tabular_attempt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-11-19T22:48:14.794178100Z",
     "start_time": "2023-11-19T22:48:08.921048100Z"
    }
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "from finrl.meta.env_portfolio_allocation import env_portfolio\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import gym\n",
    "from typing import Callable, Tuple\n",
    "from collections import defaultdict\n",
    "from tqdm import trange\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "def generate_episode(env: gym.Env, policy: Callable, es: bool = False):\n",
    "    \"\"\"A function to generate one episode and collect the sequence of (s, a, r) tuples\n",
    "\n",
    "    This function will be useful for implementing the MC methods\n",
    "\n",
    "    Args:\n",
    "        env (gym.Env): a Gym API compatible environment\n",
    "        policy (Callable): A function that represents the policy.\n",
    "        es (bool): Whether to use exploring starts or not\n",
    "    \"\"\"\n",
    "    episode = []\n",
    "    state = env.reset()\n",
    "    while True:\n",
    "        if es and len(episode) == 0:\n",
    "            action = env.action_space.sample()\n",
    "        else:\n",
    "            action = policy(state)\n",
    "\n",
    "        next_state, reward, done, _ = env.step(action)\n",
    "        episode.append((state, action, reward))\n",
    "        if done:\n",
    "            break\n",
    "        state = next_state\n",
    "\n",
    "    return episode\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-19T22:48:14.818180900Z",
     "start_time": "2023-11-19T22:48:14.796189600Z"
    }
   },
   "id": "e145df452125bc37"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wacho\\Programming\\RL_Finance\\venv\\lib\\site-packages\\pyfolio\\pos.py:26: UserWarning: Module \"zipline.assets\" not found; mutltipliers will not be applied to position notionals.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AXP', 'AMGN', 'AAPL', 'BA', 'CAT', 'CSCO', 'CVX', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'KO', 'JPM', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PG', 'TRV', 'UNH', 'CRM', 'VZ', 'V', 'WBA', 'WMT', 'DIS', 'DOW']\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "############STUFF GRABBED FROM FINRL TUTORIAL\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "matplotlib.use('Agg')\n",
    "%matplotlib inline\n",
    "import datetime\n",
    "\n",
    "from finrl import config\n",
    "from finrl import config_tickers\n",
    "from finrl.meta.preprocessor.yahoodownloader import YahooDownloader\n",
    "from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
    "from finrl.meta.env_portfolio_allocation.env_portfolio import StockPortfolioEnv\n",
    "from finrl.agents.stablebaselines3.models import DRLAgent\n",
    "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline, convert_daily_return_to_pyfolio_ts\n",
    "from finrl.meta.data_processor import DataProcessor\n",
    "from finrl.meta.data_processors.processor_yahoofinance import YahooFinanceProcessor\n",
    "import sys\n",
    "\n",
    "## 2.4. Create Folders\n",
    "import os\n",
    "\n",
    "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
    "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
    "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
    "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
    "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
    "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
    "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
    "    os.makedirs(\"./\" + config.RESULTS_DIR)\n",
    "\n",
    "print(config_tickers.DOW_30_TICKER)\n",
    "#dp = YahooFinanceProcessor()\n",
    "#df = dp.download_data\n",
    "\n",
    "df = YahooDownloader(start_date='2008-01-01',\n",
    "                     end_date='2021-10-31',\n",
    "                     ticker_list=config_tickers.DOW_30_TICKER).fetch_data()\n",
    "\n",
    "df.head()\n",
    "df.shape\n"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-11-19T22:48:14.816181600Z"
    }
   },
   "id": "fa1e3d58f36f10e4"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully added technical indicators\n"
     ]
    },
    {
     "data": {
