balance 5482.439941\n",
|
||||
"day 45: buy 1 unit at price 806.650024, total balance 4675.789917\n",
|
||||
"day 47, sell 1 unit at price 807.909973, investment 6.578808 %, total balance 5483.699890,\n",
|
||||
"day 50: buy 1 unit at price 804.609985, total balance 4679.089905\n",
|
||||
"day 51, sell 1 unit at price 806.070007, investment 7.404398 %, total balance 5485.159912,\n",
|
||||
"day 52, sell 1 unit at price 802.174988, investment 4.017815 %, total balance 6287.334900,\n",
|
||||
"day 53: buy 1 unit at price 805.020020, total balance 5482.314880\n",
|
||||
"day 54, sell 1 unit at price 819.309998, investment 3.161670 %, total balance 6301.624878,\n",
|
||||
"day 56: buy 1 unit at price 835.669983, total balance 5465.954895\n",
|
||||
"day 58, sell 1 unit at price 823.309998, investment 4.873576 %, total balance 6289.264893,\n",
|
||||
"day 59, sell 1 unit at price 802.320007, investment 2.058157 %, total balance 7091.584900,\n",
|
||||
"day 60: buy 1 unit at price 796.789978, total balance 6294.794922\n",
|
||||
"day 61, sell 1 unit at price 795.695007, investment -1.358088 %, total balance 7090.489929,\n",
|
||||
"day 62, sell 1 unit at price 798.530029, investment -0.755640 %, total balance 7889.019958,\n",
|
||||
"day 63, sell 1 unit at price 801.489990, investment -0.438502 %, total balance 8690.509948,\n",
|
||||
"day 66: buy 1 unit at price 808.380005, total balance 7882.129943\n",
|
||||
"day 67, sell 1 unit at price 809.559998, investment -3.124437 %, total balance 8691.689941,\n",
|
||||
"day 68: buy 1 unit at price 813.669983, total balance 7878.019958\n",
|
||||
"day 69, sell 1 unit at price 819.239990, investment 2.817557 %, total balance 8697.259948,\n",
|
||||
"day 70, sell 1 unit at price 820.450012, investment 1.493111 %, total balance 9517.709960,\n",
|
||||
"day 72, sell 1 unit at price 824.159973, investment 1.289219 %, total balance 10341.869933,\n",
|
||||
"day 73: buy 1 unit at price 828.070007, total balance 9513.799926\n",
|
||||
"day 74, sell 1 unit at price 831.659973, investment 0.433534 %, total balance 10345.459899,\n",
|
||||
"day 75: buy 1 unit at price 830.760010, total balance 9514.699889\n",
|
||||
"day 76: buy 1 unit at price 831.330017, total balance 8683.369872\n",
|
||||
"day 78: buy 1 unit at price 829.280029, total balance 7854.089843\n",
|
||||
"day 79: buy 1 unit at price 823.210022, total balance 7030.879821\n",
|
||||
"day 80: buy 1 unit at price 835.239990, total balance 6195.639831\n",
|
||||
"day 81, sell 1 unit at price 830.630005, investment -0.015649 %, total balance 7026.269836,\n",
|
||||
"day 82, sell 1 unit at price 829.080017, investment -0.270651 %, total balance 7855.349853,\n",
|
||||
"day 84, sell 1 unit at price 831.909973, investment 0.317136 %, total balance 8687.259826,\n",
|
||||
"day 85: buy 1 unit at price 835.369995, total balance 7851.889831\n",
|
||||
"day 86: buy 1 unit at price 838.679993, total balance 7013.209838\n",
|
||||
"day 88: buy 1 unit at price 845.539978, total balance 6167.669860\n",
|
||||
"day 89: buy 1 unit at price 845.619995, total balance 5322.049865\n",
|
||||
"day 90, sell 1 unit at price 847.200012, investment 2.914200 %, total balance 6169.249877,\n",
|
||||
"day 91: buy 1 unit at price 848.780029, total balance 5320.469848\n",
|
||||
"day 92: buy 1 unit at price 852.119995, total balance 4468.349853\n",
|
||||
"day 93, sell 1 unit at price 848.400024, investment 1.575599 %, total balance 5316.749877,\n",
|
||||
"day 94: buy 1 unit at price 830.460022, total balance 4486.289855\n",
|
||||
"day 95: buy 1 unit at price 829.590027, total balance 3656.699828\n",
|
||||
"day 96, sell 1 unit at price 817.580017, investment -2.129593 %, total balance 4474.279845,\n",
|
||||
"day 97: buy 1 unit at price 814.429993, total balance 3659.849852\n",
|
||||
"day 98, sell 1 unit at price 819.510010, investment -2.285733 %, total balance 4479.359862,\n",
|
||||
"day 100: buy 1 unit at price 831.409973, total balance 3647.949889\n",
|
||||
"day 101, sell 1 unit at price 831.500000, investment -1.660475 %, total balance 4479.449889,\n",
|
||||
"day 103: buy 1 unit at price 838.549988, total balance 3640.899901\n",
|
||||
"day 104: buy 1 unit at price 834.570007, total balance 2806.329894\n",
|
||||
"day 105, sell 1 unit at price 831.409973, investment -1.680426 %, total balance 3637.739867,\n",
|
||||
"day 106: buy 1 unit at price 827.880005, total balance 2809.859862\n",
|
||||
"day 108: buy 1 unit at price 824.729980, total balance 1985.129882\n",
|
||||
"day 109: buy 1 unit at price 823.349976, total balance 1161.779906\n",
|
||||
"day 110: buy 1 unit at price 824.320007, total balance 337.459899\n",
|
||||
"day 112, sell 1 unit at price 837.169983, investment -1.367851 %, total balance 1174.629882,\n",
|
||||
"day 113: buy 1 unit at price 836.820007, total balance 337.809875\n",
|
||||
"day 115, sell 1 unit at price 841.650024, investment -1.228697 %, total balance 1179.459899,\n",
|
||||
"day 116, sell 1 unit at price 843.190002, investment 1.532883 %, total balance 2022.649901,\n",
|
||||
"day 117, sell 1 unit at price 862.760010, investment 3.998358 %, total balance 2885.409911,\n",
|
||||
"day 118: buy 1 unit at price 872.299988, total balance 2013.109923\n",
|
||||
"day 119, sell 1 unit at price 871.729980, investment 7.035594 %, total balance 2884.839903,\n",
|
||||
"day 120, sell 1 unit at price 874.250000, investment 5.152696 %, total balance 3759.089903,\n",
|
||||
"day 123, sell 1 unit at price 916.440002, investment 9.288655 %, total balance 4675.529905,\n",
|
||||
"day 124: buy 1 unit at price 927.039978, total balance 3748.489927\n",
|
||||
"day 126, sell 1 unit at price 927.130005, investment 11.090741 %, total balance 4675.619932,\n",
|
||||
"day 127, sell 1 unit at price 934.299988, investment 12.854518 %, total balance 5609.919920,\n",
|
||||
"day 128, sell 1 unit at price 932.169983, investment 13.027294 %, total balance 6542.089903,\n",
|
||||
"day 129: buy 1 unit at price 928.780029, total balance 5613.309874\n",
|
||||
"day 133, sell 1 unit at price 943.000000, investment 14.532098 %, total balance 6556.309874,\n",
|
||||
"day 137, sell 1 unit at price 941.859985, investment 14.259023 %, total balance 7498.169859,\n",
|
||||
"day 140, sell 1 unit at price 969.539978, investment 15.860038 %, total balance 8467.709837,\n",
|
||||
"day 141: buy 1 unit at price 971.469971, total balance 7496.239866\n",
|
||||
"day 142, sell 1 unit at price 975.880005, investment 11.874357 %, total balance 8472.119871,\n",
|
||||
"day 143, sell 1 unit at price 964.859985, investment 4.079652 %, total balance 9436.979856,\n",
|
||||
"day 144, sell 1 unit at price 966.950012, investment 4.109690 %, total balance 10403.929868,\n",
|
||||
"day 145, sell 1 unit at price 975.599976, investment 0.425129 %, total balance 11379.529844,\n",
|
||||
"day 147: buy 1 unit at price 976.570007, total balance 10402.959837\n",
|
||||
"day 148: buy 1 unit at price 980.940002, total balance 9422.019835\n",
|
||||
"day 149, sell 1 unit at price 983.409973, investment 0.700407 %, total balance 10405.429808,\n",
|
||||
"day 150: buy 1 unit at price 949.830017, total balance 9455.599791\n",
|
||||
"day 151, sell 1 unit at price 942.900024, investment -3.877911 %, total balance 10398.499815,\n",
|
||||
"day 152, sell 1 unit at price 953.400024, investment 0.375857 %, total balance 11351.899839,\n",
|
||||
"day 153: buy 1 unit at price 950.760010, total balance 10401.139829\n",
|
||||
"day 155, sell 1 unit at price 939.780029, investment -1.154864 %, total balance 11340.919858,\n",
|
||||
"day 156: buy 1 unit at price 957.369995, total balance 10383.549863\n",
|
||||
"day 157: buy 1 unit at price 950.630005, total balance 9432.919858\n",
|
||||
"day 158: buy 1 unit at price 959.450012, total balance 8473.469846\n",
|
||||
"day 159, sell 1 unit at price 957.090027, investment -0.029243 %, total balance 9430.559873,\n",
|
||||
"day 160: buy 1 unit at price 965.590027, total balance 8464.969846\n",
|
||||
"day 162, sell 1 unit at price 927.330017, investment -2.451005 %, total balance 9392.299863,\n",
|
||||
"day 163: buy 1 unit at price 940.489990, total balance 8451.809873\n",
|
||||
"day 164: buy 1 unit at price 917.789978, total balance 7534.019895\n",
|
||||
"day 165: buy 1 unit at price 908.729980, total balance 6625.289915\n",
|
||||
"day 166, sell 1 unit at price 898.700012, investment -6.331752 %, total balance 7523.989927,\n",
|
||||
"day 167, sell 1 unit at price 911.710022, investment -5.580008 %, total balance 8435.699949,\n",
|
||||
"day 169: buy 1 unit at price 918.590027, total balance 7517.109922\n",
|
||||
"day 170: buy 1 unit at price 928.799988, total balance 6588.309934\n",
|
||||
"day 171: buy 1 unit at price 930.090027, total balance 5658.219907\n",
|
||||
"day 172: buy 1 unit at price 943.830017, total balance 4714.389890\n",
|
||||
"day 173: buy 1 unit at price 947.159973, total balance 3767.229917\n",
|
||||
"day 175: buy 1 unit at price 953.419983, total balance 2813.809934\n",
|
||||
"day 177: buy 1 unit at price 970.890015, total balance 1842.919919\n",
|
||||
"day 178, sell 1 unit at price 968.150024, investment 2.941024 %, total balance 2811.069943,\n",
|
||||
"day 179: buy 1 unit at price 972.919983, total balance 1838.149960\n",
|
||||
"day 180: buy 1 unit at price 980.340027, total balance 857.809933\n",
|
||||
"day 187, sell 1 unit at price 930.390015, investment 1.372867 %, total balance 1788.199948,\n",
|
||||
"day 189, sell 1 unit at price 927.960022, investment 2.116145 %, total balance 2716.159970,\n",
|
||||
"day 190, sell 1 unit at price 929.359985, investment 1.172444 %, total balance 3645.519955,\n",
|
||||
"day 191, sell 1 unit at price 926.789978, investment -0.216409 %, total balance 4572.309933,\n",
|
||||
"day 192, sell 1 unit at price 922.900024, investment -0.773044 %, total balance 5495.209957,\n",
|
||||
"day 193: buy 1 unit at price 907.239990, total balance 4587.969967\n",
|
||||
"day 196, sell 1 unit at price 922.219971, investment -2.289612 %, total balance 5510.189938,\n",
|
||||
"day 197, sell 1 unit at price 926.960022, investment -2.132686 %, total balance 6437.149960,\n",
|
||||
"day 198, sell 1 unit at price 910.979980, investment -4.451344 %, total balance 7348.129940,\n",
|
||||
"day 200, sell 1 unit at price 906.659973, investment -6.615584 %, total balance 8254.789913,\n",
|
||||
"day 201: buy 1 unit at price 924.690002, total balance 7330.099911\n",
|
||||
"day 202: buy 1 unit at price 927.000000, total balance 6403.099911\n",
|
||||
"day 204: buy 1 unit at price 915.890015, total balance 5487.209896\n",
|
||||
"day 205: buy 1 unit at price 913.809998, total balance 4573.399898\n",
|
||||
"day 206: buy 1 unit at price 921.289978, total balance 3652.109920\n",
|
||||
"day 207: buy 1 unit at price 929.570007, total balance 2722.539913\n",
|
||||
"day 208: buy 1 unit at price 939.330017, total balance 1783.209896\n",
|
||||
"day 209: buy 1 unit at price 937.340027, total balance 845.869869\n",
|
||||
"day 210, sell 1 unit at price 928.450012, investment -4.570774 %, total balance 1774.319881,\n",
|
||||
"day 211: buy 1 unit at price 927.809998, total balance 846.509883\n",
|
||||
"day 215, sell 1 unit at price 932.070007, investment -4.923804 %, total balance 1778.579890,\n",
|
||||
"day 218: buy 1 unit at price 920.289978, total balance 858.289912\n",
|
||||
"day 219, sell 1 unit at price 915.000000, investment 0.855343 %, total balance 1773.289912,\n",
|
||||
"day 220, sell 1 unit at price 921.809998, investment -0.311456 %, total balance 2695.099910,\n",
|
||||
"day 221, sell 1 unit at price 931.580017, investment 0.494069 %, total balance 3626.679927,\n",
|
||||
"day 223: buy 1 unit at price 928.530029, total balance 2698.149898\n",
|
||||
"day 225, sell 1 unit at price 924.859985, investment 0.979372 %, total balance 3623.009883,\n",
|
||||
"day 228: buy 1 unit at price 959.109985, total balance 2663.899898\n",
|
||||
"day 230, sell 1 unit at price 957.789978, investment 4.812814 %, total balance 3621.689876,\n",
|
||||
"day 231, sell 1 unit at price 951.679993, investment 3.298637 %, total balance 4573.369869,\n",
|
||||
"day 232, sell 1 unit at price 969.960022, investment 4.345021 %, total balance 5543.329891,\n",
|
||||
"day 233, sell 1 unit at price 978.890015, investment 4.211512 %, total balance 6522.219906,\n",
|
||||
"day 234, sell 1 unit at price 977.000000, investment 4.231119 %, total balance 7499.219906,\n",
|
||||
"day 235, sell 1 unit at price 972.599976, investment 4.827495 %, total balance 8471.819882,\n",
|
||||
"day 236, sell 1 unit at price 989.250000, investment 7.493293 %, total balance 9461.069882,\n",
|
||||
"day 239: buy 1 unit at price 992.000000, total balance 8469.069882\n",
|
||||
"day 240, sell 1 unit at price 992.179993, investment 6.854917 %, total balance 9461.249875,\n",
|
||||
"day 242, sell 1 unit at price 984.450012, investment 2.642036 %, total balance 10445.699887,\n",
|
||||
"day 243: buy 1 unit at price 988.200012, total balance 9457.499875\n",
|
||||
"day 245, sell 1 unit at price 970.539978, investment -2.163309 %, total balance 10428.039853,\n",
|
||||
"day 246, sell 1 unit at price 973.330017, investment -1.504756 %, total balance 11401.369870,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+633
@@ -0,0 +1,633 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class neuralnetwork:\n",
|
||||
" def __init__(self, id_, hidden_size = 128):\n",
|
||||
" self.W1 = np.random.randn(window_size, hidden_size) / np.sqrt(window_size)\n",
|
||||
" self.W2 = np.random.randn(hidden_size, 3) / np.sqrt(hidden_size)\n",
|
||||
" self.fitness = 0\n",
|
||||
" self.id = id_\n",
|
||||
"\n",
|
||||
"def relu(X):\n",
|
||||
" return np.maximum(X, 0)\n",
|
||||
" \n",
|
||||
"def softmax(X):\n",
|
||||
" e_x = np.exp(X - np.max(X, axis=-1, keepdims=True))\n",
|
||||
" return e_x / np.sum(e_x, axis=-1, keepdims=True)\n",
|
||||
"\n",
|
||||
"def feed_forward(X, nets):\n",
|
||||
" a1 = np.dot(X, nets.W1)\n",
|
||||
" z1 = relu(a1)\n",
|
||||
" a2 = np.dot(z1, nets.W2)\n",
|
||||
" return softmax(a2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class NeuroEvolution:\n",
|
||||
" def __init__(self, population_size, mutation_rate, model_generator,\n",
|
||||
" state_size, window_size, trend, skip, initial_money):\n",
|
||||
" self.population_size = population_size\n",
|
||||
" self.mutation_rate = mutation_rate\n",
|
||||
" self.model_generator = model_generator\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" self.initial_money = initial_money\n",
|
||||
" \n",
|
||||
" def _initialize_population(self):\n",
|
||||
" self.population = []\n",
|
||||
" for i in range(self.population_size):\n",
|
||||
" self.population.append(self.model_generator(i))\n",
|
||||
" \n",
|
||||
" def mutate(self, individual, scale=1.0):\n",
|
||||
" mutation_mask = np.random.binomial(1, p=self.mutation_rate, size=individual.W1.shape)\n",
|
||||
" individual.W1 += np.random.normal(loc=0, scale=scale, size=individual.W1.shape) * mutation_mask\n",
|
||||
" mutation_mask = np.random.binomial(1, p=self.mutation_rate, size=individual.W2.shape)\n",
|
||||
" individual.W2 += np.random.normal(loc=0, scale=scale, size=individual.W2.shape) * mutation_mask\n",
|
||||
" return individual\n",
|
||||
" \n",
|
||||
" def inherit_weights(self, parent, child):\n",
|
||||
" child.W1 = parent.W1.copy()\n",
|
||||
" child.W2 = parent.W2.copy()\n",
|
||||
" return child\n",
|
||||
" \n",
|
||||
" def crossover(self, parent1, parent2):\n",
|
||||
" child1 = self.model_generator((parent1.id+1)*10)\n",
|
||||
" child1 = self.inherit_weights(parent1, child1)\n",
|
||||
" child2 = self.model_generator((parent2.id+1)*10)\n",
|
||||
" child2 = self.inherit_weights(parent2, child2)\n",
|
||||
" # first W\n",
|
||||
" n_neurons = child1.W1.shape[1]\n",
|
||||
" cutoff = np.random.randint(0, n_neurons)\n",
|
||||
" child1.W1[:, cutoff:] = parent2.W1[:, cutoff:].copy()\n",
|
||||
" child2.W1[:, cutoff:] = parent1.W1[:, cutoff:].copy()\n",
|
||||
" # second W\n",
|
||||
" n_neurons = child1.W2.shape[1]\n",
|
||||
" cutoff = np.random.randint(0, n_neurons)\n",
|
||||
" child1.W2[:, cutoff:] = parent2.W2[:, cutoff:].copy()\n",
|
||||
" child2.W2[:, cutoff:] = parent1.W2[:, cutoff:].copy()\n",
|
||||
" return child1, child2\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array([res])\n",
|
||||
" \n",
|
||||
" def act(self, p, state):\n",
|
||||
" logits = feed_forward(state, p)\n",
|
||||
" return np.argmax(logits, 1)[0]\n",
|
||||
" \n",
|
||||
" def buy(self, individual):\n",
|
||||
" initial_money = self.initial_money\n",
|
||||
" starting_money = initial_money\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" inventory = []\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" \n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self.act(individual, state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((self.trend[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, self.trend[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" state = next_state\n",
|
||||
" \n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" def calculate_fitness(self):\n",
|
||||
" for i in range(self.population_size):\n",
|
||||
" initial_money = self.initial_money\n",
|
||||
" starting_money = initial_money\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" inventory = []\n",
|
||||
" \n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self.act(self.population[i], state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
"\n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
"\n",
|
||||
" state = next_state\n",
|
||||
" invest = ((starting_money - initial_money) / initial_money) * 100\n",
|
||||
" self.population[i].fitness = invest\n",
|
||||
" \n",
|
||||
" def evolve(self, generations=20, checkpoint= 5):\n",
|
||||
" self._initialize_population()\n",
|
||||
" n_winners = int(self.population_size * 0.4)\n",
|
||||
" n_parents = self.population_size - n_winners\n",
|
||||
" for epoch in range(generations):\n",
|
||||
" self.calculate_fitness()\n",
|
||||
" fitnesses = [i.fitness for i in self.population]\n",
|
||||
" sort_fitness = np.argsort(fitnesses)[::-1]\n",
|
||||
" self.population = [self.population[i] for i in sort_fitness]\n",
|
||||
" fittest_individual = self.population[0]\n",
|
||||
" if (epoch+1) % checkpoint == 0:\n",
|
||||
" print('epoch %d, fittest individual %d with accuracy %f'%(epoch+1, sort_fitness[0], \n",
|
||||
" fittest_individual.fitness))\n",
|
||||
" next_population = [self.population[i] for i in range(n_winners)]\n",
|
||||
" total_fitness = np.sum([np.abs(i.fitness) for i in self.population])\n",
|
||||
" parent_probabilities = [np.abs(i.fitness / total_fitness) for i in self.population]\n",
|
||||
" parents = np.random.choice(self.population, size=n_parents, p=parent_probabilities, replace=False)\n",
|
||||
" for i in np.arange(0, len(parents), 2):\n",
|
||||
" child1, child2 = self.crossover(parents[i], parents[i+1])\n",
|
||||
" next_population += [self.mutate(child1), self.mutate(child2)]\n",
|
||||
" self.population = next_population\n",
|
||||
" return fittest_individual"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"population_size = 100\n",
|
||||
"generations = 100\n",
|
||||
"mutation_rate = 0.1\n",
|
||||
"neural_evolve = NeuroEvolution(population_size, mutation_rate, neuralnetwork,\n",
|
||||
" window_size, window_size, close, skip, initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"epoch 5, fittest individual 0 with accuracy 10.849749\n",
|
||||
"epoch 10, fittest individual 0 with accuracy 11.095000\n",
|
||||
"epoch 15, fittest individual 0 with accuracy 11.095000\n",
|
||||
"epoch 20, fittest individual 93 with accuracy 13.756802\n",
|
||||
"epoch 25, fittest individual 95 with accuracy 23.728605\n",
|
||||
"epoch 30, fittest individual 0 with accuracy 23.728605\n",
|
||||
"epoch 35, fittest individual 0 with accuracy 23.728605\n",
|
||||
"epoch 40, fittest individual 0 with accuracy 23.728605\n",
|
||||
"epoch 45, fittest individual 0 with accuracy 23.728605\n",
|
||||
"epoch 50, fittest individual 0 with accuracy 23.728605\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fittest_nets = neural_evolve.evolve(50)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 1: buy 1 unit at price 762.130005, total balance 9237.869995\n",
|
||||
"day 2: buy 1 unit at price 762.020020, total balance 8475.849975\n",
|
||||
"day 3, sell 1 unit at price 782.520020, investment 2.675399 %, total balance 9258.369995,\n",
|
||||
"day 5, sell 1 unit at price 785.309998, investment 3.056347 %, total balance 10043.679993,\n",
|
||||
"day 6: buy 1 unit at price 762.559998, total balance 9281.119995\n",
|
||||
"day 7: buy 1 unit at price 754.020020, total balance 8527.099975\n",
|
||||
"day 8: buy 1 unit at price 736.080017, total balance 7791.019958\n",
|
||||
"day 9: buy 1 unit at price 758.489990, total balance 7032.529968\n",
|
||||
"day 10, sell 1 unit at price 764.479980, investment 0.251781 %, total balance 7797.009948,\n",
|
||||
"day 12: buy 1 unit at price 760.539978, total balance 7036.469970\n",
|
||||
"day 14: buy 1 unit at price 768.270020, total balance 6268.199950\n",
|
||||
"day 15: buy 1 unit at price 760.989990, total balance 5507.209960\n",
|
||||
"day 16: buy 1 unit at price 761.679993, total balance 4745.529967\n",
|
||||
"day 17, sell 1 unit at price 768.239990, investment 1.885888 %, total balance 5513.769957,\n",
|
||||
"day 18, sell 1 unit at price 770.840027, investment 4.722314 %, total balance 6284.609984,\n",
|
||||
"day 20: buy 1 unit at price 747.919983, total balance 5536.690001\n",
|
||||
"day 21: buy 1 unit at price 750.500000, total balance 4786.190001\n",
|
||||
"day 22: buy 1 unit at price 762.520020, total balance 4023.669981\n",
|
||||
"day 24, sell 1 unit at price 771.190002, investment 1.674381 %, total balance 4794.859983,\n",
|
||||
"day 25, sell 1 unit at price 776.419983, investment 2.087991 %, total balance 5571.279966,\n",
|
||||
"day 26, sell 1 unit at price 789.289978, investment 2.736012 %, total balance 6360.569944,\n",
|
||||
"day 27: buy 1 unit at price 789.270020, total balance 5571.299924\n",
|
||||
"day 28: buy 1 unit at price 796.099976, total balance 4775.199948\n",
|
||||
"day 29: buy 1 unit at price 797.070007, total balance 3978.129941\n",
|
||||
"day 31: buy 1 unit at price 790.799988, total balance 3187.329953\n",
|
||||
"day 32, sell 1 unit at price 794.200012, investment 4.364055 %, total balance 3981.529965,\n",
|
||||
"day 33: buy 1 unit at price 796.419983, total balance 3185.109982\n",
|
||||
"day 34, sell 1 unit at price 794.559998, investment 4.316774 %, total balance 3979.669980,\n",
|
||||
"day 35, sell 1 unit at price 791.260010, investment 5.794741 %, total balance 4770.929990,\n",
|
||||
"day 37: buy 1 unit at price 791.549988, total balance 3979.380002\n",
|
||||
"day 38, sell 1 unit at price 785.049988, investment 4.603596 %, total balance 4764.429990,\n",
|
||||
"day 39: buy 1 unit at price 782.789978, total balance 3981.640012\n",
|
||||
"day 40: buy 1 unit at price 771.820007, total balance 3209.820005\n",
|
||||
"day 41: buy 1 unit at price 786.140015, total balance 2423.679990\n",
|
||||
"day 42, sell 1 unit at price 786.900024, investment 3.197294 %, total balance 3210.580014,\n",
|
||||
"day 43: buy 1 unit at price 794.020020, total balance 2416.559994\n",
|
||||
"day 44: buy 1 unit at price 806.150024, total balance 1610.409970\n",
|
||||
"day 46, sell 1 unit at price 804.789978, investment 1.966369 %, total balance 2415.199948,\n",
|
||||
"day 47, sell 1 unit at price 807.909973, investment 1.483482 %, total balance 3223.109921,\n",
|
||||
"day 48: buy 1 unit at price 806.359985, total balance 2416.749936\n",
|
||||
"day 49, sell 1 unit at price 807.880005, investment 1.356217 %, total balance 3224.629941,\n",
|
||||
"day 50, sell 1 unit at price 804.609985, investment 1.746332 %, total balance 4029.239926,\n",
|
||||
"day 51, sell 1 unit at price 806.070007, investment 1.211675 %, total balance 4835.309933,\n",
|
||||
"day 52: buy 1 unit at price 802.174988, total balance 4033.134945\n",
|
||||
"day 53: buy 1 unit at price 805.020020, total balance 3228.114925\n",
|
||||
"day 54, sell 1 unit at price 819.309998, investment 3.507044 %, total balance 4047.424923,\n",
|
||||
"day 55, sell 1 unit at price 823.869995, investment 5.247898 %, total balance 4871.294918,\n",
|
||||
"day 56, sell 1 unit at price 835.669983, investment 8.272651 %, total balance 5706.964901,\n",
|
||||
"day 57, sell 1 unit at price 832.150024, investment 5.852648 %, total balance 6539.114925,\n",
|
||||
"day 58: buy 1 unit at price 823.309998, total balance 5715.804927\n",
|
||||
"day 59: buy 1 unit at price 802.320007, total balance 4913.484920\n",
|
||||
"day 60: buy 1 unit at price 796.789978, total balance 4116.694942\n",
|
||||
"day 61: buy 1 unit at price 795.695007, total balance 3320.999935\n",
|
||||
"day 63: buy 1 unit at price 801.489990, total balance 2519.509945\n",
|
||||
"day 65: buy 1 unit at price 806.969971, total balance 1712.539974\n",
|
||||
"day 66, sell 1 unit at price 808.380005, investment 1.808517 %, total balance 2520.919979,\n",
|
||||
"day 67: buy 1 unit at price 809.559998, total balance 1711.359981\n",
|
||||
"day 68, sell 1 unit at price 813.669983, investment 0.932824 %, total balance 2525.029964,\n",
|
||||
"day 69, sell 1 unit at price 819.239990, investment 1.597302 %, total balance 3344.269954,\n",
|
||||
"day 70, sell 1 unit at price 820.450012, investment 2.278184 %, total balance 4164.719966,\n",
|
||||
"day 71: buy 1 unit at price 818.979980, total balance 3345.739986\n",
|
||||
"day 72: buy 1 unit at price 824.159973, total balance 2521.580013\n",
|
||||
"day 73: buy 1 unit at price 828.070007, total balance 1693.510006\n",
|
||||
"day 75, sell 1 unit at price 830.760010, investment 3.197435 %, total balance 2524.270016,\n",
|
||||
"day 76, sell 1 unit at price 831.330017, investment 0.974119 %, total balance 3355.600033,\n",
|
||||
"day 77, sell 1 unit at price 828.640015, investment 3.280488 %, total balance 4184.240048,\n",
|
||||
"day 78, sell 1 unit at price 829.280029, investment 4.077618 %, total balance 5013.520077,\n",
|
||||
"day 79, sell 1 unit at price 823.210022, investment 3.457985 %, total balance 5836.730099,\n",
|
||||
"day 80: buy 1 unit at price 835.239990, total balance 5001.490109\n",
|
||||
"day 81, sell 1 unit at price 830.630005, investment 3.635730 %, total balance 5832.120114,\n",
|
||||
"day 83: buy 1 unit at price 827.780029, total balance 5004.340085\n",
|
||||
"day 84: buy 1 unit at price 831.909973, total balance 4172.430112\n",
|
||||
"day 85: buy 1 unit at price 835.369995, total balance 3337.060117\n",
|
||||
"day 86, sell 1 unit at price 838.679993, investment 3.929517 %, total balance 4175.740110,\n",
|
||||
"day 87, sell 1 unit at price 843.250000, investment 4.161520 %, total balance 5018.990110,\n",
|
||||
"day 88, sell 1 unit at price 845.539978, investment 3.243058 %, total balance 5864.530088,\n",
|
||||
"day 89: buy 1 unit at price 845.619995, total balance 5018.910093\n",
|
||||
"day 90, sell 1 unit at price 847.200012, investment 2.795578 %, total balance 5866.110105,\n",
|
||||
