FinRL_StockTrading_NeurIPS_2018.ipynb

{
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   "metadata": {
    "id": "view-in-github",
    "colab_type": "text"
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   "source": [
    "<a href=\"https://colab.research.google.com/github/AI4Finance-Foundation/FinRL/blob/master/FinRL_StockTrading_NeurIPS_2018.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gXaoZs2lh1hi"
   },
   "source": [
    "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading\n",
    "\n",
    "* **Pytorch Version** \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lGunVt8oLCVS"
   },
   "source": [
    "# Content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HOzAKQ-SLGX6"
   },
   "source": [
    "* [1. Problem Definition](#0)\n",
    "* [2. Getting Started - Load Python packages](#1)\n",
    "    * [2.1. Install Packages](#1.1)    \n",
    "    * [2.2. Check Additional Packages](#1.2)\n",
    "    * [2.3. Import Packages](#1.3)\n",
    "    * [2.4. Create Folders](#1.4)\n",
    "* [3. Download Data](#2)\n",
    "* [4. Preprocess Data](#3)        \n",
    "    * [4.1. Technical Indicators](#3.1)\n",
    "    * [4.2. Perform Feature Engineering](#3.2)\n",
    "* [5.Build Environment](#4)  \n",
    "    * [5.1. Training & Trade Data Split](#4.1)\n",
    "    * [5.2. User-defined Environment](#4.2)   \n",
    "    * [5.3. Initialize Environment](#4.3)    \n",
    "* [6.Implement DRL Algorithms](#5)  \n",
    "* [7.Backtesting Performance](#6)  \n",
    "    * [7.1. BackTestStats](#6.1)\n",
    "    * [7.2. BackTestPlot](#6.2)   \n",
    "    * [7.3. Baseline Stats](#6.3)   \n",
    "    * [7.3. Compare to Stock Market Index](#6.4)   \n",
    "* [RLlib Section](#7)            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sApkDlD9LIZv"
   },
   "source": [
    "<a id='0'></a>\n",
    "# Part 1. Problem Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HjLD2TZSLKZ-"
   },
   "source": [
    "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
    "\n",
    "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\n",
    "\n",
    "\n",
    "* Action: The action space describes the allowed actions that the agent interacts with the\n",
    "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
    "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
    "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
    "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
    "\n",
    "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
    "values at state s′ and s, respectively\n",
    "\n",
    "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
    "our trading agent observes many different features to better learn in an interactive environment.\n",
    "\n",
    "* Environment: Dow 30 consituents\n",
    "\n",
    "\n",
    "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ffsre789LY08"
   },
   "source": [
    "<a id='1'></a>\n",
    "# Part 2. Getting Started- Load Python Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Uy5_PTmOh1hj"
   },
   "source": [
    "<a id='1.1'></a>\n",
    "## 2.1. Install all the packages through FinRL library\n"
   ]
  },
  {
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   "execution_count": 1,
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    "outputId": "ef0ba8d0-f57a-4c74-bb0b-46737762677d"
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   "outputs": [
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     "name": "stdout",
     "text": [
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     ]
    }
   ],
   "source": [
    "## install finrl library\n",
    "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "w9A8CN5R5PuZ"
   },
   "outputs": [],
   "source": [
    "from finrl import config\n",
    "from finrl import config_tickers\n",
    "import os\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "osBHhVysOEzi"
   },
   "source": [
    "\n",
    "<a id='1.2'></a>\n",
    "## 2.2. Check if the additional packages needed are present, if not install them. \n",
    "* Yahoo Finance API\n",
    "* pandas\n",
    "* numpy\n",
    "* matplotlib\n",
    "* stockstats\n",
    "* OpenAI gym\n",
    "* stable-baselines\n",
    "* tensorflow\n",
    "* pyfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nGv01K8Sh1hn"
   },
   "source": [
    "<a id='1.3'></a>\n",
    "## 2.3. Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "lPqeTTwoh1hn",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "10b08480-3a0c-4826-8e51-f94ce97ab84a"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/pyfolio/pos.py:27: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
      "  'Module \"zipline.assets\" not found; multipliers will not be applied'\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "# matplotlib.use('Agg')\n",
    "import datetime\n",
    "\n",
    "%matplotlib inline\n",
    "from finrl.finrl_meta.preprocessor.yahoodownloader import YahooDownloader\n",
    "from finrl.finrl_meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
    "from finrl.finrl_meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
    "from finrl.agents.stablebaselines3.models import DRLAgent\n",
    "from finrl.finrl_meta.data_processor import DataProcessor\n",
    "\n",
    "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
    "from pprint import pprint\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"../FinRL-Library\")\n",
    "\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T2owTj985RW4"
   },
   "source": [
    "<a id='1.4'></a>\n",
    "## 2.4. Create Folders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A289rQWMh1hq"
   },
   "source": [
    "<a id='2'></a>\n",
    "# Part 3. Download Data\n",
    "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
    "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
    "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NPeQ7iS-LoMm"
   },
   "source": [
    "\n",
    "\n",
    "-----\n",
    "class YahooDownloader:\n",
    "    Provides methods for retrieving daily stock data from\n",
    "    Yahoo Finance API\n",
    "\n",
    "    Attributes\n",
    "    ----------\n",
    "        start_date : str\n",
    "            start date of the data (modified from config.py)\n",
    "        end_date : str\n",
    "            end date of the data (modified from config.py)\n",
    "        ticker_list : list\n",
    "            a list of stock tickers (modified from config.py)\n",
    "\n",
    "    Methods\n",
    "    -------\n",
    "    fetch_data()\n",
    "        Fetches data from yahoo API\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "h3XJnvrbLp-C",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "outputId": "5302d7c0-1c68-4c6e-b30e-b1395bdc109e"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'2009-01-01'"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "# from config.py start_date is a string\n",