      "text/plain": "             date       open       high        low      close      volume  \\\n0      2008-01-02   7.116786   7.152143   6.876786   5.898640  1079178800   \n3483   2008-01-02  46.599998  47.040001  46.259998  33.761406     7934400   \n6966   2008-01-02  52.090000  52.320000  50.790001  39.588005     8053700   \n10449  2008-01-02  87.570000  87.839996  86.000000  63.481621     4303000   \n13932  2008-01-02  72.559998  72.669998  70.050003  45.605480     6337800   \n\n        tic  day  macd   boll_ub   boll_lb  rsi_30     cci_30  dx_30  \\\n0      AAPL    2   0.0  5.903862  5.896146   100.0 -66.666667  100.0   \n3483   AMGN    2   0.0  5.903862  5.896146   100.0 -66.666667  100.0   \n6966    AXP    2   0.0  5.903862  5.896146   100.0 -66.666667  100.0   \n10449    BA    2   0.0  5.903862  5.896146   100.0 -66.666667  100.0   \n13932   CAT    2   0.0  5.903862  5.896146   100.0 -66.666667  100.0   \n\n       close_30_sma  close_60_sma  \n0          5.898640      5.898640  \n3483      33.761406     33.761406  \n6966      39.588005     39.588005  \n10449     63.481621     63.481621  \n13932     45.605480     45.605480  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>open</th>\n      <th>high</th>\n      <th>low</th>\n      <th>close</th>\n      <th>volume</th>\n      <th>tic</th>\n      <th>day</th>\n      <th>macd</th>\n      <th>boll_ub</th>\n      <th>boll_lb</th>\n      <th>rsi_30</th>\n      <th>cci_30</th>\n      <th>dx_30</th>\n      <th>close_30_sma</th>\n      <th>close_60_sma</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2008-01-02</td>\n      <td>7.116786</td>\n      <td>7.152143</td>\n      <td>6.876786</td>\n      <td>5.898640</td>\n      <td>1079178800</td>\n      <td>AAPL</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>5.903862</td>\n      <td>5.896146</td>\n      <td>100.0</td>\n      <td>-66.666667</td>\n      <td>100.0</td>\n      <td>5.898640</td>\n      <td>5.898640</td>\n    </tr>\n    <tr>\n      <th>3483</th>\n      <td>2008-01-02</td>\n      <td>46.599998</td>\n      <td>47.040001</td>\n      <td>46.259998</td>\n      <td>33.761406</td>\n      <td>7934400</td>\n      <td>AMGN</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>5.903862</td>\n      <td>5.896146</td>\n      <td>100.0</td>\n      <td>-66.666667</td>\n      <td>100.0</td>\n      <td>33.761406</td>\n      <td>33.761406</td>\n    </tr>\n    <tr>\n      <th>6966</th>\n      <td>2008-01-02</td>\n      <td>52.090000</td>\n      <td>52.320000</td>\n      <td>50.790001</td>\n      <td>39.588005</td>\n      <td>8053700</td>\n      <td>AXP</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>5.903862</td>\n      <td>5.896146</td>\n      <td>100.0</td>\n      <td>-66.666667</td>\n      <td>100.0</td>\n      <td>39.588005</td>\n      <td>39.588005</td>\n    </tr>\n    <tr>\n      <th>10449</th>\n      <td>2008-01-02</td>\n      <td>87.570000</td>\n      <td>87.839996</td>\n      <td>86.000000</td>\n      <td>63.481621</td>\n      <td>4303000</td>\n      <td>BA</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>5.903862</td>\n      <td>5.896146</td>\n      <td>100.0</td>\n      <td>-66.666667</td>\n      <td>100.0</td>\n      <td>63.481621</td>\n      <td>63.481621</td>\n    </tr>\n    <tr>\n      <th>13932</th>\n      <td>2008-01-02</td>\n      <td>72.559998</td>\n      <td>72.669998</td>\n      <td>70.050003</td>\n      <td>45.605480</td>\n      <td>6337800</td>\n      <td>CAT</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>5.903862</td>\n      <td>5.896146</td>\n      <td>100.0</td>\n      <td>-66.666667</td>\n      <td>100.0</td>\n      <td>45.605480</td>\n      <td>45.605480</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fe = FeatureEngineer(\n",
    "    use_technical_indicator=True,\n",
    "    use_turbulence=False,\n",
    "    user_defined_feature=False)\n",
    "\n",
    "df = fe.preprocess_data(df)\n",
    "df.shape\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-19T22:12:48.045202300Z",
     "start_time": "2023-11-19T22:12:22.674394100Z"
    }
   },
   "id": "5f03e9d7673ba9fb"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "             date        open        high         low       close    volume  \\\n83591  2021-10-29  162.130005  162.440002  160.660004  153.936371   1314100   \n87074  2021-10-29  454.410004  461.390015  453.059998  447.971100   2497800   \n90557  2021-10-29   52.500000   53.049999   52.410000   46.802307  17763200   \n94040  2021-10-29   46.860001   47.279999   46.770000   41.523605   4999000   \n97523  2021-10-29  147.910004  150.100006  147.559998  144.852417   7340900   \n\n       tic  day       macd     boll_ub     boll_lb     rsi_30      cci_30  \\\n83591  TRV    4   1.424913  156.635661  144.752585  55.691938  130.838082   \n87074  UNH    4  12.812443  459.914494  366.732088  