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 requests\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Let say\n",
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"\n",
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"Let say, TWTR.csv is my realtime data (follow [realtime-evolution-strategy.ipynb](realtime-evolution-strategy.ipynb)), remember, we trained using `Close`, and `Volume` data.\n",
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"\n",
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"So every request means new daily data.\n",
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"\n",
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"You can improve the code to bind historical data with your own database or any websocket streaming data. Imagination is your limit now."
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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>2018-05-23</td>\n",
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" <td>32.700001</td>\n",
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" <td>33.430000</td>\n",
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" <td>32.599998</td>\n",
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" <td>33.419998</td>\n",
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" <td>33.419998</td>\n",
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" <td>13407500</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>2018-05-24</td>\n",
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" <td>33.439999</td>\n",
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" <td>33.759998</td>\n",
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" <td>33.119999</td>\n",
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" <td>33.520000</td>\n",
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" <td>33.520000</td>\n",
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" <td>14491900</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>2018-05-25</td>\n",
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" <td>33.540001</td>\n",
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" <td>33.990002</td>\n",
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" <td>33.310001</td>\n",
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" <td>33.630001</td>\n",
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" <td>33.630001</td>\n",
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" <td>10424400</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>2018-05-29</td>\n",
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" <td>33.419998</td>\n",
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" <td>34.830002</td>\n",
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" <td>33.349998</td>\n",
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" <td>34.040001</td>\n",
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" <td>34.040001</td>\n",
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" <td>22086700</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>2018-05-30</td>\n",
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" <td>34.200001</td>\n",
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" <td>34.660000</td>\n",
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" <td>34.080002</td>\n",
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" <td>34.360001</td>\n",
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" <td>34.360001</td>\n",
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" <td>14588200</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 Volume\n",
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"0 2018-05-23 32.700001 33.430000 32.599998 33.419998 33.419998 13407500\n",
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"1 2018-05-24 33.439999 33.759998 33.119999 33.520000 33.520000 14491900\n",
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"2 2018-05-25 33.540001 33.990002 33.310001 33.630001 33.630001 10424400\n",
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"3 2018-05-29 33.419998 34.830002 33.349998 34.040001 34.040001 22086700\n",
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"4 2018-05-30 34.200001 34.660000 34.080002 34.360001 34.360001 14588200"
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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('TWTR.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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"source": [
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"close = df['Close'].tolist()\n",
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"volume = df['Volume'].tolist()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Check balance"
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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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"text/plain": [
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"93.51999599999999"
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]
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},
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"execution_count": 4,
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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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"requests.get('http://localhost:8005/balance').json()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This is the initial capital we have for now, you can check [agent.ipynb](https://github.com/huseinzol05/Stock-Prediction-Models/blob/master/realtime-agent/agent.ipynb) how I defined it, or you can overwrite it."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Trading"
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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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"text/plain": [
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"'[33.419998, 13407500]'"
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]
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},
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"execution_count": 5,
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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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"import json\n",
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"\n",
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"data = json.dumps([close[0], volume[0]])\n",
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"data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Remember, my last training session was only used `Close` and `Volume`, you need to edit it to accept any kind of parameters."
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'action': 'fail',\n",
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" 'balance': 93.51999599999999,\n",
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" 'status': 'data not enough to trade',\n",
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" 'timestamp': '2019-08-31 02:40:10.625022'}"
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]
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},
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"execution_count": 6,
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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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"requests.get('http://localhost:8005/trade?data='+data).json()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Reason why you got 'data not enough to trade', because, the agent waiting another data to complete the queue, atleast same as `window_size` size.\n",
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"\n",
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"Last time I defined `window_size` is 20, means, it only look back 20 historical data to trade."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Assume now, you have 100 times new datapoints going in, you want to trade these datapoints."
