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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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from tqdm import tqdm\n",
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"sns.set()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Date</th>\n",
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" <th>Open</th>\n",
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" <th>High</th>\n",
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" <th>Low</th>\n",
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" <th>Close</th>\n",
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" <th>Adj Close</th>\n",
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" <th>Volume</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>2018-03-23</td>\n",
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" <td>311.250000</td>\n",
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" <td>311.250000</td>\n",
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" <td>300.450012</td>\n",
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" <td>301.540009</td>\n",
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" <td>301.540009</td>\n",
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" <td>6654900</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2018-03-26</td>\n",
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" <td>307.339996</td>\n",
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" <td>307.589996</td>\n",
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" <td>291.359985</td>\n",
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" <td>304.179993</td>\n",
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" <td>304.179993</td>\n",
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" <td>8375200</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2018-03-27</td>\n",
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" <td>304.000000</td>\n",
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" <td>304.269989</td>\n",
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" <td>277.179993</td>\n",
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" <td>279.179993</td>\n",
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" <td>279.179993</td>\n",
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" <td>13872000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>2018-03-28</td>\n",
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" <td>264.579987</td>\n",
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" <td>268.679993</td>\n",
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" <td>252.100006</td>\n",
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" <td>257.779999</td>\n",
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" <td>257.779999</td>\n",
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" <td>21001400</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>2018-03-29</td>\n",
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" <td>256.489990</td>\n",
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" <td>270.959991</td>\n",
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" <td>248.210007</td>\n",
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" <td>266.130005</td>\n",
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" <td>266.130005</td>\n",
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" <td>15170700</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Date Open High Low Close Adj Close \\\n",
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"0 2018-03-23 311.250000 311.250000 300.450012 301.540009 301.540009 \n",
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"1 2018-03-26 307.339996 307.589996 291.359985 304.179993 304.179993 \n",
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"2 2018-03-27 304.000000 304.269989 277.179993 279.179993 279.179993 \n",
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"3 2018-03-28 264.579987 268.679993 252.100006 257.779999 257.779999 \n",
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"4 2018-03-29 256.489990 270.959991 248.210007 266.130005 266.130005 \n",
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"\n",
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" Volume \n",
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"0 6654900 \n",
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"1 8375200 \n",
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"2 13872000 \n",
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"3 21001400 \n",
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"4 15170700 "
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = pd.read_csv('../dataset/TSLA.csv')\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 100/100 [00:00<00:00, 104.14it/s]\n"
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]
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}
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],
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"source": [
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"number_simulation = 100\n",
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"predict_day = 30\n",
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"\n",
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"close = df['Close'].tolist()\n",
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"returns = pd.DataFrame(close).pct_change()\n",
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"last_price = close[-1]\n",
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"results = pd.DataFrame()\n",
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"avg_daily_ret = returns.mean()\n",
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"variance = returns.var()\n",
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"daily_vol = returns.std()\n",
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"daily_drift = avg_daily_ret - (variance / 2)\n",
