FinRL_stock_trading_NeurIPS_2018.ipynb

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      "name": "FinRL_stock_trading_NeurIPS_2018.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/AI4Finance-Foundation/FinRL/blob/master/FinRL_stock_trading_NeurIPS_2018.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "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"
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      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjLD2TZSLKZ-"
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      "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"
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      "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"
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29331bfe-48cc-40aa-ec90-47c6d125691a"
      },
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": null,
      "outputs": [
        {
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            "\u001b[?25hBuilding wheels for collected packages: finrl, pyfolio, empyrical, gputil, thriftpy2, gpustat, trading-calendars, yfinance\n",
            "  Building wheel for finrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Successfully built finrl pyfolio empyrical gputil thriftpy2 gpustat trading-calendars yfinance\n",
            "Installing collected packages: multidict, yarl, lxml, async-timeout, redis, pycares, ply, opencensus-context, hiredis, blessings, aiohttp, websockets, websocket-client, thriftpy2, tensorboardX, stable-baselines3, ray, pymysql, py-spy, psycopg2-binary, opencensus, mock, int-date, gpustat, empyrical, cryptography, colorful, aioredis, aiohttp-cors, aiodns, yfinance, wrds, trading-calendars, stockstats, pyfolio, lz4, jqdatasdk, gputil, ccxt, alpaca-trade-api, finrl\n",
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            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed aiodns-3.0.0 aiohttp-3.7.4.post0 aiohttp-cors-0.7.0 aioredis-1.3.1 alpaca-trade-api-1.2.3 async-timeout-3.0.1 blessings-1.7 ccxt-1.55.56 colorful-0.5.4 cryptography-3.4.8 empyrical-0.5.5 finrl-0.3.0 gpustat-0.6.0 gputil-1.4.0 hiredis-2.0.0 int-date-0.1.8 jqdatasdk-1.8.10 lxml-4.6.3 lz4-3.1.3 mock-4.0.3 multidict-5.1.0 opencensus-0.7.13 opencensus-context-0.1.2 ply-3.11 psycopg2-binary-2.9.1 py-spy-0.3.8 pycares-4.0.0 pyfolio-0.9.2+75.g4b901f6 pymysql-1.0.2 ray-1.6.0 redis-3.5.3 stable-baselines3-1.1.0 stockstats-0.3.2 tensorboardX-2.4 thriftpy2-0.4.14 trading-calendars-2.1.1 websocket-client-1.2.1 websockets-9.1 wrds-3.1.0 yarl-1.6.3 yfinance-0.1.63\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "lPqeTTwoh1hn"
      },
      "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.apps import config\n",
        "from finrl.neo_finrl.preprocessor.yahoodownloader import YahooDownloader\n",
        "from finrl.neo_finrl.preprocessor.preprocessors import FeatureEngineer, data_split\n",
        "from finrl.neo_finrl.env_stock_trading.env_stocktrading import StockTradingEnv\n",
        "from finrl.neo_finrl.env_stock_trading.env_stocktrading_np import StockTradingEnv as StockTradingEnv_numpy\n",
        "from finrl.drl_agents.stablebaselines3.models import DRLAgent\n",
        "from finrl.drl_agents.rllib.models import DRLAgent as DRLAgent_rllib\n",
        "from finrl.neo_finrl.data_processor import DataProcessor\n",
        "\n",
        "\n",
        "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
        "import ray\n",
        "from pprint import pprint\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")\n",
        "\n",
        "import itertools"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2owTj985RW4"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9A8CN5R5PuZ"
      },
      "source": [
        "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "h3XJnvrbLp-C",
        "outputId": "844dd9b7-161b-48c3-8c9c-3efbb7b48029"
      },
      "source": [
        "# from config.py start_date is a string\n",
        "config.START_DATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2009-01-01'"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "FUnY8WEfLq3C",
        "outputId": "67c6f114-96f5-4a82-94c2-0b594cbb5a76"
      },
      "source": [
        "# from config.py end_date is a string\n",
        "config.END_DATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2021-01-01'"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzqRRTOX6aFu",
        "outputId": "5ae3cebc-fc06-4b69-de57-da283fa87395"
      },
      "source": [
        "print(config.DOW_30_TICKER)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['AAPL', 'MSFT', 'JPM', 'V', 'RTX', 'PG', 'GS', 'NKE', 'DIS', 'AXP', 'HD', 'INTC', 'WMT', 'IBM', 'MRK', 'UNH', 'KO', 'CAT', 'TRV', 'JNJ', 'CVX', 'MCD', 'VZ', 'CSCO', 'XOM', 'BA', 'MMM', 'PFE', 'WBA', 'DD']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCKm4om-s9kE",
        "outputId": "9c93d108-53af-4059-a5b9-212f80706c95"
      },
      "source": [
        "df = YahooDownloader(start_date = '2009-01-01',\n",
        "                     end_date = '2021-07-06',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "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:  (94410, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV3HrZHLh1hy",
        "outputId": "afb5191d-a444-470f-894c-432e55047d3c"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(94410, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "4hYkeaPiICHS",
        "outputId": "995bb5cd-46e7-4137-f6c5-e0d59ccf595c"
      },
      "source": [
        "df.sort_values(['date','tic'],ignore_index=True).head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</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>2.787006</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.657365</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.655109</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.505757</td>\n",
              "      <td>40980600</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high        low      close     volume   tic  day\n",
              "0  2009-01-02   3.067143   3.251429   3.041429   2.787006  746015200  AAPL    4\n",
              "1  2009-01-02  18.570000  19.520000  18.400000  15.657365   10955700   AXP    4\n",