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.690977'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.695210'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.699224'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.702625'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.705349'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.708217'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.711154'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.714063'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.716941'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.719288'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.721568'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.723917'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.726292'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.728538'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.730832'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.733636'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.736353'}\n",
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"{'action': 'fail', 'balance': 93.51999599999999, 'status': 'data not enough to trade', 'timestamp': '2019-08-31 02:40:10.738558'}\n",
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"{'action': 'buy', 'balance': 48.56999499999999, 'status': 'buy 1 unit, cost 44.950001', 'timestamp': '2019-08-31 02:40:10.741146'}\n",
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"{'action': 'buy', 'balance': 2.4399939999999916, 'status': 'buy 1 unit, cost 46.130001', 'timestamp': '2019-08-31 02:40:10.743822'}\n",
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||||
"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.749816'}\n",
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||||
"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.752503'}\n",
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||||
"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.757819'}\n",
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||||
"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.760163'}\n",
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||||
"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.762512'}\n",
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||||
"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.764850'}\n",
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||||
"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.767202'}\n",
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"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.769545'}\n",
|
||||
"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.771889'}\n",
|
||||
"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.774284'}\n",
|
||||
"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.776661'}\n",
|
||||
"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.779031'}\n",
|
||||
"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.781386'}\n",
|
||||
"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.783746'}\n",
|
||||
"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.786126'}\n",
|
||||
"{'action': 'nothing', 'balance': 2.4399939999999916, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.788476'}\n",
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||||
"{'action': 'sell', 'balance': 46.69999199999999, 'gain': -0.6900030000000044, 'investment': -1.5350455720790848, 'status': 'sell 1 unit, price 44.259998', 'timestamp': '2019-08-31 02:40:10.790902'}\n",
|
||||
"{'action': 'buy', 'balance': 1.9899929999999912, 'status': 'buy 1 unit, cost 44.709999', 'timestamp': '2019-08-31 02:40:10.793386'}\n",
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||||
"{'action': 'sell', 'balance': 45.329992999999995, 'gain': -2.7900009999999966, 'investment': -6.048126901189525, 'status': 'sell 1 unit, price 43.340000', 'timestamp': '2019-08-31 02:40:10.795829'}\n",
|
||||
"{'action': 'sell', 'balance': 88.769992, 'gain': -1.269999999999996, 'investment': -2.8405279096517004, 'status': 'sell 1 unit, price 43.439999', 'timestamp': '2019-08-31 02:40:10.798273'}\n",
|
||||
"{'action': 'buy', 'balance': 45.349994, 'status': 'buy 1 unit, cost 43.419998', 'timestamp': '2019-08-31 02:40:10.800601'}\n",
|
||||
"{'action': 'nothing', 'balance': 45.349994, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.802931'}\n",
|
||||
"{'action': 'nothing', 'balance': 45.349994, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.805260'}\n",
|
||||
"{'action': 'sell', 'balance': 89.569995, 'gain': 0.8000030000000038, 'investment': 1.8424759024632011, 'status': 'sell 1 unit, price 44.220001', 'timestamp': '2019-08-31 02:40:10.807660'}\n",
|
||||
"{'action': 'nothing', 'balance': 89.569995, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.809990'}\n",
|
||||
"{'action': 'nothing', 'balance': 89.569995, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.812298'}\n",
|
||||
"{'action': 'nothing', 'balance': 89.569995, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.814634'}\n",
|
||||
"{'action': 'buy', 'balance': 57.699994000000004, 'status': 'buy 1 unit, cost 31.870001', 'timestamp': '2019-08-31 02:40:10.816950'}\n",
|
||||
"{'action': 'nothing', 'balance': 57.699994000000004, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.819333'}\n",
|
||||
"{'action': 'sell', 'balance': 90.519994, 'gain': 0.9499989999999983, 'investment': 2.980856511425896, 'status': 'sell 1 unit, price 32.820000', 'timestamp': '2019-08-31 02:40:10.821753'}\n",
|
||||
"{'action': 'nothing', 'balance': 90.519994, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.824076'}\n",
|
||||
"{'action': 'nothing', 'balance': 90.519994, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.826414'}\n",
|
||||
"{'action': 'nothing', 'balance': 90.519994, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.828726'}\n",
|
||||
"{'action': 'nothing', 'balance': 90.519994, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.831048'}\n",
|
||||
"{'action': 'nothing', 'balance': 90.519994, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.833373'}\n",
|
||||
"{'action': 'nothing', 'balance': 90.519994, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.835695'}\n",
|
||||
"{'action': 'nothing', 'balance': 90.519994, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.838029'}\n",
|
||||
"{'action': 'nothing', 'balance': 90.519994, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.840351'}\n",
|
||||
"{'action': 'nothing', 'balance': 90.519994, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.842692'}\n",
|
||||
"{'action': 'nothing', 'balance': 90.519994, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.845018'}\n",
|
||||
"{'action': 'nothing', 'balance': 90.519994, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.847350'}\n",
|
||||
"{'action': 'nothing', 'balance': 90.519994, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.849668'}\n",
|
||||
"{'action': 'nothing', 'balance': 90.519994, 'status': 'do nothing', 'timestamp': '2019-08-31 02:40:10.852011'}\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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||||
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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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|
||||
"{'action': 'sell', 'balance': 79.079998, 'gain': 0.7299989999999994, 'investment': 2.4148164075421743, 'status': 'sell 1 unit, price 30.959999', 'timestamp': '2019-08-31 02:40:11.140527'}\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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|
||||
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|
||||
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|
||||
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|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for i in range(200):\n",
|
||||
" data = json.dumps([close[i], volume[i]])\n",
|
||||
" requested = requests.get('http://localhost:8005/trade?data=' + data).json()\n",
|
||||
" print(requested)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
Reference in New Issue
Block a user