"day 91: buy 1 unit at price 848.780029, total balance 5017.330076\n",
|
||||
"day 92: buy 1 unit at price 852.119995, total balance 4165.210081\n",
|
||||
"day 93, sell 1 unit at price 848.400024, investment 2.455108 %, total balance 5013.610105,\n",
|
||||
"day 95: buy 1 unit at price 829.590027, total balance 4184.020078\n",
|
||||
"day 97: buy 1 unit at price 814.429993, total balance 3369.590085\n",
|
||||
"day 98, sell 1 unit at price 819.510010, investment -1.883289 %, total balance 4189.100095,\n",
|
||||
"day 100: buy 1 unit at price 831.409973, total balance 3357.690122\n",
|
||||
"day 101, sell 1 unit at price 831.500000, investment 0.449391 %, total balance 4189.190122,\n",
|
||||
"day 102: buy 1 unit at price 829.559998, total balance 3359.630124\n",
|
||||
"day 103: buy 1 unit at price 838.549988, total balance 2521.080136\n",
|
||||
"day 104: buy 1 unit at price 834.570007, total balance 1686.510129\n",
|
||||
"day 105, sell 1 unit at price 831.409973, investment -0.060103 %, total balance 2517.920102,\n",
|
||||
"day 106: buy 1 unit at price 827.880005, total balance 1690.040097\n",
|
||||
"day 108: buy 1 unit at price 824.729980, total balance 865.310117\n",
|
||||
"day 109: buy 1 unit at price 823.349976, total balance 41.960141\n",
|
||||
"day 110: buy 1 unit at price 824.320007, total balance -782.359866\n",
|
||||
"day 112, sell 1 unit at price 837.169983, investment 0.215472 %, total balance 54.810117,\n",
|
||||
"day 113, sell 1 unit at price 836.820007, investment -1.040655 %, total balance 891.630124,\n",
|
||||
"day 114, sell 1 unit at price 838.210022, investment -1.245318 %, total balance 1729.840146,\n",
|
||||
"day 115: buy 1 unit at price 841.650024, total balance 888.190122\n",
|
||||
"day 117: buy 1 unit at price 862.760010, total balance 25.430112\n",
|
||||
"day 118, sell 1 unit at price 872.299988, investment 2.368210 %, total balance 897.730100,\n",
|
||||
"day 119: buy 1 unit at price 871.729980, total balance 26.000120\n",
|
||||
"day 120, sell 1 unit at price 874.250000, investment 5.383379 %, total balance 900.250120,\n",
|
||||
"day 121, sell 1 unit at price 905.960022, investment 11.238539 %, total balance 1806.210142,\n",
|
||||
"day 122, sell 1 unit at price 912.570007, investment 9.761734 %, total balance 2718.780149,\n",
|
||||
"day 123, sell 1 unit at price 916.440002, investment 10.473022 %, total balance 3635.220151,\n",
|
||||
"day 124, sell 1 unit at price 927.039978, investment 10.552739 %, total balance 4562.260129,\n",
|
||||
"day 125, sell 1 unit at price 931.659973, investment 11.633532 %, total balance 5493.920102,\n",
|
||||
"day 126, sell 1 unit at price 927.130005, investment 11.988452 %, total balance 6421.050107,\n",
|
||||
"day 127, sell 1 unit at price 934.299988, investment 13.285561 %, total balance 7355.350095,\n",
|
||||
"day 128, sell 1 unit at price 932.169983, investment 13.216738 %, total balance 8287.520078,\n",
|
||||
"day 129: buy 1 unit at price 928.780029, total balance 7358.740049\n",
|
||||
"day 130, sell 1 unit at price 930.599976, investment 12.893047 %, total balance 8289.340025,\n",
|
||||
"day 132: buy 1 unit at price 937.080017, total balance 7352.260008\n",
|
||||
"day 133, sell 1 unit at price 943.000000, investment 12.041819 %, total balance 8295.260008,\n",
|
||||
"day 134: buy 1 unit at price 919.619995, total balance 7375.640013\n",
|
||||
"day 135: buy 1 unit at price 930.239990, total balance 6445.400023\n",
|
||||
"day 136: buy 1 unit at price 934.010010, total balance 5511.390013\n",
|
||||
"day 138: buy 1 unit at price 948.820007, total balance 4562.570006\n",
|
||||
"day 140: buy 1 unit at price 969.539978, total balance 3593.030028\n",
|
||||
"day 142, sell 1 unit at price 975.880005, investment 13.111409 %, total balance 4568.910033,\n",
|
||||
"day 143, sell 1 unit at price 964.859985, investment 10.683355 %, total balance 5533.770018,\n",
|
||||
"day 144, sell 1 unit at price 966.950012, investment 4.109690 %, total balance 6500.720030,\n",
|
||||
"day 146, sell 1 unit at price 983.679993, investment 4.972892 %, total balance 7484.400023,\n",
|
||||
"day 147: buy 1 unit at price 976.570007, total balance 6507.830016\n",
|
||||
"day 148, sell 1 unit at price 980.940002, investment 6.667972 %, total balance 7488.770018,\n",
|
||||
"day 149: buy 1 unit at price 983.409973, total balance 6505.360045\n",
|
||||
"day 150, sell 1 unit at price 949.830017, investment 2.105911 %, total balance 7455.190062,\n",
|
||||
"day 151: buy 1 unit at price 942.900024, total balance 6512.290038\n",
|
||||
"day 152, sell 1 unit at price 953.400024, investment 2.075996 %, total balance 7465.690062,\n",
|
||||
"day 154: buy 1 unit at price 942.309998, total balance 6523.380064\n",
|
||||
"day 155: buy 1 unit at price 939.780029, total balance 5583.600035\n",
|
||||
"day 157: buy 1 unit at price 950.630005, total balance 4632.970030\n",
|
||||
"day 158, sell 1 unit at price 959.450012, investment 1.120339 %, total balance 5592.420042,\n",
|
||||
"day 159: buy 1 unit at price 957.090027, total balance 4635.330015\n",
|
||||
"day 160, sell 1 unit at price 965.590027, investment -0.407405 %, total balance 5600.920042,\n",
|
||||
"day 161, sell 1 unit at price 952.270020, investment -2.488300 %, total balance 6553.190062,\n",
|
||||
"day 163: buy 1 unit at price 940.489990, total balance 5612.700072\n",
|
||||
"day 165: buy 1 unit at price 908.729980, total balance 4703.970092\n",
|
||||
"day 166, sell 1 unit at price 898.700012, investment -8.613901 %, total balance 5602.670104,\n",
|
||||
"day 169, sell 1 unit at price 918.590027, investment -2.578216 %, total balance 6521.260131,\n",
|
||||
"day 170: buy 1 unit at price 928.799988, total balance 5592.460143\n",
|
||||
"day 171, sell 1 unit at price 930.090027, investment -1.296810 %, total balance 6522.550170,\n",
|
||||
"day 172: buy 1 unit at price 943.830017, total balance 5578.720153\n",
|
||||
"day 173, sell 1 unit at price 947.159973, investment 0.785284 %, total balance 6525.880126,\n",
|
||||
"day 174: buy 1 unit at price 955.989990, total balance 5569.890136\n",
|
||||
"day 175: buy 1 unit at price 953.419983, total balance 4616.470153\n",
|
||||
"day 177, sell 1 unit at price 970.890015, investment 2.131219 %, total balance 5587.360168,\n",
|
||||
"day 178, sell 1 unit at price 968.150024, investment 1.155586 %, total balance 6555.510192,\n",
|
||||
"day 180, sell 1 unit at price 980.340027, investment 4.237157 %, total balance 7535.850219,\n",
|
||||
"day 181, sell 1 unit at price 950.700012, investment 4.618537 %, total balance 8486.550231,\n",
|
||||
"day 182: buy 1 unit at price 947.799988, total balance 7538.750243\n",
|
||||
"day 183, sell 1 unit at price 934.090027, investment 0.569556 %, total balance 8472.840270,\n",
|
||||
"day 185, sell 1 unit at price 930.500000, investment -1.412332 %, total balance 9403.340270,\n",
|
||||
"day 186: buy 1 unit at price 930.830017, total balance 8472.510253\n",
|
||||
"day 187: buy 1 unit at price 930.390015, total balance 7542.120238\n",
|
||||
"day 188, sell 1 unit at price 923.650024, investment -3.382877 %, total balance 8465.770262,\n",
|
||||
"day 189: buy 1 unit at price 927.960022, total balance 7537.810240\n",
|
||||
"day 191: buy 1 unit at price 926.789978, total balance 6611.020262\n",
|
||||
"day 192, sell 1 unit at price 922.900024, investment -3.201103 %, total balance 7533.920286,\n",
|
||||
"day 193: buy 1 unit at price 907.239990, total balance 6626.680296\n",
|
||||
"day 195: buy 1 unit at price 922.669983, total balance 5704.010313\n",
|
||||
"day 197, sell 1 unit at price 926.960022, investment -2.198773 %, total balance 6630.970335,\n",
|
||||
"day 199, sell 1 unit at price 910.669983, investment -2.165813 %, total balance 7541.640318,\n",
|
||||
"day 201, sell 1 unit at price 924.690002, investment -0.612648 %, total balance 8466.330320,\n",
|
||||
"day 202: buy 1 unit at price 927.000000, total balance 7539.330320\n",
|
||||
"day 204, sell 1 unit at price 915.890015, investment -1.300703 %, total balance 8455.220335,\n",
|
||||
"day 205, sell 1 unit at price 913.809998, investment -1.400531 %, total balance 9369.030333,\n",
|
||||
"day 206: buy 1 unit at price 921.289978, total balance 8447.740355\n",
|
||||
"day 207, sell 1 unit at price 929.570007, investment 2.461313 %, total balance 9377.310362,\n",
|
||||
"day 208: buy 1 unit at price 939.330017, total balance 8437.980345\n",
|
||||
"day 209, sell 1 unit at price 937.340027, investment 1.589956 %, total balance 9375.320372,\n",
|
||||
"day 211, sell 1 unit at price 927.809998, investment 0.087378 %, total balance 10303.130370,\n",
|
||||
"day 212: buy 1 unit at price 935.950012, total balance 9367.180358\n",
|
||||
"day 213: buy 1 unit at price 926.500000, total balance 8440.680358\n",
|
||||
"day 214, sell 1 unit at price 929.080017, investment 0.845558 %, total balance 9369.760375,\n",
|
||||
"day 215, sell 1 unit at price 932.070007, investment -0.772892 %, total balance 10301.830382,\n",
|
||||
"day 217: buy 1 unit at price 925.109985, total balance 9376.720397\n",
|
||||
"day 218: buy 1 unit at price 920.289978, total balance 8456.430419\n",
|
||||
"day 219: buy 1 unit at price 915.000000, total balance 7541.430419\n",
|
||||
"day 220, sell 1 unit at price 921.809998, investment -1.510766 %, total balance 8463.240417,\n",
|
||||
"day 223: buy 1 unit at price 928.530029, total balance 7534.710388\n",
|
||||
"day 224: buy 1 unit at price 920.969971, total balance 6613.740417\n",
|
||||
"day 225: buy 1 unit at price 924.859985, total balance 5688.880432\n",
|
||||
"day 226, sell 1 unit at price 944.489990, investment 1.941715 %, total balance 6633.370422,\n",
|
||||
"day 227, sell 1 unit at price 949.500000, investment 2.636445 %, total balance 7582.870422,\n",
|
||||
"day 229, sell 1 unit at price 953.270020, investment 3.583658 %, total balance 8536.140442,\n",
|
||||
"day 230, sell 1 unit at price 957.789978, investment 4.676500 %, total balance 9493.930420,\n",
|
||||
"day 231: buy 1 unit at price 951.679993, total balance 8542.250427\n",
|
||||
"day 232, sell 1 unit at price 969.960022, investment 4.461890 %, total balance 9512.210449,\n",
|
||||
"day 233, sell 1 unit at price 978.890015, investment 6.289026 %, total balance 10491.100464,\n",
|
||||
"day 235, sell 1 unit at price 972.599976, investment 5.161861 %, total balance 11463.700440,\n",
|
||||
"day 236, sell 1 unit at price 989.250000, investment 3.947756 %, total balance 12452.950440,\n",
|
||||
"day 238: buy 1 unit at price 989.679993, total balance 11463.270447\n",
|
||||
"day 239, sell 1 unit at price 992.000000, investment 0.234420 %, total balance 12455.270447,\n",
|
||||
"day 243: buy 1 unit at price 988.200012, total balance 11467.070435\n",
|
||||
"day 244: buy 1 unit at price 968.450012, total balance 10498.620423\n",
|
||||
"day 245: buy 1 unit at price 970.539978, total balance 9528.080445\n",
|
||||
"day 246, sell 1 unit at price 973.330017, investment -1.504756 %, total balance 10501.410462,\n",
|
||||
"day 248, sell 1 unit at price 1019.270020, investment 5.247561 %, total balance 11520.680482,\n",
|
||||
"day 249, sell 1 unit at price 1017.109985, investment 4.798361 %, total balance 12537.790467,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = neural_evolve.buy(fittest_nets)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+680
@@ -0,0 +1,680 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from sklearn.neighbors import NearestNeighbors\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"\n",
|
||||
"novelty_search_threshold = 6\n",
|
||||
"novelty_log_maxlen = 1000\n",
|
||||
"backlog_maxsize = 500\n",
|
||||
"novelty_log_add_amount = 3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class neuralnetwork:\n",
|
||||
" def __init__(self, id_, hidden_size = 128):\n",
|
||||
" self.W1 = np.random.randn(window_size, hidden_size) / np.sqrt(window_size)\n",
|
||||
" self.W2 = np.random.randn(hidden_size, 3) / np.sqrt(hidden_size)\n",
|
||||
" self.fitness = 0\n",
|
||||
" self.last_features = None\n",
|
||||
" self.id = id_\n",
|
||||
"\n",
|
||||
"def relu(X):\n",
|
||||
" return np.maximum(X, 0)\n",
|
||||
" \n",
|
||||
"def softmax(X):\n",
|
||||
" e_x = np.exp(X - np.max(X, axis=-1, keepdims=True))\n",
|
||||
" return e_x / np.sum(e_x, axis=-1, keepdims=True)\n",
|
||||
"\n",
|
||||
"def feed_forward(X, nets):\n",
|
||||
" a1 = np.dot(X, nets.W1)\n",
|
||||
" z1 = relu(a1)\n",
|
||||
" a2 = np.dot(z1, nets.W2)\n",
|
||||
" return softmax(a2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class NeuroEvolution:\n",
|
||||
" def __init__(self, population_size, mutation_rate, model_generator,\n",
|
||||
" state_size, window_size, trend, skip, initial_money):\n",
|
||||
" self.population_size = population_size\n",
|
||||
" self.mutation_rate = mutation_rate\n",
|
||||
" self.model_generator = model_generator\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" self.initial_money = initial_money\n",
|
||||
" self.generation_backlog = []\n",
|
||||
" self.novel_backlog = []\n",
|
||||
" self.novel_pop = []\n",
|
||||
" \n",
|
||||
" def _initialize_population(self):\n",
|
||||
" self.population = []\n",
|
||||
" for i in range(self.population_size):\n",
|
||||
" self.population.append(self.model_generator(i))\n",
|
||||
" \n",
|
||||
" def _memorize(self, q, i, limit):\n",
|
||||
" q.append(i)\n",
|
||||
" if len(q) > limit:\n",
|
||||
" q.pop()\n",
|
||||
" \n",
|
||||
" def mutate(self, individual, scale=1.0):\n",
|
||||
" mutation_mask = np.random.binomial(1, p=self.mutation_rate, size=individual.W1.shape)\n",
|
||||
" individual.W1 += np.random.normal(loc=0, scale=scale, size=individual.W1.shape) * mutation_mask\n",
|
||||
" mutation_mask = np.random.binomial(1, p=self.mutation_rate, size=individual.W2.shape)\n",
|
||||
" individual.W2 += np.random.normal(loc=0, scale=scale, size=individual.W2.shape) * mutation_mask\n",
|
||||
" return individual\n",
|
||||
" \n",
|
||||
" def inherit_weights(self, parent, child):\n",
|
||||
" child.W1 = parent.W1.copy()\n",
|
||||
" child.W2 = parent.W2.copy()\n",
|
||||
" return child\n",
|
||||
" \n",
|
||||
" def crossover(self, parent1, parent2):\n",
|
||||
" child1 = self.model_generator((parent1.id+1)*10)\n",
|
||||
" child1 = self.inherit_weights(parent1, child1)\n",
|
||||
" child2 = self.model_generator((parent2.id+1)*10)\n",
|
||||
" child2 = self.inherit_weights(parent2, child2)\n",
|
||||
" # first W\n",
|
||||
" n_neurons = child1.W1.shape[1]\n",
|
||||
" cutoff = np.random.randint(0, n_neurons)\n",
|
||||
" child1.W1[:, cutoff:] = parent2.W1[:, cutoff:].copy()\n",
|
||||
" child2.W1[:, cutoff:] = parent1.W1[:, cutoff:].copy()\n",
|
||||
" # second W\n",
|
||||
" n_neurons = child1.W2.shape[1]\n",
|
||||
" cutoff = np.random.randint(0, n_neurons)\n",
|
||||
" child1.W2[:, cutoff:] = parent2.W2[:, cutoff:].copy()\n",
|
||||
" child2.W2[:, cutoff:] = parent1.W2[:, cutoff:].copy()\n",
|
||||
" return child1, child2\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array([res])\n",
|
||||
" \n",
|
||||
" def act(self, p, state):\n",
|
||||
" logits = feed_forward(state, p)\n",
|
||||
" return np.argmax(logits, 1)[0]\n",
|
||||
" \n",
|
||||
" def buy(self, individual):\n",
|
||||
" initial_money = self.initial_money\n",
|
||||
" starting_money = initial_money\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" inventory = []\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" \n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self.act(individual, state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((self.trend[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, self.trend[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" state = next_state\n",
|
||||
" \n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" def calculate_fitness(self):\n",
|
||||
" for i in range(self.population_size):\n",
|
||||
" initial_money = self.initial_money\n",
|
||||
" starting_money = initial_money\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" inventory = []\n",
|
||||
" \n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self.act(self.population[i], state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
"\n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
"\n",
|
||||
" state = next_state\n",
|
||||
" invest = ((starting_money - initial_money) / initial_money) * 100\n",
|
||||
" self.population[i].fitness = invest\n",
|
||||
" self.population[i].last_features = self.population[i].W2.flatten()\n",
|
||||
" \n",
|
||||
" def evaluate(self, individual, backlog, pop, k = 4):\n",
|
||||
" score = 0\n",
|
||||
" if len(backlog):\n",
|
||||
" x = np.array(backlog)\n",
|
||||
" nn = NearestNeighbors(n_neighbors = k, metric = 'euclidean').fit(np.array(backlog))\n",
|
||||
" d, _ = nn.kneighbors([individual])\n",
|
||||
" score += np.mean(d)\n",
|
||||
" \n",
|
||||
" if len(pop):\n",
|
||||
" nn = NearestNeighbors(n_neighbors = k, metric = 'euclidean').fit(np.array(pop))\n",
|
||||
" d, _ = nn.kneighbors([individual])\n",
|
||||
" score += np.mean(d)\n",
|
||||
" \n",
|
||||
" return score\n",
|
||||
" \n",
|
||||
" def evolve(self, generations=20, checkpoint= 5):\n",
|
||||
" self._initialize_population()\n",
|
||||
" n_winners = int(self.population_size * 0.4)\n",
|
||||
" n_parents = self.population_size - n_winners\n",
|
||||
" for epoch in range(generations):\n",
|
||||
" self.calculate_fitness()\n",
|
||||
" scores = [self.evaluate(p.last_features, self.novel_backlog, self.novel_pop) for p in self.population]\n",
|
||||
" sort_fitness = np.argsort(scores)[::-1]\n",
|
||||
" self.population = [self.population[i] for i in sort_fitness]\n",
|
||||
" fittest_individual = self.population[0]\n",
|
||||
" if (epoch+1) % checkpoint == 0:\n",
|
||||
" print('epoch %d, fittest individual %d with accuracy %f'%(epoch+1, sort_fitness[0], \n",
|
||||
" fittest_individual.fitness))\n",
|
||||
" next_population = [self.population[i] for i in range(n_winners)]\n",
|
||||
" total_fitness = np.sum([np.abs(i.fitness) for i in self.population])\n",
|
||||
" parent_probabilities = [np.abs(i.fitness / total_fitness) for i in self.population]\n",
|
||||
" parents = np.random.choice(self.population, size=n_parents, p=parent_probabilities, replace=False)\n",
|
||||
" \n",
|
||||
" for p in next_population:\n",
|
||||
" if p.last_features is not None:\n",
|
||||
" self._memorize(self.novel_pop, p.last_features, backlog_maxsize)\n",
|
||||
" if np.random.randint(0,10) < novelty_search_threshold:\n",
|
||||
" self._memorize(self.novel_backlog, p.last_features, novelty_log_maxlen)\n",
|
||||
" \n",
|
||||
" for i in np.arange(0, len(parents), 2):\n",
|
||||
" child1, child2 = self.crossover(parents[i], parents[i+1])\n",
|
||||
" next_population += [self.mutate(child1), self.mutate(child2)]\n",
|
||||
" self.population = next_population\n",
|
||||
" \n",
|
||||
" if np.random.randint(0,10) < novelty_search_threshold:\n",
|
||||
" pop_sorted = sorted(self.population, key=lambda p: p.fitness, reverse=True)\n",
|
||||
" self.generation_backlog.append(pop_sorted[0])\n",
|
||||
" print('novel add fittest, score: %f, backlog size: %d'%(pop_sorted[0].fitness, \n",
|
||||
" len(self.generation_backlog)))\n",
|
||||
" generation_backlog_temp = self.generation_backlog\n",
|
||||
" if len(self.generation_backlog) > backlog_maxsize:\n",
|
||||
" generation_backlog_temp = random.sample(generation_backlog, backlog_maxsize)\n",
|
||||
" for p in generation_backlog_temp:\n",
|
||||
" if p.last_features is not None:\n",
|
||||
" self._memorize(self.novel_backlog, p.last_features, novelty_log_maxlen)\n",
|
||||
" \n",
|
||||
" return fittest_individual"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"population_size = 100\n",
|
||||
"generations = 100\n",
|
||||
"mutation_rate = 0.1\n",
|
||||
"neural_evolve = NeuroEvolution(population_size, mutation_rate, neuralnetwork,\n",
|
||||
" window_size, window_size, close, skip, initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"novel add fittest, score: 5.960001, backlog size: 16\n",
|
||||
"novel add fittest, score: 2.560349, backlog size: 17\n",
|
||||
"epoch 5, fittest individual 86 with accuracy -99.353801\n",
|
||||
"novel add fittest, score: 2.073401, backlog size: 18\n",
|
||||
"epoch 10, fittest individual 53 with accuracy -99.622801\n",
|
||||
"novel add fittest, score: 9.773855, backlog size: 19\n",
|
||||
"novel add fittest, score: 1.068502, backlog size: 20\n",
|
||||
"novel add fittest, score: 1.733602, backlog size: 21\n",
|
||||
"epoch 15, fittest individual 49 with accuracy -94.018300\n",
|
||||
"novel add fittest, score: 1.439049, backlog size: 22\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 23\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 24\n",
|
||||
"novel add fittest, score: 3.052850, backlog size: 25\n",
|
||||
"epoch 20, fittest individual 83 with accuracy -42.284500\n",
|
||||
"novel add fittest, score: 3.284498, backlog size: 26\n",
|
||||
"novel add fittest, score: 3.284498, backlog size: 27\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 28\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 29\n",
|
||||
"epoch 25, fittest individual 43 with accuracy -99.809850\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 30\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 31\n",
|
||||
"novel add fittest, score: 4.712602, backlog size: 32\n",
|
||||
"novel add fittest, score: 4.712602, backlog size: 33\n",
|
||||
"epoch 30, fittest individual 51 with accuracy -94.734501\n",
|
||||
"novel add fittest, score: 4.712602, backlog size: 34\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 35\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 36\n",
|
||||
"epoch 35, fittest individual 74 with accuracy -99.895853\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 37\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 38\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 39\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 40\n",
|
||||
"epoch 40, fittest individual 50 with accuracy -99.900900\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 41\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 42\n",
|
||||
"epoch 45, fittest individual 98 with accuracy -92.305952\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 43\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 44\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 45\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 46\n",
|
||||
"epoch 50, fittest individual 55 with accuracy -99.841901\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 47\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 48\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 49\n",
|
||||
"epoch 55, fittest individual 0 with accuracy -99.351002\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 50\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 51\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 52\n",
|
||||
"epoch 60, fittest individual 56 with accuracy -91.532553\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 53\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 54\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 55\n",
|
||||
"epoch 65, fittest individual 0 with accuracy -99.389200\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 56\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 57\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 58\n",
|
||||
"epoch 70, fittest individual 68 with accuracy -90.999901\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 59\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 60\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 61\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 62\n",
|
||||
"epoch 75, fittest individual 50 with accuracy -98.881400\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 63\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 64\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 65\n",
|
||||
"epoch 80, fittest individual 0 with accuracy -91.959200\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 66\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 67\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 68\n",
|
||||
"epoch 85, fittest individual 0 with accuracy -94.175699\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 69\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 70\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 71\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 72\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 73\n",
|
||||
"epoch 90, fittest individual 60 with accuracy -93.196199\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 74\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 75\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 76\n",
|
||||
"epoch 95, fittest individual 66 with accuracy -93.122201\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 77\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 78\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 79\n",
|
||||
"epoch 100, fittest individual 52 with accuracy -93.193801\n",
|
||||
"novel add fittest, score: 0.000000, backlog size: 80\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fittest_nets = neural_evolve.evolve(100)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 1: buy 1 unit at price 762.130005, total balance 9237.869995\n",
|
||||
"day 3: buy 1 unit at price 782.520020, total balance 8455.349975\n",
|
||||
"day 4: buy 1 unit at price 790.510010, total balance 7664.839965\n",
|
||||
"day 5, sell 1 unit at price 785.309998, investment 3.041475 %, total balance 8450.149963,\n",
|
||||
"day 9: buy 1 unit at price 758.489990, total balance 7691.659973\n",
|
||||
"day 10: buy 1 unit at price 764.479980, total balance 6927.179993\n",
|
||||
"day 11: buy 1 unit at price 771.229980, total balance 6155.950013\n",
|
||||
"day 15: buy 1 unit at price 760.989990, total balance 5394.960023\n",
|
||||
"day 16: buy 1 unit at price 761.679993, total balance 4633.280030\n",
|
||||
"day 17: buy 1 unit at price 768.239990, total balance 3865.040040\n",
|
||||
"day 21: buy 1 unit at price 750.500000, total balance 3114.540040\n",
|
||||
"day 26, sell 1 unit at price 789.289978, investment 0.865148 %, total balance 3903.830018,\n",
|
||||
"day 31: buy 1 unit at price 790.799988, total balance 3113.030030\n",
|
||||
"day 37: buy 1 unit at price 791.549988, total balance 2321.480042\n",
|
||||
"day 39: buy 1 unit at price 782.789978, total balance 1538.690064\n",
|
||||
"day 40: buy 1 unit at price 771.820007, total balance 766.870057\n",
|
||||
"day 43: buy 1 unit at price 794.020020, total balance -27.149963\n",
|
||||
"day 44: buy 1 unit at price 806.150024, total balance -833.299987\n",
|
||||
"day 45: buy 1 unit at price 806.650024, total balance -1639.950011\n",
|
||||
"day 48: buy 1 unit at price 806.359985, total balance -2446.309996\n",
|
||||
"day 49: buy 1 unit at price 807.880005, total balance -3254.190001\n",
|
||||
"day 50: buy 1 unit at price 804.609985, total balance -4058.799986\n",
|
||||
"day 52: buy 1 unit at price 802.174988, total balance -4860.974974\n",
|
||||
"day 53: buy 1 unit at price 805.020020, total balance -5665.994994\n",
|
||||
"day 54, sell 1 unit at price 819.309998, investment 3.643216 %, total balance -4846.684996,\n",