              "2  2009-01-02  42.799999  45.560001  42.779999  33.941101    7010200    BA    4\n",
              "3  2009-01-02  44.910000  46.980000  44.709999  32.655109    7117200   CAT    4\n",
              "4  2009-01-02  16.410000  17.000000  16.250000  12.505757   40980600  CSCO    4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "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",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "PmKP-1ii3RLS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91b01104-457f-4683-e73d-a63de7a1cc09"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully added technical indicators\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (3146, 8)\n",
            "Successfully added vix\n",
            "Successfully added turbulence index\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kixon2tR3RLT"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "grvhGJJII3Xn",
        "outputId": "df223031-7bd2-4952-bcce-7616519ed200"
      },
      "source": [
        "processed_full.sort_values(['date','tic'],ignore_index=True).head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>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>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</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>2.787006</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</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>AXP</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.657365</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</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>BA</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</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>CAT</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.655109</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</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>CSCO</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.505757</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</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>CVX</td>\n",
              "      <td>74.230003</td>\n",
              "      <td>77.300003</td>\n",
              "      <td>73.580002</td>\n",
              "      <td>46.800758</td>\n",
              "      <td>13695900.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</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>DD</td>\n",
              "      <td>21.605234</td>\n",
              "      <td>22.060680</td>\n",
              "      <td>20.993229</td>\n",
              "      <td>15.219840</td>\n",
              "      <td>13251037.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</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>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</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>GS</td>\n",
              "      <td>84.019997</td>\n",
              "      <td>87.620003</td>\n",
              "      <td>82.190002</td>\n",
              "      <td>72.315208</td>\n",
              "      <td>14088500.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>HD</td>\n",
              "      <td>23.070000</td>\n",
              "      <td>24.190001</td>\n",
              "      <td>22.959999</td>\n",
              "      <td>17.705078</td>\n",
              "      <td>14902500.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date   tic       open  ...  dx_30        vix  turbulence\n",
              "0  2009-01-02  AAPL   3.067143  ...  100.0  39.189999         0.0\n",
              "1  2009-01-02   AXP  18.570000  ...  100.0  39.189999         0.0\n",
              "2  2009-01-02    BA  42.799999  ...  100.0  39.189999         0.0\n",
              "3  2009-01-02   CAT  44.910000  ...  100.0  39.189999         0.0\n",
              "4  2009-01-02  CSCO  16.410000  ...  100.0  39.189999         0.0\n",
              "5  2009-01-02   CVX  74.230003  ...  100.0  39.189999         0.0\n",
              "6  2009-01-02    DD  21.605234  ...  100.0  39.189999         0.0\n",
              "7  2009-01-02   DIS  22.760000  ...  100.0  39.189999         0.0\n",
              "8  2009-01-02    GS  84.019997  ...  100.0  39.189999         0.0\n",
              "9  2009-01-02    HD  23.070000  ...  100.0  39.189999         0.0\n",
              "\n",
              "[10 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "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 2018-12-31\n",
        "## Trade data split: 2019-01-01 to 2020-09-30"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0qaVGjLtgbI",
        "outputId": "da18438f-00be-4487-d921-074252754dc7"
      },
      "source": [
        "train = data_split(processed_full, '2009-01-01','2020-07-01')\n",
        "trade = data_split(processed_full, '2020-07-01','2021-07-06')\n",
        "print(len(train))\n",
        "print(len(trade))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "86790\n",
            "7590\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "p52zNCOhTtLR",
        "outputId": "3f6fbcc8-d6e2-4d35-ef06-c9598bdb566f"
      },
      "source": [
        "train.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</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>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>vix</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2892</th>\n",
              "      <td>2020-06-30</td>\n",
              "      <td>V</td>\n",
              "      <td>191.490005</td>\n",
              "      <td>193.750000</td>\n",
              "      <td>190.160004</td>\n",
              "      <td>192.010803</td>\n",
              "      <td>9040100.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.055788</td>\n",
              "      <td>53.021033</td>\n",
              "      <td>-51.486540</td>\n",
              "      <td>2.013358</td>\n",
              "      <td>30.43</td>\n",
              "      <td>12.48667</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>52.217354</td>\n",
              "      <td>17414800.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.453081</td>\n",
              "      <td>48.097041</td>\n",
              "      <td>-50.881435</td>\n",