    "config.START_DATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "FUnY8WEfLq3C",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "outputId": "35dd8c5b-d58f-49b8-e4df-ae7e122448cd"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'2021-10-31'"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "# from config.py end_date is a string\n",
    "config.END_DATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "yCKm4om-s9kE",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "83c7f894-3757-473b-8afb-a904e6caabda"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[*********************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",
      "[*********************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",
      "Shape of DataFrame:  (94331, 8)\n"
     ]
    }
   ],
   "source": [
    "df = YahooDownloader(start_date = '2009-01-01',\n",
    "                     end_date = '2021-10-31',\n",
    "                     ticker_list = config_tickers.DOW_30_TICKER).fetch_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "JzqRRTOX6aFu",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "7a991dfe-f39c-40db-ec44-7c416cdce7dc"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "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"
     ]
    }
   ],
   "source": [
    "print(config_tickers.DOW_30_TICKER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "CV3HrZHLh1hy",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "5267773c-399c-4ec9-d4d5-13ab1e4cced0"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(94331, 8)"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "4hYkeaPiICHS",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "outputId": "210fade5-e912-40df-be99-4ad00bdb9d2f"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "<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",
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       "      <td>2009-01-02</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>58.590000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-02</td>\n",
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       "      <td>2009-01-02</td>\n",
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       "      <td>42.779999</td>\n",
       "      <td>33.941105</td>\n",
       "      <td>7010200</td>\n",
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>44.910000</td>\n",
       "      <td>46.980000</td>\n",
       "      <td>44.709999</td>\n",
       "      <td>32.475788</td>\n",
       "      <td>7117200</td>\n",
       "      <td>CAT</td>\n",
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       "         date       open       high        low      close     volume   tic  day\n",
       "0  2009-01-02   3.067143   3.251429   3.041429   2.778782  746015200  AAPL    4\n",
       "1  2009-01-02  58.590000  59.080002  57.750000  45.615879    6547900  AMGN    4\n",
       "2  2009-01-02  18.570000  19.520000  18.400000  15.618542   10955700   AXP    4\n",
       "3  2009-01-02  42.799999  45.560001  42.779999  33.941105    7010200    BA    4\n",
       "4  2009-01-02  44.910000  46.980000  44.709999  32.475788    7117200   CAT    4"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "df.sort_values(['date','tic'],ignore_index=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uqC6c40Zh1iH"
   },
   "source": [
    "# Part 4: Preprocess Data\n",
    "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
    "* Add technical indicators. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
    "* Add turbulence index. Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "PmKP-1ii3RLS",
    "pycharm": {
     "name": "#%%\n"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "0708badb-77ef-4c86-f77c-6525f0e8934d"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Successfully added technical indicators\n",
      "\r[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (3229, 8)\n",
      "Successfully added vix\n",
      "Successfully added turbulence index\n"
     ]
    }
   ],
   "source": [
    "fe = FeatureEngineer(\n",
    "                    use_technical_indicator=True,\n",
    "                    tech_indicator_list = config.TECHNICAL_INDICATORS_LIST,\n",
    "                    use_vix=True,\n",
    "                    use_turbulence=True,\n",
    "                    user_defined_feature = False)\n",
    "\n",
    "processed = fe.preprocess_data(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Kixon2tR3RLT"
   },
   "outputs": [],
   "source": [
    "list_ticker = processed[\"tic\"].unique().tolist()\n",
    "list_date = list(pd.date_range(processed['date'].min(),processed['date'].max()).astype(str))\n",
    "combination = list(itertools.product(list_date,list_ticker))\n",
    "\n",
    "processed_full = pd.DataFrame(combination,columns=[\"date\",\"tic\"]).merge(processed,on=[\"date\",\"tic\"],how=\"left\")\n",
    "processed_full = processed_full[processed_full['date'].isin(processed['date'])]\n",
    "processed_full = processed_full.sort_values(['date','tic'])\n",
    "\n",
    "processed_full = processed_full.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "grvhGJJII3Xn",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 600
    },
    "outputId": "733758c3-3552-4aa5-e1f9-789bd4ce0c92"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>tic</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</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>vix</th>\n",
       "      <th>turbulence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>3.067143</td>\n",
       "      <td>3.251429</td>\n",
       "      <td>3.041429</td>\n",
       "      <td>2.778782</td>\n",
       "      <td>746015200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.003272</td>\n",
       "      <td>2.671567</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.778782</td>\n",
       "      <td>2.778782</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>AMGN</td>\n",
       "      <td>58.590000</td>\n",
       "      <td>59.080002</td>\n",
       "      <td>57.750000</td>\n",
       "      <td>45.615879</td>\n",
       "      <td>6547900.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.003272</td>\n",
       "      <td>2.671567</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>45.615879</td>\n",
       "      <td>45.615879</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>AXP</td>\n",
       "      <td>18.570000</td>\n",
       "      <td>19.520000</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>15.618542</td>\n",
       "      <td>10955700.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.003272</td>\n",
       "      <td>2.671567</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>15.618542</td>\n",
       "      <td>15.618542</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>BA</td>\n",
       "      <td>42.799999</td>\n",