67.009715  145.348217   \n90557   VZ    4  -0.170568   48.086330   45.189775  46.685018  -39.966419   \n94040  WBA    4  -0.169815   44.202650   40.524222  45.913455  -97.394898   \n97523  WMT    4   1.795643  147.887261  129.205259  59.710661  130.750253   \n\n           dx_30  close_30_sma  close_60_sma  \n83591  19.622993    149.630914    150.155366  \n87074  52.064871    406.440819    405.562349  \n90557  12.569826     46.875141     47.474614  \n94040   0.268701     42.391107     42.889372  \n97523  35.423702    138.057023    140.642312  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>open</th>\n      <th>high</th>\n      <th>low</th>\n      <th>close</th>\n      <th>volume</th>\n      <th>tic</th>\n      <th>day</th>\n      <th>macd</th>\n      <th>boll_ub</th>\n      <th>boll_lb</th>\n      <th>rsi_30</th>\n      <th>cci_30</th>\n      <th>dx_30</th>\n      <th>close_30_sma</th>\n      <th>close_60_sma</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>83591</th>\n      <td>2021-10-29</td>\n      <td>162.130005</td>\n      <td>162.440002</td>\n      <td>160.660004</td>\n      <td>153.936371</td>\n      <td>1314100</td>\n      <td>TRV</td>\n      <td>4</td>\n      <td>1.424913</td>\n      <td>156.635661</td>\n      <td>144.752585</td>\n      <td>55.691938</td>\n      <td>130.838082</td>\n      <td>19.622993</td>\n      <td>149.630914</td>\n      <td>150.155366</td>\n    </tr>\n    <tr>\n      <th>87074</th>\n      <td>2021-10-29</td>\n      <td>454.410004</td>\n      <td>461.390015</td>\n      <td>453.059998</td>\n      <td>447.971100</td>\n      <td>2497800</td>\n      <td>UNH</td>\n      <td>4</td>\n      <td>12.812443</td>\n      <td>459.914494</td>\n      <td>366.732088</td>\n      <td>67.009715</td>\n      <td>145.348217</td>\n      <td>52.064871</td>\n      <td>406.440819</td>\n      <td>405.562349</td>\n    </tr>\n    <tr>\n      <th>90557</th>\n      <td>2021-10-29</td>\n      <td>52.500000</td>\n      <td>53.049999</td>\n      <td>52.410000</td>\n      <td>46.802307</td>\n      <td>17763200</td>\n      <td>VZ</td>\n      <td>4</td>\n      <td>-0.170568</td>\n      <td>48.086330</td>\n      <td>45.189775</td>\n      <td>46.685018</td>\n      <td>-39.966419</td>\n      <td>12.569826</td>\n      <td>46.875141</td>\n      <td>47.474614</td>\n    </tr>\n    <tr>\n      <th>94040</th>\n      <td>2021-10-29</td>\n      <td>46.860001</td>\n      <td>47.279999</td>\n      <td>46.770000</td>\n      <td>41.523605</td>\n      <td>4999000</td>\n      <td>WBA</td>\n      <td>4</td>\n      <td>-0.169815</td>\n      <td>44.202650</td>\n      <td>40.524222</td>\n      <td>45.913455</td>\n      <td>-97.394898</td>\n      <td>0.268701</td>\n      <td>42.391107</td>\n      <td>42.889372</td>\n    </tr>\n    <tr>\n      <th>97523</th>\n      <td>2021-10-29</td>\n      <td>147.910004</td>\n      <td>150.100006</td>\n      <td>147.559998</td>\n      <td>144.852417</td>\n      <td>7340900</td>\n      <td>WMT</td>\n      <td>4</td>\n      <td>1.795643</td>\n      <td>147.887261</td>\n      <td>129.205259</td>\n      <td>59.710661</td>\n      <td>130.750253</td>\n      <td>35.423702</td>\n      <td>138.057023</td>\n      <td>140.642312</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-19T22:12:48.089909Z",
     "start_time": "2023-11-19T22:12:48.055204Z"
    }
   },
   "id": "b5b90953f0e37bb"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "         date       open       high        low      close     volume   tic  \\\n0  2008-12-31   3.070357   3.133571   3.047857   2.583909  607541200  AAPL   \n1  2008-12-31  57.110001  58.220001  57.060001  41.839516    6287200  AMGN   \n2  2008-12-31  17.969999  18.750000  17.910000  14.580751    9625600   AXP   \n3  2008-12-31  41.590000  43.049999  41.500000  32.005898    5443100    BA   \n4  2008-12-31  43.700001  45.099998  43.700001  29.608656    6277400   CAT   \n\n   day      macd    boll_ub    boll_lb     rsi_30      cci_30      dx_30  \\\n0    2 -0.082603   3.093651   2.454292  42.254761  -80.462566  16.129793   \n1    2  0.156756  42.707033  40.853194  51.060605   51.523244  10.432018   \n2    2 -0.935583  18.646872  12.660473  42.554852  -75.437627  25.776759   \n3    2 -0.279799  32.174384  28.867833  47.440251  156.995057   5.366299   \n4    2  0.655612  30.348089  25.455648  51.205310   98.374159  26.331746   \n\n   close_30_sma  close_60_sma  \\\n0      2.749560      2.861671   \n1     41.058034     40.603776   \n2     15.744065     17.616375   \n3     30.327212     32.389914   \n4     26.689550     26.423593   \n\n                                            cov_list  \\\n0  [[0.00134896886695617, 0.00042841240010759493,...   \n1  [[0.00134896886695617, 0.00042841240010759493,...   \n2  [[0.00134896886695617, 0.00042841240010759493,...   \n3  [[0.00134896886695617, 0.00042841240010759493,...   \n4  [[0.00134896886695617, 0.00042841240010759493,...   \n\n                                         return_list  \n0  tic             AAPL      AMGN       AXP      ...  \n1  tic             AAPL      AMGN       AXP      ...  \n2  tic             AAPL      AMGN       AXP      ...  \n3  tic             AAPL      AMGN       AXP      ...  \n4  tic             AAPL      AMGN       AXP      ...  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>open</th>\n      <th>high</th>\n      <th>low</th>\n      <th>close</th>\n      <th>volume</th>\n      <th>tic</th>\n      <th>day</th>\n      <th>macd</th>\n      <th>boll_ub</th>\n      <th>boll_lb</th>\n      <th>rsi_30</th>\n      <th>cci_30</th>\n      <th>dx_30</th>\n      <th>close_30_sma</th>\n      <th>close_60_sma</th>\n      <th>cov_list</th>\n      <th>return_list</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2008-12-31</td>\n      <td>3.070357</td>\n      <td>3.133571</td>\n      <td>3.047857</td>\n      <td>2.583909</td>\n      <td>607541200</td>\n      <td>AAPL</td>\n      <td>2</td>\n      <td>-0.082603</td>\n      <td>3.093651</td>\n      <td>2.454292</td>\n      <td>42.254761</td>\n      <td>-80.462566</td>\n      <td>16.129793</td>\n      <td>2.749560</td>\n      <td>2.861671</td>\n      <td>[[0.00134896886695617, 0.00042841240010759493,...</td>\n      <td>tic             AAPL      AMGN       AXP      ...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2008-12-31</td>\n      <td>57.110001</td>\n      <td>58.220001</td>\n      <td>57.060001</td>\n      <td>41.839516</td>\n      <td>6287200</td>\n      <td>AMGN</td>\n      <td>2</td>\n      <td>0.156756</td>\n      <td>42.707033</td>\n      <td>40.853194</td>\n      <td>51.060605</td>\n      <td>51.523244</td>\n      <td>10.432018</td>\n      <td>41.058034</td>\n      <td>40.603776</td>\n      <td>[[0.00134896886695617, 0.00042841240010759493,...</td>\n      <td>tic             AAPL      AMGN       AXP      ...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2008-12-31</td>\n      <td>17.969999</td>\n      <td>18.750000</td>\n      <td>17.910000</td>\n      <td>14.580751</td>\n      <td>9625600</td>\n      <td>AXP</td>\n      <td>2</td>\n      <td>-0.935583</td>\n      <td>18.646872</td>\n      <td>12.660473</td>\n      <td>42.554852</td>\n      <td>-75.437627</td>\n      <td>25.776759</td>\n      <td>15.744065</td>\n      <td>17.616375</td>\n      <td>[[0.00134896886695617, 0.00042841240010759493,...</td>\n      <td>tic             AAPL      AMGN       AXP      ...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2008-12-31</td>\n      <td>41.590000</td>\n      <td>43.049999</td>\n      <td>41.500000</td>\n      <td>32.005898</td>\n      <td>5443100</td>\n      <td>BA</td>\n      <td>2</td>\n      <td>-0.279799</td>\n      <td>32.174384</td>\n      <td>28.867833</td>\n      <td>47.440251</td>\n      <td>156.995057</td>\n      <td>5.366299</td>\n      <td>30.327212</td>\n      <td>32.389914</td>\n      <td>[[0.00134896886695617, 0.00042841240010759493,...</td>\n      <td>tic             AAPL      AMGN       AXP      ...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2008-12-31</td>\n      <td>43.700001</td>\n      <td>45.099998</td>\n      <td>43.700001</td>\n      <td>29.608656</td>\n      <td>6277400</td>\n      <td>CAT</td>\n      <td>2</td>\n      <td>0.655612</td>\n      <td>30.348089</td>\n      <td>25.455648</td>\n      <td>51.205310</td>\n      <td>98.374159</td>\n      <td>26.331746</td>\n      <td>26.689550</td>\n      <td>26.423593</td>\n      <td>[[0.00134896886695617, 0.00042841240010759493,...</td>\n      <td>tic             AAPL      AMGN       AXP      ...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "## Add covariance matrix as states\n",
    "# add covariance matrix as states\n",
    "df = df.sort_values(['date', 'tic'], ignore_index=True)\n",
    "df.index = df.date.factorize()[0]\n",
    "\n",
    "cov_list = []\n",
    "return_list = []\n",
    "\n",
    "# look back is one year\n",
    "lookback = 252\n",
    "for i in range(lookback, len(df.index.unique())):\n",
    "    data_lookback = df.loc[i - lookback:i, :]\n",
    "    price_lookback = data_lookback.pivot_table(index='date', columns='tic', values='close')\n",