              "      <td>8.508886</td>\n",
              "      <td>30.43</td>\n",
              "      <td>12.48667</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>40.674686</td>\n",
              "      <td>4782100.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.087512</td>\n",
              "      <td>48.830189</td>\n",
              "      <td>-14.388463</td>\n",
              "      <td>1.500723</td>\n",
              "      <td>30.43</td>\n",
              "      <td>12.48667</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>117.897949</td>\n",
              "      <td>6836400.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.900130</td>\n",
              "      <td>48.159682</td>\n",
              "      <td>-69.889755</td>\n",
              "      <td>3.847271</td>\n",
              "      <td>30.43</td>\n",
              "      <td>12.48667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2892</th>\n",
              "      <td>2020-06-30</td>\n",
              "      <td>XOM</td>\n",
              "      <td>43.430000</td>\n",
              "      <td>44.970001</td>\n",
              "      <td>43.160000</td>\n",
              "      <td>41.502033</td>\n",
              "      <td>22153600.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.419571</td>\n",
              "      <td>48.041017</td>\n",
              "      <td>-90.469008</td>\n",
              "      <td>7.727992</td>\n",
              "      <td>30.43</td>\n",
              "      <td>12.48667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            date  tic        open  ...     dx_30    vix  turbulence\n",
              "2892  2020-06-30    V  191.490005  ...  2.013358  30.43    12.48667\n",
              "2892  2020-06-30   VZ   54.919998  ...  8.508886  30.43    12.48667\n",
              "2892  2020-06-30  WBA   42.119999  ...  1.500723  30.43    12.48667\n",
              "2892  2020-06-30  WMT  119.220001  ...  3.847271  30.43    12.48667\n",
              "2892  2020-06-30  XOM   43.430000  ...  7.727992  30.43    12.48667\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "k9zU9YaTTvFq",
        "outputId": "3b87903a-11dc-448a-b7a4-ecf01e68b7ce"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "</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>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</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>90.418251</td>\n",
              "      <td>110737200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.031827</td>\n",
              "      <td>62.807148</td>\n",
              "      <td>107.482905</td>\n",
              "      <td>29.730532</td>\n",
              "      <td>28.620001</td>\n",
              "      <td>23.958856</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>92.809692</td>\n",
              "      <td>3301000.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.392220</td>\n",
              "      <td>48.504814</td>\n",
              "      <td>-66.344801</td>\n",
              "      <td>3.142448</td>\n",
              "      <td>28.620001</td>\n",
              "      <td>23.958856</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>50.925771</td>\n",
              "      <td>24.220608</td>\n",
              "      <td>15.932920</td>\n",
              "      <td>28.620001</td>\n",
              "      <td>23.958856</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>122.491104</td>\n",
              "      <td>2807800.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.292031</td>\n",
              "      <td>52.865415</td>\n",
              "      <td>35.517990</td>\n",
              "      <td>14.457404</td>\n",
              "      <td>28.620001</td>\n",
              "      <td>23.958856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>46.540001</td>\n",
              "      <td>46.720001</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>44.289536</td>\n",
              "      <td>17129500.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.277462</td>\n",
              "      <td>53.160557</td>\n",
              "      <td>18.936292</td>\n",
              "      <td>13.380576</td>\n",
              "      <td>28.620001</td>\n",
              "      <td>23.958856</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date   tic        open  ...      dx_30        vix  turbulence\n",
              "0  2020-07-01  AAPL   91.279999  ...  29.730532  28.620001   23.958856\n",
              "0  2020-07-01   AXP   95.250000  ...   3.142448  28.620001   23.958856\n",
              "0  2020-07-01    BA  185.880005  ...  15.932920  28.620001   23.958856\n",
              "0  2020-07-01   CAT  129.380005  ...  14.457404  28.620001   23.958856\n",
              "0  2020-07-01  CSCO   46.540001  ...  13.380576  28.620001   23.958856\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYN573SOHhxG",
        "outputId": "e9df08db-3d1d-432e-92b9-d9a084063bd1"
      },
      "source": [
        "config.TECHNICAL_INDICATORS_LIST"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['macd', 'rsi_30', 'cci_30', 'dx_30']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2zqII8rMIqn",
        "outputId": "e93a1ffe-041c-4a7d-a727-9f04ef7785e7"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stock Dimension: 30, State Space: 181\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWyp84Ltto19"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64EoqOrQjiVf"
      },
      "source": [
        "## Environment for Training\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwSvvPjutpqS",
        "outputId": "f4c41d7c-7705-4dd5-ba18-7d47329df6d5"
      },
      "source": [
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "print(type(env_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "364PsqckttcQ"
      },
      "source": [
        "agent = DRLAgent(env = env_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUCnkn-HIbmj",
        "outputId": "1a345b2f-24c9-454b-91f9-facced5fa8df"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "model_a2c = agent.get_model(\"a2c\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0007}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0GVpkWGqH4-D",