       "      <td>45.560001</td>\n",
       "      <td>42.779999</td>\n",
       "      <td>33.941105</td>\n",
       "      <td>7010200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.003272</td>\n",
       "      <td>2.671567</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>33.941105</td>\n",
       "      <td>33.941105</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>CAT</td>\n",
       "      <td>44.910000</td>\n",
       "      <td>46.980000</td>\n",
       "      <td>44.709999</td>\n",
       "      <td>32.475788</td>\n",
       "      <td>7117200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.003272</td>\n",
       "      <td>2.671567</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>32.475788</td>\n",
       "      <td>32.475788</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>CRM</td>\n",
       "      <td>8.025000</td>\n",
       "      <td>8.550000</td>\n",
       "      <td>7.912500</td>\n",
       "      <td>8.505000</td>\n",
       "      <td>4069200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2.671567</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>100.0</td>\n",
       "      <td>8.505000</td>\n",
       "      <td>8.505000</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>16.410000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>12.421844</td>\n",
       "      <td>40980600.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2.671567</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12.421844</td>\n",
       "      <td>12.421844</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>CVX</td>\n",
       "      <td>74.230003</td>\n",
       "      <td>77.300003</td>\n",
       "      <td>73.580002</td>\n",
       "      <td>45.650547</td>\n",
       "      <td>13695900.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.003272</td>\n",
       "      <td>2.671567</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>45.650547</td>\n",
       "      <td>45.650547</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>DIS</td>\n",
       "      <td>22.760000</td>\n",
       "      <td>24.030001</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>20.597496</td>\n",
       "      <td>9796600.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.003272</td>\n",
       "      <td>2.671567</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>100.0</td>\n",
       "      <td>20.597496</td>\n",
       "      <td>20.597496</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>GS</td>\n",
       "      <td>84.019997</td>\n",
       "      <td>87.620003</td>\n",
       "      <td>82.190002</td>\n",
       "      <td>71.587753</td>\n",
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      "text/plain": [
       "         date   tic       open  ...  close_60_sma        vix  turbulence\n",
       "0  2009-01-02  AAPL   3.067143  ...      2.778782  39.189999         0.0\n",
       "1  2009-01-02  AMGN  58.590000  ...     45.615879  39.189999         0.0\n",
       "2  2009-01-02   AXP  18.570000  ...     15.618542  39.189999         0.0\n",
       "3  2009-01-02    BA  42.799999  ...     33.941105  39.189999         0.0\n",
       "4  2009-01-02   CAT  44.910000  ...     32.475788  39.189999         0.0\n",
       "5  2009-01-02   CRM   8.025000  ...      8.505000  39.189999         0.0\n",
       "6  2009-01-02  CSCO  16.410000  ...     12.421844  39.189999         0.0\n",
       "7  2009-01-02   CVX  74.230003  ...     45.650547  39.189999         0.0\n",
       "8  2009-01-02   DIS  22.760000  ...     20.597496  39.189999         0.0\n",
       "9  2009-01-02    GS  84.019997  ...     71.587753  39.189999         0.0\n",
       "\n",
       "[10 rows x 18 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "processed_full.sort_values(['date','tic'],ignore_index=True).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-QsYaY0Dh1iw"
   },
   "source": [
    "<a id='4'></a>\n",
    "# Part 5. Design Environment\n",
    "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
    "\n",
    "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
    "\n",
    "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5TOhcryx44bb"
   },
   "source": [
    "## Training data split: 2009-01-01 to 2020-07-01\n",
    "## Trade data split: 2020-07-01 to 2021-10-31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "W0qaVGjLtgbI",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "ca8d1a43-ffc3-4fc3-efa9-4de9a9065842"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "83897\n",
      "9744\n"
     ]
    }
   ],
   "source": [
    "train = data_split(processed_full, '2009-01-01','2020-07-01')\n",
    "trade = data_split(processed_full, '2020-07-01','2021-10-31')\n",
    "print(len(train))\n",
    "print(len(trade))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "p52zNCOhTtLR",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "outputId": "b3ad3e10-376f-4186-f875-0331708c5e14"
   },
   "outputs": [
    {
     "output_type": "execute_result",
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       "      <td>53.021038</td>\n",
       "      <td>-51.516660</td>\n",
       "      <td>2.013358</td>\n",
       "      <td>192.162884</td>\n",
       "      <td>182.320812</td>\n",
       "      <td>30.43</td>\n",
       "      <td>12.918777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>VZ</td>\n",
       "      <td>54.919998</td>\n",
       "      <td>55.290001</td>\n",
       "      <td>54.360001</td>\n",
       "      <td>51.604500</td>\n",
       "      <td>17414800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.447763</td>\n",
       "      <td>55.232496</td>\n",
       "      <td>49.916930</td>\n",
       "      <td>48.097047</td>\n",
       "      <td>-50.926627</td>\n",
       "      <td>8.508886</td>\n",
       "      <td>52.255363</td>\n",
       "      <td>52.718949</td>\n",
       "      <td>30.43</td>\n",
       "      <td>12.918777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>WBA</td>\n",
       "      <td>42.119999</td>\n",
       "      <td>42.580002</td>\n",
       "      <td>41.759998</td>\n",
       "      <td>39.892540</td>\n",
       "      <td>4782100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.085830</td>\n",
       "      <td>43.544548</td>\n",
       "      <td>37.287954</td>\n",
       "      <td>48.830190</td>\n",
       "      <td>-14.445141</td>\n",
       "      <td>1.500723</td>\n",
       "      <td>39.994176</td>\n",
       "      <td>39.789724</td>\n",
       "      <td>30.43</td>\n",
       "      <td>12.918777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>WMT</td>\n",
       "      <td>119.220001</td>\n",
       "      <td>120.129997</td>\n",
       "      <td>118.540001</td>\n",
       "      <td>116.994614</td>\n",
       "      <td>6836400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.893234</td>\n",
       "      <td>120.371806</td>\n",