    "    return_lookback = price_lookback.pct_change().dropna()\n",
    "    return_list.append(return_lookback)\n",
    "\n",
    "    covs = return_lookback.cov().values\n",
    "    cov_list.append(covs)\n",
    "\n",
    "df_cov = pd.DataFrame({'date': df.date.unique()[lookback:], 'cov_list': cov_list, 'return_list': return_list})\n",
    "df = df.merge(df_cov, on='date')\n",
    "df = df.sort_values(['date', 'tic']).reset_index(drop=True)\n",
    "\n",
    "df.shape\n",
    "df.head()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-19T22:13:16.608871400Z",
     "start_time": "2023-11-19T22:12:48.080917200Z"
    }
   },
   "id": "71443e3dcfcc97c9"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "         date       open       high        low      close     volume   tic  \\\n0  2009-01-02   3.067143   3.251429   3.041429   2.747391  746015200  AAPL   \n0  2009-01-02  58.590000  59.080002  57.750000  42.737900    6547900  AMGN   \n0  2009-01-02  18.570000  19.520000  18.400000  15.193846   10955700   AXP   \n0  2009-01-02  42.799999  45.560001  42.779999  33.941105    7010200    BA   \n0  2009-01-02  44.910000  46.980000  44.709999  31.093401    7117200   CAT   \n\n   day      macd    boll_ub    boll_lb     rsi_30      cci_30      dx_30  \\\n0    4 -0.070153   3.080129   2.452223  45.440192  -32.212842   2.140064   \n0    4  0.232162  42.852627  40.817723  52.756872   92.828067   0.814217   \n0    4 -0.832049  18.463257  12.644437  43.957548  -42.853099  16.335101   \n0    4 -0.002007  32.948630  28.452124  50.822053  272.813024  20.494464   \n0    4  0.833184  30.850188  25.434764  53.661259  129.524706  34.637448   \n\n   close_30_sma  close_60_sma  \\\n0      2.750408      2.862473   \n0     41.122031     40.691923   \n0     15.742755     17.499520   \n0     30.469477     32.344130   \n0     26.926402     26.424174   \n\n                                            cov_list  \\\n0  [[0.0013661505804949945, 0.0004339380680857294...   \n0  [[0.0013661505804949945, 0.0004339380680857294...   \n0  [[0.0013661505804949945, 0.0004339380680857294...   \n0  [[0.0013661505804949945, 0.0004339380680857294...   \n0  [[0.0013661505804949945, 0.0004339380680857294...   \n\n                                         return_list  \n0  tic             AAPL      AMGN       AXP      ...  \n0  tic             AAPL      AMGN       AXP      ...  \n0  tic             AAPL      AMGN       AXP      ...  \n0  tic             AAPL      AMGN       AXP      ...  \n0  tic             AAPL      AMGN       AXP      ...  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>open</th>\n      <th>high</th>\n      <th>low</th>\n      <th>close</th>\n      <th>volume</th>\n      <th>tic</th>\n      <th>day</th>\n      <th>macd</th>\n      <th>boll_ub</th>\n      <th>boll_lb</th>\n      <th>rsi_30</th>\n      <th>cci_30</th>\n      <th>dx_30</th>\n      <th>close_30_sma</th>\n      <th>close_60_sma</th>\n      <th>cov_list</th>\n      <th>return_list</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2009-01-02</td>\n      <td>3.067143</td>\n      <td>3.251429</td>\n      <td>3.041429</td>\n      <td>2.747391</td>\n      <td>746015200</td>\n      <td>AAPL</td>\n      <td>4</td>\n      <td>-0.070153</td>\n      <td>3.080129</td>\n      <td>2.452223</td>\n      <td>45.440192</td>\n      <td>-32.212842</td>\n      <td>2.140064</td>\n      <td>2.750408</td>\n      <td>2.862473</td>\n      <td>[[0.0013661505804949945, 0.0004339380680857294...</td>\n      <td>tic             AAPL      AMGN       AXP      ...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>2009-01-02</td>\n      <td>58.590000</td>\n      <td>59.080002</td>\n      <td>57.750000</td>\n      <td>42.737900</td>\n      <td>6547900</td>\n      <td>AMGN</td>\n      <td>4</td>\n      <td>0.232162</td>\n      <td>42.852627</td>\n      <td>40.817723</td>\n      <td>52.756872</td>\n      <td>92.828067</td>\n      <td>0.814217</td>\n      <td>41.122031</td>\n      <td>40.691923</td>\n      <td>[[0.0013661505804949945, 0.0004339380680857294...</td>\n      <td>tic             AAPL      AMGN       AXP      ...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>2009-01-02</td>\n      <td>18.570000</td>\n      <td>19.520000</td>\n      <td>18.400000</td>\n      <td>15.193846</td>\n      <td>10955700</td>\n      <td>AXP</td>\n      <td>4</td>\n      <td>-0.832049</td>\n      <td>18.463257</td>\n      <td>12.644437</td>\n      <td>43.957548</td>\n      <td>-42.853099</td>\n      <td>16.335101</td>\n      <td>15.742755</td>\n      <td>17.499520</td>\n      <td>[[0.0013661505804949945, 0.0004339380680857294...