        "outputId": "10c60083-c962-4fe7-bb45-61527fd6cf35"
      },
      "source": [
        "trained_a2c = agent.train_model(model=model_a2c, \n",
        "                             tb_log_name='a2c',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/a2c/a2c_1\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 5        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | -0.324   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | -23.4    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.514    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 11       |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -0.28    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -54      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 2.09     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 300      |\n",
            "|    time_elapsed       | 17       |\n",
            "|    total_timesteps    | 1500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | 0.0323   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 299      |\n",
            "|    policy_loss        | -253     |\n",
            "|    std                | 0.999    |\n",
            "|    value_loss         | 36.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 400      |\n",
            "|    time_elapsed       | 23       |\n",
            "|    total_timesteps    | 2000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 399      |\n",
            "|    policy_loss        | -14.4    |\n",
            "|    std                | 0.999    |\n",
            "|    value_loss         | 0.884    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 500      |\n",
            "|    time_elapsed       | 29       |\n",
            "|    total_timesteps    | 2500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 499      |\n",
            "|    policy_loss        | 449      |\n",
            "|    std                | 0.999    |\n",
            "|    value_loss         | 127      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 600      |\n",
            "|    time_elapsed       | 34       |\n",
            "|    total_timesteps    | 3000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -0.36    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 599      |\n",
            "|    policy_loss        | 179      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 17.8     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 700      |\n",
            "|    time_elapsed       | 40       |\n",
            "|    total_timesteps    | 3500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 699      |\n",
            "|    policy_loss        | -58      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 3.26     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 800      |\n",
            "|    time_elapsed       | 46       |\n",
            "|    total_timesteps    | 4000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 799      |\n",
            "|    policy_loss        | -49.3    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.87     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 900      |\n",
            "|    time_elapsed       | 52       |\n",
            "|    total_timesteps    | 4500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 899      |\n",
            "|    policy_loss        | 19.5     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 2.32     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 1000     |\n",
            "|    time_elapsed       | 57       |\n",
            "|    total_timesteps    | 5000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 999      |\n",
            "|    policy_loss        | 33.2     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.86     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 1100     |\n",
            "|    time_elapsed       | 63       |\n",
            "|    total_timesteps    | 5500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1099     |\n",
            "|    policy_loss        | -274     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 47.1     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 1200     |\n",
            "|    time_elapsed       | 69       |\n",
            "|    total_timesteps    | 6000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -2.19    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1199     |\n",
            "|    policy_loss        | -246     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 36.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 1300     |\n",
            "|    time_elapsed       | 75       |\n",
            "|    total_timesteps    | 6500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.0993   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1299     |\n",
            "|    policy_loss        | 23.4     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 4.28     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 1400     |\n",
            "|    time_elapsed       | 81       |\n",
            "|    total_timesteps    | 7000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.166    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1399     |\n",
            "|    policy_loss        | 20.2     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.955    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 1500      |\n",
            "|    time_elapsed       | 87        |\n",
            "|    total_timesteps    | 7500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.8     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1499      |\n",
            "|    policy_loss        | 106       |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 9.64      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 1600     |\n",
            "|    time_elapsed       | 93       |\n",
            "|    total_timesteps    | 8000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 2.38e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1599     |\n",
            "|    policy_loss        | 3.61     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.518    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 1700     |\n",