|
||||
"day 55: buy 1 unit at price 823.869995, total balance -5670.554991\n",
|
||||
"day 60: buy 1 unit at price 796.789978, total balance -6467.344969\n",
|
||||
"day 61: buy 1 unit at price 795.695007, total balance -7263.039976\n",
|
||||
"day 63: buy 1 unit at price 801.489990, total balance -8064.529966\n",
|
||||
"day 65: buy 1 unit at price 806.969971, total balance -8871.499937\n",
|
||||
"day 66: buy 1 unit at price 808.380005, total balance -9679.879942\n",
|
||||
"day 67: buy 1 unit at price 809.559998, total balance -10489.439940\n",
|
||||
"day 68: buy 1 unit at price 813.669983, total balance -11303.109923\n",
|
||||
"day 69: buy 1 unit at price 819.239990, total balance -12122.349913\n",
|
||||
"day 73, sell 1 unit at price 828.070007, investment 9.173492 %, total balance -11294.279906,\n",
|
||||
"day 80: buy 1 unit at price 835.239990, total balance -12129.519896\n",
|
||||
"day 84: buy 1 unit at price 831.909973, total balance -12961.429869\n",
|
||||
"day 85, sell 1 unit at price 835.369995, investment 9.272972 %, total balance -12126.059874,\n",
|
||||
"day 86, sell 1 unit at price 838.679993, investment 8.745772 %, total balance -11287.379881,\n",
|
||||
"day 88: buy 1 unit at price 845.539978, total balance -12132.919859\n",
|
||||
"day 89: buy 1 unit at price 845.619995, total balance -12978.539854\n",
|
||||
"day 90: buy 1 unit at price 847.200012, total balance -13825.739866\n",
|
||||
"day 92: buy 1 unit at price 852.119995, total balance -14677.859861\n",
|
||||
"day 93: buy 1 unit at price 848.400024, total balance -15526.259885\n",
|
||||
"day 94: buy 1 unit at price 830.460022, total balance -16356.719907\n",
|
||||
"day 96: buy 1 unit at price 817.580017, total balance -17174.299924\n",
|
||||
"day 97: buy 1 unit at price 814.429993, total balance -17988.729917\n",
|
||||
"day 98: buy 1 unit at price 819.510010, total balance -18808.239927\n",
|
||||
"day 99: buy 1 unit at price 820.919983, total balance -19629.159910\n",
|
||||
"day 101, sell 1 unit at price 831.500000, investment 9.265563 %, total balance -18797.659910,\n",
|
||||
"day 102: buy 1 unit at price 829.559998, total balance -19627.219908\n",
|
||||
"day 103, sell 1 unit at price 838.549988, investment 10.092164 %, total balance -18788.669920,\n",
|
||||
"day 104: buy 1 unit at price 834.570007, total balance -19623.239927\n",
|
||||
"day 105: buy 1 unit at price 831.409973, total balance -20454.649900\n",
|
||||
"day 107: buy 1 unit at price 824.669983, total balance -21279.319883\n",
|
||||
"day 108: buy 1 unit at price 824.729980, total balance -22104.049863\n",
|
||||
"day 109: buy 1 unit at price 823.349976, total balance -22927.399839\n",
|
||||
"day 112: buy 1 unit at price 837.169983, total balance -23764.569822\n",
|
||||
"day 116: buy 1 unit at price 843.190002, total balance -24607.759824\n",
|
||||
"day 119: buy 1 unit at price 871.729980, total balance -25479.489804\n",
|
||||
"day 125: buy 1 unit at price 931.659973, total balance -26411.149777\n",
|
||||
"day 127: buy 1 unit at price 934.299988, total balance -27345.449765\n",
|
||||
"day 129: buy 1 unit at price 928.780029, total balance -28274.229794\n",
|
||||
"day 130: buy 1 unit at price 930.599976, total balance -29204.829770\n",
|
||||
"day 133: buy 1 unit at price 943.000000, total balance -30147.829770\n",
|
||||
"day 134: buy 1 unit at price 919.619995, total balance -31067.449765\n",
|
||||
"day 137, sell 1 unit at price 941.859985, investment 22.599708 %, total balance -30125.589780,\n",
|
||||
"day 139: buy 1 unit at price 954.960022, total balance -31080.549802\n",
|
||||
"day 140: buy 1 unit at price 969.539978, total balance -32050.089780\n",
|
||||
"day 141, sell 1 unit at price 971.469971, investment 29.443034 %, total balance -31078.619809,\n",
|
||||
"day 142: buy 1 unit at price 975.880005, total balance -32054.499814\n",
|
||||
"day 143: buy 1 unit at price 964.859985, total balance -33019.359799\n",
|
||||
"day 145: buy 1 unit at price 975.599976, total balance -33994.959775\n",
|
||||
"day 147: buy 1 unit at price 976.570007, total balance -34971.529782\n",
|
||||
"day 148: buy 1 unit at price 980.940002, total balance -35952.469784\n",
|
||||
"day 149: buy 1 unit at price 983.409973, total balance -36935.879757\n",
|
||||
"day 151: buy 1 unit at price 942.900024, total balance -37878.779781\n",
|
||||
"day 152: buy 1 unit at price 953.400024, total balance -38832.179805\n",
|
||||
"day 153: buy 1 unit at price 950.760010, total balance -39782.939815\n",
|
||||
"day 154: buy 1 unit at price 942.309998, total balance -40725.249813\n",
|
||||
"day 156: buy 1 unit at price 957.369995, total balance -41682.619808\n",
|
||||
"day 157, sell 1 unit at price 950.630005, investment 20.211181 %, total balance -40731.989803,\n",
|
||||
"day 158: buy 1 unit at price 959.450012, total balance -41691.439815\n",
|
||||
"day 159: buy 1 unit at price 957.090027, total balance -42648.529842\n",
|
||||
"day 164: buy 1 unit at price 917.789978, total balance -43566.319820\n",
|
||||
"day 165: buy 1 unit at price 908.729980, total balance -44475.049800\n",
|
||||
"day 167: buy 1 unit at price 911.710022, total balance -45386.759822\n",
|
||||
"day 168, sell 1 unit at price 906.690002, investment 14.546146 %, total balance -44480.069820,\n",
|
||||
"day 169, sell 1 unit at price 918.590027, investment 17.348210 %, total balance -43561.479793,\n",
|
||||
"day 170: buy 1 unit at price 928.799988, total balance -44490.279781\n",
|
||||
"day 173, sell 1 unit at price 947.159973, investment 22.717728 %, total balance -43543.119808,\n",
|
||||
"day 184: buy 1 unit at price 941.530029, total balance -44484.649837\n",
|
||||
"day 186: buy 1 unit at price 930.830017, total balance -45415.479854\n",
|
||||
"day 187: buy 1 unit at price 930.390015, total balance -46345.869869\n",
|
||||
"day 190: buy 1 unit at price 929.359985, total balance -47275.229854\n",
|
||||
"day 191: buy 1 unit at price 926.789978, total balance -48202.019832\n",
|
||||
"day 194: buy 1 unit at price 914.390015, total balance -49116.409847\n",
|
||||
"day 196: buy 1 unit at price 922.219971, total balance -50038.629818\n",
|
||||
"day 198: buy 1 unit at price 910.979980, total balance -50949.609798\n",
|
||||
"day 201: buy 1 unit at price 924.690002, total balance -51874.299800\n",
|
||||
"day 202: buy 1 unit at price 927.000000, total balance -52801.299800\n",
|
||||
"day 206, sell 1 unit at price 921.289978, investment 16.028558 %, total balance -51880.009822,\n",
|
||||
"day 207: buy 1 unit at price 929.570007, total balance -52809.579829\n",
|
||||
"day 208: buy 1 unit at price 939.330017, total balance -53748.909846\n",
|
||||
"day 213: buy 1 unit at price 926.500000, total balance -54675.409846\n",
|
||||
"day 218: buy 1 unit at price 920.289978, total balance -55595.699824\n",
|
||||
"day 219: buy 1 unit at price 915.000000, total balance -56510.699824\n",
|
||||
"day 222: buy 1 unit at price 932.450012, total balance -57443.149836\n",
|
||||
"day 224: buy 1 unit at price 920.969971, total balance -58364.119807\n",
|
||||
"day 227: buy 1 unit at price 949.500000, total balance -59313.619807\n",
|
||||
"day 228: buy 1 unit at price 959.109985, total balance -60272.729792\n",
|
||||
"day 229: buy 1 unit at price 953.270020, total balance -61225.999812\n",
|
||||
"day 232: buy 1 unit at price 969.960022, total balance -62195.959834\n",
|
||||
"day 238: buy 1 unit at price 989.679993, total balance -63185.639827\n",
|
||||
"day 240: buy 1 unit at price 992.179993, total balance -64177.819820\n",
|
||||
"day 242: buy 1 unit at price 984.450012, total balance -65162.269832\n",
|
||||
"day 244: buy 1 unit at price 968.450012, total balance -66130.719844\n",
|
||||
"day 247: buy 1 unit at price 972.559998, total balance -67103.279842\n",
|
||||
"day 248: buy 1 unit at price 1019.270020, total balance -68122.549862\n",
|
||||
"day 249: buy 1 unit at price 1017.109985, total balance -69139.659847\n",
|
||||
"day 250: buy 1 unit at price 1016.640015, total balance -70156.299862\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = neural_evolve.buy(fittest_nets)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+399
@@ -0,0 +1,399 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
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|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
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|
||||
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|
||||
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|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def abcd(trend, skip_loop = 4, ma = 7):\n",
|
||||
" ma = pd.Series(trend).rolling(ma).mean().values\n",
|
||||
" x = []\n",
|
||||
" for a in range(ma.shape[0]):\n",
|
||||
" for b in range(a, ma.shape[0], skip_loop):\n",
|
||||
" for c in range(b, ma.shape[0], skip_loop):\n",
|
||||
" for d in range(c, ma.shape[0], skip_loop):\n",
|
||||
" if ma[b] > ma[a] and \\\n",
|
||||
" (ma[c] < ma[b] and ma[c] > ma[a]) \\\n",
|
||||
" and ma[d] > ma[b]:\n",
|
||||
" x.append([a,b,c,d])\n",
|
||||
" x_np = np.array(x)\n",
|
||||
" ac = x_np[:,0].tolist() + x_np[:,2].tolist()\n",
|
||||
" bd = x_np[:,1].tolist() + x_np[:,3].tolist()\n",
|
||||
" ac_set = set(ac)\n",
|
||||
" bd_set = set(bd)\n",
|
||||
" signal = np.zeros(len(trend))\n",
|
||||
" buy = list(ac_set - bd_set)\n",
|
||||
" sell = list(list(bd_set - ac_set))\n",
|
||||
" signal[buy] = 1.0\n",
|
||||
" signal[sell] = -1.0\n",
|
||||
" return signal"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CPU times: user 1.08 s, sys: 8 ms, total: 1.09 s\n",
|
||||
"Wall time: 1.09 s\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"signal = abcd(df['Close'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def buy_stock(\n",
|
||||
" real_movement,\n",
|
||||
" signal,\n",
|
||||
" initial_money = 10000,\n",
|
||||
" max_buy = 1,\n",
|
||||
" max_sell = 1,\n",
|
||||
"):\n",
|
||||
" \"\"\"\n",
|
||||
" real_movement = actual movement in the real world\n",
|
||||
" delay = how much interval you want to delay to change our decision from buy to sell, vice versa\n",
|
||||
" initial_state = 1 is buy, 0 is sell\n",
|
||||
" initial_money = 10000, ignore what kind of currency\n",
|
||||
" max_buy = max quantity for share to buy\n",
|
||||
" max_sell = max quantity for share to sell\n",
|
||||
" \"\"\"\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" states_money = []\n",
|
||||
" current_inventory = 0\n",
|
||||
" \n",
|
||||
" def buy(i, initial_money, current_inventory):\n",
|
||||
" shares = initial_money // real_movement[i]\n",
|
||||
" if shares < 1:\n",
|
||||
" print(\n",
|
||||
" 'day %d: total balances %f, not enough money to buy a unit price %f'\n",
|
||||
" % (i, initial_money, real_movement[i])\n",
|
||||
" )\n",
|
||||
" else:\n",
|
||||
" if shares > max_buy:\n",
|
||||
" buy_units = max_buy\n",
|
||||
" else:\n",
|
||||
" buy_units = shares\n",
|
||||
" initial_money -= buy_units * real_movement[i]\n",
|
||||
" current_inventory += buy_units\n",
|
||||
" print(\n",
|
||||
" 'day %d: buy %d units at price %f, total balance %f'\n",
|
||||
" % (i, buy_units, buy_units * real_movement[i], initial_money)\n",
|
||||
" )\n",
|
||||
" states_buy.append(0)\n",
|
||||
" return initial_money, current_inventory\n",
|
||||
" \n",
|
||||
" for i in range(real_movement.shape[0]):\n",
|
||||
" state = signal[i]\n",
|
||||
" if state == 1:\n",
|
||||
" initial_money, current_inventory = buy(\n",
|
||||
" i, initial_money, current_inventory\n",
|
||||
" )\n",
|
||||
" states_buy.append(i)\n",
|
||||
" elif state == -1:\n",
|
||||
" if current_inventory == 0:\n",
|
||||
" print('day %d: cannot sell anything, inventory 0' % (i))\n",
|
||||
" else:\n",
|
||||
" if current_inventory > max_sell:\n",
|
||||
" sell_units = max_sell\n",
|
||||
" else:\n",
|
||||
" sell_units = current_inventory\n",
|
||||
" current_inventory -= sell_units\n",
|
||||
" total_sell = sell_units * real_movement[i]\n",
|
||||
" initial_money += total_sell\n",
|
||||
" try:\n",
|
||||
" invest = (\n",
|
||||
" (real_movement[i] - real_movement[states_buy[-1]])\n",
|
||||
" / real_movement[states_buy[-1]]\n",
|
||||
" ) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell %d units at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (i, sell_units, total_sell, invest, initial_money)\n",
|
||||
" )\n",
|
||||
" states_sell.append(i)\n",
|
||||
" states_money.append(initial_money)\n",
|
||||
" \n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest, states_money"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 6: buy 1 units at price 762.559998, total balance 9237.440002\n",
|
||||
"day 7: buy 1 units at price 754.020020, total balance 8483.419982\n",
|
||||
"day 8: buy 1 units at price 736.080017, total balance 7747.339965\n",
|
||||
"day 9: buy 1 units at price 758.489990, total balance 6988.849975\n",
|
||||
"day 10: buy 1 units at price 764.479980, total balance 6224.369995\n",
|
||||
"day 11: buy 1 units at price 771.229980, total balance 5453.140015\n",
|
||||
"day 12: buy 1 units at price 760.539978, total balance 4692.600037\n",
|
||||
"day 13: buy 1 units at price 769.200012, total balance 3923.400025\n",
|
||||
"day 14: buy 1 units at price 768.270020, total balance 3155.130005\n",
|
||||
"day 15: buy 1 units at price 760.989990, total balance 2394.140015\n",
|
||||
"day 19: buy 1 units at price 758.039978, total balance 1636.100037\n",
|
||||
"day 21: buy 1 units at price 750.500000, total balance 885.600037\n",
|
||||
"day 22: buy 1 units at price 762.520020, total balance 123.080017\n",
|
||||
"day 23: total balances 123.080017, not enough money to buy a unit price 759.109985\n",
|
||||
"day 24: total balances 123.080017, not enough money to buy a unit price 771.190002\n",
|
||||
"day 25: total balances 123.080017, not enough money to buy a unit price 776.419983\n",
|
||||
"day 26: total balances 123.080017, not enough money to buy a unit price 789.289978\n",
|
||||
"day 27: total balances 123.080017, not enough money to buy a unit price 789.270020\n",
|
||||
"day 43: total balances 123.080017, not enough money to buy a unit price 794.020020\n",
|
||||
"day 148, sell 1 units at price 980.940002, investment 23.540966 %, total balance 1104.020019,\n",
|
||||
"day 149, sell 1 units at price 983.409973, investment 23.852038 %, total balance 2087.429992,\n",
|
||||
"day 239, sell 1 units at price 992.000000, investment 24.933878 %, total balance 3079.429992,\n",
|
||||
"day 240, sell 1 units at price 992.179993, investment 24.956546 %, total balance 4071.609985,\n",
|
||||
"day 241, sell 1 units at price 992.809998, investment 25.035890 %, total balance 5064.419983,\n",
|
||||
"day 242, sell 1 units at price 984.450012, investment 23.983021 %, total balance 6048.869995,\n",
|
||||
"day 243, sell 1 units at price 988.200012, investment 24.455302 %, total balance 7037.070007,\n",
|
||||
"day 244, sell 1 units at price 968.450012, investment 21.967959 %, total balance 8005.520019,\n",
|
||||
"day 245, sell 1 units at price 970.539978, investment 22.231172 %, total balance 8976.059997,\n",
|
||||
"day 248, sell 1 units at price 1019.270020, investment 28.368302 %, total balance 9995.330017,\n",
|
||||
"day 249, sell 1 units at price 1017.109985, investment 28.096264 %, total balance 11012.440002,\n",
|
||||
"day 250, sell 1 units at price 1016.640015, investment 28.037076 %, total balance 12029.080017,\n",
|
||||
"day 251, sell 1 units at price 1025.500000, investment 29.152915 %, total balance 13054.580017,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest, states_money = buy_stock(df.Close, signal)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df['Close']\n",
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(states_money, color='r', lw=2.)\n",
|
||||
"plt.plot(states_money, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(states_money, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
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|
||||
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|
||||
"language_info": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+337
@@ -0,0 +1,337 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def buy_stock(\n",
|
||||
" real_movement,\n",
|
||||
" delay = 5,\n",
|
||||
" initial_state = 1,\n",
|
||||
" initial_money = 10000,\n",
|
||||
" max_buy = 1,\n",
|
||||
" max_sell = 1,\n",
|
||||
"):\n",
|
||||
" \"\"\"\n",
|
||||
" real_movement = actual movement in the real world\n",
|
||||
" delay = how much interval you want to delay to change our decision from buy to sell, vice versa\n",
|
||||
" initial_state = 1 is buy, 0 is sell\n",
|
||||
" initial_money = 1000, ignore what kind of currency\n",
|
||||
" max_buy = max quantity for share to buy\n",
|
||||
" max_sell = max quantity for share to sell\n",
|
||||
" \"\"\"\n",
|
||||
" starting_money = initial_money\n",
|
||||
" delay_change_decision = delay\n",
|
||||
" current_decision = 0\n",
|
||||
" state = initial_state\n",
|
||||
" current_val = real_movement[0]\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" current_inventory = 0\n",
|
||||
"\n",
|
||||
" def buy(i, initial_money, current_inventory):\n",
|
||||
" shares = initial_money // real_movement[i]\n",
|
||||
" if shares < 1:\n",
|
||||
" print(\n",
|
||||
" 'day %d: total balances %f, not enough money to buy a unit price %f'\n",
|
||||
" % (i, initial_money, real_movement[i])\n",
|
||||
" )\n",
|
||||
" else:\n",
|
||||
" if shares > max_buy:\n",
|
||||
" buy_units = max_buy\n",
|
||||
" else:\n",
|
||||
" buy_units = shares\n",
|
||||
" initial_money -= buy_units * real_movement[i]\n",
|
||||
" current_inventory += buy_units\n",
|
||||
" print(\n",
|
||||
" 'day %d: buy %d units at price %f, total balance %f'\n",
|
||||
" % (i, buy_units, buy_units * real_movement[i], initial_money)\n",
|
||||
" )\n",
|
||||
" states_buy.append(0)\n",
|
||||
" return initial_money, current_inventory\n",
|
||||
"\n",
|
||||
" if state == 1:\n",
|
||||
" initial_money, current_inventory = buy(\n",
|
||||
" 0, initial_money, current_inventory\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" for i in range(1, real_movement.shape[0], 1):\n",
|
||||
" if real_movement[i] < current_val and state == 0:\n",
|
||||
" if current_decision < delay_change_decision:\n",
|
||||
" current_decision += 1\n",
|
||||
" else:\n",
|
||||
" state = 1\n",
|
||||
" initial_money, current_inventory = buy(\n",
|
||||
" i, initial_money, current_inventory\n",
|
||||
" )\n",
|
||||
" current_decision = 0\n",
|
||||
" states_buy.append(i)\n",
|
||||
" if real_movement[i] > current_val and state == 1:\n",
|
||||
" if current_decision < delay_change_decision:\n",
|
||||
" current_decision += 1\n",
|
||||
" else:\n",
|
||||
" state = 0\n",
|
||||
"\n",
|
||||
" if current_inventory == 0:\n",
|
||||
" print('day %d: cannot sell anything, inventory 0' % (i))\n",
|
||||
" else:\n",
|
||||
" if current_inventory > max_sell:\n",
|
||||
" sell_units = max_sell\n",
|
||||
" else:\n",
|
||||
" sell_units = current_inventory\n",
|
||||
" current_inventory -= sell_units\n",
|
||||
" total_sell = sell_units * real_movement[i]\n",
|
||||
" initial_money += total_sell\n",
|
||||
" try:\n",
|
||||
" invest = (\n",
|
||||
" (real_movement[i] - real_movement[states_buy[-1]])\n",
|
||||
" / real_movement[states_buy[-1]]\n",
|
||||
" ) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell %d units at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (i, sell_units, total_sell, invest, initial_money)\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" current_decision = 0\n",
|
||||
" states_sell.append(i)\n",
|
||||
" current_val = real_movement[i]\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 0: buy 1 units at price 768.700012, total balance 9231.299988\n",
|
||||
"day 11, sell 1 units at price 771.229980, investment 0.329123 %, total balance 10002.529968,\n",
|
||||
"day 20: buy 1 units at price 747.919983, total balance 9254.609985\n",
|
||||
"day 26, sell 1 units at price 789.289978, investment 5.531340 %, total balance 10043.899963,\n",
|
||||
"day 36: buy 1 units at price 789.909973, total balance 9253.989990\n",
|
||||
"day 44, sell 1 units at price 806.150024, investment 2.055937 %, total balance 10060.140014,\n",
|
||||
"day 57: buy 1 units at price 832.150024, total balance 9227.989990\n",
|
||||
"day 67, sell 1 units at price 809.559998, investment -2.714658 %, total balance 10037.549988,\n",
|
||||
"day 81: buy 1 units at price 830.630005, total balance 9206.919983\n",
|
||||
"day 88, sell 1 units at price 845.539978, investment 1.795020 %, total balance 10052.459961,\n",
|
||||
"day 97: buy 1 units at price 814.429993, total balance 9238.029968\n",
|
||||
"day 103, sell 1 units at price 838.549988, investment 2.961580 %, total balance 10076.579956,\n",
|
||||
"day 109: buy 1 units at price 823.349976, total balance 9253.229980\n",
|
||||
"day 116, sell 1 units at price 843.190002, investment 2.409671 %, total balance 10096.419982,\n",
|
||||
"day 134: buy 1 units at price 919.619995, total balance 9176.799987\n",
|
||||
"day 139, sell 1 units at price 954.960022, investment 3.842895 %, total balance 10131.760009,\n",
|
||||
"day 153: buy 1 units at price 950.760010, total balance 9180.999999\n",
|
||||
"day 167, sell 1 units at price 911.710022, investment -4.107239 %, total balance 10092.710021,\n",
|
||||
"day 182: buy 1 units at price 947.799988, total balance 9144.910033\n",
|
||||
"day 194, sell 1 units at price 914.390015, investment -3.525002 %, total balance 10059.300048,\n",
|
||||
"day 203: buy 1 units at price 921.280029, total balance 9138.020019\n",
|
||||
"day 214, sell 1 units at price 929.080017, investment 0.846647 %, total balance 10067.100036,\n",
|
||||
"day 224: buy 1 units at price 920.969971, total balance 9146.130065\n",
|
||||
"day 230, sell 1 units at price 957.789978, investment 3.997960 %, total balance 10103.920043,\n",
|
||||
"day 242: buy 1 units at price 984.450012, total balance 9119.470031\n",
|
||||
"day 251, sell 1 units at price 1025.500000, investment 4.169840 %, total balance 10144.970031,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = buy_stock(df.Close, initial_state = 1, \n",
|
||||
" delay = 4, initial_money = 10000)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df['Close']\n",
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+526
@@ -0,0 +1,526 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" LEARNING_RATE = 1e-4\n",
|
||||
" LAYER_SIZE = 256\n",
|
||||
" GAMMA = 0.9\n",
|
||||
" OUTPUT_SIZE = 3\n",
|
||||
"\n",
|
||||
" def __init__(self, state_size, window_size, trend, skip):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, self.state_size))\n",
|
||||
" self.REWARDS = tf.placeholder(tf.float32, (None))\n",
|
||||
" self.ACTIONS = tf.placeholder(tf.int32, (None))\n",
|
||||
" feed_forward = tf.layers.dense(self.X, self.LAYER_SIZE, activation = tf.nn.relu)\n",
|
||||
" self.logits = tf.layers.dense(feed_forward, self.OUTPUT_SIZE, activation = tf.nn.softmax)\n",
|
||||
" input_y = tf.one_hot(self.ACTIONS, self.OUTPUT_SIZE)\n",
|
||||
" loglike = tf.log((input_y * (input_y - self.logits) + (1 - input_y) * (input_y + self.logits)) + 1)\n",
|
||||
" rewards = tf.tile(tf.reshape(self.REWARDS, (-1,1)), [1, self.OUTPUT_SIZE])\n",
|
||||
" self.cost = -tf.reduce_mean(loglike * (rewards + 1)) \n",
|
||||
" self.optimizer = tf.train.AdamOptimizer(learning_rate = self.LEARNING_RATE).minimize(self.cost)\n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
" \n",
|
||||
" def predict(self, inputs):\n",
|
||||
" return self.sess.run(self.logits, feed_dict={self.X:inputs})\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array([res])\n",
|
||||
" \n",
|
||||
" def discount_rewards(self, r):\n",
|
||||
" discounted_r = np.zeros_like(r)\n",
|
||||
" running_add = 0\n",
|
||||
" for t in reversed(range(0, r.size)):\n",
|
||||
" running_add = running_add * self.GAMMA + r[t]\n",
|
||||
" discounted_r[t] = running_add\n",
|
||||
" return discounted_r\n",
|
||||
" \n",
|
||||
" def get_predicted_action(self, sequence):\n",
|
||||
" prediction = self.predict(np.array(sequence))[0]\n",
|
||||
" return np.argmax(prediction)\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self.get_predicted_action(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t] and t < (len(self.trend) - self.half_window):\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" state = next_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" ep_history = []\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self.get_predicted_action(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" if action == 1 and starting_money >= self.trend[t] and t < (len(self.trend) - self.half_window):\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= close[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" ep_history.append([state,action,starting_money,next_state])\n",
|
||||
" state = next_state\n",
|
||||
" ep_history = np.array(ep_history)\n",
|
||||
" ep_history[:,2] = self.discount_rewards(ep_history[:,2])\n",
|
||||
" cost, _ = self.sess.run([self.cost, self.optimizer], feed_dict={self.X:np.vstack(ep_history[:,0]),\n",
|
||||
" self.REWARDS:ep_history[:,2],\n",
|
||||
" self.ACTIONS:ep_history[:,1]})\n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"epoch: 10, total rewards: 1781.590144.3, cost: -3782.833740, total money: 7062.900203\n",
|
||||
"epoch: 20, total rewards: 1808.720396.3, cost: -6238.727539, total money: 10819.470396\n",
|
||||
"epoch: 30, total rewards: 644.675288.3, cost: -10399.220703, total money: 10644.675288\n",
|
||||
"epoch: 40, total rewards: 1696.944943.3, cost: -9798.079102, total money: 11696.944943\n",
|
||||
"epoch: 50, total rewards: 593.719845.3, cost: -13938.982422, total money: 10593.719845\n",
|
||||
"epoch: 60, total rewards: 634.539913.3, cost: -14890.398438, total money: 9645.289913\n",
|
||||
"epoch: 70, total rewards: 1586.160156.3, cost: -10411.115234, total money: 11586.160156\n",
|
||||
"epoch: 80, total rewards: 368.749937.3, cost: -15986.910156, total money: 10368.749937\n",
|
||||
"epoch: 90, total rewards: 733.844603.3, cost: -15352.789062, total money: 8857.304625\n",
|
||||
"epoch: 100, total rewards: 645.715148.3, cost: -15976.339844, total money: 10645.715148\n",
|
||||
"epoch: 110, total rewards: 994.814937.3, cost: -11198.958984, total money: 4471.054988\n",
|
||||
"epoch: 120, total rewards: 1771.289852.3, cost: -6539.313477, total money: 5164.829891\n",
|
||||
"epoch: 130, total rewards: 1643.744995.3, cost: -11630.438477, total money: 11643.744995\n",
|
||||
"epoch: 140, total rewards: 1877.095029.3, cost: -7103.230957, total money: 9104.255063\n",
|
||||
"epoch: 150, total rewards: 481.749932.3, cost: -18531.593750, total money: 10481.749932\n",
|
||||
"epoch: 160, total rewards: 638.035152.3, cost: -16995.314453, total money: 10638.035152\n",
|
||||
"epoch: 170, total rewards: 1188.049925.3, cost: -13348.065430, total money: 10263.189940\n",
|
||||
"epoch: 180, total rewards: 633.885008.3, cost: -14666.952148, total money: 10633.885008\n",
|
||||
"epoch: 190, total rewards: 1675.079952.3, cost: -9106.298828, total money: 5977.189998\n",
|
||||