</td>\n      <td>tic             AAPL      AMGN       AXP      ...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>2009-01-02</td>\n      <td>42.799999</td>\n      <td>45.560001</td>\n      <td>42.779999</td>\n      <td>33.941105</td>\n      <td>7010200</td>\n      <td>BA</td>\n      <td>4</td>\n      <td>-0.002007</td>\n      <td>32.948630</td>\n      <td>28.452124</td>\n      <td>50.822053</td>\n      <td>272.813024</td>\n      <td>20.494464</td>\n      <td>30.469477</td>\n      <td>32.344130</td>\n      <td>[[0.0013661505804949945, 0.0004339380680857294...</td>\n      <td>tic             AAPL      AMGN       AXP      ...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>2009-01-02</td>\n      <td>44.910000</td>\n      <td>46.980000</td>\n      <td>44.709999</td>\n      <td>31.093401</td>\n      <td>7117200</td>\n      <td>CAT</td>\n      <td>4</td>\n      <td>0.833184</td>\n      <td>30.850188</td>\n      <td>25.434764</td>\n      <td>53.661259</td>\n      <td>129.524706</td>\n      <td>34.637448</td>\n      <td>26.926402</td>\n      <td>26.424174</td>\n      <td>[[0.0013661505804949945, 0.0004339380680857294...</td>\n      <td>tic             AAPL      AMGN       AXP      ...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Training data split: 2009-01-01 to 2020-07-01\n",
    "train = data_split(df, '2009-01-01', '2020-07-01')\n",
    "#trade = data_split(df, '2020-01-01', config.END_DATE)\n",
    "train.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-19T22:13:16.801872600Z",
     "start_time": "2023-11-19T22:13:16.622870300Z"
    }
   },
   "id": "83da70d25c48258e"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stock Dimension: 28, State Space: 28\n"
     ]
    }
   ],
   "source": [
    "stock_dimension = len(train.tic.unique())\n",
    "state_space = stock_dimension\n",
    "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n",
    "\n",
    "env_kwargs = {\n",
    "    \"hmax\": 100, \n",
    "    \"initial_amount\": 1000000, \n",
    "    \"transaction_cost_pct\": 0.001, \n",
    "    \"state_space\": state_space, \n",
    "    \"stock_dim\": stock_dimension, \n",
    "    \"tech_indicator_list\": config.INDICATORS, \n",
    "    \"action_space\": stock_dimension, \n",
    "    \"reward_scaling\": 1e-4\n",
    "    \n",
    "}\n",
    "\n",
    "e_train_gym = StockPortfolioEnv(df = train, **env_kwargs)\n",
    "e_train_gym_two = StockPortfolioEnv(df = train, **env_kwargs)\n",
    "e_train_gym_three = StockPortfolioEnv(df = train, **env_kwargs)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-19T22:13:16.853904200Z",
     "start_time": "2023-11-19T22:13:16.802879300Z"
    }
   },
   "id": "3796b3ffad8bbbfe"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 18)\n",
      "AAPL\n",
      "[3.067142963409424]\n",
      "AMGN\n",
      "[58.59000015258789]\n",
      "AXP\n",
      "[18.56999969482422]\n",
      "BA\n",
      "[42.79999923706055]\n",
      "CAT\n",
      "[44.90999984741211]\n",
      "CRM\n",
      "[8.024999618530273]\n",
      "CSCO\n",
      "[16.40999984741211]\n",
      "CVX\n",
      "[74.2300033569336]\n",
      "DIS\n",
      "[22.760000228881836]\n",
      "GS\n",
      "[84.0199966430664]\n",
      "HD\n",
      "[23.06999969482422]\n",
      "HON\n",
      "[31.394458770751953]\n",
      "IBM\n",
      "[80.20076751708984]\n",
      "INTC\n",
      "[14.6899995803833]\n",
      "JNJ\n",
      "[60.130001068115234]\n",
      "JPM\n",
      "[31.190000534057617]\n",
      "KO\n",
      "[22.700000762939453]\n",
      "MCD\n",
      "[62.380001068115234]\n",
      "MMM\n",
      "[57.54999923706055]\n",
      "MRK\n",
      "[29.064884185791016]\n",
      "MSFT\n",
      "[19.530000686645508]\n",
      "NKE\n",
      "[12.737500190734863]\n",
      "PG\n",
      "[61.689998626708984]\n",
      "TRV\n",
      "[45.2599983215332]\n",
      "UNH\n",
      "[26.700000762939453]\n",
      "VZ\n",
      "[32.00077438354492]\n",
      "WBA\n",
      "[24.649999618530273]\n",
      "WMT\n",
      "[55.97999954223633]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x2496a53fc10>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(e_train_gym_three.data.shape)\n",
    "for ticker in e_train_gym_three.data['tic'].unique():\n",
    "    print(ticker)\n",
    "    \n",
    "    print(e_train_gym_three.data[e_train_gym_three.data['tic'] == ticker]['open'].tolist())\n",
    "    plt.plot(e_train_gym_three.df[e_train_gym_three.df['tic'] == ticker]['open'].tolist(), label=ticker)\n",