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"drift = daily_drift - 0.5 * daily_vol ** 2\n",
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"\n",
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"results = pd.DataFrame()\n",
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"\n",
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"for i in tqdm(range(number_simulation)):\n",
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" prices = []\n",
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" prices.append(df.Close.iloc[-1])\n",
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" for d in range(predict_day):\n",
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" shock = [drift + daily_vol * np.random.normal()]\n",
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" shock = np.mean(shock)\n",
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" price = prices[-1] * np.exp(shock)\n",
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" prices.append(price)\n",
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" results[i] = prices"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 720x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"plt.figure(figsize=(10,5))\n",
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"plt.plot(results)\n",
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"plt.ylabel('Value')\n",
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"plt.xlabel('Simulated days')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<Figure size 1224x360 with 3 Axes>"
|
||||
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|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"raveled = results.values.ravel()\n",
|
||||
"raveled.sort()\n",
|
||||
"cp_raveled = raveled.copy()\n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(17,5))\n",
|
||||
"plt.subplot(1,3,1)\n",
|
||||
"plt.plot(results)\n",
|
||||
"plt.ylabel('Value')\n",
|
||||
"plt.xlabel('Simulated days')\n",
|
||||
"plt.subplot(1,3,2)\n",
|
||||
"sns.distplot(df.Close,norm_hist=True)\n",
|
||||
"plt.title('$\\mu$ = %.2f, $\\sigma$ = %.2f'%(df.Close.mean(),df.Close.std()))\n",
|
||||
"plt.subplot(1,3,3)\n",
|
||||
"sns.distplot(raveled,norm_hist=True,label='monte carlo samples')\n",
|
||||
"sns.distplot(df.Close,norm_hist=True,label='real samples')\n",
|
||||
"plt.title('simulation $\\mu$ = %.2f, $\\sigma$ = %.2f'%(raveled.mean(),raveled.std()))\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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"
|
||||
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|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
BIN
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|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"from tqdm import tqdm\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",
|
||||
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|
||||
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|
||||
" .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>2018-03-23</td>\n",
|
||||
" <td>311.250000</td>\n",
|
||||
" <td>311.250000</td>\n",
|
||||
" <td>300.450012</td>\n",
|
||||
" <td>301.540009</td>\n",
|
||||
" <td>301.540009</td>\n",
|
||||
" <td>6654900</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2018-03-26</td>\n",
|
||||
" <td>307.339996</td>\n",
|
||||
" <td>307.589996</td>\n",
|
||||
" <td>291.359985</td>\n",
|
||||
" <td>304.179993</td>\n",
|
||||
" <td>304.179993</td>\n",
|
||||
" <td>8375200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2018-03-27</td>\n",
|
||||
" <td>304.000000</td>\n",
|
||||
" <td>304.269989</td>\n",
|
||||
" <td>277.179993</td>\n",
|
||||
" <td>279.179993</td>\n",
|
||||
" <td>279.179993</td>\n",
|
||||
" <td>13872000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2018-03-28</td>\n",
|
||||
" <td>264.579987</td>\n",
|
||||
" <td>268.679993</td>\n",
|
||||
" <td>252.100006</td>\n",
|
||||
" <td>257.779999</td>\n",
|
||||
" <td>257.779999</td>\n",
|
||||
" <td>21001400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2018-03-29</td>\n",
|
||||
" <td>256.489990</td>\n",
|
||||
" <td>270.959991</td>\n",
|
||||
" <td>248.210007</td>\n",
|
||||
" <td>266.130005</td>\n",
|
||||
" <td>266.130005</td>\n",
|
||||
" <td>15170700</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2018-03-23 311.250000 311.250000 300.450012 301.540009 301.540009 \n",
|
||||
"1 2018-03-26 307.339996 307.589996 291.359985 304.179993 304.179993 \n",
|
||||
"2 2018-03-27 304.000000 304.269989 277.179993 279.179993 279.179993 \n",
|
||||
"3 2018-03-28 264.579987 268.679993 252.100006 257.779999 257.779999 \n",
|
||||
"4 2018-03-29 256.489990 270.959991 248.210007 266.130005 266.130005 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 6654900 \n",
|
||||
"1 8375200 \n",
|
||||
"2 13872000 \n",
|
||||
"3 21001400 \n",
|
||||
"4 15170700 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/TSLA.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def pct_change(x,period=1):\n",
|
||||
" x = np.array(x)\n",
|
||||
" return ((x[period:] - x[:-period]) / x[:-period])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 100/100 [00:00<00:00, 689.32it/s]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"number_simulation = 100\n",
|
||||
"predict_day = 30\n",
|
||||
"\n",
|
||||
"results = pd.DataFrame()\n",
|
||||
"\n",
|
||||
"for i in tqdm(range(number_simulation)):\n",
|
||||
" prices = df.Close.values[-predict_day:].tolist()\n",
|
||||
" volatility = pct_change(prices[-predict_day:]).std()\n",
|
||||
" for d in range(predict_day):\n",
|
||||
" prices.append(prices[-1] * (1 + np.random.normal(0, volatility)))\n",
|
||||
" volatility = pct_change(prices[-predict_day:]).std()\n",
|
||||
" results[i] = pd.Series(prices[-predict_day:]).values"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"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",
|
||||
"plt.plot(results)\n",
|
||||
"plt.ylabel('Value')\n",
|
||||