       "      <td>114.363670</td>\n",
       "      <td>48.159665</td>\n",
       "      <td>-69.914694</td>\n",
       "      <td>3.847271</td>\n",
       "      <td>118.672996</td>\n",
       "      <td>120.623193</td>\n",
       "      <td>30.43</td>\n",
       "      <td>12.918777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-618bcac5-931b-4e49-bb84-d69c1785cb5c')\"\n",
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       "  </svg>\n",
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       "\n",
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       "\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-618bcac5-931b-4e49-bb84-d69c1785cb5c button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-618bcac5-931b-4e49-bb84-d69c1785cb5c');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "            date  tic        open  ...  close_60_sma    vix  turbulence\n",
       "2892  2020-06-30  UNH  288.570007  ...    281.832978  30.43   12.918777\n",
       "2892  2020-06-30    V  191.490005  ...    182.320812  30.43   12.918777\n",
       "2892  2020-06-30   VZ   54.919998  ...     52.718949  30.43   12.918777\n",
       "2892  2020-06-30  WBA   42.119999  ...     39.789724  30.43   12.918777\n",
       "2892  2020-06-30  WMT  119.220001  ...    120.623193  30.43   12.918777\n",
       "\n",
       "[5 rows x 18 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "k9zU9YaTTvFq",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "outputId": "72213585-39a3-4bff-c031-874ec0ca06f9"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "\n",
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       "      <div>\n",
       "<style scoped>\n",
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       "\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>tic</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</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>vix</th>\n",
       "      <th>turbulence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>91.279999</td>\n",
       "      <td>91.839996</td>\n",
       "      <td>90.977501</td>\n",
       "      <td>90.151405</td>\n",
       "      <td>110737200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.022880</td>\n",
       "      <td>92.953798</td>\n",
       "      <td>80.400058</td>\n",
       "      <td>62.807136</td>\n",
       "      <td>107.487539</td>\n",
       "      <td>29.730532</td>\n",
       "      <td>84.164182</td>\n",
       "      <td>77.930892</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>53.068262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>AMGN</td>\n",
       "      <td>235.520004</td>\n",
       "      <td>256.230011</td>\n",
       "      <td>232.580002</td>\n",
       "      <td>244.159134</td>\n",
       "      <td>6575800.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.697038</td>\n",
       "      <td>236.273230</td>\n",
       "      <td>203.552000</td>\n",
       "      <td>61.279647</td>\n",
       "      <td>271.769196</td>\n",
       "      <td>46.806139</td>\n",
       "      <td>218.441970</td>\n",
       "      <td>219.532869</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>53.068262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>AXP</td>\n",
       "      <td>95.250000</td>\n",
       "      <td>96.959999</td>\n",
       "      <td>93.639999</td>\n",
       "      <td>92.579544</td>\n",
       "      <td>3301000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.391248</td>\n",
       "      <td>111.015324</td>\n",
       "      <td>88.229326</td>\n",
       "      <td>48.504813</td>\n",
       "      <td>-66.339693</td>\n",
       "      <td>3.142448</td>\n",
       "      <td>97.765431</td>\n",
       "      <td>91.181246</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>53.068262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>BA</td>\n",
       "      <td>185.880005</td>\n",
       "      <td>190.610001</td>\n",
       "      <td>180.039993</td>\n",
       "      <td>180.320007</td>\n",
       "      <td>49036700.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.443193</td>\n",
       "      <td>220.721139</td>\n",
       "      <td>160.932863</td>\n",
       "      <td>50.925771</td>\n",
       "      <td>24.220608</td>\n",
       "      <td>15.932920</td>\n",
       "      <td>176.472335</td>\n",
       "      <td>155.614168</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>53.068262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>CAT</td>\n",
       "      <td>129.380005</td>\n",
       "      <td>129.399994</td>\n",
       "      <td>125.879997</td>\n",
       "      <td>121.818489</td>\n",
       "      <td>2807800.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.284937</td>\n",
       "      <td>131.887581</td>\n",
       "      <td>114.449377</td>\n",
       "      <td>52.865426</td>\n",
       "      <td>35.546958</td>\n",
       "      <td>14.457404</td>\n",
       "      <td>120.567700</td>\n",
       "      <td>114.745772</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>53.068262</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-bf423a8f-07cb-4099-80bf-84651b53c5c4 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-bf423a8f-07cb-4099-80bf-84651b53c5c4');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "         date   tic        open  ...  close_60_sma        vix  turbulence\n",
       "0  2020-07-01  AAPL   91.279999  ...     77.930892  28.620001   53.068262\n",
       "0  2020-07-01  AMGN  235.520004  ...    219.532869  28.620001   53.068262\n",
       "0  2020-07-01   AXP   95.250000  ...     91.181246  28.620001   53.068262\n",
       "0  2020-07-01    BA  185.880005  ...    155.614168  28.620001   53.068262\n",
       "0  2020-07-01   CAT  129.380005  ...    114.745772  28.620001   53.068262\n",
       "\n",
       "[5 rows x 18 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "trade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "zYN573SOHhxG",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "7f228183-abe3-4477-f574-3c9b25c62cd8"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['macd',\n",
       " 'boll_ub',\n",
       " 'boll_lb',\n",
       " 'rsi_30',\n",
       " 'cci_30',\n",
       " 'dx_30',\n",
       " 'close_30_sma',\n",
       " 'close_60_sma']"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "config.TECHNICAL_INDICATORS_LIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "Q2zqII8rMIqn",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "1f54d044-e2d3-4a34-c041-e913d686654e"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Stock Dimension: 29, State Space: 291\n"
     ]
    }
   ],
   "source": [
    "stock_dimension = len(train.tic.unique())\n",
    "state_space = 1 + 2*stock_dimension + len(config.TECHNICAL_INDICATORS_LIST)*stock_dimension\n",
    "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "AWyp84Ltto19"