    "plt.legend(loc='lower left')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-19T22:13:17.817869Z",
     "start_time": "2023-11-19T22:13:16.827871500Z"
    }
   },
   "id": "45f93cc7f8fed17c"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'AAPL': 3.067142963409424}\n",
      "{'AMGN': 58.59000015258789}\n",
      "{'AXP': 18.56999969482422}\n",
      "{'BA': 42.79999923706055}\n",
      "{'CAT': 44.90999984741211}\n",
      "{'CRM': 8.024999618530273}\n",
      "{'CSCO': 16.40999984741211}\n",
      "{'CVX': 74.2300033569336}\n",
      "{'DIS': 22.760000228881836}\n",
      "{'GS': 84.0199966430664}\n",
      "{'HD': 23.06999969482422}\n",
      "{'HON': 31.394458770751953}\n",
      "{'IBM': 80.20076751708984}\n",
      "{'INTC': 14.6899995803833}\n",
      "{'JNJ': 60.130001068115234}\n",
      "{'JPM': 31.190000534057617}\n",
      "{'KO': 22.700000762939453}\n",
      "{'MCD': 62.380001068115234}\n",
      "{'MMM': 57.54999923706055}\n",
      "{'MRK': 29.064884185791016}\n",
      "{'MSFT': 19.530000686645508}\n",
      "{'NKE': 12.737500190734863}\n",
      "{'PG': 61.689998626708984}\n",
      "{'TRV': 45.2599983215332}\n",
      "{'UNH': 26.700000762939453}\n",
      "{'VZ': 32.00077438354492}\n",
      "{'WBA': 24.649999618530273}\n",
      "{'WMT': 55.97999954223633}\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "0    0.0\n",
      "Name: open, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "e_train_gym_three.data[['tic','open']]\n",
    "for row in e_train_gym_three.data[['tic','open']].values:\n",
    "    print({row[0]: row[1]})\n",
    "    \n",
    "print(e_train_gym_three.data['open'] - e_train_gym_three.data['open'])\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-19T22:13:17.820868100Z",
     "start_time": "2023-11-19T22:13:17.808868800Z"
    }
   },
   "id": "6cbdd7c4bb77bf0d"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "from typing import Sequence\n",
    "from enum import Enum\n",
    "import random\n",
    "from itertools import product\n",
    "def random_agent(env: StockPortfolioEnv, iter=2000):\n",
    "    env.reset()\n",
    "    for iteration in range(iter):\n",
    "        action = [0] * len(env.action_space.sample())\n",
    "        action[random.randint(0,  len(env.action_space.sample()) - 1)] = 1\n",
    "        state, reward, terminal, _, _ = env.step(action)\n",
    "    return env\n",
    "def argmax(arr: Sequence[float]) -> int:\n",
    "    \"\"\"Argmax that breaks ties randomly\n",
    "\n",
    "    Takes in a list of values and returns the index of the item with the highest value, breaking ties randomly.\n",
    "\n",
    "    Note: np.argmax returns the first index that matches the maximum, so we define this method to use in EpsilonGreedy and UCB agents.\n",
    "    Args:\n",
    "        arr: sequence of values\n",
    "    \"\"\"\n",
    "    largest = max(arr)\n",
    "    in_case_of_ties = []\n",
    "    for i in range(len(arr)):\n",
    "        if arr[i] == largest:\n",
    "            in_case_of_ties.append(i)\n",
    "    return random.choice(in_case_of_ties)\n",
    "\n",
    "def create_epsilon_policy(Q: defaultdict, epsilon: float, actions) -> Callable:\n",
    "    \"\"\"Creates an epsilon soft policy from Q values.\n",
    "\n",
    "    A policy is represented as a function here because the policies are simple. More complex policies can be represented using classes.\n",
    "\n",
    "    Args:\n",
    "        Q (defaultdict): current Q-values\n",
    "        epsilon (float): softness parameter\n",
    "    Returns:\n",
    "        get_action (Callable): Takes a state as input and outputs an action\n",
    "    \"\"\"\n",
    "    # Get number of actions\n",
    "    num_actions = len(Q[0])\n",
    "\n",
    "    def get_action(state: Tuple) -> int:\n",
    "        # You can reuse code from ex1\n",
    "        # Make sure to break ties arbitrarily\n",
    "        a_star = argmax(Q[state])\n",
    "        \n",
    "        if np.random.random() < epsilon:\n",
    "            action = random.choice(actions)\n",
    "        else:\n",
    "            probabilities = [(1 -epsilon + (epsilon/num_actions)) if a == a_star else epsilon / num_actions for a in actions]\n",
    "            return np.random.choice(actions, p=probabilities)\n",
    "\n",
    "        return action\n",
    "\n",
    "    return get_action\n",
    "\n",
    "\n",
    "class PortfolioAction(Enum):\n",
    "    BUY = 0\n",
    "    SELL = 1\n",
    "    HOLD = 2\n",
    "    \n",
    "\n",