"plt.xlabel('Simulated days')\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA/IAAAFUCAYAAACHuAfiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd5gV1f348fftW9m+sJSluHDovRcbYCG2KHYQS6zRFGOJJjH+bDHxq4lRsRMElKixgij2QhEBAVHgUJa6C+yyvd0+vz/mLm6F7Y3P63n2YWfmzJnPzL17mDOnjMUwDIQQQgghhBBCCNE+WFs7ACGEEEIIIYQQQtSdVOSFEEIIIYQQQoh2RCryQgghhBBCCCFEOyIVeSGEEEIIIYQQoh2RirwQQgghhBBCCNGOSEVeCCGEEEIIIYRoR6QiL4QQQgghhBBCtCNSkRdCCCGEEEIIIdoRqcgLIYQQQghxglJK/aSUOrUZ8t2jlJrWiP2bJS4hOgp7awcghBBCiOqUUouAqUAkcAj4h9b6pQrbbwWuBoYAi7XWV1fYFg+8DJwBHAHu0Vq/doxjfQmMB/yhVRlaa9WQvFqaUsoFzAWmAfHALswYP6yQplcozQTAA/wP+J3W2l9DfsVVVoUDc7XWt4W2H/NzEaK90VoPau0YlFJ7gF9prT8tX9cW4mqIupRJoXSXAX8FUjHLkqu11t9U2N4X2Az8T2s96xjH+5Lay+86xdJamqH8rlP5XNdr29ZJi7wQQgjRNv0N6KW17gScBzyklBpVYXsm8BAwr4Z9nwG8QGfgSuBZpdTxbopv1VpHhX5UI/NqSXZgP3AKEAP8GXgjdPNXbi6QBaQAw0Npb6kpswrXIAroApQBb1ZIcrzPRQhxYjtumaSUmg78HbgGiAZOBtKr5PMMsLaOx6yt/K5L+diamrT8pu7lc32ubZslLfJCCCFEPSml/gT00FrfFFqOw7zRiNZau5viGFrrnyosGqGfk4D1oe1vh449GuheIbZI4CJgsNa6GFihlHofmA38sT4xNEVeSikHcD/mQ4Bu/HzvsVlrPbQ+8dREa10Syr/cUqXUbmAUsCe0rjfwdOizOaSU+gioy8OIizA/16OtZMf7XIRoq5RSdwO/ATphPgi8RWv9WcXW8NDvz2D+jZ8E/Be4F5gPTAbWABdrrfNCeRpAX631ztDyfOCA1vrPNRz/j8D1QDJm5e1PWut3lFILMVullyilAsADWut/VIlrAPAsZkUuA7PV9v1QvnuAp4GrgJ7AR8Cc2sri5i6/61gm/b/QeX4bWs6oEuNlQD6wCkhr5lhq1d7K77qUz011bdsCqcgLIYQQ9TcE+LLC8nBA13QTqJRainkDXJMVWutzajuIUmouZvf5cGADsKwOsfUD/Frr7RXWbcJsxTiWvymlHgU05g32l43Iq6KHQumnALnAe0AhcEfVhI25VhXy6ByKu+IN3b+Ay0JdUOOAs4G/1CH2OcACrbVR5RgN+VyEaDVKKQXcCozRWmeGWjxttSS/CJiOWU/YAIwArgO2Yn7Xf4NZEa2vXZjlwCHgYmCRUipNaz1bKTWFKl3rK8TuAJZg9j46A7OMeE8pNVprrUPJLgHOAtzASsy/z+dqiaNFyu8KeVQqk5RSNmA08L5SaicQBrwL3Km1LlNKdQIeAE4HfnW8/ENqKr+PG0sdtLvy+1jlcwOvbZslFXkhhBCi/oZg3lyUG45Zwa2mLjcvtdFa36KUug1zbOCpmOMDjycK80arogLM7pu1uRvYgtmF/jLMlrHhDczrKKVUNOZN/1Ct9f7QureAS7XWVbuRNupahfJ2AK8Cr2itt1XY9DVwA+a52IBXMG+cj5VXT8wb2OtqiLMhn4sQrSkAuICBSqlsrfWeY6R9Smt9GEAp9Q2QpbXeEFp+B3MMcr1prSsOUXldKXUPMBazcngs4zHLoke11kHg81Cl8XJ+bs39t9Y6MxTjEswyuTYtUn6HYqmpTOoMOICZmBVkH+Y1+DPwJ+BB4GWt9QHz+ctx1Vh+a6131SGWY8XeLsvv45TP9b22bZqMkRdCCCHqQSnlxOyq90OF1cOAjc1xPK11QGu9ArP7/M112KUYs+tsRZ2AomMcY43Wukhr7dFav4LZojWjIXlVcTKQrrXeUWFdHGaLXJNSSlmBhZg3s7dWWf8R8DbmBEiJoRj+fpwsZ2O2Iu2uaWMDPhchWk2o6/vvMCu+WUqp/yqlutaS/HCF38tqWI5qSAxKqauUUhuVUvlKqXxgMObf4/F0BfaHKvHl9mJ29S5XsUwprS3Gliy/ayuTMK8hmA9MDmqtjwBPADNCD1CnAf+s63GOUX7XJZZjabfld03lc0OubVsnLfJCCCFE/QzAnBW4FEApZcF86l/jTO5KqQ8xW11q8o3W+uw6HteOeQN6PNsBu1Kqb4UbsGHUvSslmOMKLU2QVxKQV74Qula/BJ6sKXFDr1Uo35cxW7pmaK19FTbHY46/fVpr7QE8Sqn/YHYZvesYsV8FPHqM7eXq+rkI0aq0+baJ10Ldi5/HrAzNbmS2pUBEheUuwIGqiUI9XF7EbM1frbUOKKU2YpYzYJY5tckEeiilrBUq86mY5VN9tUj5fawySWudp5Q6QOVzLv/9VKAXsC/UYhwF2JRSA7XWI+twfuV5lV/X45WPx9Key+9yFcvnU2n8tW1TpCIvhBBC1M9QIFkpdRLmDeafMCdY2lNT4npU1I9SSiVjjuFbitl6Mw2zG+nlFdLYMf8ft2HejIRhjmcvUUq9DTyglPoVZrfR84GJtRwrFhgHfIX5+qJLMVtifluXvEKTW6ErvP6ugh+BkaGWEI35qiUDeL2mWBpyrUKexbxBn6a1Lqu4QWt9JDR50s1Kqf/DvHmbQ+UWuUqUUhMxW/verLL+uJ+LEG1RaIx8N8zWWjfm97e2MfL1sRG4Qin1E+a4+lOAdTWki8T8288OxXMNZot8ucNAn1qOsQbzgcFdSqnHgUnAucCYBsTb7OV3SK1lUsh/gNtCE7f5gN9jlisvYE4wWO4OzMpnjb1+jlV+1yWWjlR+16F8rte1bQ+ka70QQghRP0OA5ZiTJe3E7GZ+APOGsKkYmDcXBzBbRP4P872571dI82fMm5U/ArNCv5fPFH0L5kQ/WcBi4OaKs/kqpT5USt0bWnRgtm5kY74n/jbgggoT3B0zL6AHZuWgGq31OuBhzMmG0jFb6+rTInRcoZa+GzEfMhxSShWHfq6skOxCzImwsjE/s/Ib56rXotwc4G2tddUhBHX5XIRoi1yYPUyOYHaNTgbuaYJ8f4tZqc7HnNm8xrHLWustwOPAasxK+xAqlxt/A/4c6nZ/R5V9vaFjnB2Kfy5wVV3Gedeg2cvvOpZJD2K+/mw75iSCG4CHtdalWutD5T+Yw5vcWuvsCvnXufyuQywdqfw+Zvlcl2vb3lgM41g9WYQQQghRUaj74Eta67daO5bWFhpvuglzMqQmu7kTQojmIOX3z6T8bv+ka70QQghRP0MwW1BOeKGWsgGtHYcQQtSRlN8hUn63f9K1XgghhKgjpVQcZpfUHcdLK4QQou2Q8lt0NNK1XgghhBBCCCGEaEc6cou8HXMmQhk+IIRoSlK2CCGEEEKIVtWRb0R7Ys5sOIUa3mcphBAN1B34BkgDdrVyLM3BhflKn4NAoJVjEUJ0HDYgBXOmbk8rx9LUpNwUQjSHY5abHbkinxL695tWjUII0VGl0DEr8mOQclMI0XymACtaO4gmJuWmEKI51VhuduSK/EGAvLwSgsG6zQOQkBBFTk5xswbVlCTe5tWe4m1PsUL7jtdqtRAXFwmhMqYDqnfZ2Va1t+9Zc5PrUZ1ck8qa83p08LKzUeVmR/geyjm0vvYeP8g5VHW8crMjV+QDAMGgUa9Ctb3duEq8zas9xdueYoUOEW9H7T7ZoLKzreoI59CU5HpUJ9eksha4Hh2x7Gx0udkRvodyDq2vvccPcg61qLHc7MiT3QkhhBBCCCGEEB2OVOSFEEIIIYQQQoh2pCN3rRdCCCGEEEKIBgkE/OTlZeP3e1s7lOPKyrISDAZbO4xGOZHPwW53EheXhM1W9+q5VOSFEEIIIYQQooq8vGzCwiKIjOyCxWJp7XCOyW634ve370rwiXoOhmFQUlJIXl42iYkpx9+h/Fj1DU4IIYQQQojmoJTqB7wCJAA5wFVa6x1V0tiAfwNnAQbwqNb6pdC2vwCXYU4O5QPu1VovD22LAP4DjAL8wB1a66XH2yZOXH6/t11U4kX7ZrFYiIzsRHFxfr32kzHyQgghhBCirXgOeEZr3Q94Bni+hjRXAmlAX2ACcL9Sqldo23fAGK31UOBa4HWlVHho2x1AodY6DTgXeEkpFVWHbeIEJpV40RIa8j2TirwQQgghhGh1SqlkYCSwOLRqMTBSKZVUJemlwIta66DWOht4F7gYQGu9XGtdGkr3A2DBbN0v3+/5ULodwDrg7DpsE0KINke61gshhBBCiLagB5ChtQ4AaK0DSqnM0PrsCulSgb0VlveF0lR1FbBLa32gDvvVNc9aJSQ0vAE/KSm6wfu2FR3xHLKyrNjtP7d7egMGbq+/yY8b5rTjtDW+5b9irA2xfv06/H4f48ZNaHQsDT3+U0/9k/nzX22V49fXAw/8lQEDBnDxxZdVWt/Qz8Fqtdbr70gq8kK0c74937P/rbcwOqVgTeqFLbE3tqReWFyRrR1am+f+KQuLxYJrYNXGHiGEEO2ZUuoU4EFgeksdMyenmGDQqPd+SUnRZGcXNUNELaejnkMwGKw0cVmZx8/arYeb/NhjBnTG6mpctawpJopbt24tZWVljBo1rlH5NITfbz4gMQzazYR3hmEQDBqV4m3M5xAMBit9B61WyzEfEEpFXoh2zAj68axejDXoI+jbh3/3uqPbLNFJ2JLMSr01sRe2pN5YnOHHyO3EYgSCuDcdxtkztrVDEaJLine truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1224x360 with 3 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"raveled = results.values.ravel()\n",
|
||||
"raveled.sort()\n",
|
||||
"cp_raveled = raveled.copy()\n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(17,5))\n",
|
||||
"plt.subplot(1,3,1)\n",
|
||||
"plt.plot(results)\n",
|
||||
"plt.ylabel('Value')\n",
|
||||
"plt.xlabel('Simulated days')\n",
|
||||
"plt.subplot(1,3,2)\n",
|
||||
"sns.distplot(df.Close,norm_hist=True)\n",
|
||||
"plt.title('$\\mu$ = %.2f, $\\sigma$ = %.2f'%(df.Close.mean(),df.Close.std()))\n",
|
||||
"plt.subplot(1,3,3)\n",
|
||||
"sns.distplot(raveled,norm_hist=True,label='monte carlo samples')\n",
|
||||
"sns.distplot(df.Close,norm_hist=True,label='real samples')\n",
|
||||
"plt.title('simulation $\\mu$ = %.2f, $\\sigma$ = %.2f'%(raveled.mean(),raveled.std()))\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
|
||||
}
|
||||
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|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"from tqdm import tqdm\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",
|
||||