"epoch: 200, total rewards: 567.955136.3, cost: -17828.587891, total money: 10567.955136\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"agent = Agent(state_size = window_size,\n",
|
||||
" window_size = window_size,\n",
|
||||
" trend = close,\n",
|
||||
" skip = skip)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 12: buy 1 unit at price 760.539978, total balance 9239.460022\n",
|
||||
"day 19, sell 1 unit at price 758.039978, investment -0.328714 %, total balance 9997.500000,\n",
|
||||
"day 23: buy 1 unit at price 759.109985, total balance 9238.390015\n",
|
||||
"day 26, sell 1 unit at price 789.289978, investment 3.975708 %, total balance 10027.679993,\n",
|
||||
"day 27: buy 1 unit at price 789.270020, total balance 9238.409973\n",
|
||||
"day 28, sell 1 unit at price 796.099976, investment 0.865351 %, total balance 10034.509949,\n",
|
||||
"day 31: buy 1 unit at price 790.799988, total balance 9243.709961\n",
|
||||
"day 33, sell 1 unit at price 796.419983, investment 0.710672 %, total balance 10040.129944,\n",
|
||||
"day 35: buy 1 unit at price 791.260010, total balance 9248.869934\n",
|
||||
"day 36: buy 1 unit at price 789.909973, total balance 8458.959961\n",
|
||||
"day 37, sell 1 unit at price 791.549988, investment 0.036648 %, total balance 9250.509949,\n",
|
||||
"day 38: buy 1 unit at price 785.049988, total balance 8465.459961\n",
|
||||
"day 39: buy 1 unit at price 782.789978, total balance 7682.669983\n",
|
||||
"day 40: buy 1 unit at price 771.820007, total balance 6910.849976\n",
|
||||
"day 41, sell 1 unit at price 786.140015, investment -0.477264 %, total balance 7696.989991,\n",
|
||||
"day 42, sell 1 unit at price 786.900024, investment 0.235658 %, total balance 8483.890015,\n",
|
||||
"day 44: buy 1 unit at price 806.150024, total balance 7677.739991\n",
|
||||
"day 46: buy 1 unit at price 804.789978, total balance 6872.950013\n",
|
||||
"day 50: buy 1 unit at price 804.609985, total balance 6068.340028\n",
|
||||
"day 51: buy 1 unit at price 806.070007, total balance 5262.270021\n",
|
||||
"day 52, sell 1 unit at price 802.174988, investment 2.476400 %, total balance 6064.445009,\n",
|
||||
"day 53, sell 1 unit at price 805.020020, investment 4.301523 %, total balance 6869.465029,\n",
|
||||
"day 54, sell 1 unit at price 819.309998, investment 1.632447 %, total balance 7688.775027,\n",
|
||||
"day 56: buy 1 unit at price 835.669983, total balance 6853.105044\n",
|
||||
"day 57: buy 1 unit at price 832.150024, total balance 6020.955020\n",
|
||||
"day 59, sell 1 unit at price 802.320007, investment -0.306909 %, total balance 6823.275027,\n",
|
||||
"day 61: buy 1 unit at price 795.695007, total balance 6027.580020\n",
|
||||
"day 63: buy 1 unit at price 801.489990, total balance 5226.090030\n",
|
||||
"day 64, sell 1 unit at price 801.340027, investment -0.406403 %, total balance 6027.430057,\n",
|
||||
"day 65: buy 1 unit at price 806.969971, total balance 5220.460086\n",
|
||||
"day 71: buy 1 unit at price 818.979980, total balance 4401.480106\n",
|
||||
"day 72: buy 1 unit at price 824.159973, total balance 3577.320133\n",
|
||||
"day 73, sell 1 unit at price 828.070007, investment 2.729291 %, total balance 4405.390140,\n",
|
||||
"day 74, sell 1 unit at price 831.659973, investment -0.479856 %, total balance 5237.050113,\n",
|
||||
"day 78, sell 1 unit at price 829.280029, investment -0.344889 %, total balance 6066.330142,\n",
|
||||
"day 79: buy 1 unit at price 823.210022, total balance 5243.120120\n",
|
||||
"day 80: buy 1 unit at price 835.239990, total balance 4407.880130\n",
|
||||
"day 81, sell 1 unit at price 830.630005, investment 4.390501 %, total balance 5238.510135,\n",
|
||||
"day 83: buy 1 unit at price 827.780029, total balance 4410.730106\n",
|
||||
"day 86, sell 1 unit at price 838.679993, investment 4.640108 %, total balance 5249.410099,\n",
|
||||
"day 88, sell 1 unit at price 845.539978, investment 4.779609 %, total balance 6094.950077,\n",
|
||||
"day 89, sell 1 unit at price 845.619995, investment 3.252829 %, total balance 6940.570072,\n",
|
||||
"day 90: buy 1 unit at price 847.200012, total balance 6093.370060\n",
|
||||
"day 92, sell 1 unit at price 852.119995, investment 3.392548 %, total balance 6945.490055,\n",
|
||||
"day 93, sell 1 unit at price 848.400024, investment 3.059973 %, total balance 7793.890079,\n",
|
||||
"day 94: buy 1 unit at price 830.460022, total balance 6963.430057\n",
|
||||
"day 95, sell 1 unit at price 829.590027, investment -0.676448 %, total balance 7793.020084,\n",
|
||||
"day 98: buy 1 unit at price 819.510010, total balance 6973.510074\n",
|
||||
"day 101: buy 1 unit at price 831.500000, total balance 6142.010074\n",
|
||||
"day 102: buy 1 unit at price 829.559998, total balance 5312.450076\n",
|
||||
"day 103, sell 1 unit at price 838.549988, investment 1.301065 %, total balance 6151.000064,\n",
|
||||
"day 105, sell 1 unit at price 831.409973, investment -1.863791 %, total balance 6982.410037,\n",
|
||||
"day 106: buy 1 unit at price 827.880005, total balance 6154.530032\n",
|
||||
"day 108, sell 1 unit at price 824.729980, investment -0.689984 %, total balance 6979.260012,\n",
|
||||
"day 109: buy 1 unit at price 823.349976, total balance 6155.910036\n",
|
||||
"day 110, sell 1 unit at price 824.320007, investment 0.586936 %, total balance 6980.230043,\n",
|
||||
"day 111, sell 1 unit at price 823.559998, investment -0.954901 %, total balance 7803.790041,\n",
|
||||
"day 113, sell 1 unit at price 836.820007, investment 0.875164 %, total balance 8640.610048,\n",
|
||||
"day 114: buy 1 unit at price 838.210022, total balance 7802.400026\n",
|
||||
"day 115: buy 1 unit at price 841.650024, total balance 6960.750002\n",
|
||||
"day 116, sell 1 unit at price 843.190002, investment 1.849301 %, total balance 7803.940004,\n",
|
||||
"day 117, sell 1 unit at price 862.760010, investment 4.786547 %, total balance 8666.700014,\n",
|
||||
"day 118, sell 1 unit at price 872.299988, investment 4.066996 %, total balance 9539.000002,\n",
|
||||
"day 119: buy 1 unit at price 871.729980, total balance 8667.270022\n",
|
||||
"day 120, sell 1 unit at price 874.250000, investment 3.873341 %, total balance 9541.520022,\n",
|
||||
"day 121: buy 1 unit at price 905.960022, total balance 8635.560000\n",
|
||||
"day 123, sell 1 unit at price 916.440002, investment 5.128884 %, total balance 9552.000002,\n",
|
||||
"day 124: buy 1 unit at price 927.039978, total balance 8624.960024\n",
|
||||
"day 127: buy 1 unit at price 934.299988, total balance 7690.660036\n",
|
||||
"day 128: buy 1 unit at price 932.169983, total balance 6758.490053\n",
|
||||
"day 129, sell 1 unit at price 928.780029, investment 2.518876 %, total balance 7687.270082,\n",
|
||||
"day 130: buy 1 unit at price 930.599976, total balance 6756.670106\n",
|
||||
"day 131: buy 1 unit at price 932.219971, total balance 5824.450135\n",
|
||||
"day 132: buy 1 unit at price 937.080017, total balance 4887.370118\n",
|
||||
"day 133: buy 1 unit at price 943.000000, total balance 3944.370118\n",
|
||||
"day 134, sell 1 unit at price 919.619995, investment -0.800395 %, total balance 4863.990113,\n",
|
||||
"day 135: buy 1 unit at price 930.239990, total balance 3933.750123\n",
|
||||
"day 136: buy 1 unit at price 934.010010, total balance 2999.740113\n",
|
||||
"day 137, sell 1 unit at price 941.859985, investment 0.809162 %, total balance 3941.600098,\n",
|
||||
"day 138, sell 1 unit at price 948.820007, investment 1.786157 %, total balance 4890.420105,\n",
|
||||
"day 139: buy 1 unit at price 954.960022, total balance 3935.460083\n",
|
||||
"day 140, sell 1 unit at price 969.539978, investment 4.184397 %, total balance 4905.000061,\n",
|
||||
"day 141: buy 1 unit at price 971.469971, total balance 3933.530090\n",
|
||||
"day 142: buy 1 unit at price 975.880005, total balance 2957.650085\n",
|
||||
"day 143, sell 1 unit at price 964.859985, investment 3.501321 %, total balance 3922.510070,\n",
|
||||
"day 145: buy 1 unit at price 975.599976, total balance 2946.910094\n",
|
||||
"day 146, sell 1 unit at price 983.679993, investment 4.972892 %, total balance 3930.590087,\n",
|
||||
"day 147: buy 1 unit at price 976.570007, total balance 2954.020080\n",
|
||||
"day 148, sell 1 unit at price 980.940002, investment 4.023330 %, total balance 3934.960082,\n",
|
||||
"day 149, sell 1 unit at price 983.409973, investment 5.715728 %, total balance 4918.370055,\n",
|
||||
"day 150: buy 1 unit at price 949.830017, total balance 3968.540038\n",
|
||||
"day 151: buy 1 unit at price 942.900024, total balance 3025.640014\n",
|
||||
"day 153, sell 1 unit at price 950.760010, investment 1.793343 %, total balance 3976.400024,\n",
|
||||
"day 154: buy 1 unit at price 942.309998, total balance 3034.090026\n",
|
||||
"day 155: buy 1 unit at price 939.780029, total balance 2094.309997\n",
|
||||
"day 156: buy 1 unit at price 957.369995, total balance 1136.940002\n",
|
||||
"day 157, sell 1 unit at price 950.630005, investment -0.453424 %, total balance 2087.570007,\n",
|
||||
"day 158: buy 1 unit at price 959.450012, total balance 1128.119995\n",
|
||||
"day 159: buy 1 unit at price 957.090027, total balance 171.029968\n",
|
||||
"day 161, sell 1 unit at price 952.270020, investment -1.976381 %, total balance 1123.299988,\n",
|
||||
"day 164, sell 1 unit at price 917.789978, investment -5.952579 %, total balance 2041.089966,\n",
|
||||
"day 165, sell 1 unit at price 908.729980, investment -6.854243 %, total balance 2949.819946,\n",
|
||||
"day 166: buy 1 unit at price 898.700012, total balance 2051.119934\n",
|
||||
"day 168, sell 1 unit at price 906.690002, investment -7.155658 %, total balance 2957.809936,\n",
|
||||
"day 170: buy 1 unit at price 928.799988, total balance 2029.009948\n",
|
||||
"day 172, sell 1 unit at price 943.830017, investment -0.631692 %, total balance 2972.839965,\n",
|
||||
"day 173, sell 1 unit at price 947.159973, investment 0.451792 %, total balance 3919.999938,\n",
|
||||
"day 175: buy 1 unit at price 953.419983, total balance 2966.579955\n",
|
||||
"day 176, sell 1 unit at price 965.400024, investment 2.450364 %, total balance 3931.979979,\n",
|
||||
"day 177, sell 1 unit at price 970.890015, investment 3.310348 %, total balance 4902.869994,\n",
|
||||
"day 179, sell 1 unit at price 972.919983, investment 1.624240 %, total balance 5875.789977,\n",
|
||||
"day 182, sell 1 unit at price 947.799988, investment -1.214240 %, total balance 6823.589965,\n",
|
||||
"day 184: buy 1 unit at price 941.530029, total balance 5882.059936\n",
|
||||
"day 185: buy 1 unit at price 930.500000, total balance 4951.559936\n",
|
||||
"day 186, sell 1 unit at price 930.830017, investment -2.743735 %, total balance 5882.389953,\n",
|
||||
"day 187: buy 1 unit at price 930.390015, total balance 4951.999938\n",
|
||||
"day 188, sell 1 unit at price 923.650024, investment 2.776234 %, total balance 5875.649962,\n",
|
||||
"day 189: buy 1 unit at price 927.960022, total balance 4947.689940\n",
|
||||
"day 191, sell 1 unit at price 926.789978, investment -0.216409 %, total balance 5874.479918,\n",
|
||||
"day 192, sell 1 unit at price 922.900024, investment -3.201103 %, total balance 6797.379942,\n",
|
||||
"day 194, sell 1 unit at price 914.390015, investment -2.882544 %, total balance 7711.769957,\n",
|
||||
"day 195, sell 1 unit at price 922.669983, investment -0.841485 %, total balance 8634.439940,\n",
|
||||
"day 196: buy 1 unit at price 922.219971, total balance 7712.219969\n",
|
||||
"day 198: buy 1 unit at price 910.979980, total balance 6801.239989\n",
|
||||
"day 199, sell 1 unit at price 910.669983, investment -2.119545 %, total balance 7711.909972,\n",
|
||||
"day 201: buy 1 unit at price 924.690002, total balance 6787.219970\n",
|
||||
"day 202, sell 1 unit at price 927.000000, investment -0.103455 %, total balance 7714.219970,\n",
|
||||
"day 203, sell 1 unit at price 921.280029, investment -0.101922 %, total balance 8635.499999,\n",
|
||||
"day 204: buy 1 unit at price 915.890015, total balance 7719.609984\n",
|
||||
"day 206: buy 1 unit at price 921.289978, total balance 6798.320006\n",
|
||||
"day 207, sell 1 unit at price 929.570007, investment 2.040663 %, total balance 7727.890013,\n",
|
||||
"day 208, sell 1 unit at price 939.330017, investment 1.583235 %, total balance 8667.220030,\n",
|
||||
"day 210, sell 1 unit at price 928.450012, investment 1.371343 %, total balance 9595.670042,\n",
|
||||
"day 213: buy 1 unit at price 926.500000, total balance 8669.170042\n",
|
||||
"day 215: buy 1 unit at price 932.070007, total balance 7737.100035\n",
|
||||
"day 217, sell 1 unit at price 925.109985, investment 0.414637 %, total balance 8662.210020,\n",
|
||||
"day 218: buy 1 unit at price 920.289978, total balance 7741.920042\n",
|
||||
"day 219: buy 1 unit at price 915.000000, total balance 6826.920042\n",
|
||||
"day 221: buy 1 unit at price 931.580017, total balance 5895.340025\n",
|
||||
"day 222, sell 1 unit at price 932.450012, investment 0.642203 %, total balance 6827.790037,\n",
|
||||
"day 223, sell 1 unit at price 928.530029, investment -0.379797 %, total balance 7756.320066,\n",
|
||||
"day 224: buy 1 unit at price 920.969971, total balance 6835.350095\n",
|
||||
"day 225: buy 1 unit at price 924.859985, total balance 5910.490110\n",
|
||||
"day 227, sell 1 unit at price 949.500000, investment 3.174002 %, total balance 6859.990110,\n",
|
||||
"day 228, sell 1 unit at price 959.109985, investment 4.820763 %, total balance 7819.100095,\n",
|
||||
"day 230: buy 1 unit at price 957.789978, total balance 6861.310117\n",
|
||||
"day 233, sell 1 unit at price 978.890015, investment 5.078469 %, total balance 7840.200132,\n",
|
||||
"day 234, sell 1 unit at price 977.000000, investment 6.083806 %, total balance 8817.200132,\n",
|
||||
"day 235: buy 1 unit at price 972.599976, total balance 7844.600156\n",
|
||||
"day 236: buy 1 unit at price 989.250000, total balance 6855.350156\n",
|
||||
"day 237, sell 1 unit at price 987.830017, investment 6.808602 %, total balance 7843.180173,\n",
|
||||
"day 240, sell 1 unit at price 992.179993, investment 3.590559 %, total balance 8835.360166,\n",
|
||||
"day 243, sell 1 unit at price 988.200012, investment 1.603952 %, total balance 9823.560178,\n",
|
||||
"day 245, sell 1 unit at price 970.539978, investment -1.891334 %, total balance 10794.100156,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+497
@@ -0,0 +1,497 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from collections import deque\n",
|
||||
"import random\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class Agent:\n",
|
||||
" def __init__(self, state_size, window_size, trend, skip, batch_size):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" self.action_size = 3\n",
|
||||
" self.batch_size = batch_size\n",
|
||||
" self.memory = deque(maxlen = 1000)\n",
|
||||
" self.inventory = []\n",
|
||||
"\n",
|
||||
" self.gamma = 0.95\n",
|
||||
" self.epsilon = 0.5\n",
|
||||
" self.epsilon_min = 0.01\n",
|
||||
" self.epsilon_decay = 0.999\n",
|
||||
"\n",
|
||||
" tf.reset_default_graph()\n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.X = tf.placeholder(tf.float32, [None, self.state_size])\n",
|
||||
" self.Y = tf.placeholder(tf.float32, [None, self.action_size])\n",
|
||||
" feed = tf.layers.dense(self.X, 256, activation = tf.nn.relu)\n",
|
||||
" self.logits = tf.layers.dense(feed, self.action_size)\n",
|
||||
" self.cost = tf.reduce_mean(tf.square(self.Y - self.logits))\n",
|
||||
" self.optimizer = tf.train.GradientDescentOptimizer(1e-5).minimize(\n",
|
||||
" self.cost\n",
|
||||
" )\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
"\n",
|
||||
" def act(self, state):\n",
|
||||
" if random.random() <= self.epsilon:\n",
|
||||
" return random.randrange(self.action_size)\n",
|
||||
" return np.argmax(\n",
|
||||
" self.sess.run(self.logits, feed_dict = {self.X: state})[0]\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array([res])\n",
|
||||
"\n",
|
||||
" def replay(self, batch_size):\n",
|
||||
" mini_batch = []\n",
|
||||
" l = len(self.memory)\n",
|
||||
" for i in range(l - batch_size, l):\n",
|
||||
" mini_batch.append(self.memory[i])\n",
|
||||
" replay_size = len(mini_batch)\n",
|
||||
" X = np.empty((replay_size, self.state_size))\n",
|
||||
" Y = np.empty((replay_size, self.action_size))\n",
|
||||
" states = np.array([a[0][0] for a in mini_batch])\n",
|
||||
" new_states = np.array([a[3][0] for a in mini_batch])\n",
|
||||
" Q = self.sess.run(self.logits, feed_dict = {self.X: states})\n",
|
||||
" Q_new = self.sess.run(self.logits, feed_dict = {self.X: new_states})\n",
|
||||
" for i in range(len(mini_batch)):\n",
|
||||
" state, action, reward, next_state, done = mini_batch[i]\n",
|
||||
" target = Q[i]\n",
|
||||
" target[action] = reward\n",
|
||||
" if not done:\n",
|
||||
" target[action] += self.gamma * np.amax(Q_new[i])\n",
|
||||
" X[i] = state\n",
|
||||
" Y[i] = target\n",
|
||||
" cost, _ = self.sess.run(\n",
|
||||
" [self.cost, self.optimizer], feed_dict = {self.X: X, self.Y: Y}\n",
|
||||
" )\n",
|
||||
" if self.epsilon > self.epsilon_min:\n",
|
||||
" self.epsilon *= self.epsilon_decay\n",
|
||||
" return cost\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self.act(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t] and t < (len(self.trend) - self.half_window):\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" state = next_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self.act(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t] and t < (len(self.trend) - self.half_window):\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" \n",
|
||||
" invest = ((starting_money - initial_money) / initial_money)\n",
|
||||
" self.memory.append((state, action, invest, \n",
|
||||
" next_state, starting_money < initial_money))\n",
|
||||
" state = next_state\n",
|
||||
" batch_size = min(self.batch_size, len(self.memory))\n",
|
||||
" cost = self.replay(batch_size)\n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"epoch: 10, total rewards: 274.710201.3, cost: 0.810730, total money: 10274.710201\n",
|
||||
"epoch: 20, total rewards: 161.429929.3, cost: 0.406487, total money: 10161.429929\n",
|
||||
"epoch: 30, total rewards: 89.659849.3, cost: 0.225568, total money: 10089.659849\n",
|
||||
"epoch: 40, total rewards: 121.209836.3, cost: 0.152499, total money: 10121.209836\n",
|
||||
"epoch: 50, total rewards: 94.869810.3, cost: 0.120762, total money: 10094.869810\n",
|
||||
"epoch: 60, total rewards: 123.609922.3, cost: 0.097353, total money: 10123.609922\n",
|
||||
"epoch: 70, total rewards: 130.149901.3, cost: 0.131718, total money: 10130.149901\n",
|
||||
"epoch: 80, total rewards: 55.369871.3, cost: 0.072531, total money: 10055.369871\n",
|
||||
"epoch: 90, total rewards: 177.780026.3, cost: 0.062346, total money: 10177.780026\n",
|
||||
"epoch: 100, total rewards: 151.249997.3, cost: 0.056566, total money: 10151.249997\n",
|
||||
"epoch: 110, total rewards: 101.629942.3, cost: 0.050717, total money: 10101.629942\n",
|
||||
"epoch: 120, total rewards: 138.329892.3, cost: 0.075178, total money: 10138.329892\n",
|
||||
"epoch: 130, total rewards: 187.559812.3, cost: 0.039170, total money: 10187.559812\n",
|
||||
"epoch: 140, total rewards: 125.699889.3, cost: 0.035156, total money: 10125.699889\n",
|
||||
"epoch: 150, total rewards: 138.249876.3, cost: 0.403965, total money: 10138.249876\n",
|
||||
"epoch: 160, total rewards: 141.329832.3, cost: 0.029966, total money: 10141.329832\n",
|
||||
"epoch: 170, total rewards: 179.989928.3, cost: 0.027219, total money: 10179.989928\n",
|
||||
"epoch: 180, total rewards: 191.619871.3, cost: 0.025002, total money: 10191.619871\n",
|
||||
"epoch: 190, total rewards: 191.929868.3, cost: 0.149151, total money: 10191.929868\n",
|
||||
"epoch: 200, total rewards: 113.759886.3, cost: 0.021398, total money: 10113.759886\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"batch_size = 32\n",
|
||||
"agent = Agent(state_size = window_size, \n",
|
||||
" window_size = window_size, \n",
|
||||
" trend = close, \n",
|
||||
" skip = skip, \n",
|
||||
" batch_size = batch_size)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 4: buy 1 unit at price 790.510010, total balance 9209.489990\n",
|
||||
"day 5, sell 1 unit at price 785.309998, investment -0.657805 %, total balance 9994.799988,\n",
|
||||
"day 14: buy 1 unit at price 768.270020, total balance 9226.529968\n",
|
||||
"day 16, sell 1 unit at price 761.679993, investment -0.857775 %, total balance 9988.209961,\n",
|
||||
"day 22: buy 1 unit at price 762.520020, total balance 9225.689941\n",
|
||||
"day 23, sell 1 unit at price 759.109985, investment -0.447206 %, total balance 9984.799926,\n",
|
||||
"day 28: buy 1 unit at price 796.099976, total balance 9188.699950\n",
|
||||
"day 29, sell 1 unit at price 797.070007, investment 0.121848 %, total balance 9985.769957,\n",
|
||||
"day 32: buy 1 unit at price 794.200012, total balance 9191.569945\n",
|
||||
"day 35: buy 1 unit at price 791.260010, total balance 8400.309935\n",
|
||||
"day 36: buy 1 unit at price 789.909973, total balance 7610.399962\n",
|
||||
"day 37, sell 1 unit at price 791.549988, investment -0.333672 %, total balance 8401.949950,\n",
|
||||
"day 38, sell 1 unit at price 785.049988, investment -0.784827 %, total balance 9186.999938,\n",
|
||||
"day 39, sell 1 unit at price 782.789978, investment -0.901368 %, total balance 9969.789916,\n",
|
||||
"day 42: buy 1 unit at price 786.900024, total balance 9182.889892\n",
|
||||
"day 43: buy 1 unit at price 794.020020, total balance 8388.869872\n",
|
||||
"day 44: buy 1 unit at price 806.150024, total balance 7582.719848\n",
|
||||
"day 45: buy 1 unit at price 806.650024, total balance 6776.069824\n",
|
||||
"day 46: buy 1 unit at price 804.789978, total balance 5971.279846\n",
|
||||
"day 48, sell 1 unit at price 806.359985, investment 2.472990 %, total balance 6777.639831,\n",
|
||||
"day 49, sell 1 unit at price 807.880005, investment 1.745546 %, total balance 7585.519836,\n",
|
||||
"day 50, sell 1 unit at price 804.609985, investment -0.191036 %, total balance 8390.129821,\n",
|
||||
"day 51, sell 1 unit at price 806.070007, investment -0.071904 %, total balance 9196.199828,\n",
|
||||
"day 53: buy 1 unit at price 805.020020, total balance 8391.179808\n",
|
||||
"day 56, sell 1 unit at price 835.669983, investment 3.837027 %, total balance 9226.849791,\n",
|
||||
"day 57, sell 1 unit at price 832.150024, investment 3.370103 %, total balance 10058.999815,\n",
|
||||
"day 63: buy 1 unit at price 801.489990, total balance 9257.509825\n",
|
||||
"day 66: buy 1 unit at price 808.380005, total balance 8449.129820\n",
|
||||
"day 67, sell 1 unit at price 809.559998, investment 1.006876 %, total balance 9258.689818,\n",
|
||||
"day 68, sell 1 unit at price 813.669983, investment 0.654392 %, total balance 10072.359801,\n",
|
||||
"day 71: buy 1 unit at price 818.979980, total balance 9253.379821\n",
|
||||
"day 72: buy 1 unit at price 824.159973, total balance 8429.219848\n",
|
||||
"day 73: buy 1 unit at price 828.070007, total balance 7601.149841\n",
|
||||
"day 74: buy 1 unit at price 831.659973, total balance 6769.489868\n",
|
||||
"day 75: buy 1 unit at price 830.760010, total balance 5938.729858\n",
|
||||
"day 81, sell 1 unit at price 830.630005, investment 1.422504 %, total balance 6769.359863,\n",
|
||||
"day 82, sell 1 unit at price 829.080017, investment 0.596977 %, total balance 7598.439880,\n",
|
||||
"day 85, sell 1 unit at price 835.369995, investment 0.881567 %, total balance 8433.809875,\n",
|
||||
"day 87, sell 1 unit at price 843.250000, investment 1.393602 %, total balance 9277.059875,\n",
|
||||
"day 88, sell 1 unit at price 845.539978, investment 1.779090 %, total balance 10122.599853,\n",
|
||||
"day 92: buy 1 unit at price 852.119995, total balance 9270.479858\n",
|
||||
"day 93, sell 1 unit at price 848.400024, investment -0.436555 %, total balance 10118.879882,\n",
|
||||
"day 99: buy 1 unit at price 820.919983, total balance 9297.959899\n",
|
||||
"day 101: buy 1 unit at price 831.500000, total balance 8466.459899\n",
|
||||
"day 104, sell 1 unit at price 834.570007, investment 1.662772 %, total balance 9301.029906,\n",
|
||||
"day 105, sell 1 unit at price 831.409973, investment -0.010827 %, total balance 10132.439879,\n",
|
||||
"day 111: buy 1 unit at price 823.559998, total balance 9308.879881\n",
|
||||
"day 113, sell 1 unit at price 836.820007, investment 1.610084 %, total balance 10145.699888,\n",
|
||||
"day 116: buy 1 unit at price 843.190002, total balance 9302.509886\n",
|
||||
"day 117: buy 1 unit at price 862.760010, total balance 8439.749876\n",
|
||||
"day 118: buy 1 unit at price 872.299988, total balance 7567.449888\n",
|
||||
"day 119, sell 1 unit at price 871.729980, investment 3.384762 %, total balance 8439.179868,\n",
|
||||
"day 120: buy 1 unit at price 874.250000, total balance 7564.929868\n",
|
||||
"day 121: buy 1 unit at price 905.960022, total balance 6658.969846\n",
|
||||
"day 122, sell 1 unit at price 912.570007, investment 5.773332 %, total balance 7571.539853,\n",
|
||||
"day 123, sell 1 unit at price 916.440002, investment 5.060187 %, total balance 8487.979855,\n",
|
||||
"day 124, sell 1 unit at price 927.039978, investment 6.038316 %, total balance 9415.019833,\n",
|
||||
"day 125: buy 1 unit at price 931.659973, total balance 8483.359860\n",
|
||||
"day 126: buy 1 unit at price 927.130005, total balance 7556.229855\n",
|
||||
"day 127: buy 1 unit at price 934.299988, total balance 6621.929867\n",
|
||||
"day 128: buy 1 unit at price 932.169983, total balance 5689.759884\n",
|
||||
"day 129, sell 1 unit at price 928.780029, investment 2.518876 %, total balance 6618.539913,\n",
|
||||
"day 130, sell 1 unit at price 930.599976, investment -0.113775 %, total balance 7549.139889,\n",
|
||||
"day 131: buy 1 unit at price 932.219971, total balance 6616.919918\n",
|
||||
"day 133, sell 1 unit at price 943.000000, investment 1.711734 %, total balance 7559.919918,\n",
|
||||
"day 134, sell 1 unit at price 919.619995, investment -1.571229 %, total balance 8479.539913,\n",
|
||||
"day 136, sell 1 unit at price 934.010010, investment 0.197392 %, total balance 9413.549923,\n",
|
||||
"day 137, sell 1 unit at price 941.859985, investment 1.034092 %, total balance 10355.409908,\n",
|
||||
"day 139: buy 1 unit at price 954.960022, total balance 9400.449886\n",
|
||||
"day 141, sell 1 unit at price 971.469971, investment 1.728863 %, total balance 10371.919857,\n",
|
||||
"day 144: buy 1 unit at price 966.950012, total balance 9404.969845\n",
|
||||
"day 145, sell 1 unit at price 975.599976, investment 0.894562 %, total balance 10380.569821,\n",
|
||||
"day 149: buy 1 unit at price 983.409973, total balance 9397.159848\n",
|
||||
"day 151: buy 1 unit at price 942.900024, total balance 8454.259824\n",
|
||||
"day 154, sell 1 unit at price 942.309998, investment -4.179333 %, total balance 9396.569822,\n",
|
||||
"day 157: buy 1 unit at price 950.630005, total balance 8445.939817\n",
|
||||
"day 159, sell 1 unit at price 957.090027, investment 1.504932 %, total balance 9403.029844,\n",
|
||||
"day 160: buy 1 unit at price 965.590027, total balance 8437.439817\n",
|
||||