    "def convert_prices_to_discrete_state(prev_data_df: pd.DataFrame, current_data_df: pd.DataFrame) -> Tuple:\n",
    "    if prev_data_df['tic'] != current_data_df['tic']:\n",
    "        raise IndexError(\"Dfs are wrong\")\n",
    "    percent_diffs = ((current_data_df['open'] - prev_data_df['open']) / prev_data_df['open']) * 100\n",
    "    \n",
    "    discrete = []\n",
    "    for dif in percent_diffs.values:\n",
    "        if dif > 5:\n",
    "            discrete.append(0)\n",
    "        elif 5 > dif > 0:\n",
    "            discrete.append(1)\n",
    "        elif 0 > dif > -5:\n",
    "            discrete.append(2)\n",
    "        elif -5 > dif:\n",
    "            discrete.append(3)\n",
    "        else:\n",
    "            discrete.append(4)\n",
    "    return tuple(discrete)\n",
    "\n",
    "def discrete_action_to_portfolio(list_of_actions: PortfolioAction, current_portfolio: tuple):\n",
    "    buy_indexes = []\n",
    "    sell_indexes = []\n",
    "    \n",
    "    updated_portfolio = [val for val in current_portfolio]\n",
    "    # get indexes\n",
    "    for idx, action in enumerate(list_of_actions):\n",
    "        if action == PortfolioAction.BUY:\n",
    "            buy_indexes.append(idx)\n",
    "        elif action == PortfolioAction.SELL:\n",
    "            sell_indexes.append(idx)\n",
    "        else:\n",
    "            continue\n",
    "    \n",
    "    if len(buy_indexes) == 0 or len(sell_indexes) == 0:\n",
    "        # cannot buy or sell without doing both in order to keep all capital deployed\n",
    "        # doesn't reflect actual action space but an initial naive approach\n",
    "        return current_portfolio\n",
    "            \n",
    "    \n",
    "    # determine how much of the portfolio weight we are shifting\n",
    "    sold_percent = sum([current_portfolio[index] for index in sell_indexes])\n",
    "    \n",
    "    \n",
    "    # distribute portfolio weight\n",
    "    distribute_percent = sold_percent / float(len(buy_indexes))\n",
    "    \n",
    "    for index in buy_indexes:\n",
    "        updated_portfolio[index] += distribute_percent\n",
    "    for index in sell_indexes:\n",
    "        updated_portfolio[index] = 0.0\n",
    "    return tuple(updated_portfolio)\n",
    "\n",
    "def tabular_agent(env: StockPortfolioEnv, epsilon, step_size, gamma, iter):\n",
    "    env.reset()\n",
    "    print(\"ENV RESET\")\n",
    "    actions = list(product(PortfolioAction, repeat=28))\n",
    "    print(\"ACTIONS MADE\")\n",
    "    current_data = env.data\n",
    "    # take single step to have data in prev\n",
    "    state, reward, terminal, _, _ = env.step([1] * 28)\n",
    "    prev_data = current_data\n",
    "    current_data = env.data\n",
    "    \n",
    "    \n",
    "    Q = defaultdict(lambda: np.zeros(len(actions)))\n",
    "    policy = create_epsilon_policy(Q, epsilon, actions)\n",
    "\n",
    "    # run SARSA for iterations\n",
    "    env.reset()\n",
    "    S = convert_prices_to_discrete_state(prev_data, current_data)\n",
    "    A = policy(S)\n",
    "    episode = []\n",
    "    \n",
    "    for _ in range(iter):\n",
    "        next_state, reward, terminal, _, _ = env.step(discrete_action_to_portfolio(A, env.actions_memory[-1]))\n",
    "        episode.append((S, A, reward))\n",
    "        S = convert_prices_to_discrete_state(prev_data, current_data)\n",
    "        A_star = policy(S)\n",
    "        Q[S][A] = Q[S][A] + (step_size * (reward + (gamma * Q[next_state][A_star]) - Q[S][A]))\n",
    "        S = next_state\n",
    "        A = A_star\n",
    "        if terminal:\n",
    "            break\n",
    "\n",
    "    return env\n",
    "\n",
    "    "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-19T22:15:18.652303700Z",
     "start_time": "2023-11-19T22:15:18.627303300Z"
    }
   },
   "id": "b022eb8cdd89eb32"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "env_after_training = random_agent(e_train_gym)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-19T22:14:34.040770Z",
     "start_time": "2023-11-19T22:14:29.918877Z"
    }
   },
   "id": "54919c61764fe536"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "other_agent_env = tabular_agent(e_train_gym_two,  gamma=0.5, epsilon=0.1, step_size=0.5, iter=2)\n"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-11-19T22:15:22.043793200Z"
    }
   },
   "id": "21f321d6231e5f1d"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "ae751b1a53ad87d1"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}