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|
||||
" }\n",
|
||||
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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>2018-03-23</td>\n",
|
||||
" <td>311.250000</td>\n",
|
||||
" <td>311.250000</td>\n",
|
||||
" <td>300.450012</td>\n",
|
||||
" <td>301.540009</td>\n",
|
||||
" <td>301.540009</td>\n",
|
||||
" <td>6654900</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2018-03-26</td>\n",
|
||||
" <td>307.339996</td>\n",
|
||||
" <td>307.589996</td>\n",
|
||||
" <td>291.359985</td>\n",
|
||||
" <td>304.179993</td>\n",
|
||||
" <td>304.179993</td>\n",
|
||||
" <td>8375200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2018-03-27</td>\n",
|
||||
" <td>304.000000</td>\n",
|
||||
" <td>304.269989</td>\n",
|
||||
" <td>277.179993</td>\n",
|
||||
" <td>279.179993</td>\n",
|
||||
" <td>279.179993</td>\n",
|
||||
" <td>13872000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2018-03-28</td>\n",
|
||||
" <td>264.579987</td>\n",
|
||||
" <td>268.679993</td>\n",
|
||||
" <td>252.100006</td>\n",
|
||||
" <td>257.779999</td>\n",
|
||||
" <td>257.779999</td>\n",
|
||||
" <td>21001400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2018-03-29</td>\n",
|
||||
" <td>256.489990</td>\n",
|
||||
" <td>270.959991</td>\n",
|
||||
" <td>248.210007</td>\n",
|
||||
" <td>266.130005</td>\n",
|
||||
" <td>266.130005</td>\n",
|
||||
" <td>15170700</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Date Open High Low Close Adj Close \\\n",
|
||||
"0 2018-03-23 311.250000 311.250000 300.450012 301.540009 301.540009 \n",
|
||||
"1 2018-03-26 307.339996 307.589996 291.359985 304.179993 304.179993 \n",
|
||||
"2 2018-03-27 304.000000 304.269989 277.179993 279.179993 279.179993 \n",
|
||||
"3 2018-03-28 264.579987 268.679993 252.100006 257.779999 257.779999 \n",
|
||||
"4 2018-03-29 256.489990 270.959991 248.210007 266.130005 266.130005 \n",
|
||||
"\n",
|
||||
" Volume \n",
|
||||
"0 6654900 \n",
|
||||
"1 8375200 \n",
|
||||
"2 13872000 \n",
|
||||
"3 21001400 \n",
|
||||
"4 15170700 "
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('../dataset/TSLA.csv')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def pct_change(x,period=1):\n",
|
||||
" x = np.array(x)\n",
|
||||
" return ((x[period:] - x[:-period]) / x[:-period])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 100/100 [00:00<00:00, 1819.19it/s]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"number_simulation = 100\n",
|
||||
"predict_day = 30\n",
|
||||
"returns = df.Close.pct_change()\n",
|
||||
"volatility = returns.std()\n",
|
||||
"results = pd.DataFrame()\n",
|
||||
"\n",
|
||||
"for i in tqdm(range(number_simulation)):\n",
|
||||
" prices = []\n",
|
||||
" prices.append(df.Close.iloc[-1])\n",
|
||||
" for d in range(predict_day):\n",
|
||||
" prices.append(prices[d] * (1 + np.random.normal(0, volatility)))\n",
|
||||
" results[i] = pd.Series(prices).values"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"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",
|
||||
"plt.plot(results)\n",
|
||||
"plt.ylabel('Value')\n",
|
||||
"plt.xlabel('Simulated days')\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1224x360 with 3 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"raveled = results.values.ravel()\n",
|
||||
"raveled.sort()\n",
|
||||
"cp_raveled = raveled.copy()\n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(17,5))\n",
|
||||
"plt.subplot(1,3,1)\n",
|
||||
"plt.plot(results)\n",
|
||||
"plt.ylabel('Value')\n",
|
||||
"plt.xlabel('Simulated days')\n",
|
||||
"plt.subplot(1,3,2)\n",
|
||||
"sns.distplot(df.Close,norm_hist=True)\n",
|
||||
"plt.title('$\\mu$ = %.2f, $\\sigma$ = %.2f'%(df.Close.mean(),df.Close.std()))\n",
|
||||
"plt.subplot(1,3,3)\n",
|
||||
"sns.distplot(raveled,norm_hist=True,label='monte carlo samples')\n",
|
||||
"sns.distplot(df.Close,norm_hist=True,label='real samples')\n",
|
||||
"plt.title('simulation $\\mu$ = %.2f, $\\sigma$ = %.2f'%(raveled.mean(),raveled.std()))\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
|
||||
}
|
||||
BIN
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|
After Width: | Height: | Size: 140 KiB |
+641
@@ -0,0 +1,641 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"import requests\n",
|
||||
"sns.set()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Pull sentiment, fear greed and BTC/USDT data from bitcurate API.\n",
|
||||
"\n",
|
||||
"Can read more about this API at https://doc.api.bitcurate.com/"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"dict_keys(['momentum', 'sentiment', 'timestamp', 'volatility'])"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"r = requests.get('https://datascience.api.dev.bitcurate.com/social_sentiment?query=(BTC%20OR%20bitcoin)%20AND%20binance&before_date=8/15/2019%200:0')\n",
|
||||
"sentiment = r.json()\n",
|
||||
"sentiment.keys()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"dict_keys(['fear', 'greed', 'label', 'timestamp'])"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"r = requests.get('https://datascience.api.dev.bitcurate.com/social_feargreed?query=(BTC%20OR%20bitcoin)%20AND%20binance&before_date=8/15/2019%200:0')\n",
|
||||
"feargreed = r.json()\n",
|
||||
"feargreed.keys()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"dict_keys(['close', 'high', 'low', 'momentum', 'open', 'timestamp', 'volatility', 'volume'])"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"r = requests.get('https://datascience.api.dev.bitcurate.com/pair?before_date=8/15/2019%200:0&pair=BTC/USDT&exchange=binance')\n",
|
||||
"btc = r.json()\n",
|
||||
"btc.keys()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.6/dist-packages/scipy/stats/stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
|
||||
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1080x216 with 4 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.figure(figsize=(15,3))\n",
|
||||
"plt.subplot(1,4,1)\n",
|
||||
"sns.distplot(btc['close'])\n",
|
||||