"day 161, sell 1 unit at price 952.270020, investment 0.172519 %, total balance 9389.709837,\n",
|
||||
"day 162, sell 1 unit at price 927.330017, investment -3.962345 %, total balance 10317.039854,\n",
|
||||
"day 173: buy 1 unit at price 947.159973, total balance 9369.879881\n",
|
||||
"day 176, sell 1 unit at price 965.400024, investment 1.925762 %, total balance 10335.279905,\n",
|
||||
"day 180: buy 1 unit at price 980.340027, total balance 9354.939878\n",
|
||||
"day 184, sell 1 unit at price 941.530029, investment -3.958830 %, total balance 10296.469907,\n",
|
||||
"day 185: buy 1 unit at price 930.500000, total balance 9365.969907\n",
|
||||
"day 186: buy 1 unit at price 930.830017, total balance 8435.139890\n",
|
||||
"day 187, sell 1 unit at price 930.390015, investment -0.011820 %, total balance 9365.529905,\n",
|
||||
"day 189, sell 1 unit at price 927.960022, investment -0.308326 %, total balance 10293.489927,\n",
|
||||
"day 197: buy 1 unit at price 926.960022, total balance 9366.529905\n",
|
||||
"day 200, sell 1 unit at price 906.659973, investment -2.189959 %, total balance 10273.189878,\n",
|
||||
"day 203: buy 1 unit at price 921.280029, total balance 9351.909849\n",
|
||||
"day 205: buy 1 unit at price 913.809998, total balance 8438.099851\n",
|
||||
"day 210, sell 1 unit at price 928.450012, investment 0.778263 %, total balance 9366.549863,\n",
|
||||
"day 213, sell 1 unit at price 926.500000, investment 1.388692 %, total balance 10293.049863,\n",
|
||||
"day 229: buy 1 unit at price 953.270020, total balance 9339.779843\n",
|
||||
"day 232, sell 1 unit at price 969.960022, investment 1.750816 %, total balance 10309.739865,\n",
|
||||
"day 234: buy 1 unit at price 977.000000, total balance 9332.739865\n",
|
||||
"day 239, sell 1 unit at price 992.000000, investment 1.535312 %, total balance 10324.739865,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+673
@@ -0,0 +1,673 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import time\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"import random\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"seaborn==0.9.0\n",
|
||||
"pandas==0.23.4\n",
|
||||
"numpy==1.14.5\n",
|
||||
"matplotlib==3.0.2\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pkg_resources\n",
|
||||
"import types\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def get_imports():\n",
|
||||
" for name, val in globals().items():\n",
|
||||
" if isinstance(val, types.ModuleType):\n",
|
||||
" name = val.__name__.split('.')[0]\n",
|
||||
" elif isinstance(val, type):\n",
|
||||
" name = val.__module__.split('.')[0]\n",
|
||||
" poorly_named_packages = {'PIL': 'Pillow', 'sklearn': 'scikit-learn'}\n",
|
||||
" if name in poorly_named_packages.keys():\n",
|
||||
" name = poorly_named_packages[name]\n",
|
||||
" yield name\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"imports = list(set(get_imports()))\n",
|
||||
"requirements = []\n",
|
||||
"for m in pkg_resources.working_set:\n",
|
||||
" if m.project_name in imports and m.project_name != 'pip':\n",
|
||||
" requirements.append((m.project_name, m.version))\n",
|
||||
"\n",
|
||||
"for r in requirements:\n",
|
||||
" print('{}=={}'.format(*r))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class Deep_Evolution_Strategy:\n",
|
||||
"\n",
|
||||
" inputs = None\n",
|
||||
"\n",
|
||||
" def __init__(\n",
|
||||
" self, weights, reward_function, population_size, sigma, learning_rate\n",
|
||||
" ):\n",
|
||||
" self.weights = weights\n",
|
||||
" self.reward_function = reward_function\n",
|
||||
" self.population_size = population_size\n",
|
||||
" self.sigma = sigma\n",
|
||||
" self.learning_rate = learning_rate\n",
|
||||
"\n",
|
||||
" def _get_weight_from_population(self, weights, population):\n",
|
||||
" weights_population = []\n",
|
||||
" for index, i in enumerate(population):\n",
|
||||
" jittered = self.sigma * i\n",
|
||||
" weights_population.append(weights[index] + jittered)\n",
|
||||
" return weights_population\n",
|
||||
"\n",
|
||||
" def get_weights(self):\n",
|
||||
" return self.weights\n",
|
||||
"\n",
|
||||
" def train(self, epoch = 100, print_every = 1):\n",
|
||||
" lasttime = time.time()\n",
|
||||
" for i in range(epoch):\n",
|
||||
" population = []\n",
|
||||
" rewards = np.zeros(self.population_size)\n",
|
||||
" for k in range(self.population_size):\n",
|
||||
" x = []\n",
|
||||
" for w in self.weights:\n",
|
||||
" x.append(np.random.randn(*w.shape))\n",
|
||||
" population.append(x)\n",
|
||||
" for k in range(self.population_size):\n",
|
||||
" weights_population = self._get_weight_from_population(\n",
|
||||
" self.weights, population[k]\n",
|
||||
" )\n",
|
||||
" rewards[k] = self.reward_function(weights_population)\n",
|
||||
" rewards = (rewards - np.mean(rewards)) / (np.std(rewards) + 1e-7)\n",
|
||||
" for index, w in enumerate(self.weights):\n",
|
||||
" A = np.array([p[index] for p in population])\n",
|
||||
" self.weights[index] = (\n",
|
||||
" w\n",
|
||||
" + self.learning_rate\n",
|
||||
" / (self.population_size * self.sigma)\n",
|
||||
" * np.dot(A.T, rewards).T\n",
|
||||
" )\n",
|
||||
" if (i + 1) % print_every == 0:\n",
|
||||
" print(\n",
|
||||
" 'iter %d. reward: %f'\n",
|
||||
" % (i + 1, self.reward_function(self.weights))\n",
|
||||
" )\n",
|
||||
" print('time taken to train:', time.time() - lasttime, 'seconds')\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class Model:\n",
|
||||
" def __init__(self, input_size, layer_size, output_size):\n",
|
||||
" self.weights = [\n",
|
||||
" np.random.randn(input_size, layer_size),\n",
|
||||
" np.random.randn(layer_size, output_size),\n",
|
||||
" np.random.randn(1, layer_size),\n",
|
||||
" ]\n",
|
||||
"\n",
|
||||
" def predict(self, inputs):\n",
|
||||
" feed = np.dot(inputs, self.weights[0]) + self.weights[-1]\n",
|
||||
" decision = np.dot(feed, self.weights[1])\n",
|
||||
" return decision\n",
|
||||
"\n",
|
||||
" def get_weights(self):\n",
|
||||
" return self.weights\n",
|
||||
"\n",
|
||||
" def set_weights(self, weights):\n",
|
||||
" self.weights = weights"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" POPULATION_SIZE = 15\n",
|
||||
" SIGMA = 0.1\n",
|
||||
" LEARNING_RATE = 0.03\n",
|
||||
"\n",
|
||||
" def __init__(self, model, window_size, trend, skip, initial_money):\n",
|
||||
" self.model = model\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" self.initial_money = initial_money\n",
|
||||
" self.es = Deep_Evolution_Strategy(\n",
|
||||
" self.model.get_weights(),\n",
|
||||
" self.get_reward,\n",
|
||||
" self.POPULATION_SIZE,\n",
|
||||
" self.SIGMA,\n",
|
||||
" self.LEARNING_RATE,\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" def act(self, sequence):\n",
|
||||
" decision = self.model.predict(np.array(sequence))\n",
|
||||
" return np.argmax(decision[0])\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array([res])\n",
|
||||
"\n",
|
||||
" def get_reward(self, weights):\n",
|
||||
" initial_money = self.initial_money\n",
|
||||
" starting_money = initial_money\n",
|
||||
" self.model.weights = weights\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" inventory = []\n",
|
||||
" quantity = 0\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self.act(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= close[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
"\n",
|
||||
" state = next_state\n",
|
||||
" return ((starting_money - initial_money) / initial_money) * 100\n",
|
||||
"\n",
|
||||
" def fit(self, iterations, checkpoint):\n",
|
||||
" self.es.train(iterations, print_every = checkpoint)\n",
|
||||
"\n",
|
||||
" def buy(self):\n",
|
||||
" initial_money = self.initial_money\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self.act(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" state = next_state\n",
|
||||
"\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"iter 10. reward: 8.610248\n",
|
||||
"iter 20. reward: 12.257399\n",
|
||||
"iter 30. reward: 7.689600\n",
|
||||
"iter 40. reward: 18.719300\n",
|
||||
"iter 50. reward: 16.883897\n",
|
||||
"iter 60. reward: 18.100399\n",
|
||||
"iter 70. reward: 17.280399\n",
|
||||
"iter 80. reward: 15.865947\n",
|
||||
"iter 90. reward: 17.435298\n",
|
||||
"iter 100. reward: 22.108749\n",
|
||||
"iter 110. reward: 21.537897\n",
|
||||
"iter 120. reward: 21.986898\n",
|
||||
"iter 130. reward: 22.303096\n",
|
||||
"iter 140. reward: 27.540547\n",
|
||||
"iter 150. reward: 24.151497\n",
|
||||
"iter 160. reward: 26.339196\n",
|
||||
"iter 170. reward: 26.184596\n",
|
||||
"iter 180. reward: 25.859546\n",
|
||||
"iter 190. reward: 28.623797\n",
|
||||
"iter 200. reward: 30.171547\n",
|
||||
"iter 210. reward: 29.712899\n",
|
||||
"iter 220. reward: 28.880399\n",
|
||||
"iter 230. reward: 29.221448\n",
|
||||
"iter 240. reward: 26.622551\n",
|
||||
"iter 250. reward: 21.736548\n",
|
||||
"iter 260. reward: 32.192049\n",
|
||||
"iter 270. reward: 31.077749\n",
|
||||
"iter 280. reward: 30.869947\n",
|
||||
"iter 290. reward: 30.829648\n",
|
||||
"iter 300. reward: 32.587899\n",
|
||||
"iter 310. reward: 32.627998\n",
|
||||
"iter 320. reward: 32.198498\n",
|
||||
"iter 330. reward: 31.940298\n",
|
||||
"iter 340. reward: 32.789998\n",
|
||||
"iter 350. reward: 33.619697\n",
|
||||
"iter 360. reward: 32.738196\n",
|
||||
"iter 370. reward: 34.456997\n",
|
||||
"iter 380. reward: 34.972598\n",
|
||||
"iter 390. reward: 34.632198\n",
|
||||
"iter 400. reward: 32.573597\n",
|
||||
"iter 410. reward: 35.826097\n",
|
||||
"iter 420. reward: 33.999698\n",
|
||||
"iter 430. reward: 35.530297\n",
|
||||
"iter 440. reward: 35.589196\n",
|
||||
"iter 450. reward: 32.944796\n",
|
||||
"iter 460. reward: 36.473798\n",
|
||||
"iter 470. reward: 38.662997\n",
|
||||
"iter 480. reward: 37.648599\n",
|
||||
"iter 490. reward: 37.361099\n",
|
||||
"iter 500. reward: 37.407198\n",
|
||||
"time taken to train: 33.66626238822937 seconds\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"initial_money = 10000\n",
|
||||
"\n",
|
||||
"model = Model(input_size = window_size, layer_size = 500, output_size = 3)\n",
|
||||
"agent = Agent(model = model, \n",
|
||||
" window_size = window_size,\n",
|
||||
" trend = close,\n",
|
||||
" skip = skip,\n",
|
||||
" initial_money = initial_money)\n",
|
||||
"agent.fit(iterations = 500, checkpoint = 10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 1: buy 1 unit at price 762.130005, total balance 9237.869995\n",
|
||||
"day 3: buy 1 unit at price 782.520020, total balance 8455.349975\n",
|
||||
"day 4, sell 1 unit at price 790.510010, investment 3.723775 %, total balance 9245.859985,\n",
|
||||
"day 5, sell 1 unit at price 785.309998, investment 0.356538 %, total balance 10031.169983,\n",
|
||||
"day 6: buy 1 unit at price 762.559998, total balance 9268.609985\n",
|
||||
"day 10: buy 1 unit at price 764.479980, total balance 8504.130005\n",
|
||||
"day 11: buy 1 unit at price 771.229980, total balance 7732.900025\n",
|
||||
"day 12: buy 1 unit at price 760.539978, total balance 6972.360047\n",
|
||||
"day 17: buy 1 unit at price 768.239990, total balance 6204.120057\n",
|
||||
"day 18: buy 1 unit at price 770.840027, total balance 5433.280030\n",
|
||||
"day 19: buy 1 unit at price 758.039978, total balance 4675.240052\n",
|
||||
"day 20: buy 1 unit at price 747.919983, total balance 3927.320069\n",
|
||||
"day 21: buy 1 unit at price 750.500000, total balance 3176.820069\n",
|
||||
"day 24, sell 1 unit at price 771.190002, investment 1.131715 %, total balance 3948.010071,\n",
|
||||
"day 25: buy 1 unit at price 776.419983, total balance 3171.590088\n",
|
||||
"day 26, sell 1 unit at price 789.289978, investment 3.245343 %, total balance 3960.880066,\n",
|
||||
"day 27: buy 1 unit at price 789.270020, total balance 3171.610046\n",
|
||||
"day 29, sell 1 unit at price 797.070007, investment 3.350496 %, total balance 3968.680053,\n",
|
||||
"day 30, sell 1 unit at price 797.849976, investment 4.905725 %, total balance 4766.530029,\n",
|
||||
"day 31, sell 1 unit at price 790.799988, investment 2.936582 %, total balance 5557.330017,\n",
|
||||
"day 32: buy 1 unit at price 794.200012, total balance 4763.130005\n",
|
||||
"day 33: buy 1 unit at price 796.419983, total balance 3966.710022\n",
|
||||
"day 34: buy 1 unit at price 794.559998, total balance 3172.150024\n",
|
||||
"day 36: buy 1 unit at price 789.909973, total balance 2382.240051\n",
|
||||
"day 37, sell 1 unit at price 791.549988, investment 2.686674 %, total balance 3173.790039,\n",
|
||||
"day 40: buy 1 unit at price 771.820007, total balance 2401.970032\n",
|
||||
"day 41: buy 1 unit at price 786.140015, total balance 1615.830017\n",
|
||||
"day 42: buy 1 unit at price 786.900024, total balance 828.929993\n",
|
||||
"day 44: buy 1 unit at price 806.150024, total balance 22.779969\n",
|
||||
"day 45, sell 1 unit at price 806.650024, investment 6.412597 %, total balance 829.429993,\n",
|
||||
"day 48, sell 1 unit at price 806.359985, investment 7.813670 %, total balance 1635.789978,\n",
|
||||
"day 49, sell 1 unit at price 807.880005, investment 7.645570 %, total balance 2443.669983,\n",
|
||||
"day 50, sell 1 unit at price 804.609985, investment 3.630767 %, total balance 3248.279968,\n",
|
||||
"day 51: buy 1 unit at price 806.070007, total balance 2442.209961\n",
|
||||
"day 52: buy 1 unit at price 802.174988, total balance 1640.034973\n",
|
||||
"day 53: buy 1 unit at price 805.020020, total balance 835.014953\n",
|
||||
"day 56, sell 1 unit at price 835.669983, investment 5.878845 %, total balance 1670.684936,\n",
|
||||
"day 57, sell 1 unit at price 832.150024, investment 4.778395 %, total balance 2502.834960,\n",
|
||||
"day 59: buy 1 unit at price 802.320007, total balance 1700.514953\n",
|
||||
"day 61: buy 1 unit at price 795.695007, total balance 904.819946\n",
|
||||
"day 62: buy 1 unit at price 798.530029, total balance 106.289917\n",
|
||||
"day 69, sell 1 unit at price 819.239990, investment 2.865323 %, total balance 925.529907,\n",
|
||||
"day 70, sell 1 unit at price 820.450012, investment 3.258409 %, total balance 1745.979919,\n",
|
||||
"day 71: buy 1 unit at price 818.979980, total balance 926.999939\n",
|
||||
"day 72: buy 1 unit at price 824.159973, total balance 102.839966\n",
|
||||
"day 74, sell 1 unit at price 831.659973, investment 5.285412 %, total balance 934.499939,\n",
|
||||
"day 76, sell 1 unit at price 831.330017, investment 7.710348 %, total balance 1765.829956,\n",
|
||||
"day 77, sell 1 unit at price 828.640015, investment 5.406162 %, total balance 2594.469971,\n",
|
||||
"day 78, sell 1 unit at price 829.280029, investment 5.385691 %, total balance 3423.750000,\n",
|
||||
"day 79: buy 1 unit at price 823.210022, total balance 2600.539978\n",
|
||||
"day 80: buy 1 unit at price 835.239990, total balance 1765.299988\n",
|
||||
"day 81: buy 1 unit at price 830.630005, total balance 934.669983\n",
|
||||
"day 83: buy 1 unit at price 827.780029, total balance 106.889954\n",
|
||||
"day 85, sell 1 unit at price 835.369995, investment 3.624632 %, total balance 942.259949,\n",
|
||||
"day 88: buy 1 unit at price 845.539978, total balance 96.719971\n",
|
||||
"day 89, sell 1 unit at price 845.619995, investment 4.906520 %, total balance 942.339966,\n",
|
||||
"day 90, sell 1 unit at price 847.200012, investment 5.612868 %, total balance 1789.539978,\n",
|
||||
"day 92, sell 1 unit at price 852.119995, investment 5.850783 %, total balance 2641.659973,\n",
|
||||
"day 93, sell 1 unit at price 848.400024, investment 5.743346 %, total balance 3490.059997,\n",
|
||||
"day 95: buy 1 unit at price 829.590027, total balance 2660.469970\n",
|
||||
"day 96: buy 1 unit at price 817.580017, total balance 1842.889953\n",
|
||||
"day 98: buy 1 unit at price 819.510010, total balance 1023.379943\n",
|
||||
"day 99: buy 1 unit at price 820.919983, total balance 202.459960\n",
|
||||
"day 105, sell 1 unit at price 831.409973, investment 4.488525 %, total balance 1033.869933,\n",
|
||||
"day 107: buy 1 unit at price 824.669983, total balance 209.199950\n",
|
||||
"day 113, sell 1 unit at price 836.820007, investment 4.795058 %, total balance 1046.019957,\n",
|
||||
"day 115: buy 1 unit at price 841.650024, total balance 204.369933\n",
|
||||
"day 116, sell 1 unit at price 843.190002, investment 2.956119 %, total balance 1047.559935,\n",
|
||||
"day 118: buy 1 unit at price 872.299988, total balance 175.259947\n",
|
||||
"day 119, sell 1 unit at price 871.729980, investment 5.771939 %, total balance 1046.989927,\n",
|
||||
"day 120: buy 1 unit at price 874.250000, total balance 172.739927\n",
|
||||
"day 121, sell 1 unit at price 905.960022, investment 10.052113 %, total balance 1078.699949,\n",
|
||||
"day 122, sell 1 unit at price 912.570007, investment 9.258419 %, total balance 1991.269956,\n",
|
||||
"day 123, sell 1 unit at price 916.440002, investment 10.330712 %, total balance 2907.709958,\n",
|
||||
"day 124, sell 1 unit at price 927.039978, investment 11.991102 %, total balance 3834.749936,\n",
|
||||
"day 125, sell 1 unit at price 931.659973, investment 10.185207 %, total balance 4766.409909,\n",
|
||||
"day 126, sell 1 unit at price 927.130005, investment 11.757612 %, total balance 5693.539914,\n",
|
||||
"day 127, sell 1 unit at price 934.299988, investment 14.276275 %, total balance 6627.839902,\n",
|
||||
"day 128, sell 1 unit at price 932.169983, investment 13.747236 %, total balance 7560.009885,\n",
|
||||
"day 129: buy 1 unit at price 928.780029, total balance 6631.229856\n",
|
||||
"day 130: buy 1 unit at price 930.599976, total balance 5700.629880\n",
|
||||
"day 132: buy 1 unit at price 937.080017, total balance 4763.549863\n",
|
||||
"day 133: buy 1 unit at price 943.000000, total balance 3820.549863\n",
|
||||
"day 136: buy 1 unit at price 934.010010, total balance 2886.539853\n",
|
||||
"day 137, sell 1 unit at price 941.859985, investment 14.732252 %, total balance 3828.399838,\n",
|
||||
"day 139, sell 1 unit at price 954.960022, investment 15.799052 %, total balance 4783.359860,\n",
|
||||
"day 140, sell 1 unit at price 969.539978, investment 15.195146 %, total balance 5752.899838,\n",
|
||||
"day 141, sell 1 unit at price 971.469971, investment 11.368793 %, total balance 6724.369809,\n",
|
||||
"day 142, sell 1 unit at price 975.880005, investment 11.624822 %, total balance 7700.249814,\n",
|
||||
"day 143, sell 1 unit at price 964.859985, investment 3.884661 %, total balance 8665.109799,\n",
|
||||
"day 144, sell 1 unit at price 966.950012, investment 3.906086 %, total balance 9632.059811,\n",
|
||||
"day 145, sell 1 unit at price 975.599976, investment 4.110637 %, total balance 10607.659787,\n",
|
||||
"day 146, sell 1 unit at price 983.679993, investment 4.313891 %, total balance 11591.339780,\n",
|
||||
"day 147: buy 1 unit at price 976.570007, total balance 10614.769773\n",
|
||||
"day 148, sell 1 unit at price 980.940002, investment 5.024571 %, total balance 11595.709775,\n",
|
||||
"day 153, sell 1 unit at price 950.760010, investment -2.642923 %, total balance 12546.469785,\n",
|
||||
"day 154: buy 1 unit at price 942.309998, total balance 11604.159787\n",
|
||||
"day 155: buy 1 unit at price 939.780029, total balance 10664.379758\n",
|
||||
"day 156: buy 1 unit at price 957.369995, total balance 9707.009763\n",
|
||||
"day 158, sell 1 unit at price 959.450012, investment 1.818936 %, total balance 10666.459775,\n",
|
||||
"day 160, sell 1 unit at price 965.590027, investment 2.746387 %, total balance 11632.049802,\n",
|
||||
"day 161, sell 1 unit at price 952.270020, investment -0.532707 %, total balance 12584.319822,\n",
|
||||
"day 163: buy 1 unit at price 940.489990, total balance 11643.829832\n",
|
||||
"day 164: buy 1 unit at price 917.789978, total balance 10726.039854\n",
|
||||
"day 166: buy 1 unit at price 898.700012, total balance 9827.339842\n",
|
||||
"day 167: buy 1 unit at price 911.710022, total balance 8915.629820\n",
|
||||
"day 168: buy 1 unit at price 906.690002, total balance 8008.939818\n",
|
||||
"day 171: buy 1 unit at price 930.090027, total balance 7078.849791\n",
|
||||
"day 172, sell 1 unit at price 943.830017, investment 0.355137 %, total balance 8022.679808,\n",
|
||||
"day 174, sell 1 unit at price 955.989990, investment 4.162174 %, total balance 8978.669798,\n",
|
||||
"day 175: buy 1 unit at price 953.419983, total balance 8025.249815\n",
|
||||
"day 176, sell 1 unit at price 965.400024, investment 7.421833 %, total balance 8990.649839,\n",
|
||||
"day 178, sell 1 unit at price 968.150024, investment 6.190565 %, total balance 9958.799863,\n",
|
||||
"day 179, sell 1 unit at price 972.919983, investment 7.304589 %, total balance 10931.719846,\n",
|
||||
"day 180, sell 1 unit at price 980.340027, investment 5.402703 %, total balance 11912.059873,\n",
|
||||
"day 181, sell 1 unit at price 950.700012, investment -0.285286 %, total balance 12862.759885,\n",
|
||||
"day 183: buy 1 unit at price 934.090027, total balance 11928.669858\n",
|
||||
"day 185: buy 1 unit at price 930.500000, total balance 10998.169858\n",
|
||||
"day 186: buy 1 unit at price 930.830017, total balance 10067.339841\n",
|
||||
"day 187: buy 1 unit at price 930.390015, total balance 9136.949826\n",
|
||||
"day 188: buy 1 unit at price 923.650024, total balance 8213.299802\n",
|
||||
"day 192: buy 1 unit at price 922.900024, total balance 7290.399778\n",
|
||||
"day 194: buy 1 unit at price 914.390015, total balance 6376.009763\n",
|
||||
"day 195: buy 1 unit at price 922.669983, total balance 5453.339780\n",
|
||||
"day 197: buy 1 unit at price 926.960022, total balance 4526.379758\n",
|
||||
"day 202: buy 1 unit at price 927.000000, total balance 3599.379758\n",
|
||||
"day 203: buy 1 unit at price 921.280029, total balance 2678.099729\n",
|
||||
"day 204: buy 1 unit at price 915.890015, total balance 1762.209714\n",
|
||||
"day 205: buy 1 unit at price 913.809998, total balance 848.399716\n",
|
||||
"day 210, sell 1 unit at price 928.450012, investment -0.603798 %, total balance 1776.849728,\n",
|
||||
"day 212, sell 1 unit at price 935.950012, investment 0.585708 %, total balance 2712.799740,\n",
|
||||
"day 214: buy 1 unit at price 929.080017, total balance 1783.719723\n",
|
||||
"day 215: buy 1 unit at price 932.070007, total balance 851.649716\n",
|
||||
"day 216, sell 1 unit at price 935.090027, investment 0.457657 %, total balance 1786.739743,\n",
|
||||
"day 217: buy 1 unit at price 925.109985, total balance 861.629758\n",
|
||||
"day 222, sell 1 unit at price 932.450012, investment 0.221412 %, total balance 1794.079770,\n",
|
||||
"day 223: buy 1 unit at price 928.530029, total balance 865.549741\n",
|
||||
"day 227, sell 1 unit at price 949.500000, investment 2.798676 %, total balance 1815.049741,\n",
|
||||
"day 228, sell 1 unit at price 959.109985, investment 3.923498 %, total balance 2774.159726,\n",
|
||||
"day 229: buy 1 unit at price 953.270020, total balance 1820.889706\n",
|
||||
"day 230, sell 1 unit at price 957.789978, investment 4.746329 %, total balance 2778.679684,\n",
|
||||
"day 232, sell 1 unit at price 969.960022, investment 5.125347 %, total balance 3748.639706,\n",
|
||||
"day 233, sell 1 unit at price 978.890015, investment 5.602183 %, total balance 4727.529721,\n",
|
||||
"day 234, sell 1 unit at price 977.000000, investment 5.393743 %, total balance 5704.529721,\n",
|
||||
"day 235: buy 1 unit at price 972.599976, total balance 4731.929745\n",
|
||||
"day 236, sell 1 unit at price 989.250000, investment 7.377775 %, total balance 5721.179745,\n",
|
||||
"day 237, sell 1 unit at price 987.830017, investment 7.854655 %, total balance 6709.009762,\n",
|
||||
"day 238, sell 1 unit at price 989.679993, investment 8.302601 %, total balance 7698.689755,\n",
|
||||
"day 239, sell 1 unit at price 992.000000, investment 6.772289 %, total balance 8690.689755,\n",
|
||||
"day 241, sell 1 unit at price 992.809998, investment 6.516677 %, total balance 9683.499753,\n",
|
||||
"day 242, sell 1 unit at price 984.450012, investment 6.414375 %, total balance 10667.949765,\n",
|
||||
"day 243, sell 1 unit at price 988.200012, investment 6.426285 %, total balance 11656.149777,\n",
|
||||
"day 244: buy 1 unit at price 968.450012, total balance 10687.699765\n",
|
||||
"day 248, sell 1 unit at price 1019.270020, investment 6.923537 %, total balance 11706.969785,\n",
|
||||
"day 249, sell 1 unit at price 1017.109985, investment 4.576394 %, total balance 12724.079770,\n",
|
||||
"day 250, sell 1 unit at price 1016.640015, investment 4.975993 %, total balance 13740.719785,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+548
@@ -0,0 +1,548 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from collections import deque\n",
|
||||
"import random\n",
|
||||
"\n",
|
||||
"class Model:\n",
|
||||
" def __init__(self, input_size, output_size, layer_size, learning_rate):\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, input_size))\n",
|
||||
" self.Y = tf.placeholder(tf.float32, (None, output_size))\n",
|
||||
" feed_forward = tf.layers.dense(self.X, layer_size, activation = tf.nn.relu)\n",
|
||||
" self.logits = tf.layers.dense(feed_forward, output_size)\n",
|
||||
" self.cost = tf.reduce_sum(tf.square(self.Y - self.logits))\n",
|
||||
" self.optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(self.cost)\n",
|
||||
" \n",
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" LEARNING_RATE = 0.003\n",
|
||||
" BATCH_SIZE = 32\n",
|
||||
" LAYER_SIZE = 500\n",
|
||||
" OUTPUT_SIZE = 3\n",
|
||||
" EPSILON = 0.5\n",
|
||||
" DECAY_RATE = 0.005\n",
|
||||
" MIN_EPSILON = 0.1\n",
|
||||
" GAMMA = 0.99\n",
|
||||
" MEMORIES = deque()\n",
|
||||
" COPY = 1000\n",
|
||||
" T_COPY = 0\n",
|
||||
" MEMORY_SIZE = 300\n",
|
||||
" \n",
|
||||
" def __init__(self, state_size, window_size, trend, skip):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" tf.reset_default_graph()\n",
|
||||
" self.model = Model(self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE, self.LEARNING_RATE)\n",
|
||||
" self.model_negative = Model(self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE, self.LEARNING_RATE)\n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
" self.trainable = tf.trainable_variables()\n",
|
||||
" \n",
|
||||
" def _assign(self):\n",
|
||||
" for i in range(len(self.trainable)//2):\n",
|
||||
" assign_op = self.trainable[i+len(self.trainable)//2].assign(self.trainable[i])\n",
|
||||
" self.sess.run(assign_op)\n",
|
||||
"\n",
|
||||
" def _memorize(self, state, action, reward, new_state, done):\n",
|
||||
" self.MEMORIES.append((state, action, reward, new_state, done))\n",
|
||||
" if len(self.MEMORIES) > self.MEMORY_SIZE:\n",
|
||||
" self.MEMORIES.popleft()\n",
|
||||
"\n",
|
||||
" def _select_action(self, state):\n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action = self.get_predicted_action([state])\n",
|
||||
" return action\n",
|
||||
"\n",
|
||||
" def _construct_memories(self, replay):\n",
|
||||
" states = np.array([a[0] for a in replay])\n",
|
||||
" new_states = np.array([a[3] for a in replay])\n",
|
||||
" Q = self.predict(states)\n",
|
||||
" Q_new = self.predict(new_states)\n",
|
||||
" Q_new_negative = self.sess.run(self.model_negative.logits, feed_dict={self.model_negative.X:new_states})\n",
|
||||
" replay_size = len(replay)\n",
|
||||