   },
   "outputs": [],
   "source": [
    "env_kwargs = {\n",
    "    \"hmax\": 100, \n",
    "    \"initial_amount\": 1000000, \n",
    "    \"buy_cost_pct\": 0.001,\n",
    "    \"sell_cost_pct\": 0.001,\n",
    "    \"state_space\": state_space, \n",
    "    \"stock_dim\": stock_dimension, \n",
    "    \"tech_indicator_list\": config.TECHNICAL_INDICATORS_LIST, \n",
    "    \"action_space\": stock_dimension, \n",
    "    \"reward_scaling\": 1e-4\n",
    "    \n",
    "}\n",
    "\n",
    "e_train_gym = StockTradingEnv(df = train, **env_kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "64EoqOrQjiVf"
   },
   "source": [
    "## Environment for Training\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "xwSvvPjutpqS",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "deeaef07-afda-4ca1-fea8-99384224c7cf"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv'>\n"
     ]
    }
   ],
   "source": [
    "env_train, _ = e_train_gym.get_sb_env()\n",
    "print(type(env_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HMNR5nHjh1iz"
   },
   "source": [
    "<a id='5'></a>\n",
    "# Part 6: Implement DRL Algorithms\n",
    "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
    "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
    "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
    "design their own DRL algorithms by adapting these DRL algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "364PsqckttcQ"
   },
   "outputs": [],
   "source": [
    "agent = DRLAgent(env = env_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YDmqOyF9h1iz"
   },
   "source": [
    "### Model Training: 5 models, A2C DDPG, PPO, TD3, SAC\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uijiWgkuh1jB"
   },
   "source": [
    "### Model 1: A2C\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "GUCnkn-HIbmj",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "a90a7a60-21a5-47e1-b683-1f7cbb4b8bc0"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0007}\n",
      "Using cpu device\n"
     ]
    }
   ],
   "source": [
    "agent = DRLAgent(env = env_train)\n",
    "model_a2c = agent.get_model(\"a2c\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "0GVpkWGqH4-D",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "outputId": "570d540f-abe9-402b-e0cc-f9b007228e8e"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 77         |\n",
      "|    iterations         | 100        |\n",
      "|    time_elapsed       | 6          |\n",
      "|    total_timesteps    | 500        |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | -1.19e-07  |\n",
      "|    learning_rate      | 0.0007     |\n",
      "|    n_updates          | 99         |\n",
      "|    policy_loss        | -23.3      |\n",
      "|    reward             | 0.16407855 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 1.02       |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 80         |\n",
      "|    iterations         | 200        |\n",
      "|    time_elapsed       | 12         |\n",
      "|    total_timesteps    | 1000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.3      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0007     |\n",
      "|    n_updates          | 199        |\n",
      "|    policy_loss        | -23.2      |\n",
      "|    reward             | -0.7490468 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 2.69       |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 80        |\n",
      "|    iterations         | 300       |\n",
      "|    time_elapsed       | 18        |\n",
      "|    total_timesteps    | 1500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 299       |\n",
      "|    policy_loss        | -394      |\n",
      "|    reward             | 7.1691494 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 87        |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 79        |\n",
      "|    iterations         | 400       |\n",
      "|    time_elapsed       | 25        |\n",
      "|    total_timesteps    | 2000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | 1.19e-07  |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 399       |\n",
      "|    policy_loss        | -19       |\n",
      "|    reward             | 2.3222716 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 7.21      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 81        |\n",
      "|    iterations         | 500       |\n",
      "|    time_elapsed       | 30        |\n",
      "|    total_timesteps    | 2500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | 0         |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 499       |\n",
      "|    policy_loss        | 571       |\n",
      "|    reward             | -8.450254 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 284       |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 81         |\n",
      "|    iterations         | 600        |\n",
      "|    time_elapsed       | 36         |\n",
      "|    total_timesteps    | 3000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | 1.19e-07   |\n",
      "|    learning_rate      | 0.0007     |\n",
      "|    n_updates          | 599        |\n",
      "|    policy_loss        | 112        |\n",
      "|    reward             | -0.5293981 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 8.02       |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 82        |\n",
      "|    iterations         | 700       |\n",
      "|    time_elapsed       | 42        |\n",
      "|    total_timesteps    | 3500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | -0.0134   |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 699       |\n",
      "|    policy_loss        | 60.6      |\n",
      "|    reward             | -3.293049 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 3.7       |\n",
      "-------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 82         |\n",
      "|    iterations         | 800        |\n",
      "|    time_elapsed       | 48         |\n",
      "|    total_timesteps    | 4000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0007     |\n",
      "|    n_updates          | 799        |\n",
      "|    policy_loss        | -14.9      |\n",
      "|    reward             | -2.4913642 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 0.629      |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 83         |\n",
      "|    iterations         | 900        |\n",
      "|    time_elapsed       | 54         |\n",
      "|    total_timesteps    | 4500       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | 0          |\n",
      "|    learning_rate      | 0.0007     |\n",
      "|    n_updates          | 899        |\n",
      "|    policy_loss        | 228        |\n",
      "|    reward             | -1.1507895 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 33.2       |\n",