"plt.title('BTC/USDT histogram')\n",
|
||||
"plt.subplot(1,4,2)\n",
|
||||
"sns.distplot(sentiment['sentiment'])\n",
|
||||
"plt.title('sentiment histogram')\n",
|
||||
"plt.subplot(1,4,3)\n",
|
||||
"sns.distplot(feargreed['fear'])\n",
|
||||
"plt.title('fear histogram')\n",
|
||||
"plt.subplot(1,4,4)\n",
|
||||
"sns.distplot(feargreed['greed'])\n",
|
||||
"plt.title('greed histogram')\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"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>momentum_x</th>\n",
|
||||
" <th>sentiment</th>\n",
|
||||
" <th>timestamp</th>\n",
|
||||
" <th>volatility_x</th>\n",
|
||||
" <th>close</th>\n",
|
||||
" <th>high</th>\n",
|
||||
" <th>low</th>\n",
|
||||
" <th>momentum_y</th>\n",
|
||||
" <th>open</th>\n",
|
||||
" <th>volatility_y</th>\n",
|
||||
" <th>volume</th>\n",
|
||||
" <th>fear</th>\n",
|
||||
" <th>greed</th>\n",
|
||||
" <th>label</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>1.726086</td>\n",
|
||||
" <td>-0.305778</td>\n",
|
||||
" <td>2019-08-15 00:00:00</td>\n",
|
||||
" <td>70.186031</td>\n",
|
||||
" <td>10142.664026</td>\n",
|
||||
" <td>10854.806026</td>\n",
|
||||
" <td>9928.099609</td>\n",
|
||||
" <td>96.233277</td>\n",
|
||||
" <td>10843.803747</td>\n",
|
||||
" <td>0.005316</td>\n",
|
||||
" <td>5.790672e+08</td>\n",
|
||||
" <td>0.497674</td>\n",
|
||||
" <td>0.502326</td>\n",
|
||||
" <td>greed</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>1.726086</td>\n",
|
||||
" <td>0.184823</td>\n",
|
||||
" <td>2019-08-15 01:00:00</td>\n",
|
||||
" <td>70.186031</td>\n",
|
||||
" <td>10086.199284</td>\n",
|
||||
" <td>10739.315820</td>\n",
|
||||
" <td>9928.099609</td>\n",
|
||||
" <td>96.233277</td>\n",
|
||||
" <td>10689.825716</td>\n",
|
||||
" <td>0.005316</td>\n",
|
||||
" <td>5.641456e+08</td>\n",
|
||||
" <td>0.801826</td>\n",
|
||||
" <td>0.198174</td>\n",
|
||||
" <td>fear</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>1.726086</td>\n",
|
||||
" <td>0.726358</td>\n",
|
||||
" <td>2019-08-15 02:00:00</td>\n",
|
||||
" <td>70.186031</td>\n",
|
||||
" <td>10095.049805</td>\n",
|
||||
" <td>10712.450195</td>\n",
|
||||
" <td>9928.099609</td>\n",
|
||||
" <td>96.233277</td>\n",
|
||||
" <td>10611.636387</td>\n",
|
||||
" <td>0.005316</td>\n",
|
||||
" <td>5.416016e+08</td>\n",
|
||||
" <td>0.809082</td>\n",
|
||||
" <td>0.190918</td>\n",
|
||||
" <td>fear</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>1.726086</td>\n",
|
||||
" <td>0.100070</td>\n",
|
||||
" <td>2019-08-15 03:00:00</td>\n",
|
||||
" <td>70.186031</td>\n",
|
||||
" <td>10095.049805</td>\n",
|
||||
" <td>10697.000000</td>\n",
|
||||
" <td>9928.099609</td>\n",
|
||||
" <td>96.233277</td>\n",
|
||||
" <td>10640.818994</td>\n",
|
||||
" <td>0.005316</td>\n",
|
||||
" <td>5.159930e+08</td>\n",
|
||||
" <td>0.320567</td>\n",
|
||||
" <td>0.679433</td>\n",
|
||||
" <td>greed</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>1.726086</td>\n",
|
||||
" <td>0.219240</td>\n",
|
||||
" <td>2019-08-15 04:00:00</td>\n",
|
||||
" <td>70.186031</td>\n",
|
||||
" <td>10095.049805</td>\n",
|
||||
" <td>10697.000000</td>\n",
|
||||
" <td>9928.099609</td>\n",
|
||||
" <td>96.233277</td>\n",
|
||||
" <td>10642.563684</td>\n",
|
||||
" <td>0.005316</td>\n",
|
||||
" <td>5.106988e+08</td>\n",
|
||||
" <td>0.381714</td>\n",
|
||||
" <td>0.618286</td>\n",
|
||||
" <td>greed</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" momentum_x sentiment timestamp volatility_x close \\\n",
|
||||
"0 1.726086 -0.305778 2019-08-15 00:00:00 70.186031 10142.664026 \n",
|
||||
"1 1.726086 0.184823 2019-08-15 01:00:00 70.186031 10086.199284 \n",
|
||||
"2 1.726086 0.726358 2019-08-15 02:00:00 70.186031 10095.049805 \n",
|
||||
"3 1.726086 0.100070 2019-08-15 03:00:00 70.186031 10095.049805 \n",
|
||||
"4 1.726086 0.219240 2019-08-15 04:00:00 70.186031 10095.049805 \n",
|
||||
"\n",
|
||||
" high low momentum_y open volatility_y \\\n",
|
||||
"0 10854.806026 9928.099609 96.233277 10843.803747 0.005316 \n",
|
||||
"1 10739.315820 9928.099609 96.233277 10689.825716 0.005316 \n",
|
||||
"2 10712.450195 9928.099609 96.233277 10611.636387 0.005316 \n",
|
||||
"3 10697.000000 9928.099609 96.233277 10640.818994 0.005316 \n",
|
||||
"4 10697.000000 9928.099609 96.233277 10642.563684 0.005316 \n",
|
||||
"\n",
|
||||
" volume fear greed label \n",
|
||||
"0 5.790672e+08 0.497674 0.502326 greed \n",
|
||||
"1 5.641456e+08 0.801826 0.198174 fear \n",
|
||||
"2 5.416016e+08 0.809082 0.190918 fear \n",
|
||||
"3 5.159930e+08 0.320567 0.679433 greed \n",
|
||||
"4 5.106988e+08 0.381714 0.618286 greed "
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df_sentiment = pd.DataFrame(sentiment)\n",
|
||||
"df_btc = pd.DataFrame(btc)\n",
|
||||
"df_feargreed = pd.DataFrame(feargreed)\n",
|
||||
"merged = df_sentiment.merge(df_btc, on = 'timestamp')\n",
|
||||
"merged = merged.merge(df_feargreed, on = 'timestamp')\n",
|
||||
"merged.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Monte carlo simulation using sentiment and fear\n",
|
||||
"\n",
|
||||
"I want to simulate 30 hours ahead for 100 times. More simulation, more precise it will be."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"number_simulation = 100\n",
|
||||
"predict_hour = 30"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in sqrt\n",
|
||||
" This is separate from the ipykernel package so we can avoid doing imports until\n",
|
||||
"100%|██████████| 100/100 [00:00<00:00, 1995.27it/s]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"v = merged[['sentiment', 'fear', 'close']].pct_change(1).dropna().values\n",
|
||||
"variance = np.linalg.cholesky(np.cov(v.T))\n",
|
||||
"daily_vol = np.sqrt(variance)\n",
|
||||
"avg_daily_ret = np.mean(v,axis=0)\n",
|
||||
"daily_drift = avg_daily_ret - (variance / 2)\n",
|
||||
"drift = daily_drift - 0.5 * daily_vol ** 2\n",
|
||||
"\n",
|
||||
"results_close_fear = pd.DataFrame()\n",
|
||||
"\n",
|
||||
"for i in tqdm(range(number_simulation)):\n",
|
||||
" prices = []\n",