" X = np.empty((replay_size, self.state_size))\n",
|
||||
" Y = np.empty((replay_size, self.OUTPUT_SIZE))\n",
|
||||
" for i in range(replay_size):\n",
|
||||
" state_r, action_r, reward_r, new_state_r, done_r = replay[i]\n",
|
||||
" target = Q[i]\n",
|
||||
" target[action_r] = reward_r\n",
|
||||
" if not done_r:\n",
|
||||
" target[action_r] += self.GAMMA * Q_new_negative[i, np.argmax(Q_new[i])]\n",
|
||||
" X[i] = state_r\n",
|
||||
" Y[i] = target\n",
|
||||
" return X, Y\n",
|
||||
"\n",
|
||||
" def predict(self, inputs):\n",
|
||||
" return self.sess.run(self.model.logits, feed_dict={self.model.X:inputs})\n",
|
||||
" \n",
|
||||
" def get_predicted_action(self, sequence):\n",
|
||||
" prediction = self.predict(np.array(sequence))[0]\n",
|
||||
" return np.argmax(prediction)\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array(res)\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action = self._select_action(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" state = next_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" if (self.T_COPY + 1) % self.COPY == 0:\n",
|
||||
" self._assign()\n",
|
||||
" \n",
|
||||
" action = self._select_action(state)\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" \n",
|
||||
" invest = ((starting_money - initial_money) / initial_money)\n",
|
||||
" \n",
|
||||
" self._memorize(state, action, invest, next_state, starting_money < initial_money)\n",
|
||||
" batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)\n",
|
||||
" replay = random.sample(self.MEMORIES, batch_size)\n",
|
||||
" state = next_state\n",
|
||||
" X, Y = self._construct_memories(replay)\n",
|
||||
" \n",
|
||||
" cost, _ = self.sess.run([self.model.cost, self.model.optimizer], \n",
|
||||
" feed_dict={self.model.X: X, self.model.Y:Y})\n",
|
||||
" self.T_COPY += 1\n",
|
||||
" self.EPSILON = self.MIN_EPSILON + (1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)\n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"epoch: 10, total rewards: 1241.885127.3, cost: 1.110860, total money: 1744.875178\n",
|
||||
"epoch: 20, total rewards: 89.105106.3, cost: 0.649060, total money: 8097.275088\n",
|
||||
"epoch: 30, total rewards: 719.079470.3, cost: 0.823131, total money: 9699.809450\n",
|
||||
"epoch: 40, total rewards: 684.040043.3, cost: 1.931746, total money: 134.750004\n",
|
||||
"epoch: 50, total rewards: 1744.829771.3, cost: 0.895153, total money: 11744.829771\n",
|
||||
"epoch: 60, total rewards: 149.195010.3, cost: 1.097174, total money: 5196.854982\n",
|
||||
"epoch: 70, total rewards: 1389.289786.3, cost: 0.860031, total money: 9399.319754\n",
|
||||
"epoch: 80, total rewards: 529.019898.3, cost: 0.305593, total money: 10529.019898\n",
|
||||
"epoch: 90, total rewards: 1285.264893.3, cost: 1.882383, total money: 9251.514893\n",
|
||||
"epoch: 100, total rewards: 409.474970.3, cost: 0.146280, total money: 551.414972\n",
|
||||
"epoch: 110, total rewards: 1074.725155.3, cost: 0.661549, total money: 2231.475154\n",
|
||||
"epoch: 120, total rewards: 1713.854676.3, cost: 1.219318, total money: 11713.854676\n",
|
||||
"epoch: 130, total rewards: 871.945621.3, cost: 1.460638, total money: 8947.665652\n",
|
||||
"epoch: 140, total rewards: 1564.314818.3, cost: 1.133385, total money: 2767.354796\n",
|
||||
"epoch: 150, total rewards: 855.729796.3, cost: 1.886093, total money: 10855.729796\n",
|
||||
"epoch: 160, total rewards: 302.970157.3, cost: 0.642825, total money: 6320.700137\n",
|
||||
"epoch: 170, total rewards: 512.139521.3, cost: 3.411159, total money: 1801.649470\n",
|
||||
"epoch: 180, total rewards: 769.354739.3, cost: 0.379282, total money: 10769.354739\n",
|
||||
"epoch: 190, total rewards: 332.274720.3, cost: 1.111366, total money: 10332.274720\n",
|
||||
"epoch: 200, total rewards: 395.419923.3, cost: 0.270106, total money: 5401.389893\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"batch_size = 32\n",
|
||||
"agent = Agent(state_size = window_size, \n",
|
||||
" window_size = window_size, \n",
|
||||
" trend = close, \n",
|
||||
" skip = skip)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 7: buy 1 unit at price 754.020020, total balance 9245.979980\n",
|
||||
"day 9, sell 1 unit at price 758.489990, investment 0.592818 %, total balance 10004.469970,\n",
|
||||
"day 10: buy 1 unit at price 764.479980, total balance 9239.989990\n",
|
||||
"day 11: buy 1 unit at price 771.229980, total balance 8468.760010\n",
|
||||
"day 12, sell 1 unit at price 760.539978, investment -0.515383 %, total balance 9229.299988,\n",
|
||||
"day 13, sell 1 unit at price 769.200012, investment -0.263212 %, total balance 9998.500000,\n",
|
||||
"day 17: buy 1 unit at price 768.239990, total balance 9230.260010\n",
|
||||
"day 19, sell 1 unit at price 758.039978, investment -1.327712 %, total balance 9988.299988,\n",
|
||||
"day 21: buy 1 unit at price 750.500000, total balance 9237.799988\n",
|
||||
"day 22, sell 1 unit at price 762.520020, investment 1.601602 %, total balance 10000.320008,\n",
|
||||
"day 26: buy 1 unit at price 789.289978, total balance 9211.030030\n",
|
||||
"day 27, sell 1 unit at price 789.270020, investment -0.002529 %, total balance 10000.300050,\n",
|
||||
"day 30: buy 1 unit at price 797.849976, total balance 9202.450074\n",
|
||||
"day 33, sell 1 unit at price 796.419983, investment -0.179231 %, total balance 9998.870057,\n",
|
||||
"day 41: buy 1 unit at price 786.140015, total balance 9212.730042\n",
|
||||
"day 42, sell 1 unit at price 786.900024, investment 0.096676 %, total balance 9999.630066,\n",
|
||||
"day 45: buy 1 unit at price 806.650024, total balance 9192.980042\n",
|
||||
"day 46: buy 1 unit at price 804.789978, total balance 8388.190064\n",
|
||||
"day 47, sell 1 unit at price 807.909973, investment 0.156195 %, total balance 9196.100037,\n",
|
||||
"day 49, sell 1 unit at price 807.880005, investment 0.383954 %, total balance 10003.980042,\n",
|
||||
"day 54: buy 1 unit at price 819.309998, total balance 9184.670044\n",
|
||||
"day 55, sell 1 unit at price 823.869995, investment 0.556566 %, total balance 10008.540039,\n",
|
||||
"day 61: buy 1 unit at price 795.695007, total balance 9212.845032\n",
|
||||
"day 62, sell 1 unit at price 798.530029, investment 0.356295 %, total balance 10011.375061,\n",
|
||||
"day 64: buy 1 unit at price 801.340027, total balance 9210.035034\n",
|
||||
"day 65, sell 1 unit at price 806.969971, investment 0.702566 %, total balance 10017.005005,\n",
|
||||
"day 66: buy 1 unit at price 808.380005, total balance 9208.625000\n",
|
||||
"day 67: buy 1 unit at price 809.559998, total balance 8399.065002\n",
|
||||
"day 68: buy 1 unit at price 813.669983, total balance 7585.395019\n",
|
||||
"day 69, sell 1 unit at price 819.239990, investment 1.343426 %, total balance 8404.635009,\n",
|
||||
"day 70, sell 1 unit at price 820.450012, investment 1.345177 %, total balance 9225.085021,\n",
|
||||
"day 71, sell 1 unit at price 818.979980, investment 0.652598 %, total balance 10044.065001,\n",
|
||||
"day 74: buy 1 unit at price 831.659973, total balance 9212.405028\n",
|
||||
"day 76, sell 1 unit at price 831.330017, investment -0.039674 %, total balance 10043.735045,\n",
|
||||
"day 79: buy 1 unit at price 823.210022, total balance 9220.525023\n",
|
||||
"day 80, sell 1 unit at price 835.239990, investment 1.461349 %, total balance 10055.765013,\n",
|
||||
"day 83: buy 1 unit at price 827.780029, total balance 9227.984984\n",
|
||||
"day 84, sell 1 unit at price 831.909973, investment 0.498918 %, total balance 10059.894957,\n",
|
||||
"day 97: buy 1 unit at price 814.429993, total balance 9245.464964\n",
|
||||
"day 98, sell 1 unit at price 819.510010, investment 0.623751 %, total balance 10064.974974,\n",
|
||||
"day 102: buy 1 unit at price 829.559998, total balance 9235.414976\n",
|
||||
"day 103, sell 1 unit at price 838.549988, investment 1.083706 %, total balance 10073.964964,\n",
|
||||
"day 104: buy 1 unit at price 834.570007, total balance 9239.394957\n",
|
||||
"day 105, sell 1 unit at price 831.409973, investment -0.378642 %, total balance 10070.804930,\n",
|
||||
"day 107: buy 1 unit at price 824.669983, total balance 9246.134947\n",
|
||||
"day 108: buy 1 unit at price 824.729980, total balance 8421.404967\n",
|
||||
"day 109, sell 1 unit at price 823.349976, investment -0.160065 %, total balance 9244.754943,\n",
|
||||
"day 110, sell 1 unit at price 824.320007, investment -0.049710 %, total balance 10069.074950,\n",
|
||||
"day 113: buy 1 unit at price 836.820007, total balance 9232.254943\n",
|
||||
"day 114, sell 1 unit at price 838.210022, investment 0.166107 %, total balance 10070.464965,\n",
|
||||
"day 117: buy 1 unit at price 862.760010, total balance 9207.704955\n",
|
||||
"day 118, sell 1 unit at price 872.299988, investment 1.105751 %, total balance 10080.004943,\n",
|
||||
"day 120: buy 1 unit at price 874.250000, total balance 9205.754943\n",
|
||||
"day 123, sell 1 unit at price 916.440002, investment 4.825851 %, total balance 10122.194945,\n",
|
||||
"day 125: buy 1 unit at price 931.659973, total balance 9190.534972\n",
|
||||
"day 126: buy 1 unit at price 927.130005, total balance 8263.404967\n",
|
||||
"day 127, sell 1 unit at price 934.299988, investment 0.283367 %, total balance 9197.704955,\n",
|
||||
"day 128, sell 1 unit at price 932.169983, investment 0.543611 %, total balance 10129.874938,\n",
|
||||
"day 132: buy 1 unit at price 937.080017, total balance 9192.794921\n",
|
||||
"day 133, sell 1 unit at price 943.000000, investment 0.631748 %, total balance 10135.794921,\n",
|
||||
"day 135: buy 1 unit at price 930.239990, total balance 9205.554931\n",
|
||||
"day 138, sell 1 unit at price 948.820007, investment 1.997336 %, total balance 10154.374938,\n",
|
||||
"day 139: buy 1 unit at price 954.960022, total balance 9199.414916\n",
|
||||
"day 140, sell 1 unit at price 969.539978, investment 1.526761 %, total balance 10168.954894,\n",
|
||||
"day 141: buy 1 unit at price 971.469971, total balance 9197.484923\n",
|
||||
"day 143, sell 1 unit at price 964.859985, investment -0.680411 %, total balance 10162.344908,\n",
|
||||
"day 153: buy 1 unit at price 950.760010, total balance 9211.584898\n",
|
||||
"day 154, sell 1 unit at price 942.309998, investment -0.888764 %, total balance 10153.894896,\n",
|
||||
"day 157: buy 1 unit at price 950.630005, total balance 9203.264891\n",
|
||||
"day 158, sell 1 unit at price 959.450012, investment 0.927807 %, total balance 10162.714903,\n",
|
||||
"day 161: buy 1 unit at price 952.270020, total balance 9210.444883\n",
|
||||
"day 162: buy 1 unit at price 927.330017, total balance 8283.114866\n",
|
||||
"day 163, sell 1 unit at price 940.489990, investment -1.237047 %, total balance 9223.604856,\n",
|
||||
"day 164, sell 1 unit at price 917.789978, investment -1.028764 %, total balance 10141.394834,\n",
|
||||
"day 171: buy 1 unit at price 930.090027, total balance 9211.304807\n",
|
||||
"day 172, sell 1 unit at price 943.830017, investment 1.477275 %, total balance 10155.134824,\n",
|
||||
"day 178: buy 1 unit at price 968.150024, total balance 9186.984800\n",
|
||||
"day 179, sell 1 unit at price 972.919983, investment 0.492688 %, total balance 10159.904783,\n",
|
||||
"day 180: buy 1 unit at price 980.340027, total balance 9179.564756\n",
|
||||
"day 181: buy 1 unit at price 950.700012, total balance 8228.864744\n",
|
||||
"day 182: buy 1 unit at price 947.799988, total balance 7281.064756\n",
|
||||
"day 183: buy 1 unit at price 934.090027, total balance 6346.974729\n",
|
||||
"day 185: buy 1 unit at price 930.500000, total balance 5416.474729\n",
|
||||
"day 186, sell 1 unit at price 930.830017, investment -5.050290 %, total balance 6347.304746,\n",
|
||||
"day 188: buy 1 unit at price 923.650024, total balance 5423.654722\n",
|
||||
"day 189: buy 1 unit at price 927.960022, total balance 4495.694700\n",
|
||||
"day 190, sell 1 unit at price 929.359985, investment -2.244665 %, total balance 5425.054685,\n",
|
||||
"day 193, sell 1 unit at price 907.239990, investment -4.279384 %, total balance 6332.294675,\n",
|
||||
"day 195, sell 1 unit at price 922.669983, investment -1.222585 %, total balance 7254.964658,\n",
|
||||
"day 196, sell 1 unit at price 922.219971, investment -0.889847 %, total balance 8177.184629,\n",
|
||||
"day 197, sell 1 unit at price 926.960022, investment 0.358361 %, total balance 9104.144651,\n",
|
||||
"day 198, sell 1 unit at price 910.979980, investment -1.829825 %, total balance 10015.124631,\n",
|
||||
"day 202: buy 1 unit at price 927.000000, total balance 9088.124631\n",
|
||||
"day 205: buy 1 unit at price 913.809998, total balance 8174.314633\n",
|
||||
"day 207, sell 1 unit at price 929.570007, investment 0.277239 %, total balance 9103.884640,\n",
|
||||
"day 208, sell 1 unit at price 939.330017, investment 2.792705 %, total balance 10043.214657,\n",
|
||||
"day 215: buy 1 unit at price 932.070007, total balance 9111.144650\n",
|
||||
"day 217: buy 1 unit at price 925.109985, total balance 8186.034665\n",
|
||||
"day 218: buy 1 unit at price 920.289978, total balance 7265.744687\n",
|
||||
"day 219: buy 1 unit at price 915.000000, total balance 6350.744687\n",
|
||||
"day 220: buy 1 unit at price 921.809998, total balance 5428.934689\n",
|
||||
"day 221, sell 1 unit at price 931.580017, investment -0.052570 %, total balance 6360.514706,\n",
|
||||
"day 222: buy 1 unit at price 932.450012, total balance 5428.064694\n",
|
||||
"day 223: buy 1 unit at price 928.530029, total balance 4499.534665\n",
|
||||
"day 224, sell 1 unit at price 920.969971, investment -0.447516 %, total balance 5420.504636,\n",
|
||||
"day 225: buy 1 unit at price 924.859985, total balance 4495.644651\n",
|
||||
"day 226: buy 1 unit at price 944.489990, total balance 3551.154661\n",
|
||||
"day 228, sell 1 unit at price 959.109985, investment 4.218236 %, total balance 4510.264646,\n",
|
||||
"day 229, sell 1 unit at price 953.270020, investment 4.182516 %, total balance 5463.534666,\n",
|
||||
"day 230, sell 1 unit at price 957.789978, investment 3.903188 %, total balance 6421.324644,\n",
|
||||
"day 231, sell 1 unit at price 951.679993, investment 2.062307 %, total balance 7373.004637,\n",
|
||||
"day 232, sell 1 unit at price 969.960022, investment 4.461890 %, total balance 8342.964659,\n",
|
||||
"day 235: buy 1 unit at price 972.599976, total balance 7370.364683\n",
|
||||
"day 236, sell 1 unit at price 989.250000, investment 6.962137 %, total balance 8359.614683,\n",
|
||||
"day 238: buy 1 unit at price 989.679993, total balance 7369.934690\n",
|
||||
"day 241: buy 1 unit at price 992.809998, total balance 6377.124692\n",
|
||||
"day 242, sell 1 unit at price 984.450012, investment 4.230857 %, total balance 7361.574704,\n",
|
||||
"day 243: buy 1 unit at price 988.200012, total balance 6373.374692\n",
|
||||
"day 244: buy 1 unit at price 968.450012, total balance 5404.924680\n",
|
||||
"day 245, sell 1 unit at price 970.539978, investment -0.211803 %, total balance 6375.464658,\n",
|
||||
"day 246: buy 1 unit at price 973.330017, total balance 5402.134641\n",
|
||||
"day 247, sell 1 unit at price 972.559998, investment -1.729852 %, total balance 6374.694639,\n",
|
||||
"day 248: buy 1 unit at price 1019.270020, total balance 5355.424619\n",
|
||||
"day 249, sell 1 unit at price 1017.109985, investment 2.447597 %, total balance 6372.534604,\n",
|
||||
"day 250: buy 1 unit at price 1016.640015, total balance 5355.894589\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+458
@@ -0,0 +1,458 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from collections import deque\n",
|
||||
"import random\n",
|
||||
" \n",
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" LEARNING_RATE = 0.003\n",
|
||||
" BATCH_SIZE = 32\n",
|
||||
" LAYER_SIZE = 256\n",
|
||||
" OUTPUT_SIZE = 3\n",
|
||||
" EPSILON = 0.5\n",
|
||||
" DECAY_RATE = 0.005\n",
|
||||
" MIN_EPSILON = 0.1\n",
|
||||
" GAMMA = 0.99\n",
|
||||
" MEMORIES = deque()\n",
|
||||
" MEMORY_SIZE = 300\n",
|
||||
" \n",
|
||||
" def __init__(self, state_size, window_size, trend, skip):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" tf.reset_default_graph()\n",
|
||||
" self.INITIAL_FEATURES = np.zeros((4, self.state_size))\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, None, self.state_size))\n",
|
||||
" self.Y = tf.placeholder(tf.float32, (None, self.OUTPUT_SIZE))\n",
|
||||
" cell = tf.nn.rnn_cell.LSTMCell(self.LAYER_SIZE, state_is_tuple = False)\n",
|
||||
" self.hidden_layer = tf.placeholder(tf.float32, (None, 2 * self.LAYER_SIZE))\n",
|
||||
" self.rnn,self.last_state = tf.nn.dynamic_rnn(inputs=self.X,cell=cell,\n",
|
||||
" dtype=tf.float32,\n",
|
||||
" initial_state=self.hidden_layer)\n",
|
||||
" self.logits = tf.layers.dense(self.rnn[:,-1], self.OUTPUT_SIZE)\n",
|
||||
" self.cost = tf.reduce_sum(tf.square(self.Y - self.logits))\n",
|
||||
" self.optimizer = tf.train.AdamOptimizer(learning_rate = self.LEARNING_RATE).minimize(self.cost)\n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
" \n",
|
||||
" def _memorize(self, state, action, reward, new_state, dead, rnn_state):\n",
|
||||
" self.MEMORIES.append((state, action, reward, new_state, dead, rnn_state))\n",
|
||||
" if len(self.MEMORIES) > self.MEMORY_SIZE:\n",
|
||||
" self.MEMORIES.popleft()\n",
|
||||
"\n",
|
||||
" def _construct_memories(self, replay):\n",
|
||||
" states = np.array([a[0] for a in replay])\n",
|
||||
" new_states = np.array([a[3] for a in replay])\n",
|
||||
" init_values = np.array([a[-1] for a in replay])\n",
|
||||
" Q = self.sess.run(self.logits, feed_dict={self.X:states, self.hidden_layer:init_values})\n",
|
||||
" Q_new = self.sess.run(self.logits, feed_dict={self.X:new_states, self.hidden_layer:init_values})\n",
|
||||
" replay_size = len(replay)\n",
|
||||
" X = np.empty((replay_size, 4, self.state_size))\n",
|
||||
" Y = np.empty((replay_size, self.OUTPUT_SIZE))\n",
|
||||
" INIT_VAL = np.empty((replay_size, 2 * self.LAYER_SIZE))\n",
|
||||
" for i in range(replay_size):\n",
|
||||
" state_r, action_r, reward_r, new_state_r, dead_r, rnn_memory = replay[i]\n",
|
||||
" target = Q[i]\n",
|
||||
" target[action_r] = reward_r\n",
|
||||
" if not dead_r:\n",
|
||||
" target[action_r] += self.GAMMA * np.amax(Q_new[i])\n",
|
||||
" X[i] = state_r\n",
|
||||
" Y[i] = target\n",
|
||||
" INIT_VAL[i] = rnn_memory\n",
|
||||
" return X, Y, INIT_VAL\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array(res)\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" init_value = np.zeros((1, 2 * self.LAYER_SIZE))\n",
|
||||
" for k in range(self.INITIAL_FEATURES.shape[0]):\n",
|
||||
" self.INITIAL_FEATURES[k,:] = state\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action, last_state = self.sess.run([self.logits,self.last_state],\n",
|
||||
" feed_dict={self.X:[self.INITIAL_FEATURES],\n",
|
||||
" self.hidden_layer:init_value})\n",
|
||||
" action, init_value = np.argmax(action[0]), last_state\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" new_state = np.append([self.get_state(t + 1)], self.INITIAL_FEATURES[:3, :], axis = 0)\n",
|
||||
" self.INITIAL_FEATURES = new_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" init_value = np.zeros((1, 2 * self.LAYER_SIZE))\n",
|
||||
" for k in range(self.INITIAL_FEATURES.shape[0]):\n",
|
||||
" self.INITIAL_FEATURES[k,:] = state\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" \n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action, last_state = self.sess.run([self.logits,\n",
|
||||
" self.last_state],\n",
|
||||
" feed_dict={self.X:[self.INITIAL_FEATURES],\n",
|
||||
" self.hidden_layer:init_value})\n",
|
||||
" action, init_value = np.argmax(action[0]), last_state\n",
|
||||
" \n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" \n",
|
||||
" invest = ((starting_money - initial_money) / initial_money)\n",
|
||||
" new_state = np.append([self.get_state(t + 1)], self.INITIAL_FEATURES[:3, :], axis = 0)\n",
|
||||
" self._memorize(self.INITIAL_FEATURES, action, invest, new_state, \n",
|
||||
" starting_money < initial_money, init_value[0])\n",
|
||||
" self.INITIAL_FEATURES = new_state\n",
|
||||
" batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)\n",
|
||||
" replay = random.sample(self.MEMORIES, batch_size)\n",
|
||||
" X, Y, INIT_VAL = self._construct_memories(replay)\n",
|
||||
" \n",
|
||||
" cost, _ = self.sess.run([self.cost, self.optimizer], \n",
|
||||
" feed_dict={self.X: X, self.Y:Y,\n",
|
||||
" self.hidden_layer: INIT_VAL})\n",
|
||||
" self.EPSILON = self.MIN_EPSILON + (1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)\n",
|
||||
" \n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7fef003b2d30>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"epoch: 10, total rewards: 449.400388.3, cost: 0.117951, total money: 7420.680355\n",
|
||||
"epoch: 20, total rewards: 513.109983.3, cost: 0.187314, total money: 7552.130003\n",
|
||||
"epoch: 30, total rewards: 1755.114813.3, cost: 0.337607, total money: 6759.834784\n",
|
||||
"epoch: 40, total rewards: 545.719909.3, cost: 0.555657, total money: 9529.079894\n",
|
||||
"epoch: 50, total rewards: 593.435182.3, cost: 0.399239, total money: 6611.165162\n",
|
||||
"epoch: 60, total rewards: 285.174678.3, cost: 0.071772, total money: 6314.564631\n",
|
||||
"epoch: 70, total rewards: 169.200014.3, cost: 0.796504, total money: 4264.030030\n",
|
||||
"epoch: 80, total rewards: 520.019840.3, cost: 0.567794, total money: 6501.959842\n",
|
||||
"epoch: 90, total rewards: 498.320189.3, cost: 0.245750, total money: 9481.210204\n",
|
||||
"epoch: 100, total rewards: 1572.605044.3, cost: 1.142984, total money: 11572.605044\n",
|
||||
"epoch: 110, total rewards: 297.584960.3, cost: 0.973414, total money: 10297.584960\n",
|
||||
"epoch: 120, total rewards: 912.394901.3, cost: 2.032860, total money: 6987.034854\n",
|
||||
"epoch: 130, total rewards: 22.109988.3, cost: 0.097879, total money: 10022.109988\n",
|
||||
"epoch: 140, total rewards: 471.779909.3, cost: 0.532008, total money: 10471.779909\n",
|
||||
"epoch: 150, total rewards: 215.255126.3, cost: 0.236825, total money: 10215.255126\n",
|
||||
"epoch: 160, total rewards: 147.780093.3, cost: 0.432537, total money: 9174.450076\n",
|
||||
"epoch: 170, total rewards: 203.309817.3, cost: 0.413111, total money: 10203.309817\n",
|
||||
"epoch: 180, total rewards: 76.350403.3, cost: 0.132205, total money: 8084.520385\n",
|
||||
"epoch: 190, total rewards: 173.749880.3, cost: 1.325852, total money: 10173.749880\n",
|
||||
"epoch: 200, total rewards: 4.325196.3, cost: 0.500293, total money: 8987.685181\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"batch_size = 32\n",
|
||||
"agent = Agent(state_size = window_size, \n",
|
||||
" window_size = window_size, \n",
|
||||
" trend = close, \n",
|
||||
" skip = skip)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 13: buy 1 unit at price 769.200012, total balance 9230.799988\n",
|
||||
"day 14: buy 1 unit at price 768.270020, total balance 8462.529968\n",
|
||||
"day 15, sell 1 unit at price 760.989990, investment -1.067346 %, total balance 9223.519958,\n",
|
||||
"day 17: buy 1 unit at price 768.239990, total balance 8455.279968\n",
|
||||
"day 18, sell 1 unit at price 770.840027, investment 0.334519 %, total balance 9226.119995,\n",
|
||||
"day 19, sell 1 unit at price 758.039978, investment -1.327712 %, total balance 9984.159973,\n",
|
||||
"day 29: buy 1 unit at price 797.070007, total balance 9187.089966\n",
|
||||
"day 30: buy 1 unit at price 797.849976, total balance 8389.239990\n",
|
||||
"day 33, sell 1 unit at price 796.419983, investment -0.081552 %, total balance 9185.659973,\n",
|
||||
"day 34: buy 1 unit at price 794.559998, total balance 8391.099975\n",
|
||||
"day 36, sell 1 unit at price 789.909973, investment -0.995175 %, total balance 9181.009948,\n",
|
||||
"day 37, sell 1 unit at price 791.549988, investment -0.378827 %, total balance 9972.559936,\n",
|
||||
"day 39: buy 1 unit at price 782.789978, total balance 9189.769958\n",
|
||||
"day 40, sell 1 unit at price 771.820007, investment -1.401394 %, total balance 9961.589965,\n",
|
||||
"day 46: buy 1 unit at price 804.789978, total balance 9156.799987\n",
|
||||
"day 47: buy 1 unit at price 807.909973, total balance 8348.890014\n",
|
||||
"day 49, sell 1 unit at price 807.880005, investment 0.383954 %, total balance 9156.770019,\n",
|
||||
"day 50, sell 1 unit at price 804.609985, investment -0.408460 %, total balance 9961.380004,\n",
|
||||
"day 51: buy 1 unit at price 806.070007, total balance 9155.309997\n",
|
||||
"day 54, sell 1 unit at price 819.309998, investment 1.642536 %, total balance 9974.619995,\n",
|
||||
"day 110: buy 1 unit at price 824.320007, total balance 9150.299988\n",
|
||||
"day 111, sell 1 unit at price 823.559998, investment -0.092198 %, total balance 9973.859986,\n",
|
||||
"day 128: buy 1 unit at price 932.169983, total balance 9041.690003\n",
|
||||
"day 129: buy 1 unit at price 928.780029, total balance 8112.909974\n",
|
||||
"day 130, sell 1 unit at price 930.599976, investment -0.168425 %, total balance 9043.509950,\n",
|
||||
"day 131, sell 1 unit at price 932.219971, investment 0.370372 %, total balance 9975.729921,\n",
|
||||
"day 173: buy 1 unit at price 947.159973, total balance 9028.569948\n",
|
||||
"day 175, sell 1 unit at price 953.419983, investment 0.660924 %, total balance 9981.989931,\n",
|
||||
"day 182: buy 1 unit at price 947.799988, total balance 9034.189943\n",
|
||||
"day 183, sell 1 unit at price 934.090027, investment -1.446504 %, total balance 9968.279970,\n",
|
||||
"day 197: buy 1 unit at price 926.960022, total balance 9041.319948\n",
|
||||
"day 198, sell 1 unit at price 910.979980, investment -1.723919 %, total balance 9952.299928,\n",
|
||||
"day 204: buy 1 unit at price 915.890015, total balance 9036.409913\n",
|
||||
"day 205, sell 1 unit at price 913.809998, investment -0.227103 %, total balance 9950.219911,\n",
|
||||
"day 207: buy 1 unit at price 929.570007, total balance 9020.649904\n",
|
||||
"day 209, sell 1 unit at price 937.340027, investment 0.835872 %, total balance 9957.989931,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+598
@@ -0,0 +1,598 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2016-11-02</td>\n",
|
||||
" <td>778.200012</td>\n",
|
||||
" <td>781.650024</td>\n",
|
||||
" <td>763.450012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>768.700012</td>\n",
|
||||
" <td>1872400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2016-11-03</td>\n",
|
||||
" <td>767.250000</td>\n",
|
||||
" <td>769.950012</td>\n",
|
||||
" <td>759.030029</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>762.130005</td>\n",
|
||||
" <td>1943200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2016-11-04</td>\n",
|
||||
" <td>750.659973</td>\n",
|
||||
" <td>770.359985</td>\n",
|
||||
" <td>750.560974</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>762.020020</td>\n",
|
||||
" <td>2134800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2016-11-07</td>\n",
|
||||
" <td>774.500000</td>\n",
|
||||
" <td>785.190002</td>\n",
|
||||
" <td>772.549988</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>782.520020</td>\n",
|
||||
" <td>1585100</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2016-11-08</td>\n",