      "--------------------------------------\n",
      "--------------------------------------\n",
      "| time/                 |            |\n",
      "|    fps                | 83         |\n",
      "|    iterations         | 1000       |\n",
      "|    time_elapsed       | 59         |\n",
      "|    total_timesteps    | 5000       |\n",
      "| train/                |            |\n",
      "|    entropy_loss       | -41.2      |\n",
      "|    explained_variance | -1.19e-07  |\n",
      "|    learning_rate      | 0.0007     |\n",
      "|    n_updates          | 999        |\n",
      "|    policy_loss        | 73.6       |\n",
      "|    reward             | -1.4470645 |\n",
      "|    std                | 1          |\n",
      "|    value_loss         | 4.06       |\n",
      "--------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 83        |\n",
      "|    iterations         | 1100      |\n",
      "|    time_elapsed       | 65        |\n",
      "|    total_timesteps    | 5500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -41.2     |\n",
      "|    explained_variance | 1.19e-07  |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 1099      |\n",
      "|    policy_loss        | -50.7     |\n",
      "|    reward             | 0.6880832 |\n",
      "|    std                | 1         |\n",
      "|    value_loss         | 9.48      |\n",
      "-------------------------------------\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "KeyboardInterrupt",
     "evalue": "ignored",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-22-10fe1e0717a3>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m()\u001B[0m\n\u001B[1;32m      1\u001B[0m trained_a2c = agent.train_model(model=model_a2c, \n\u001B[1;32m      2\u001B[0m                              \u001B[0mtb_log_name\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m'a2c'\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 3\u001B[0;31m                              total_timesteps=50000)\n\u001B[0m",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/finrl/drl_agents/stablebaselines3/models.py\u001B[0m in \u001B[0;36mtrain_model\u001B[0;34m(self, model, tb_log_name, total_timesteps)\u001B[0m\n\u001B[1;32m    103\u001B[0m             \u001B[0mtotal_timesteps\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mtotal_timesteps\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    104\u001B[0m             \u001B[0mtb_log_name\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mtb_log_name\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 105\u001B[0;31m             \u001B[0mcallback\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mTensorboardCallback\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    106\u001B[0m         )\n\u001B[1;32m    107\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mmodel\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/a2c/a2c.py\u001B[0m in \u001B[0;36mlearn\u001B[0;34m(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps)\u001B[0m\n\u001B[1;32m    199\u001B[0m             \u001B[0mtb_log_name\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mtb_log_name\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    200\u001B[0m             \u001B[0meval_log_path\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0meval_log_path\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 201\u001B[0;31m             \u001B[0mreset_num_timesteps\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mreset_num_timesteps\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    202\u001B[0m         )\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/on_policy_algorithm.py\u001B[0m in \u001B[0;36mlearn\u001B[0;34m(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps)\u001B[0m\n\u001B[1;32m    235\u001B[0m         \u001B[0;32mwhile\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnum_timesteps\u001B[0m \u001B[0;34m<\u001B[0m \u001B[0mtotal_timesteps\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    236\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 237\u001B[0;31m             \u001B[0mcontinue_training\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcollect_rollouts\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0menv\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcallback\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mrollout_buffer\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mn_rollout_steps\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mn_steps\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    238\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    239\u001B[0m             \u001B[0;32mif\u001B[0m \u001B[0mcontinue_training\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mFalse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/on_policy_algorithm.py\u001B[0m in \u001B[0;36mcollect_rollouts\u001B[0;34m(self, env, callback, rollout_buffer, n_rollout_steps)\u001B[0m\n\u001B[1;32m    176\u001B[0m                 \u001B[0mclipped_actions\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mclip\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mactions\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0maction_space\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mlow\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0maction_space\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mhigh\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    177\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 178\u001B[0;31m             \u001B[0mnew_obs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mrewards\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdones\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0minfos\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0menv\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mstep\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mclipped_actions\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    179\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    180\u001B[0m             \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnum_timesteps\u001B[0m \u001B[0;34m+=\u001B[0m \u001B[0menv\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnum_envs\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/vec_env/base_vec_env.py\u001B[0m in \u001B[0;36mstep\u001B[0;34m(self, actions)\u001B[0m\n\u001B[1;32m    160\u001B[0m         \"\"\"\n\u001B[1;32m    161\u001B[0m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mstep_async\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mactions\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 162\u001B[0;31m         \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mstep_wait\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    163\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    