|
||||
" prices.append(merged['close'].iloc[-1])\n",
|
||||
" for d in range(predict_hour):\n",
|
||||
" shock = drift + daily_vol * np.random.normal()\n",
|
||||
" price = prices[-1] * np.exp(shock)[-1,-1]\n",
|
||||
" prices.append(price)\n",
|
||||
" results_close_fear[i] = prices"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Monte carlo simulation using sentiment and greed"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in sqrt\n",
|
||||
" \"\"\"\n",
|
||||
"100%|██████████| 100/100 [00:00<00:00, 1956.93it/s]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"number_simulation = 100\n",
|
||||
"predict_hour = 30\n",
|
||||
"v = merged[['sentiment', 'greed', 'close']].pct_change(1).dropna().values\n",
|
||||
"variance = np.linalg.cholesky(np.cov(v.T))\n",
|
||||
"daily_vol = np.sqrt(variance)\n",
|
||||
"avg_daily_ret = np.mean(v,axis=0)\n",
|
||||
"daily_drift = avg_daily_ret - (variance / 2)\n",
|
||||
"drift = daily_drift - 0.5 * daily_vol ** 2\n",
|
||||
"\n",
|
||||
"results_close_greed = pd.DataFrame()\n",
|
||||
"\n",
|
||||
"for i in tqdm(range(number_simulation)):\n",
|
||||
" prices = []\n",
|
||||
" prices.append(merged['close'].iloc[-1])\n",
|
||||
" for d in range(predict_hour):\n",
|
||||
" shock = drift + daily_vol * np.random.normal()\n",
|
||||
" price = prices[-1] * np.exp(shock)[-1,-1]\n",
|
||||
" prices.append(price)\n",
|
||||
" results_close_greed[i] = prices"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Monte carlo simulation univariate\n",
|
||||
"\n",
|
||||
"**Just historical close volatility, univariate**."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 100/100 [00:01<00:00, 79.04it/s]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"number_simulation = 100\n",
|
||||
"predict_hour = 30\n",
|
||||
"\n",
|
||||
"close = merged['close'].tolist()\n",
|
||||
"returns = pd.DataFrame(close).pct_change()\n",
|
||||
"last_price = close[-1]\n",
|
||||
"results = pd.DataFrame()\n",
|
||||
"avg_daily_ret = returns.mean()\n",
|
||||
"variance = returns.var()\n",
|
||||
"daily_vol = returns.std()\n",
|
||||
"daily_drift = avg_daily_ret - (variance / 2)\n",
|
||||
"drift = daily_drift - 0.5 * daily_vol ** 2\n",
|
||||
"\n",
|
||||
"results = pd.DataFrame()\n",
|
||||
"\n",
|
||||
"for i in tqdm(range(number_simulation)):\n",
|
||||
" prices = []\n",
|
||||
" prices.append(merged['close'].iloc[-1])\n",
|
||||
" for d in range(predict_hour):\n",
|
||||
" shock = drift + daily_vol * np.random.normal()\n",
|
||||
" price = prices[-1] * np.exp(shock)\n",
|
||||
" prices.append(price[0])\n",
|
||||
" results[i] = prices"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAABKcAAAFSCAYAAAAn57xwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3xU15nw8d80TVHvFSEhxKFjg8FgjHED95bYsZ3ESVzidTbJZje777bsZp3su9m8u5tsNmXjJE7iFhdcMW4YbAOm2XRTj5BQR72XGWnKff+4IxBCFRAS4vl+PvrAzG3nzNy59z7nnvNci2EYCCGEEEIIIYQQQggxFqxjXQAhhBBCCCGEEEIIcfGSxikhhBBCCCGEEEIIMWakcUoIIYQQQgghhBBCjBlpnBJCCCGEEEIIIYQQY0Yap4QQQgghhBBCCCHEmJHGKSGEEEIIIYQQQggxZqRxSgghhBBjTilVopS6fqzLMZEppQ4qpa4eZPoGpdQj57FIQ1JKZSul2pVStrEuy0SglHpcKfXcWJdDCCGE6Ms+1gUQFy+lVAnwiNZ6/RgXZcJSSh0Evqm13jDA9A3Ac1rrJ89nuQajlMoGDgGxWuvgWJfnXFBK3QX8HIgHlmmt94xxkcQ4Fj42ZgAZWuv6Xu/vAS4BcrXWJWe5jQ2c5W9fKXUD8D3gUsCH+bv9idb6zbMp2xmU42vA7wFv+K1a4D+11r9WSi0D3g2/bwE8QEevxWdqrcuGUxel1G+AXZjfzVSt9Zf7lMMA8rXWhUqpWcB/A5dh3ggsAv5Za/1OuHHoQ6AzvGgzsDVc5h29joE9IsPzGuHXN2mtPx7hxwSA1npWr/I+3l89xlrfawOtdRkQNUZlOfGdDjJPLvAUMB9oxPyenzk/JRRCiFNJfCUuZNI4NQ5IIDLicnwNCUSGRQKREZdltAKR/wK+pbVefa7KKia8YuB+4BcASqk5mMezcUEpdTfwB+C7wG1AG7AM+DIwonOCUsqutQ6cZZG2aa2vDK/vUmCTUmp7+NgZFX4/B/Nzjeu9vRHU5SbgX4Hh9CxaA/wauDX8eiHmOanHca11llLKAmQCjwIfK6Vu0Vp/QK9jYPi4NG+w45IYUz8CSoDrgUQg61yu/Bz9PoS46Eh8NeJyfA2Jr8QYk8ap8UMCkZGRQESMlTMJRCYDB891QSRomdCeBb5C+JwAfBV4Bvi/PTMopWLD02/CvOD6HfAjrXUofJH5CLAdeBjzou3PtdbvKqX+DfOYt1gp9TPgKa31t5RS08PrWwDUYV4ArupbsPBx7KfAv/a5IN8Y/kMplRcuzzzMC8G1mL04m8PTSzCPmV8yX6rIPttwAv8P+EL4rVXA32mtu4b64LTWe5RSh4EZwKC9FIdTl/B8c4FmrXWFUmrQ7SulkoBc4Hda6+7w21sGKKsBVADfV0olYNb5skE3cPr2rgF+rrWeE369DvO8tzD8+mPM4OCNnpsBmNd//whYlFJ3AkVa63nhVU5WSm0B5gLbgC/2Duz61PMp4EoghHmMWx7e/zIw96WrgHbgv7XWPw8v9zgwEzNwuQsoA76qtd6plHoWyAbWKKWCwA8xv/tiwKG1DoSDwc3AteEyfgR8DbN36m2ABu7pCToH26+VUk9hBlg54bIeCte3SCm1KVzVfeHz8sNa65f6+Qr8QIXW2g9Uh/8GpZT6Cub1RRTwM8zf6CNa6/Xhz2d2+PO5HfiuUuoPwN8CXwfigA+Ax7TWjeH1Lcbcj2cCpcB3enpM97mhsj38+QhxsZD4amQkvhJjShqnxg8JRCQQGRYJRC6cQCS8XzcAtvB6q7XWeUN8XouA/8Hcn73Aq8B3e/atcNm+Bfwl5veaO9D2xQVtO/CAUmoGUADcByyl1zkBcx+KBaZgNpS+D1Rh3vkEuBx4GkjCvGD7vVIqU2v9PaXUUnrd7Q0fk9cB38c8x8wB1imlDmite995BFDAJOCVQcpvAf4d2ATEYO7Hj2Putz3uB24B6sO/9d7Lfw9YjHl32wBWA/8E/PMg2yRcl4XANGDnUPMOsy4ANwNvD2N9YP7mC4HnlFJPYl7s1wxjudeAP1dKRWqtO4ac+6TtQH74GN2CeZwMKKWigQDmOeaUu8Na6/eUUj+i/960X8TcB8ox75T/DfD3/Wz3rzHPZ8nh14sBQyllxQwoVmN+x1nAeqWU1lqvDc97O/A54EHMffqXwGKt9QPhO/QnetOGA6G+7gNuAOoxz1vbgD/HvHb6A/AvwIPD3K/vC0/bjfl7+TfgPq31VcMMZnYA/6WU+lhr/d4g8xGuz0zgf4EbgU8xb3hk9pntDuAezOtCJ/Bt4E5gOeZ57efAr4D7lVKZmPvmA8B7wHXAq0qp6VrrOuD58OezEvOY8DbmdyPExUDiK4mvxAVEEqKPH9uBGKXUDGUm/bwP6JuwsncgshzzYPtgr+mXYwbqScB/YAYiFq319zAvTL+ltY4KHzh7LtieB1LC2/vf8EVTXyMJRDIwD2KTMAOR3noCkVNa2sN6ByLzgEWYgciQxlkgcqdSKnWYy70GzO97IhmGE4GIUsqBGYhkKKWilVJuBghEMC+AXwrvA/N6Tf4i5n6UAkRgBiL96R2IpGI2dvUORPZhXmBfB/ylMrv29rgdeBHzju+bmIEIWusHMBurbguX6z8G2PZ9mBfemUAe5oX2H4EE4DBmIMIw9+v7gB9g5n8qxAxE0FpfFZ4+L1yW/hqmwAxE/kYpdeMA00/QWndprXvu2swLN0wN9XkFgb/C/B0vCU//8z6rvhPz997f71VMHD0X1Ssw9/PKngm9zhP/oLVuCzfO/gTzd9KjVGv9O23mbnsaSMf87fbnVqBEa/1HrXVAm3nRXsUMkPtKDP9bNVDBtdaFWut14d9AHeZF6/I+s/1ca12utfb2s4ovAT/UWteGl/9Bn7r1tVgp1ayUasMM+J8Fjg4y/7DrEnYL8M4w1tdzkXwNZg/LnwBVSqlNSqn8IRY9jnkujRvOdnptz4t5XLoKMxjah3kBvxTzvHpUa90wglX+UWtdEF7vKszzcn/8mPvUZK21X2v9cbjuC4FkrfUPtdbdWutjmMHVfb2W3ay1fie8bz6Led4fiT9qrYu01i2YDWhFWuv14WuLlzGHlMDw9uvXtdafhpf90yD1PU24kfe7mA0/T/acF5RSU5VS9eGAra+7gTVa683hgOv7nBxm0mOb1voNrXUo/D08BnxPa10RDiofB+5WStkxeyW8E/48Q1rrdZjXQzcrc1jLQszguEtrvQnz/CPExULiK4mvxAVEek6NLz2ByEYGDkQu0Vq3AW1KqZ5ApOcueanW+nfh+Z/GvDOXSv89O05csIVf71FK9Vyw/aDPvMMKRDAPIAB1SqmfEm4w6OXnWuvyAVbxJeDbWuvacPl/APyGge+SL1ZKNWP2SInCbOw414HIPw5jfWitjXBvpr/HDERylVKbMXveDFam3oHIsO+Sa629SqmeQOQ4ZiDSjBmIdHGGgQiAUmoVZkNSf3oHIoWEG8DCPX2StdY/DM93TCnVE4j03CXfrLV+Jzz/s5zae2K4ZSwKL/8u5tj2nrvqL2N2D4bh7deva60/DS/7J8ygeVj6BCJ/Uko9Eu6BMBXzAig5HJwN5kTgFn59yueltd7Va94SZY7NX4459KPHv+vwcA4xoT2L2fMoF/NOb29JgANzCE+PUk7tgXHi2K+17gzfpRwon9tk4PLwcbWHPVyGvnqOL+mYvRxPE76I/B/Mu8rRmDfDmvrMNtD5AMwL8b51yxhk/u365FCEVOAFzAb5fxhkGRheXeKA6Zi5LMDsjeToM0/Paz+A1roCs4cjSqlJwG8xv8Mlg5QlE7ORonmQeQayEbga8wbCRszPejnmOWHjwIv1q/c1QycD7zP/iRkkvR/et36rtf4x5r6U0WdfsnHqTZO+23CpkQ1J6d0TzdvP654yD2e/Hm59+/MtzHpvVOaDL95RSj2Aee310QDngwx67fvh32bfc3bf38Zk4HWlVKjXe8HwdiYD9yilbus1zYHZyzgDaNKn9sQrxQwihbhYSHwl8ZW4QEjj1PgigchJEogMTgKRU1+P50Ckr0E/L6XUNMwGs8sw8yLYMRNH9jbYb0lMEFrrUqVUMeadxof7TK7HPP5M5mTCz2x6XXQPoe++Wg5s1FqvGE7RwvN/HjPZf39+FN7GHK11ozKHE/9yiDL0dpxTc7Vlh98bunBa14SDgW8w9DlhOHW5AfhQn3x6aBnmkOLecjHPFad9/lrrcqXUrzDPU4O5C9itRzakr8dGzIv3MuDHmOeE32GeE341wDLDOV4NKBzI/TXw10qp2cCH4Rsn5UCx1nqonmIDOaty9TGS/fpM2AlfH2gzAe+9mHftm4B7B1imCrOXAQDhHs+Jfebp7/f5kNb6tCEtSqly4Fmt9df7mTYZiFenDhXN7mf9QkxkEl+dJPGVGNekcWockUBEApERkECkf+MxEOlrqM/r15hj++/XWrcppf4ScxhIbxJYXDweBuK11h3hITwAaK2D4Z6O/6bM5MoJmL36Bjqu9VWDOYShx1vAj8ONrS+G37sEaNdaH+69YPhu5ncxhzY0YA6TageuAL6itX4U8yK6BWhRZk6c/zOiWpvHz38KH2MMzKFPfYdi9EsplYh5fB3yIQTDrEvfYQjvAb/o9VlFY54DX9Vm7qx4zN6hzwLHML+bhzB7V/YtqwUzUHgk/DdQz9WhbMVs8EgDPtVad/c0TDDwsakGWKGUsmqtQwPMMyCl1K3AEcynJ7Vg9uQJYQ6rbFNK/R1mbqRuzOEobq31jmGsuu++eTaGvV8PUZaBck69jDlkZwNmbsQqzOGcCvMaoT+vANuVUldgDpd5nFMT/PbnCczf+lfD14rJwBXafALsc8AOZQ4NX495jloMFIbn3Qn8QCn1j5hDem5jhImWhbiQSXwl8ZW4cEjj1PgjgYgLine truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1440x360 with 3 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.figure(figsize=(20,5))\n",
|
||||
"plt.subplot(1,3,1)\n",
|
||||
"plt.plot(results_close_fear)\n",
|
||||
"plt.ylabel('Value')\n",
|
||||
"plt.xlabel('Simulated hours')\n",
|
||||
"plt.title('Monte Carlo BTC/USDT with sentiment & fear')\n",
|
||||
"plt.subplot(1,3,2)\n",
|
||||
"plt.plot(results_close_greed)\n",
|
||||
"plt.ylabel('Value')\n",
|
||||
"plt.xlabel('Simulated hours')\n",
|
||||
"plt.title('Monte Carlo BTC/USDT with sentiment & greed')\n",
|
||||
"plt.subplot(1,3,3)\n",
|
||||
"plt.plot(results)\n",
|
||||
"plt.ylabel('Value')\n",
|
||||
"plt.xlabel('Simulated hours')\n",
|
||||
"plt.title('Monte Carlo BTC/USDT')\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Value-at-Risk"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"99% VaR for sentiment & fear: 3460.0421119349726\n",
|
||||
"99% VaR for sentiment & greed: 3587.808611402619\n",
|
||||
"99% VaR: 9061.239256181689\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"price_array = results_close_fear.iloc[-1, :]\n",
|
||||
"price_array = sorted(price_array, key = int)\n",
|
||||
"var99 = np.percentile(price_array, 0.99)\n",
|
||||
"print('99% VaR for sentiment & fear:', var99)\n",
|
||||
"\n",
|
||||
"price_array = results_close_greed.iloc[-1, :]\n",
|
||||
"price_array = sorted(price_array, key = int)\n",
|
||||
"var99 = np.percentile(price_array, 0.99)\n",
|
||||
"print('99% VaR for sentiment & greed:', var99)\n",
|
||||
"\n",
|
||||
"price_array = results.iloc[-1, :]\n",
|
||||
"price_array = sorted(price_array, key = int)\n",
|
||||
"var99 = np.percentile(price_array, 0.99)\n",
|
||||
"print('99% VaR:', var99)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**If you observed from both `fear` and `greed` histograms, some of simulations dropped less than 5k of `BTC/USDT`. What is the probability going to happen for going less than 5k based on the monte carlo?**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"probability < 5k for sentiment & fear 0.14\n",
|
||||
"probability < 5k for sentiment & greed 0.09\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"v = results_close_fear.iloc[-1, :].values\n",
|
||||
"print('probability < 5k for sentiment & fear', v[v < 5000].shape[0] / number_simulation)\n",
|
||||
"v = results_close_greed.iloc[-1, :].values\n",
|
||||
"print('probability < 5k for sentiment & greed', v[v < 5000].shape[0] / number_simulation)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"I believe it is pretty reasonable why probability on `fear` is higher than `greed`, `fear` factors can caused bearish."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
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||||
"<Figure size 1224x360 with 3 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
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|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"raveled = results.values.ravel()\n",
|
||||
"raveled.sort()\n",
|
||||
"cp_raveled = raveled.copy()\n",
|
||||
"\n",
|
||||
"raveled_close_fear = results_close_fear.values.ravel()\n",
|
||||
"raveled_close_fear.sort()\n",
|
||||
"cp_raveled_close_fear = raveled_close_fear.copy()\n",
|
||||
"\n",
|
||||
"raveled_close_greed = results_close_greed.values.ravel()\n",
|
||||
"raveled_close_greed.sort()\n",
|
||||
"cp_raveled_close_greed = raveled_close_greed.copy()\n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(17,5))\n",
|
||||
"plt.subplot(1,3,1)\n",
|
||||
"plt.plot(results)\n",
|
||||
"plt.ylabel('Value')\n",
|
||||
"plt.xlabel('Simulated days')\n",
|
||||
"plt.subplot(1,3,2)\n",
|
||||
"sns.distplot(close,norm_hist=True)\n",
|
||||
"plt.title('$\\mu$ = %.2f, $\\sigma$ = %.2f'%(np.mean(close),np.std(close)))\n",
|
||||
"plt.subplot(1,3,3)\n",
|
||||
"sns.distplot(raveled,norm_hist=True,label='univariate monte carlo samples')\n",
|
||||
"sns.distplot(raveled_close_fear,norm_hist=True,label='multivariate monte carlo samples sentiment & fear')\n",
|
||||
"sns.distplot(raveled_close_greed,norm_hist=True,label='multivariate monte carlo samples sentiment & greed')\n",
|
||||
"sns.distplot(close,norm_hist=True,label='real samples')\n",
|
||||
"plt.title('simulation $\\mu$ = %.2f, $\\sigma$ = %.2f'%(raveled.mean(),raveled.std()))\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"
|
||||
},
|
||||
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|
||||