|
||||
" <td>783.400024</td>\n",
|
||||
" <td>795.632996</td>\n",
|
||||
" <td>780.190002</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>790.510010</td>\n",
|
||||
" <td>1350800</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 \n",
|
||||
"1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 \n",
|
||||
"2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 \n",
|
||||
"3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 \n",
|
||||
"4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 1872400 \n",
|
||||
"1 1943200 \n",
|
||||
"2 2134800 \n",
|
||||
"3 1585100 \n",
|
||||
"4 1350800 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/GOOG-year.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from collections import deque\n",
|
||||
"import random\n",
|
||||
"\n",
|
||||
"class Model:\n",
|
||||
" def __init__(self, input_size, output_size, layer_size, learning_rate, name):\n",
|
||||
" with tf.variable_scope(name):\n",
|
||||
" self.X = tf.placeholder(tf.float32, (None, None, input_size))\n",
|
||||
" self.Y = tf.placeholder(tf.float32, (None, output_size))\n",
|
||||
" cell = tf.nn.rnn_cell.LSTMCell(layer_size, state_is_tuple = False)\n",
|
||||
" self.hidden_layer = tf.placeholder(tf.float32, (None, 2 * layer_size))\n",
|
||||
" self.rnn,self.last_state = tf.nn.dynamic_rnn(inputs=self.X,cell=cell,\n",
|
||||
" dtype=tf.float32,\n",
|
||||
" initial_state=self.hidden_layer)\n",
|
||||
" self.logits = tf.layers.dense(self.rnn[:,-1], output_size)\n",
|
||||
" self.cost = tf.reduce_sum(tf.square(self.Y - self.logits))\n",
|
||||
" self.optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(self.cost)\n",
|
||||
" \n",
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" LEARNING_RATE = 0.003\n",
|
||||
" BATCH_SIZE = 32\n",
|
||||
" LAYER_SIZE = 256\n",
|
||||
" OUTPUT_SIZE = 3\n",
|
||||
" EPSILON = 0.5\n",
|
||||
" DECAY_RATE = 0.005\n",
|
||||
" MIN_EPSILON = 0.1\n",
|
||||
" GAMMA = 0.99\n",
|
||||
" MEMORIES = deque()\n",
|
||||
" COPY = 1000\n",
|
||||
" T_COPY = 0\n",
|
||||
" MEMORY_SIZE = 300\n",
|
||||
" \n",
|
||||
" def __init__(self, state_size, window_size, trend, skip):\n",
|
||||
" self.state_size = state_size\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.half_window = window_size // 2\n",
|
||||
" self.trend = trend\n",
|
||||
" self.skip = skip\n",
|
||||
" tf.reset_default_graph()\n",
|
||||
" self.INITIAL_FEATURES = np.zeros((4, self.state_size))\n",
|
||||
" self.model = Model(self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE, self.LEARNING_RATE,\n",
|
||||
" 'real_model')\n",
|
||||
" self.model_negative = Model(self.state_size, self.OUTPUT_SIZE, self.LAYER_SIZE, self.LEARNING_RATE,\n",
|
||||
" 'negative_model')\n",
|
||||
" self.sess = tf.InteractiveSession()\n",
|
||||
" self.sess.run(tf.global_variables_initializer())\n",
|
||||
" self.trainable = tf.trainable_variables()\n",
|
||||
" \n",
|
||||
" def _assign(self, from_name, to_name):\n",
|
||||
" from_w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=from_name)\n",
|
||||
" to_w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=to_name)\n",
|
||||
" for i in range(len(from_w)):\n",
|
||||
" assign_op = to_w[i].assign(from_w[i])\n",
|
||||
" self.sess.run(assign_op)\n",
|
||||
"\n",
|
||||
" def _memorize(self, state, action, reward, new_state, dead, rnn_state):\n",
|
||||
" self.MEMORIES.append((state, action, reward, new_state, dead, rnn_state))\n",
|
||||
" if len(self.MEMORIES) > self.MEMORY_SIZE:\n",
|
||||
" self.MEMORIES.popleft()\n",
|
||||
"\n",
|
||||
" def _select_action(self, state):\n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action = self.get_predicted_action([state])\n",
|
||||
" return action\n",
|
||||
"\n",
|
||||
" def _construct_memories(self, replay):\n",
|
||||
" states = np.array([a[0] for a in replay])\n",
|
||||
" new_states = np.array([a[3] for a in replay])\n",
|
||||
" init_values = np.array([a[-1] for a in replay])\n",
|
||||
" Q = self.sess.run(self.model.logits, feed_dict={self.model.X:states, \n",
|
||||
" self.model.hidden_layer:init_values})\n",
|
||||
" Q_new = self.sess.run(self.model.logits, feed_dict={self.model.X:new_states, \n",
|
||||
" self.model.hidden_layer:init_values})\n",
|
||||
" Q_new_negative = self.sess.run(self.model_negative.logits, \n",
|
||||
" feed_dict={self.model_negative.X:new_states, \n",
|
||||
" self.model_negative.hidden_layer:init_values})\n",
|
||||
" replay_size = len(replay)\n",
|
||||
" X = np.empty((replay_size, 4, self.state_size))\n",
|
||||
" Y = np.empty((replay_size, self.OUTPUT_SIZE))\n",
|
||||
" INIT_VAL = np.empty((replay_size, 2 * self.LAYER_SIZE))\n",
|
||||
" for i in range(replay_size):\n",
|
||||
" state_r, action_r, reward_r, new_state_r, dead_r, rnn_memory = replay[i]\n",
|
||||
" target = Q[i]\n",
|
||||
" target[action_r] = reward_r\n",
|
||||
" if not dead_r:\n",
|
||||
" target[action_r] += self.GAMMA * Q_new_negative[i, np.argmax(Q_new[i])]\n",
|
||||
" X[i] = state_r\n",
|
||||
" Y[i] = target\n",
|
||||
" INIT_VAL[i] = rnn_memory\n",
|
||||
" return X, Y, INIT_VAL\n",
|
||||
" \n",
|
||||
" def get_state(self, t):\n",
|
||||
" window_size = self.window_size + 1\n",
|
||||
" d = t - window_size + 1\n",
|
||||
" block = self.trend[d : t + 1] if d >= 0 else -d * [self.trend[0]] + self.trend[0 : t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(window_size - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array(res)\n",
|
||||
" \n",
|
||||
" def buy(self, initial_money):\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" init_value = np.zeros((1, 2 * self.LAYER_SIZE))\n",
|
||||
" for k in range(self.INITIAL_FEATURES.shape[0]):\n",
|
||||
" self.INITIAL_FEATURES[k,:] = state\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" action, last_state = self.sess.run([self.model.logits,self.model.last_state],\n",
|
||||
" feed_dict={self.model.X:[self.INITIAL_FEATURES],\n",
|
||||
" self.model.hidden_layer:init_value})\n",
|
||||
" action, init_value = np.argmax(action[0]), last_state\n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and initial_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" initial_money -= self.trend[t]\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money))\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory):\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" initial_money += self.trend[t]\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((close[t] - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, close[t], invest, initial_money)\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" new_state = np.append([self.get_state(t + 1)], self.INITIAL_FEATURES[:3, :], axis = 0)\n",
|
||||
" self.INITIAL_FEATURES = new_state\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" total_gains = initial_money - starting_money\n",
|
||||
" return states_buy, states_sell, total_gains, invest\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" def train(self, iterations, checkpoint, initial_money):\n",
|
||||
" for i in range(iterations):\n",
|
||||
" total_profit = 0\n",
|
||||
" inventory = []\n",
|
||||
" state = self.get_state(0)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" init_value = np.zeros((1, 2 * self.LAYER_SIZE))\n",
|
||||
" for k in range(self.INITIAL_FEATURES.shape[0]):\n",
|
||||
" self.INITIAL_FEATURES[k,:] = state\n",
|
||||
" for t in range(0, len(self.trend) - 1, self.skip):\n",
|
||||
" if (self.T_COPY + 1) % self.COPY == 0:\n",
|
||||
" self._assign('real_model', 'negative_model')\n",
|
||||
" \n",
|
||||
" if np.random.rand() < self.EPSILON:\n",
|
||||
" action = np.random.randint(self.OUTPUT_SIZE)\n",
|
||||
" else:\n",
|
||||
" action, last_state = self.sess.run([self.model.logits,\n",
|
||||
" self.model.last_state],\n",
|
||||
" feed_dict={self.model.X:[self.INITIAL_FEATURES],\n",
|
||||
" self.model.hidden_layer:init_value})\n",
|
||||
" action, init_value = np.argmax(action[0]), last_state\n",
|
||||
" \n",
|
||||
" next_state = self.get_state(t + 1)\n",
|
||||
" \n",
|
||||
" if action == 1 and starting_money >= self.trend[t]:\n",
|
||||
" inventory.append(self.trend[t])\n",
|
||||
" starting_money -= self.trend[t]\n",
|
||||
" \n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" total_profit += self.trend[t] - bought_price\n",
|
||||
" starting_money += self.trend[t]\n",
|
||||
" \n",
|
||||
" invest = ((starting_money - initial_money) / initial_money)\n",
|
||||
" new_state = np.append([self.get_state(t + 1)], self.INITIAL_FEATURES[:3, :], axis = 0)\n",
|
||||
" \n",
|
||||
" self._memorize(self.INITIAL_FEATURES, action, invest, new_state, \n",
|
||||
" starting_money < initial_money, init_value[0])\n",
|
||||
" self.INITIAL_FEATURES = new_state\n",
|
||||
" batch_size = min(len(self.MEMORIES), self.BATCH_SIZE)\n",
|
||||
" replay = random.sample(self.MEMORIES, batch_size)\n",
|
||||
" X, Y, INIT_VAL = self._construct_memories(replay)\n",
|
||||
" \n",
|
||||
" cost, _ = self.sess.run([self.model.cost, self.model.optimizer], \n",
|
||||
" feed_dict={self.model.X: X, self.model.Y:Y,\n",
|
||||
" self.model.hidden_layer: INIT_VAL})\n",
|
||||
" self.T_COPY += 1\n",
|
||||
" self.EPSILON = self.MIN_EPSILON + (1.0 - self.MIN_EPSILON) * np.exp(-self.DECAY_RATE * i)\n",
|
||||
" if (i+1) % checkpoint == 0:\n",
|
||||
" print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost,\n",
|
||||
" starting_money))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7fb85fd10940>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7fb85f9de7b8>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n",
|
||||
"epoch: 10, total rewards: 1305.274912.3, cost: 0.402263, total money: 777.284860\n",
|
||||
"epoch: 20, total rewards: 582.070375.3, cost: 0.782595, total money: 804.650331\n",
|
||||
"epoch: 30, total rewards: 420.380369.3, cost: 1.481925, total money: 80.210326\n",
|
||||
"epoch: 40, total rewards: 1502.554748.3, cost: 0.343374, total money: 2823.564757\n",
|
||||
"epoch: 50, total rewards: 589.170222.3, cost: 0.370314, total money: 6597.640193\n",
|
||||
"epoch: 60, total rewards: 1069.864985.3, cost: 0.733583, total money: 10052.755000\n",
|
||||
"epoch: 70, total rewards: 900.360168.3, cost: 0.154633, total money: 8866.610168\n",
|
||||
"epoch: 80, total rewards: 625.559509.3, cost: 0.573019, total money: 9652.999511\n",
|
||||
"epoch: 90, total rewards: 966.905028.3, cost: 0.080430, total money: 6971.785033\n",
|
||||
"epoch: 100, total rewards: 784.169802.3, cost: 0.568819, total money: 10784.169802\n",
|
||||
"epoch: 110, total rewards: 658.149963.3, cost: 0.052230, total money: 9641.509948\n",
|
||||
"epoch: 120, total rewards: 615.210201.3, cost: 0.802322, total money: 9595.940181\n",
|
||||
"epoch: 130, total rewards: 623.289978.3, cost: 0.278659, total money: 10623.289978\n",
|
||||
"epoch: 140, total rewards: 595.960078.3, cost: 0.094435, total money: 10595.960078\n",
|
||||
"epoch: 150, total rewards: 594.979550.3, cost: 0.360762, total money: 1819.289547\n",
|
||||
"epoch: 160, total rewards: 794.614687.3, cost: 1.058314, total money: 3118.034730\n",
|
||||
"epoch: 170, total rewards: 1225.854981.3, cost: 0.226553, total money: 5322.584961\n",
|
||||
"epoch: 180, total rewards: 1099.610169.3, cost: 0.275357, total money: 6189.200135\n",
|
||||
"epoch: 190, total rewards: 857.554813.3, cost: 0.417154, total money: 7946.004825\n",
|
||||
"epoch: 200, total rewards: 1049.100096.3, cost: 0.839669, total money: 3317.970090\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = df.Close.values.tolist()\n",
|
||||
"initial_money = 10000\n",
|
||||
"window_size = 30\n",
|
||||
"skip = 1\n",
|
||||
"batch_size = 32\n",
|
||||
"agent = Agent(state_size = window_size, \n",
|
||||
" window_size = window_size, \n",
|
||||
" trend = close, \n",
|
||||
" skip = skip)\n",
|
||||
"agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 0: buy 1 unit at price 768.700012, total balance 9231.299988\n",
|
||||
"day 1, sell 1 unit at price 762.130005, investment -0.854691 %, total balance 9993.429993,\n",
|
||||
"day 3: buy 1 unit at price 782.520020, total balance 9210.909973\n",
|
||||
"day 4, sell 1 unit at price 790.510010, investment 1.021059 %, total balance 10001.419983,\n",
|
||||
"day 5: buy 1 unit at price 785.309998, total balance 9216.109985\n",
|
||||
"day 6, sell 1 unit at price 762.559998, investment -2.896945 %, total balance 9978.669983,\n",
|
||||
"day 7: buy 1 unit at price 754.020020, total balance 9224.649963\n",
|
||||
"day 8, sell 1 unit at price 736.080017, investment -2.379248 %, total balance 9960.729980,\n",
|
||||
"day 13: buy 1 unit at price 769.200012, total balance 9191.529968\n",
|
||||
"day 16, sell 1 unit at price 761.679993, investment -0.977642 %, total balance 9953.209961,\n",
|
||||
"day 19: buy 1 unit at price 758.039978, total balance 9195.169983\n",
|
||||
"day 20, sell 1 unit at price 747.919983, investment -1.335021 %, total balance 9943.089966,\n",
|
||||
"day 24: buy 1 unit at price 771.190002, total balance 9171.899964\n",
|
||||
"day 28: buy 1 unit at price 796.099976, total balance 8375.799988\n",
|
||||
"day 29: buy 1 unit at price 797.070007, total balance 7578.729981\n",
|
||||
"day 31: buy 1 unit at price 790.799988, total balance 6787.929993\n",
|
||||
"day 32, sell 1 unit at price 794.200012, investment 2.983702 %, total balance 7582.130005,\n",
|
||||
"day 33, sell 1 unit at price 796.419983, investment 0.040197 %, total balance 8378.549988,\n",
|
||||
"day 35, sell 1 unit at price 791.260010, investment -0.728919 %, total balance 9169.809998,\n",
|
||||
"day 36, sell 1 unit at price 789.909973, investment -0.112546 %, total balance 9959.719971,\n",
|
||||
"day 38: buy 1 unit at price 785.049988, total balance 9174.669983\n",
|
||||
"day 41: buy 1 unit at price 786.140015, total balance 8388.529968\n",
|
||||
"day 42, sell 1 unit at price 786.900024, investment 0.235658 %, total balance 9175.429992,\n",
|
||||
"day 43, sell 1 unit at price 794.020020, investment 1.002367 %, total balance 9969.450012,\n",
|
||||
"day 44: buy 1 unit at price 806.150024, total balance 9163.299988\n",
|
||||
"day 46, sell 1 unit at price 804.789978, investment -0.168709 %, total balance 9968.089966,\n",
|
||||
"day 47: buy 1 unit at price 807.909973, total balance 9160.179993\n",
|
||||
"day 49, sell 1 unit at price 807.880005, investment -0.003709 %, total balance 9968.059998,\n",
|
||||
"day 51: buy 1 unit at price 806.070007, total balance 9161.989991\n",
|
||||
"day 52: buy 1 unit at price 802.174988, total balance 8359.815003\n",
|
||||
"day 54: buy 1 unit at price 819.309998, total balance 7540.505005\n",
|
||||
"day 55, sell 1 unit at price 823.869995, investment 2.208243 %, total balance 8364.375000,\n",
|
||||
"day 56: buy 1 unit at price 835.669983, total balance 7528.705017\n",
|
||||
"day 57: buy 1 unit at price 832.150024, total balance 6696.554993\n",
|
||||
"day 58, sell 1 unit at price 823.309998, investment 2.634713 %, total balance 7519.864991,\n",
|
||||
"day 59, sell 1 unit at price 802.320007, investment -2.073695 %, total balance 8322.184998,\n",
|
||||
"day 61, sell 1 unit at price 795.695007, investment -4.783584 %, total balance 9117.880005,\n",
|
||||
"day 62, sell 1 unit at price 798.530029, investment -4.040136 %, total balance 9916.410034,\n",
|
||||
"day 68: buy 1 unit at price 813.669983, total balance 9102.740051\n",
|
||||
"day 69, sell 1 unit at price 819.239990, investment 0.684554 %, total balance 9921.980041,\n",
|
||||
"day 76: buy 1 unit at price 831.330017, total balance 9090.650024\n",
|
||||
"day 77: buy 1 unit at price 828.640015, total balance 8262.010009\n",
|
||||
"day 79, sell 1 unit at price 823.210022, investment -0.976747 %, total balance 9085.220031,\n",
|
||||
"day 81, sell 1 unit at price 830.630005, investment 0.240151 %, total balance 9915.850036,\n",
|
||||
"day 86: buy 1 unit at price 838.679993, total balance 9077.170043\n",
|
||||
"day 88: buy 1 unit at price 845.539978, total balance 8231.630065\n",
|
||||
"day 89, sell 1 unit at price 845.619995, investment 0.827491 %, total balance 9077.250060,\n",
|
||||
"day 91, sell 1 unit at price 848.780029, investment 0.383193 %, total balance 9926.030089,\n",
|
||||
"day 95: buy 1 unit at price 829.590027, total balance 9096.440062\n",
|
||||
"day 96, sell 1 unit at price 817.580017, investment -1.447704 %, total balance 9914.020079,\n",
|
||||
"day 97: buy 1 unit at price 814.429993, total balance 9099.590086\n",
|
||||
"day 101: buy 1 unit at price 831.500000, total balance 8268.090086\n",
|
||||
"day 102: buy 1 unit at price 829.559998, total balance 7438.530088\n",
|
||||
"day 104, sell 1 unit at price 834.570007, investment 2.472897 %, total balance 8273.100095,\n",
|
||||
"day 105, sell 1 unit at price 831.409973, investment -0.010827 %, total balance 9104.510068,\n",
|
||||
"day 106, sell 1 unit at price 827.880005, investment -0.202516 %, total balance 9932.390073,\n",
|
||||
"day 108: buy 1 unit at price 824.729980, total balance 9107.660093\n",
|
||||
"day 109, sell 1 unit at price 823.349976, investment -0.167328 %, total balance 9931.010069,\n",
|
||||
"day 114: buy 1 unit at price 838.210022, total balance 9092.800047\n",
|
||||
"day 117, sell 1 unit at price 862.760010, investment 2.928859 %, total balance 9955.560057,\n",
|
||||
"day 121: buy 1 unit at price 905.960022, total balance 9049.600035\n",
|
||||
"day 122: buy 1 unit at price 912.570007, total balance 8137.030028\n",
|
||||
"day 124: buy 1 unit at price 927.039978, total balance 7209.990050\n",
|
||||
"day 125: buy 1 unit at price 931.659973, total balance 6278.330077\n",
|
||||
"day 130, sell 1 unit at price 930.599976, investment 2.719762 %, total balance 7208.930053,\n",
|
||||
"day 131, sell 1 unit at price 932.219971, investment 2.153256 %, total balance 8141.150024,\n",
|
||||
"day 132, sell 1 unit at price 937.080017, investment 1.083021 %, total balance 9078.230041,\n",
|
||||
"day 133, sell 1 unit at price 943.000000, investment 1.217185 %, total balance 10021.230041,\n",
|
||||
"day 137: buy 1 unit at price 941.859985, total balance 9079.370056\n",
|
||||
"day 138, sell 1 unit at price 948.820007, investment 0.738966 %, total balance 10028.190063,\n",
|
||||
"day 142: buy 1 unit at price 975.880005, total balance 9052.310058\n",
|
||||
"day 143: buy 1 unit at price 964.859985, total balance 8087.450073\n",
|
||||
"day 144: buy 1 unit at price 966.950012, total balance 7120.500061\n",
|
||||
"day 145, sell 1 unit at price 975.599976, investment -0.028695 %, total balance 8096.100037,\n",
|
||||
"day 146, sell 1 unit at price 983.679993, investment 1.950543 %, total balance 9079.780030,\n",
|
||||
"day 147: buy 1 unit at price 976.570007, total balance 8103.210023\n",
|
||||
"day 148: buy 1 unit at price 980.940002, total balance 7122.270021\n",
|
||||
"day 150, sell 1 unit at price 949.830017, investment -1.770515 %, total balance 8072.100038,\n",
|
||||
"day 151: buy 1 unit at price 942.900024, total balance 7129.200014\n",
|
||||
"day 152, sell 1 unit at price 953.400024, investment -2.372588 %, total balance 8082.600038,\n",
|
||||
"day 153: buy 1 unit at price 950.760010, total balance 7131.840028\n",
|
||||
"day 154, sell 1 unit at price 942.309998, investment -3.938060 %, total balance 8074.150026,\n",
|
||||
"day 155, sell 1 unit at price 939.780029, investment -0.330894 %, total balance 9013.930055,\n",
|
||||
"day 156, sell 1 unit at price 957.369995, investment 0.695232 %, total balance 9971.300050,\n",
|
||||
"day 159: buy 1 unit at price 957.090027, total balance 9014.210023\n",
|
||||
"day 160: buy 1 unit at price 965.590027, total balance 8048.619996\n",
|
||||
"day 161, sell 1 unit at price 952.270020, investment -0.503611 %, total balance 9000.890016,\n",
|
||||
"day 162: buy 1 unit at price 927.330017, total balance 8073.559999\n",
|
||||
"day 163: buy 1 unit at price 940.489990, total balance 7133.070009\n",
|
||||
"day 165: buy 1 unit at price 908.729980, total balance 6224.340029\n",
|
||||
"day 167: buy 1 unit at price 911.710022, total balance 5312.630007\n",
|
||||
"day 169, sell 1 unit at price 918.590027, investment -4.867490 %, total balance 6231.220034,\n",
|
||||
"day 170, sell 1 unit at price 928.799988, investment 0.158516 %, total balance 7160.020022,\n",
|
||||
"day 173, sell 1 unit at price 947.159973, investment 0.709203 %, total balance 8107.179995,\n",
|
||||
"day 174: buy 1 unit at price 955.989990, total balance 7151.190005\n",
|
||||
"day 175: buy 1 unit at price 953.419983, total balance 6197.770022\n",
|
||||
"day 176: buy 1 unit at price 965.400024, total balance 5232.369998\n",
|
||||
"day 177, sell 1 unit at price 970.890015, investment 6.840320 %, total balance 6203.260013,\n",
|
||||
"day 178: buy 1 unit at price 968.150024, total balance 5235.109989\n",
|
||||
"day 179: buy 1 unit at price 972.919983, total balance 4262.190006\n",
|
||||
"day 180: buy 1 unit at price 980.340027, total balance 3281.849979\n",
|
||||
"day 181, sell 1 unit at price 950.700012, investment 4.276578 %, total balance 4232.549991,\n",
|
||||
"day 182, sell 1 unit at price 947.799988, investment -0.856704 %, total balance 5180.349979,\n",
|
||||
"day 184, sell 1 unit at price 941.530029, investment -1.247085 %, total balance 6121.880008,\n",
|
||||
"day 185, sell 1 unit at price 930.500000, investment -3.615084 %, total balance 7052.380008,\n",
|
||||
"day 186: buy 1 unit at price 930.830017, total balance 6121.549991\n",
|
||||
"day 190: buy 1 unit at price 929.359985, total balance 5192.190006\n",
|
||||
"day 191, sell 1 unit at price 926.789978, investment -4.272070 %, total balance 6118.979984,\n",
|
||||
"day 192, sell 1 unit at price 922.900024, investment -5.141220 %, total balance 7041.880008,\n",
|
||||
"day 193, sell 1 unit at price 907.239990, investment -7.456600 %, total balance 7949.119998,\n",
|
||||
"day 196: buy 1 unit at price 922.219971, total balance 7026.900027\n",
|
||||
"day 198: buy 1 unit at price 910.979980, total balance 6115.920047\n",
|
||||
"day 199, sell 1 unit at price 910.669983, investment -2.165813 %, total balance 7026.590030,\n",
|
||||
"day 200, sell 1 unit at price 906.659973, investment -2.442542 %, total balance 7933.250003,\n",
|
||||
"day 201, sell 1 unit at price 924.690002, investment 0.267835 %, total balance 8857.940005,\n",
|
||||
"day 204, sell 1 unit at price 915.890015, investment 0.538984 %, total balance 9773.830020,\n",
|
||||
"day 205: buy 1 unit at price 913.809998, total balance 8860.020022\n",
|
||||
"day 206, sell 1 unit at price 921.289978, investment 0.818549 %, total balance 9781.310000,\n",
|
||||
"day 209: buy 1 unit at price 937.340027, total balance 8843.969973\n",
|
||||
"day 210: buy 1 unit at price 928.450012, total balance 7915.519961\n",
|
||||
"day 211, sell 1 unit at price 927.809998, investment -1.016710 %, total balance 8843.329959,\n",
|
||||
"day 212: buy 1 unit at price 935.950012, total balance 7907.379947\n",
|
||||
"day 214, sell 1 unit at price 929.080017, investment 0.067856 %, total balance 8836.459964,\n",
|
||||
"day 216, sell 1 unit at price 935.090027, investment -0.091884 %, total balance 9771.549991,\n",
|
||||
"day 217: buy 1 unit at price 925.109985, total balance 8846.440006\n",
|
||||
"day 219, sell 1 unit at price 915.000000, investment -1.092841 %, total balance 9761.440006,\n",
|
||||
"day 220: buy 1 unit at price 921.809998, total balance 8839.630008\n",
|
||||
"day 221: buy 1 unit at price 931.580017, total balance 7908.049991\n",
|
||||
"day 222: buy 1 unit at price 932.450012, total balance 6975.599979\n",
|
||||
"day 226, sell 1 unit at price 944.489990, investment 2.460376 %, total balance 7920.089969,\n",
|
||||
"day 227: buy 1 unit at price 949.500000, total balance 6970.589969\n",
|
||||
"day 229, sell 1 unit at price 953.270020, investment 2.328303 %, total balance 7923.859989,\n",
|
||||
"day 230: buy 1 unit at price 957.789978, total balance 6966.070011\n",
|
||||
"day 231: buy 1 unit at price 951.679993, total balance 6014.390018\n",
|
||||
"day 232: buy 1 unit at price 969.960022, total balance 5044.429996\n",
|
||||
"day 233, sell 1 unit at price 978.890015, investment 4.980428 %, total balance 6023.320011,\n",
|
||||
"day 234: buy 1 unit at price 977.000000, total balance 5046.320011\n",
|
||||
"day 237, sell 1 unit at price 987.830017, investment 4.036863 %, total balance 6034.150028,\n",
|
||||
"day 239, sell 1 unit at price 992.000000, investment 3.571767 %, total balance 7026.150028,\n",
|
||||
"day 240, sell 1 unit at price 992.179993, investment 4.255632 %, total balance 8018.330021,\n",
|
||||
"day 243, sell 1 unit at price 988.200012, investment 1.880489 %, total balance 9006.530033,\n",
|
||||
"day 244, sell 1 unit at price 968.450012, investment -0.875127 %, total balance 9974.980045,\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"states_buy, states_sell, total_gains, invest = agent.buy(initial_money = initial_money)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize = (15,5))\n",
|
||||
"plt.plot(close, color='r', lw=2.)\n",
|
||||
"plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
|
||||
"plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
|
||||
"plt.title('total gains %f, total investment %f%%'%(total_gains, invest))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+940
@@ -0,0 +1,940 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 720x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.figure(figsize = (10, 5))\n",
|
||||
"bins = np.linspace(-10, 10, 100)\n",
|
||||
"\n",
|
||||
"solution = np.random.randn(100)\n",
|
||||
"w = np.random.randn(100)\n",
|
||||
"\n",
|
||||
"plt.hist(solution, bins, alpha = 0.5, label = 'solution', color = 'r')\n",
|
||||
"plt.hist(w, bins, alpha = 0.5, label = 'random', color = 'y')\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"iter 1000. w: 0.0952791586701015, solution: 0.5720518054873052, reward: -20.148099\n",
|
||||
"iter 2000. w: 0.5750455468679501, solution: 0.5720518054873052, reward: -0.008058\n",
|
||||
"iter 3000. w: 0.5751585748688035, solution: 0.5720518054873052, reward: -0.008793\n",
|
||||
"iter 4000. w: 0.5665604300033952, solution: 0.5720518054873052, reward: -0.007711\n",
|
||||
"iter 5000. w: 0.5619489293298067, solution: 0.5720518054873052, reward: -0.005604\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def f(w):\n",
|
||||
" return -np.sum(np.square(solution - w))\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"npop = 50\n",
|
||||
"sigma = 0.1\n",
|
||||
"alpha = 0.001\n",
|
||||
"\n",
|
||||
"for i in range(5000):\n",
|
||||
"\n",
|
||||
" if (i + 1) % 1000 == 0:\n",
|
||||
" print(\n",
|
||||
" 'iter %d. w: %s, solution: %s, reward: %f'\n",
|
||||
" % (i + 1, str(w[-1]), str(solution[-1]), f(w))\n",
|
||||
" )\n",
|
||||
" N = np.random.randn(npop, 100)\n",
|
||||
" R = np.zeros(npop)\n",
|
||||
" for j in range(npop):\n",
|
||||
" w_try = w + sigma * N[j]\n",
|
||||
" R[j] = f(w_try)\n",
|
||||
"\n",
|
||||
" A = (R - np.mean(R)) / np.std(R)\n",
|
||||
" w = w + alpha / (npop * sigma) * np.dot(N.T, A)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 720x360 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"'''\n",
|
||||
"I want to compare my first two individuals with my real w\n",
|
||||