164\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mget_images\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m->\u001B[0m \u001B[0mSequence\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mndarray\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/vec_env/dummy_vec_env.py\u001B[0m in \u001B[0;36mstep_wait\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m     42\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0menv_idx\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mrange\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnum_envs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     43\u001B[0m             obs, self.buf_rews[env_idx], self.buf_dones[env_idx], self.buf_infos[env_idx] = self.envs[env_idx].step(\n\u001B[0;32m---> 44\u001B[0;31m                 \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mactions\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0menv_idx\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     45\u001B[0m             )\n\u001B[1;32m     46\u001B[0m             \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mbuf_dones\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0menv_idx\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/finrl/finrl_meta/env_stock_trading/env_stocktrading.py\u001B[0m in \u001B[0;36mstep\u001B[0;34m(self, actions)\u001B[0m\n\u001B[1;32m    187\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    188\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mstep\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mactions\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 189\u001B[0;31m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mterminal\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mday\u001B[0m \u001B[0;34m>=\u001B[0m \u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdf\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mindex\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0munique\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m-\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    190\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mterminal\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    191\u001B[0m             \u001B[0;31m# print(f\"Episode: {self.episode}\")\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001B[0m in \u001B[0;36munique\u001B[0;34m(self, level)\u001B[0m\n\u001B[1;32m   2239\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mlevel\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   2240\u001B[0m             \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_validate_index_level\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlevel\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 2241\u001B[0;31m         \u001B[0mresult\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0msuper\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0munique\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   2242\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_shallow_copy\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mresult\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   2243\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/base.py\u001B[0m in \u001B[0;36munique\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m   1263\u001B[0m                     \u001B[0mresult\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0masarray\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mresult\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1264\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1265\u001B[0;31m             \u001B[0mresult\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0munique1d\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1266\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1267\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mresult\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/algorithms.py\u001B[0m in \u001B[0;36munique\u001B[0;34m(values)\u001B[0m\n\u001B[1;32m    397\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    398\u001B[0m     \u001B[0mtable\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mhtable\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 399\u001B[0;31m     \u001B[0muniques\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mtable\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0munique\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    400\u001B[0m     \u001B[0muniques\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0m_reconstruct_data\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0muniques\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0moriginal\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdtype\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0moriginal\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    401\u001B[0m     \u001B[0;32mreturn\u001B[0m \u001B[0muniques\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "trained_a2c = agent.train_model(model=model_a2c, \n",
    "                             tb_log_name='a2c',\n",
    "                             total_timesteps=50000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MRiOtrywfAo1"
   },
   "source": [
    "### Model 2: DDPG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "M2YadjfnLwgt"
   },
   "outputs": [],
   "source": [
    "agent = DRLAgent(env = env_train)\n",
    "model_ddpg = agent.get_model(\"ddpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tCDa78rqfO_a",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "trained_ddpg = agent.train_model(model=model_ddpg, \n",
    "                             tb_log_name='ddpg',\n",
    "                             total_timesteps=50000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_gDkU-j-fCmZ"
   },
   "source": [
    "### Model 3: PPO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "y5D5PFUhMzSV"
   },
   "outputs": [],
   "source": [
    "agent = DRLAgent(env = env_train)\n",
    "PPO_PARAMS = {\n",
    "    \"n_steps\": 2048,\n",
    "    \"ent_coef\": 0.01,\n",
    "    \"learning_rate\": 0.00025,\n",
    "    \"batch_size\": 128,\n",
    "}\n",
    "model_ppo = agent.get_model(\"ppo\",model_kwargs = PPO_PARAMS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Gt8eIQKYM4G3",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "trained_ppo = agent.train_model(model=model_ppo, \n",
    "                             tb_log_name='ppo',\n",
    "                             total_timesteps=50000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3Zpv4S0-fDBv"
   },
   "source": [
    "### Model 4: TD3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JSAHhV4Xc-bh"
   },
   "outputs": [],
   "source": [
    "agent = DRLAgent(env = env_train)\n",
    "TD3_PARAMS = {\"batch_size\": 100, \n",
    "              \"buffer_size\": 1000000, \n",