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|
||||
"name": "ipython",
|
||||
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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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||||
"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/plain": [
|
||||
"['../dataset/AMD.csv',\n",
|
||||
" '../dataset/FB.csv',\n",
|
||||
" '../dataset/TSLA.csv',\n",
|
||||
" '../dataset/TWTR.csv',\n",
|
||||
" '../dataset/MONDY.csv']"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"directory = '../dataset/'\n",
|
||||
"stocks = ['AMD.csv', 'FB.csv', 'TSLA.csv', 'TWTR.csv', 'MONDY.csv']\n",
|
||||
"stocks = [directory + s for s in stocks]\n",
|
||||
"stocks"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dfs = [pd.read_csv(s)[['Date', 'Close']] for s in stocks]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"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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||||
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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>Close_x</th>\n",
|
||||
" <th>Close_y</th>\n",
|
||||
" <th>Close_x</th>\n",
|
||||
" <th>Close_y</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>16.270000</td>\n",
|
||||
" <td>207.320007</td>\n",
|
||||
" <td>318.869995</td>\n",
|
||||
" <td>44.490002</td>\n",
|
||||
" <td>56.889999</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>16.580000</td>\n",
|
||||
" <td>207.229996</td>\n",
|
||||
" <td>310.100006</td>\n",
|
||||
" <td>44.259998</td>\n",
|
||||
" <td>56.639999</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>16.870001</td>\n",
|
||||
" <td>209.990005</td>\n",
|
||||
" <td>322.690002</td>\n",
|
||||
" <td>44.709999</td>\n",
|
||||
" <td>57.730000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>16.850000</td>\n",
|
||||
" <td>209.360001</td>\n",
|
||||
" <td>323.850006</td>\n",
|
||||
" <td>43.340000</td>\n",
|
||||
" <td>57.810001</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>16.709999</td>\n",
|
||||
" <td>208.089996</td>\n",
|
||||
" <td>320.230011</td>\n",
|
||||
" <td>43.439999</td>\n",
|
||||
" <td>52.380001</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Close_x Close_y Close_x Close_y Close\n",
|
||||
"0 16.270000 207.320007 318.869995 44.490002 56.889999\n",
|
||||
"1 16.580000 207.229996 310.100006 44.259998 56.639999\n",
|
||||
"2 16.870001 209.990005 322.690002 44.709999 57.730000\n",
|
||||
"3 16.850000 209.360001 323.850006 43.340000 57.810001\n",
|
||||
"4 16.709999 208.089996 320.230011 43.439999 52.380001"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from functools import reduce\n",
|
||||
"data = reduce(lambda left,right: pd.merge(left,right,on='Date'), dfs).iloc[:, 1:]\n",
|
||||
"data.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"returns = data.pct_change()\n",
|
||||
"mean_daily_returns = returns.mean()\n",
|
||||
"cov_matrix = returns.cov()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
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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>Close_x</th>\n",
|
||||
" <th>Close_y</th>\n",
|
||||
" <th>Close_x</th>\n",
|
||||
" <th>Close_y</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>Close_x</th>\n",
|
||||
" <td>0.002342</td>\n",
|
||||
" <td>0.000316</td>\n",
|
||||
" <td>0.000368</td>\n",
|
||||
" <td>0.000387</td>\n",
|
||||
" <td>0.000215</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Close_y</th>\n",
|
||||
" <td>0.000316</td>\n",
|
||||
" <td>0.000694</td>\n",
|
||||
" <td>0.000216</td>\n",
|
||||
" <td>0.000463</td>\n",
|
||||
" <td>0.000043</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Close_x</th>\n",
|
||||
" <td>0.000368</td>\n",
|
||||
" <td>0.000216</td>\n",
|
||||
" <td>0.001643</td>\n",
|
||||
" <td>0.000516</td>\n",
|
||||
" <td>0.000004</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Close_y</th>\n",
|
||||
" <td>0.000387</td>\n",
|
||||
" <td>0.000463</td>\n",
|
||||
" <td>0.000516</td>\n",
|
||||
" <td>0.001240</td>\n",
|
||||
" <td>0.000177</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <td>0.000215</td>\n",
|
||||
" <td>0.000043</td>\n",
|
||||
" <td>0.000004</td>\n",
|
||||
" <td>0.000177</td>\n",
|
||||
" <td>0.000985</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Close_x Close_y Close_x Close_y Close\n",
|
||||
"Close_x 0.002342 0.000316 0.000368 0.000387 0.000215\n",
|
||||
"Close_y 0.000316 0.000694 0.000216 0.000463 0.000043\n",
|
||||
"Close_x 0.000368 0.000216 0.001643 0.000516 0.000004\n",
|
||||
"Close_y 0.000387 0.000463 0.000516 0.001240 0.000177\n",
|
||||
"Close 0.000215 0.000043 0.000004 0.000177 0.000985"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"cov_matrix"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"num_portfolios = 25000\n",
|
||||
"results = np.zeros((3,num_portfolios))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for i in range(num_portfolios):\n",
|
||||
" weights = np.random.random(cov_matrix.shape[0])\n",
|
||||
" weights /= np.sum(weights)\n",
|
||||
" portfolio_return = np.sum(mean_daily_returns * weights) * 252\n",
|
||||
" portfolio_std_dev = np.sqrt(np.dot(weights.T,np.dot(cov_matrix, weights))) * np.sqrt(252)\n",
|
||||
" results[0,i] = portfolio_return\n",
|
||||
" results[1,i] = portfolio_std_dev\n",
|
||||
" results[2,i] = results[0,i] / results[1,i]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"results_frame = pd.DataFrame(results.T,columns=['ret','stdev','sharpe'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 504x360 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.figure(figsize = (7, 5))\n",
|
||||
"plt.scatter(results_frame.stdev,results_frame.ret,c=results_frame.sharpe,cmap='RdYlBu')\n",
|
||||
"plt.colorbar()\n",
|
||||
"plt.xlabel('volatility')\n",
|
||||
"plt.ylabel('returns')\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
|
||||
}
|
||||
BIN
Binary file not shown.
|
After Width: | Height: | Size: 35 KiB |
Reference in new issue
Block a user