"'''\n",
|
||||
"plt.figure(figsize=(10,5))\n",
|
||||
"\n",
|
||||
"sigma = 0.1\n",
|
||||
"N = np.random.randn(npop, 100)\n",
|
||||
"individuals = []\n",
|
||||
"for j in range(2):\n",
|
||||
" individuals.append(w + sigma * N[j])\n",
|
||||
" \n",
|
||||
" \n",
|
||||
"plt.hist(w, bins, alpha=0.5, label='w',color='r')\n",
|
||||
"plt.hist(individuals[0], bins, alpha=0.5, label='individual 1')\n",
|
||||
"plt.hist(individuals[1], bins, alpha=0.5, label='individual 2')\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2017-10-16</td>\n",
|
||||
" <td>992.099976</td>\n",
|
||||
" <td>993.906982</td>\n",
|
||||
" <td>984.000000</td>\n",
|
||||
" <td>992.000000</td>\n",
|
||||
" <td>992.000000</td>\n",
|
||||
" <td>910500</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2017-10-17</td>\n",
|
||||
" <td>990.289978</td>\n",
|
||||
" <td>996.440002</td>\n",
|
||||
" <td>988.590027</td>\n",
|
||||
" <td>992.179993</td>\n",
|
||||
" <td>992.179993</td>\n",
|
||||
" <td>1290200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2017-10-18</td>\n",
|
||||
" <td>991.770020</td>\n",
|
||||
" <td>996.719971</td>\n",
|
||||
" <td>986.974976</td>\n",
|
||||
" <td>992.809998</td>\n",
|
||||
" <td>992.809998</td>\n",
|
||||
" <td>1057600</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2017-10-19</td>\n",
|
||||
" <td>986.000000</td>\n",
|
||||
" <td>988.880005</td>\n",
|
||||
" <td>978.390015</td>\n",
|
||||
" <td>984.450012</td>\n",
|
||||
" <td>984.450012</td>\n",
|
||||
" <td>1313600</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2017-10-20</td>\n",
|
||||
" <td>989.440002</td>\n",
|
||||
" <td>991.000000</td>\n",
|
||||
" <td>984.580017</td>\n",
|
||||
" <td>988.200012</td>\n",
|
||||
" <td>988.200012</td>\n",
|
||||
" <td>1183200</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2017-10-16 992.099976 993.906982 984.000000 992.000000 992.000000 \n",
|
||||
"1 2017-10-17 990.289978 996.440002 988.590027 992.179993 992.179993 \n",
|
||||
"2 2017-10-18 991.770020 996.719971 986.974976 992.809998 992.809998 \n",
|
||||
"3 2017-10-19 986.000000 988.880005 978.390015 984.450012 984.450012 \n",
|
||||
"4 2017-10-20 989.440002 991.000000 984.580017 988.200012 988.200012 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 910500 \n",
|
||||
"1 1290200 \n",
|
||||
"2 1057600 \n",
|
||||
"3 1313600 \n",
|
||||
"4 1183200 "
|
||||
]
|
||||
},
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"google = pd.read_csv('/Users/huseinzolkepli/Desktop/GOOG.csv')\n",
|
||||
"google.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 58,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_state(data, t, n):\n",
|
||||
" d = t - n + 1\n",
|
||||
" block = data[d : t + 1] if d >= 0 else -d * [data[0]] + data[: t + 1]\n",
|
||||
" res = []\n",
|
||||
" for i in range(n - 1):\n",
|
||||
" res.append(block[i + 1] - block[i])\n",
|
||||
" return np.array([res])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 60,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"close = google.Close.values.tolist()\n",
|
||||
"get_state(close, 0, 10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[0. , 0. , 0. , 0. , 0. , 0. ,\n",
|
||||
" 0. , 0. , 0.179993]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 61,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"get_state(close, 1, 10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[0. , 0. , 0. , 0. , 0. , 0. ,\n",
|
||||
" 0. , 0.179993, 0.630005]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 62,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"get_state(close, 2, 10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class Deep_Evolution_Strategy:\n",
|
||||
" def __init__(\n",
|
||||
" self, weights, reward_function, population_size, sigma, learning_rate\n",
|
||||
" ):\n",
|
||||
" self.weights = weights\n",
|
||||
" self.reward_function = reward_function\n",
|
||||
" self.population_size = population_size\n",
|
||||
" self.sigma = sigma\n",
|
||||
" self.learning_rate = learning_rate\n",
|
||||
"\n",
|
||||
" def _get_weight_from_population(self, weights, population):\n",
|
||||
" weights_population = []\n",
|
||||
" for index, i in enumerate(population):\n",
|
||||
" jittered = self.sigma * i\n",
|
||||
" weights_population.append(weights[index] + jittered)\n",
|
||||
" return weights_population\n",
|
||||
"\n",
|
||||
" def get_weights(self):\n",
|
||||
" return self.weights\n",
|
||||
"\n",
|
||||
" def train(self, epoch = 100, print_every = 1):\n",
|
||||
" lasttime = time.time()\n",
|
||||
" for i in range(epoch):\n",
|
||||
" population = []\n",
|
||||
" rewards = np.zeros(self.population_size)\n",
|
||||
" for k in range(self.population_size):\n",
|
||||
" x = []\n",
|
||||
" for w in self.weights:\n",
|
||||
" x.append(np.random.randn(*w.shape))\n",
|
||||
" population.append(x)\n",
|
||||
" for k in range(self.population_size):\n",
|
||||
" weights_population = self._get_weight_from_population(\n",
|
||||
" self.weights, population[k]\n",
|
||||
" )\n",
|
||||
" rewards[k] = self.reward_function(weights_population)\n",
|
||||
" rewards = (rewards - np.mean(rewards)) / np.std(rewards)\n",
|
||||
" for index, w in enumerate(self.weights):\n",
|
||||
" A = np.array([p[index] for p in population])\n",
|
||||
" self.weights[index] = (\n",
|
||||
" w\n",
|
||||
" + self.learning_rate\n",
|
||||
" / (self.population_size * self.sigma)\n",
|
||||
" * np.dot(A.T, rewards).T\n",
|
||||
" )\n",
|
||||
" if (i + 1) % print_every == 0:\n",
|
||||
" print(\n",
|
||||
" 'iter %d. reward: %f'\n",
|
||||
" % (i + 1, self.reward_function(self.weights))\n",
|
||||
" )\n",
|
||||
" print('time taken to train:', time.time() - lasttime, 'seconds')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class Model:\n",
|
||||
" def __init__(self, input_size, layer_size, output_size):\n",
|
||||
" self.weights = [\n",
|
||||
" np.random.randn(input_size, layer_size),\n",
|
||||
" np.random.randn(layer_size, output_size),\n",
|
||||
" np.random.randn(layer_size, 1),\n",
|
||||
" np.random.randn(1, layer_size),\n",
|
||||
" ]\n",
|
||||
"\n",
|
||||
" def predict(self, inputs):\n",
|
||||
" feed = np.dot(inputs, self.weights[0]) + self.weights[-1]\n",
|
||||
" decision = np.dot(feed, self.weights[1])\n",
|
||||
" buy = np.dot(feed, self.weights[2])\n",
|
||||
" return decision, buy\n",
|
||||
"\n",
|
||||
" def get_weights(self):\n",
|
||||
" return self.weights\n",
|
||||
"\n",
|
||||
" def set_weights(self, weights):\n",
|
||||
" self.weights = weights"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"window_size = 30\n",
|
||||
"model = Model(window_size, 500, 3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 67,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"-89.2658852200001"
|
||||
]
|
||||
},
|
||||
"execution_count": 67,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"initial_money = 10000\n",
|
||||
"starting_money = initial_money\n",
|
||||
"len_close = len(close) - 1\n",
|
||||
"weight = model\n",
|
||||
"skip = 1\n",
|
||||
"\n",
|
||||
"state = get_state(close, 0, window_size + 1)\n",
|
||||
"inventory = []\n",
|
||||
"quantity = 0\n",
|
||||
"\n",
|
||||
"max_buy = 5\n",
|
||||
"max_sell = 5\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def act(model, sequence):\n",
|
||||
" decision, buy = model.predict(np.array(sequence))\n",
|
||||
" return np.argmax(decision[0]), int(buy[0])\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"for t in range(0, len_close, skip):\n",
|
||||
" action, buy = act(weight, state)\n",
|
||||
" next_state = get_state(close, t + 1, window_size + 1)\n",
|
||||
" if action == 1 and initial_money >= close[t]:\n",
|
||||
" if buy < 0:\n",
|
||||
" buy = 1\n",
|
||||
" if buy > max_buy:\n",
|
||||
" buy_units = max_buy\n",
|
||||
" else:\n",
|
||||
" buy_units = buy\n",
|
||||
" total_buy = buy_units * close[t]\n",
|
||||
" initial_money -= total_buy\n",
|
||||
" inventory.append(total_buy)\n",
|
||||
" quantity += buy_units\n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" if quantity > max_sell:\n",
|
||||
" sell_units = max_sell\n",
|
||||
" else:\n",
|
||||
" sell_units = quantity\n",
|
||||
" quantity -= sell_units\n",
|
||||
" total_sell = sell_units * close[t]\n",
|
||||
" initial_money += total_sell\n",
|
||||
"\n",
|
||||
" state = next_state\n",
|
||||
"((initial_money - starting_money) / starting_money) * 100"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 77,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import time\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class Agent:\n",
|
||||
"\n",
|
||||
" POPULATION_SIZE = 15\n",
|
||||
" SIGMA = 0.1\n",
|
||||
" LEARNING_RATE = 0.03\n",
|
||||
"\n",
|
||||
" def __init__(\n",
|
||||
" self, model, money, max_buy, max_sell, close, window_size, skip\n",
|
||||
" ):\n",
|
||||
" self.window_size = window_size\n",
|
||||
" self.skip = skip\n",
|
||||
" self.close = close\n",
|
||||
" self.model = model\n",
|
||||
" self.initial_money = money\n",
|
||||
" self.max_buy = max_buy\n",
|
||||
" self.max_sell = max_sell\n",
|
||||
" self.es = Deep_Evolution_Strategy(\n",
|
||||
" self.model.get_weights(),\n",
|
||||
" self.get_reward,\n",
|
||||
" self.POPULATION_SIZE,\n",
|
||||
" self.SIGMA,\n",
|
||||
" self.LEARNING_RATE,\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" def act(self, sequence):\n",
|
||||
" decision, buy = self.model.predict(np.array(sequence))\n",
|
||||
" return np.argmax(decision[0]), int(buy[0])\n",
|
||||
"\n",
|
||||
" def get_reward(self, weights):\n",
|
||||
" initial_money = self.initial_money\n",
|
||||
" starting_money = initial_money\n",
|
||||
" len_close = len(self.close) - 1\n",
|
||||
"\n",
|
||||
" self.model.weights = weights\n",
|
||||
" state = get_state(self.close, 0, self.window_size + 1)\n",
|
||||
" inventory = []\n",
|
||||
" quantity = 0\n",
|
||||
" for t in range(0, len_close, self.skip):\n",
|
||||
" action, buy = self.act(state)\n",
|
||||
" next_state = get_state(self.close, t + 1, self.window_size + 1)\n",
|
||||
" if action == 1 and initial_money >= self.close[t]:\n",
|
||||
" if buy < 0:\n",
|
||||
" buy = 1\n",
|
||||
" if buy > self.max_buy:\n",
|
||||
" buy_units = self.max_buy\n",
|
||||
" else:\n",
|
||||
" buy_units = buy\n",
|
||||
" total_buy = buy_units * self.close[t]\n",
|
||||
" initial_money -= total_buy\n",
|
||||
" inventory.append(total_buy)\n",
|
||||
" quantity += buy_units\n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" if quantity > self.max_sell:\n",
|
||||
" sell_units = self.max_sell\n",
|
||||
" else:\n",
|
||||
" sell_units = quantity\n",
|
||||
" quantity -= sell_units\n",
|
||||
" total_sell = sell_units * self.close[t]\n",
|
||||
" initial_money += total_sell\n",
|
||||
"\n",
|
||||
" state = next_state\n",
|
||||
" return ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
"\n",
|
||||
" def fit(self, iterations, checkpoint):\n",
|
||||
" self.es.train(iterations, print_every = checkpoint)\n",
|
||||
"\n",
|
||||
" def buy(self):\n",
|
||||
" initial_money = self.initial_money\n",
|
||||
" len_close = len(self.close) - 1\n",
|
||||
" state = get_state(self.close, 0, self.window_size + 1)\n",
|
||||
" starting_money = initial_money\n",
|
||||
" states_sell = []\n",
|
||||
" states_buy = []\n",
|
||||
" inventory = []\n",
|
||||
" quantity = 0\n",
|
||||
" for t in range(0, len_close, self.skip):\n",
|
||||
" action, buy = self.act(state)\n",
|
||||
" next_state = get_state(self.close, t + 1, self.window_size + 1)\n",
|
||||
" if action == 1 and initial_money >= self.close[t]:\n",
|
||||
" if buy < 0:\n",
|
||||
" buy = 1\n",
|
||||
" if buy > self.max_buy:\n",
|
||||
" buy_units = self.max_buy\n",
|
||||
" else:\n",
|
||||
" buy_units = buy\n",
|
||||
" total_buy = buy_units * self.close[t]\n",
|
||||
" initial_money -= total_buy\n",
|
||||
" inventory.append(total_buy)\n",
|
||||
" quantity += buy_units\n",
|
||||
" states_buy.append(t)\n",
|
||||
" print(\n",
|
||||
" 'day %d: buy %d units at price %f, total balance %f'\n",
|
||||
" % (t, buy_units, total_buy, initial_money)\n",
|
||||
" )\n",
|
||||
" elif action == 2 and len(inventory) > 0:\n",
|
||||
" bought_price = inventory.pop(0)\n",
|
||||
" if quantity > self.max_sell:\n",
|
||||
" sell_units = self.max_sell\n",
|
||||
" else:\n",
|
||||
" sell_units = quantity\n",
|
||||
" if sell_units < 1:\n",
|
||||
" continue\n",
|
||||
" quantity -= sell_units\n",
|
||||
" total_sell = sell_units * self.close[t]\n",
|
||||
" initial_money += total_sell\n",
|
||||
" states_sell.append(t)\n",
|
||||
" try:\n",
|
||||
" invest = ((total_sell - bought_price) / bought_price) * 100\n",
|
||||
" except:\n",
|
||||
" invest = 0\n",
|
||||
" print(\n",
|
||||
" 'day %d, sell %d units at price %f, investment %f %%, total balance %f,'\n",
|
||||
" % (t, sell_units, total_sell, invest, initial_money)\n",
|
||||
" )\n",
|
||||
" state = next_state\n",
|
||||
"\n",
|
||||
" invest = ((initial_money - starting_money) / starting_money) * 100\n",
|
||||
" print(\n",
|
||||
" '\\ntotal gained %f, total investment %f %%'\n",
|
||||
" % (initial_money - starting_money, invest)\n",
|
||||
" )\n",
|
||||
" plt.figure(figsize = (20, 10))\n",
|
||||
" plt.plot(close, label = 'true close', c = 'g')\n",
|
||||
" plt.plot(\n",
|
||||
" close, 'X', label = 'predict buy', markevery = states_buy, c = 'b'\n",
|
||||
" )\n",
|
||||
" plt.plot(\n",
|
||||
" close, 'o', label = 'predict sell', markevery = states_sell, c = 'r'\n",
|
||||
" )\n",
|
||||
" plt.legend()\n",
|
||||
" plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 78,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = Model(input_size = window_size, layer_size = 500, output_size = 3)\n",
|
||||
"agent = Agent(\n",
|
||||
" model = model,\n",
|
||||
" money = 10000,\n",
|
||||
" max_buy = 5,\n",
|
||||
" max_sell = 5,\n",
|
||||
" close = close,\n",
|
||||
" window_size = window_size,\n",
|
||||
" skip = 1,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 79,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"iter 10. reward: 36.181611\n",
|
||||
"iter 20. reward: 50.767101\n",
|
||||
"iter 30. reward: 65.467698\n",
|
||||
"iter 40. reward: 71.316103\n",
|
||||
"iter 50. reward: 82.881994\n",
|
||||
"iter 60. reward: 84.293704\n",
|
||||
"iter 70. reward: 78.501997\n",
|
||||
"iter 80. reward: 94.488579\n",
|
||||
"iter 90. reward: 86.526799\n",
|
||||
"iter 100. reward: 85.882890\n",
|
||||
"iter 110. reward: 86.063284\n",
|
||||
"iter 120. reward: 90.334301\n",
|
||||
"iter 130. reward: 85.850098\n",
|
||||
"iter 140. reward: 91.399606\n",
|
||||
"iter 150. reward: 87.862805\n",
|
||||
"iter 160. reward: 97.226486\n",
|
||||
"iter 170. reward: 86.767297\n",
|
||||
"iter 180. reward: 97.016782\n",
|
||||
"iter 190. reward: 97.843791\n",
|
||||
"iter 200. reward: 89.146606\n",
|
||||
"iter 210. reward: 96.508885\n",
|
||||
"iter 220. reward: 97.765979\n",
|
||||
"iter 230. reward: 98.256375\n",
|
||||
"iter 240. reward: 99.942482\n",
|
||||
"iter 250. reward: 94.536183\n",
|
||||
"iter 260. reward: 96.916185\n",
|
||||
"iter 270. reward: 93.193185\n",
|
||||
"iter 280. reward: 100.844085\n",
|
||||
"iter 290. reward: 100.994682\n",
|
||||
"iter 300. reward: 101.523774\n",
|
||||
"iter 310. reward: 102.090896\n",
|
||||
"iter 320. reward: 102.176091\n",
|
||||
"iter 330. reward: 92.306981\n",
|
||||
"iter 340. reward: 105.409190\n",
|
||||
"iter 350. reward: 103.159886\n",
|
||||
"iter 360. reward: 99.091287\n",
|
||||
"iter 370. reward: 108.475085\n",
|
||||
"iter 380. reward: 102.349682\n",
|
||||
"iter 390. reward: 110.289382\n",
|
||||
"iter 400. reward: 103.371389\n",
|
||||
"iter 410. reward: 110.951287\n",
|
||||
"iter 420. reward: 111.561078\n",
|
||||
"iter 430. reward: 112.275285\n",
|
||||
"iter 440. reward: 113.112587\n",
|
||||
"iter 450. reward: 110.838887\n",
|
||||
"iter 460. reward: 111.243782\n",
|
||||
"iter 470. reward: 112.924874\n",
|
||||
"iter 480. reward: 111.705677\n",
|
||||
"iter 490. reward: 110.903074\n",
|
||||
"iter 500. reward: 112.986871\n",
|
||||
"time taken to train: 60.56475520133972 seconds\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent.fit(iterations = 500, checkpoint = 10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 80,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"day 0: buy 1 units at price 992.000000, total balance 9008.000000\n",
|
||||
"day 1: buy 1 units at price 992.179993, total balance 8015.820007\n",
|
||||
"day 2: buy 1 units at price 992.809998, total balance 7023.010009\n",
|
||||
"day 3: buy 5 units at price 4922.250060, total balance 2100.759949\n",
|
||||
"day 4, sell 5 units at price 4941.000060, investment 398.084683 %, total balance 7041.760009,\n",
|
||||
"day 5: buy 5 units at price 4842.250060, total balance 2199.509949\n",
|
||||
"day 7: buy 5 units at price 4866.650085, total balance -2667.140136\n",
|
||||
"day 9, sell 5 units at price 5096.350100, investment 413.651770 %, total balance 2429.209964,\n",
|
||||
"day 10: buy 5 units at price 5085.549925, total balance -2656.339961\n",
|
||||
"day 12, sell 5 units at price 5127.500000, investment 416.463373 %, total balance 2471.160039,\n",
|
||||
"day 13, sell 5 units at price 5127.899780, investment 4.177962 %, total balance 7599.059819,\n",
|
||||
"day 14, sell 3 units at price 3097.439940, investment -36.033045 %, total balance 10696.499759,\n",
|
||||
"day 22: buy 1 units at price 1020.909973, total balance 9675.589786\n",
|
||||
"day 24: buy 1 units at price 1019.090027, total balance 8656.499759\n",
|
||||
"day 25: buy 5 units at price 5091.900025, total balance 3564.599734\n",
|
||||
"day 27: buy 5 units at price 5179.799805, total balance -1615.200071\n",
|
||||
"day 29, sell 5 units at price 5271.049805, investment 416.308974 %, total balance 3655.849734,\n",
|
||||
"day 30, sell 5 units at price 5237.050170, investment 413.894752 %, total balance 8892.899904,\n",
|
||||
"day 33: buy 5 units at price 5050.849915, total balance 3842.049989\n",
|
||||
"day 35: buy 5 units at price 5025.750120, total balance -1183.700131\n",
|
||||
"day 42, sell 5 units at price 5245.750120, investment 3.021467 %, total balance 4062.049989,\n",
|
||||
"day 43, sell 5 units at price 5320.949705, investment 2.725007 %, total balance 9382.999694,\n",
|
||||
"day 44, sell 2 units at price 2154.280030, investment -57.348168 %, total balance 11537.279724,\n",
|
||||
"day 45: buy 1 units at price 1070.680054, total balance 10466.599670\n",
|
||||
"day 48: buy 1 units at price 1060.119995, total balance 9406.479675\n",
|
||||
"day 51: buy 5 units at price 5240.700075, total balance 4165.779600\n",
|
||||
"day 52: buy 5 units at price 5232.000120, total balance -1066.220520\n",
|
||||
"day 56, sell 5 units at price 5511.149900, investment 9.658255 %, total balance 4444.929380,\n",
|
||||
"day 57, sell 5 units at price 5534.699705, investment 416.933110 %, total balance 9979.629085,\n",
|
||||
"day 58, sell 2 units at price 2212.520020, investment 108.704678 %, total balance 12192.149105,\n",
|
||||
"day 59: buy 5 units at price 5513.049925, total balance 6679.099180\n",
|
||||
"day 60: buy 5 units at price 5527.600100, total balance 1151.499080\n",
|
||||
"day 62: buy 5 units at price 5608.800050, total balance -4457.300970\n",
|
||||
"day 69, sell 5 units at price 5851.849975, investment 11.661608 %, total balance 1394.549005,\n",
|
||||
"day 70, sell 5 units at price 5879.199830, investment 12.370025 %, total balance 7273.748835,\n",
|
||||
"day 71, sell 5 units at price 5877.899780, investment 6.617931 %, total balance 13151.648615,\n",
|
||||
"day 72: buy 5 units at price 5818.449705, total balance 7333.198910\n",
|
||||
"day 73, sell 5 units at price 5849.699705, investment 5.827115 %, total balance 13182.898615,\n",
|
||||
"day 78: buy 5 units at price 5242.899780, total balance 7939.998835\n",
|
||||
"day 79: buy 5 units at price 5007.600100, total balance 2932.398735\n",
|
||||
"day 80: buy 5 units at price 5188.900145, total balance -2256.501410\n",
|
||||
"day 87, sell 5 units at price 5556.699830, investment -0.928901 %, total balance 3300.198420,\n",
|
||||
"day 89: buy 1 units at price 1126.790039, total balance 2173.408381\n",
|
||||
"day 90, sell 5 units at price 5718.750000, investment -1.713510 %, total balance 7892.158381,\n",
|
||||
"day 93: buy 5 units at price 5347.600100, total balance 2544.558281\n",
|
||||
"day 96: buy 5 units at price 5475.300295, total balance -2930.742014\n",
|
||||
"day 98, sell 5 units at price 5630.000000, investment 7.383323 %, total balance 2699.257986,\n",
|
||||
"day 99, sell 5 units at price 5800.200195, investment 15.827943 %, total balance 8499.458181,\n",
|
||||
"day 100, sell 5 units at price 5822.500000, investment 12.210677 %, total balance 14321.958181,\n",
|
||||
"day 101: buy 1 units at price 1138.170044, total balance 13183.788137\n",
|
||||
"day 102, sell 2 units at price 2298.979980, investment 104.029136 %, total balance 15482.768117,\n",
|
||||
"day 111: buy 5 units at price 5025.499880, total balance 10457.268237\n",
|
||||
"day 113: buy 5 units at price 5158.950195, total balance 5298.318042\n",
|
||||
"day 114: buy 5 units at price 5032.349855, total balance 265.968187\n",
|
||||
"day 116, sell 5 units at price 5125.700075, investment 1.993835 %, total balance 5391.668262,\n",
|
||||
"day 118: buy 1 units at price 1007.039978, total balance 4384.628284\n",
|
||||
"day 119: buy 5 units at price 5077.250060, total balance -692.621776\n",
|
||||
"day 126, sell 5 units at price 5360.399780, investment 3.904856 %, total balance 4667.778004,\n",
|
||||
"day 128, sell 5 units at price 5364.799805, investment 6.606257 %, total balance 10032.577809,\n",
|
||||
"day 129, sell 5 units at price 5337.249755, investment 429.993831 %, total balance 15369.827564,\n",
|
||||
"day 131, sell 1 units at price 1021.179993, investment -79.887144 %, total balance 16391.007557,\n",
|
||||
"day 132: buy 1 units at price 1040.040039, total balance 15350.967518\n",
|
||||
"day 135, sell 1 units at price 1037.310059, investment -0.262488 %, total balance 16388.277577,\n",
|
||||
"day 136: buy 5 units at price 5121.900025, total balance 11266.377552\n",
|
||||
"day 137: buy 1 units at price 1023.719971, total balance 10242.657581\n",
|
||||
"day 138: buy 5 units at price 5241.049805, total balance 5001.607776\n",
|
||||
"day 139: buy 5 units at price 5273.950195, total balance -272.342419\n",
|
||||
"day 141, sell 5 units at price 5413.800050, investment 5.699057 %, total balance 5141.457631,\n",
|
||||
"day 142, sell 5 units at price 5487.849730, investment 436.069422 %, total balance 10629.307361,\n",
|
||||
"day 144: buy 1 units at price 1100.199951, total balance 9529.107410\n",
|
||||
"day 147: buy 1 units at price 1078.589966, total balance 8450.517444\n",
|
||||
"day 148: buy 5 units at price 5331.799925, total balance 3118.717519\n",
|
||||
"day 150: buy 5 units at price 5348.649900, total balance -2229.932381\n",
|
||||
"day 159, sell 5 units at price 5698.300170, investment 8.724404 %, total balance 3468.367789,\n",
|
||||
"day 161, sell 5 units at price 5619.299925, investment 6.548218 %, total balance 9087.667714,\n",
|
||||
"day 162: buy 5 units at price 5604.349975, total balance 3483.317739\n",
|
||||
"day 168: buy 1 units at price 1173.459961, total balance 2309.857778\n",
|
||||
"day 170, sell 5 units at price 5849.199830, investment 431.648799 %, total balance 8159.057608,\n",
|
||||
"day 171, sell 5 units at price 5788.300170, investment 436.654368 %, total balance 13947.357778,\n",
|
||||
"day 172, sell 4 units at price 4621.919920, investment -13.314078 %, total balance 18569.277698,\n",
|
||||
"day 173: buy 5 units at price 5624.050295, total balance 12945.227403\n",
|
||||
"day 175: buy 5 units at price 5519.899900, total balance 7425.327503\n",
|
||||
"day 176: buy 5 units at price 5571.099855, total balance 1854.227648\n",
|
||||
"day 179: buy 5 units at price 5514.450075, total balance -3660.222427\n",
|
||||
"day 184, sell 5 units at price 5769.500120, investment 7.868345 %, total balance 2109.277693,\n",
|
||||
"day 187: buy 5 units at price 5919.299925, total balance -3810.022232\n",
|
||||
"day 194, sell 5 units at price 6318.499755, investment 12.742776 %, total balance 2508.477523,\n",
|
||||
"day 195, sell 5 units at price 6341.649780, investment 440.423192 %, total balance 8850.127303,\n",
|
||||
"day 196, sell 5 units at price 6192.500000, investment 10.107479 %, total balance 15042.627303,\n",
|
||||
"day 197, sell 5 units at price 6098.699950, investment 10.485698 %, total balance 21141.327253,\n",
|
||||
"day 204: buy 5 units at price 6228.049925, total balance 14913.277328\n",
|
||||
"day 205: buy 5 units at price 6245.499880, total balance 8667.777448\n",
|
||||
"day 206: buy 5 units at price 6188.049925, total balance 2479.727523\n",
|
||||
"day 207, sell 5 units at price 6175.050050, investment -0.850987 %, total balance 8654.777573,\n",
|
||||
"day 208, sell 5 units at price 6210.499880, investment -0.560404 %, total balance 14865.277453,\n",
|
||||
"day 209: buy 5 units at price 6071.900025, total balance 8793.377428\n",
|
||||
"day 210: buy 5 units at price 6032.449950, total balance 2760.927478\n",
|
||||
"day 211: buy 5 units at price 6004.799805, total balance -3243.872327\n",
|
||||
"day 219, sell 5 units at price 6246.500245, investment 0.944568 %, total balance 3002.627918,\n",
|
||||
"day 220, sell 5 units at price 6195.599975, investment 2.037253 %, total balance 9198.227893,\n",
|
||||
"day 221, sell 5 units at price 6090.949705, investment 0.969751 %, total balance 15289.177598,\n",
|
||||
"day 227: buy 1 units at price 1177.359985, total balance 14111.817613\n",
|
||||
"day 229: buy 5 units at price 5876.649780, total balance 8235.167833\n",
|
||||
"day 230: buy 5 units at price 5862.650145, total balance 2372.517688\n",
|
||||
"day 231: buy 1 units at price 1156.050049, total balance 1216.467639\n",
|
||||
"day 232: buy 1 units at price 1161.219971, total balance 55.247668\n",
|
||||
"day 233, sell 5 units at price 5855.449830, investment -2.487177 %, total balance 5910.697498,\n",
|
||||
"day 234, sell 5 units at price 5934.349975, investment 404.038701 %, total balance 11845.047473,\n",
|
||||
"day 235: buy 5 units at price 5830.449830, total balance 6014.597643\n",
|
||||
"day 238: buy 1 units at price 1180.489990, total balance 4834.107653\n",
|
||||
"day 242, sell 5 units at price 6000.549925, investment 2.108347 %, total balance 10834.657578,\n",
|
||||
"day 243, sell 5 units at price 6014.749755, investment 2.594383 %, total balance 16849.407333,\n",
|
||||
"day 245, sell 4 units at price 4629.399904, investment 300.449782 %, total balance 21478.807237,\n",
|
||||
"\n",
|
||||
"total gained 11478.807237, total investment 114.788072 %\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1440x720 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent.buy()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"inputHidden": false,
|
||||
"outputHidden": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernel_info": {
|
||||
"name": "python3"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
},
|
||||
"nteract": {
|
||||
"version": "0.11.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Reference in new issue
Block a user