    "              \"learning_rate\": 0.001}\n",
    "\n",
    "model_td3 = agent.get_model(\"td3\",model_kwargs = TD3_PARAMS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OSRxNYAxdKpU"
   },
   "outputs": [],
   "source": [
    "trained_td3 = agent.train_model(model=model_td3, \n",
    "                             tb_log_name='td3',\n",
    "                             total_timesteps=30000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dr49PotrfG01"
   },
   "source": [
    "### Model 5: SAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xwOhVjqRkCdM"
   },
   "outputs": [],
   "source": [
    "agent = DRLAgent(env = env_train)\n",
    "SAC_PARAMS = {\n",
    "    \"batch_size\": 128,\n",
    "    \"buffer_size\": 1000000,\n",
    "    \"learning_rate\": 0.0001,\n",
    "    \"learning_starts\": 100,\n",
    "    \"ent_coef\": \"auto_0.1\",\n",
    "}\n",
    "\n",
    "model_sac = agent.get_model(\"sac\",model_kwargs = SAC_PARAMS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "K8RSdKCckJyH"
   },
   "outputs": [],
   "source": [
    "trained_sac = agent.train_model(model=model_sac, \n",
    "                             tb_log_name='sac',\n",
    "                             total_timesteps=60000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f2wZgkQXh1jE"
   },
   "source": [
    "## Trading\n",
    "Assume that we have $1,000,000 initial capital at 2020-07-01. We use the DDPG model to trade Dow jones 30 stocks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bEv5KGC8h1jE"
   },
   "source": [
    "### Set turbulence threshold\n",
    "Set the turbulence threshold to be greater than the maximum of insample turbulence data, if current turbulence index is greater than the threshold, then we assume that the current market is volatile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "efwBi84ch1jE"
   },
   "outputs": [],
   "source": [
    "data_risk_indicator = processed_full[(processed_full.date<'2020-07-01') & (processed_full.date>='2009-01-01')]\n",
    "insample_risk_indicator = data_risk_indicator.drop_duplicates(subset=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VHZMBpSqh1jG"
   },
   "outputs": [],
   "source": [
    "insample_risk_indicator.vix.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BDkszkMloRWT"
   },
   "outputs": [],
   "source": [
    "insample_risk_indicator.vix.quantile(0.996)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AL7hs7svnNWT"
   },
   "outputs": [],
   "source": [
    "insample_risk_indicator.turbulence.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "N78hfHckoqJ9"
   },
   "outputs": [],
   "source": [
    "insample_risk_indicator.turbulence.quantile(0.996)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U5mmgQF_h1jQ"
   },
   "source": [
    "### Trade\n",
    "\n",
    "DRL model needs to update periodically in order to take full advantage of the data, ideally we need to retrain our model yearly, quarterly, or monthly. We also need to tune the parameters along the way, in this notebook I only use the in-sample data from 2009-01 to 2020-07 to tune the parameters once, so there is some alpha decay here as the length of trade date extends. \n",
    "\n",
    "Numerous hyperparameters – e.g. the learning rate, the total number of samples to train on – influence the learning process and are usually determined by testing some variations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cIqoV0GSI52v"
   },
   "outputs": [],
   "source": [
    "#trade = data_split(processed_full, '2020-07-01','2021-10-31')\n",
    "e_trade_gym = StockTradingEnv(df = trade, turbulence_threshold = 70,risk_indicator_col='vix', **env_kwargs)\n",
    "# env_trade, obs_trade = e_trade_gym.get_sb_env()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "W_XNgGsBMeVw"
   },
   "outputs": [],
   "source": [
    "trade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eLOnL5eYh1jR"
   },
   "outputs": [],
   "source": [
    "df_account_value, df_actions = DRLAgent.DRL_prediction(\n",
    "    model=trained_sac, \n",
    "    environment = e_trade_gym)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ERxw3KqLkcP4"
   },
   "outputs": [],
   "source": [
    "df_account_value.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2yRkNguY5yvp"
   },
   "outputs": [],
   "source": [
    "df_account_value.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nFlK5hNbWVFk"
   },
   "outputs": [],
   "source": [
    "df_actions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W6vvNSC6h1jZ"
   },
   "source": [
    "<a id='6'></a>\n",
    "# Part 7: Backtest Our Strategy\n",
    "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lr2zX7ZxNyFQ"
   },
   "source": [
    "<a id='6.1'></a>\n",
    "## 7.1 BackTestStats\n",
    "pass in df_account_value, this information is stored in env class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Nzkr9yv-AdV_"
   },
   "outputs": [],
   "source": [
    "print(\"==============Get Backtest Results===========\")\n",
    "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
    "\n",
    "perf_stats_all = backtest_stats(account_value=df_account_value)\n",
    "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
    "perf_stats_all.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QkV-LB66iwhD"
   },
   "outputs": [],
   "source": [
    "#baseline stats\n",
    "print(\"==============Get Baseline Stats===========\")\n",
    "baseline_df = get_baseline(\n",
    "        ticker=\"^DJI\", \n",
    "        start = df_account_value.loc[0,'date'],\n",
    "        end = df_account_value.loc[len(df_account_value)-1,'date'])\n",
    "\n",
    "stats = backtest_stats(baseline_df, value_col_name = 'close')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qg1kvfemrrQH"
   },
   "outputs": [],
   "source": [
    "df_account_value.loc[0,'date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tt1bzL5OrsTa"
   },
   "outputs": [],
   "source": [
    "df_account_value.loc[len(df_account_value)-1,'date']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9U6Suru3h1jc"
   },
   "source": [
    "<a id='6.2'></a>\n",
    "## 7.2 BackTestPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lKRGftSS7pNM"
   },
   "outputs": [],
   "source": [
    "print(\"==============Compare to DJIA===========\")\n",
    "%matplotlib inline\n",
    "# S&P 500: ^GSPC\n",
    "# Dow Jones Index: ^DJI\n",
    "# NASDAQ 100: ^NDX\n",
    "backtest_plot(df_account_value, \n",
    "             baseline_ticker = '^DJI', \n",
    "             baseline_start = df_account_value.loc[0,'date'],\n",
    "             baseline_end = df_account_value.loc[len(df_account_value)-1,'date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BzBaE63H3RLc"
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZYeOjax-7H_5"
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
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