            "|    time_elapsed       | 99       |\n",
            "|    total_timesteps    | 8500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -0.0143  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1699     |\n",
            "|    policy_loss        | -213     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 57.1     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 1800     |\n",
            "|    time_elapsed       | 104      |\n",
            "|    total_timesteps    | 9000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0.0905   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1799     |\n",
            "|    policy_loss        | 16.3     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.386    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 1900     |\n",
            "|    time_elapsed       | 110      |\n",
            "|    total_timesteps    | 9500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.0885  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1899     |\n",
            "|    policy_loss        | 17.1     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.41     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 2000     |\n",
            "|    time_elapsed       | 116      |\n",
            "|    total_timesteps    | 10000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1999     |\n",
            "|    policy_loss        | -207     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 32.8     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 2100      |\n",
            "|    time_elapsed       | 122       |\n",
            "|    total_timesteps    | 10500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.8     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 2099      |\n",
            "|    policy_loss        | -21.6     |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 15.3      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 2200     |\n",
            "|    time_elapsed       | 128      |\n",
            "|    total_timesteps    | 11000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2199     |\n",
            "|    policy_loss        | 108      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 21.1     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 2300      |\n",
            "|    time_elapsed       | 134       |\n",
            "|    total_timesteps    | 11500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.8     |\n",
            "|    explained_variance | 1.19e-07  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 2299      |\n",
            "|    policy_loss        | -4.72e+03 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 1.39e+04  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 2400     |\n",
            "|    time_elapsed       | 139      |\n",
            "|    total_timesteps    | 12000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.023    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2399     |\n",
            "|    policy_loss        | 49.5     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 5.42     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 2500     |\n",
            "|    time_elapsed       | 145      |\n",
            "|    total_timesteps    | 12500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2499     |\n",
            "|    policy_loss        | -109     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 8.11     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 2600     |\n",
            "|    time_elapsed       | 151      |\n",
            "|    total_timesteps    | 13000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2599     |\n",
            "|    policy_loss        | 102      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 8.16     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 2700     |\n",
            "|    time_elapsed       | 157      |\n",
            "|    total_timesteps    | 13500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2699     |\n",
            "|    policy_loss        | -20.7    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.864    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 2800     |\n",
            "|    time_elapsed       | 162      |\n",
            "|    total_timesteps    | 14000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2799     |\n",
            "|    policy_loss        | 120      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 13.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 2900     |\n",
            "|    time_elapsed       | 168      |\n",
            "|    total_timesteps    | 14500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2899     |\n",
            "|    policy_loss        | -102     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 6.25     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 3000     |\n",
            "|    time_elapsed       | 174      |\n",
            "|    total_timesteps    | 15000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2999     |\n",
            "|    policy_loss        | 5.01     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.297    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 3100      |\n",
            "|    time_elapsed       | 180       |\n",
            "|    total_timesteps    | 15500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.8     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3099      |\n",
            "|    policy_loss        | 39.1      |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 2.24      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 3200     |\n",
            "|    time_elapsed       | 186      |\n",
            "|    total_timesteps    | 16000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3199     |\n",
            "|    policy_loss        | -168     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 33.2     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 3300     |\n",
            "|    time_elapsed       | 191      |\n",
            "|    total_timesteps    | 16500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3299     |\n",
            "|    policy_loss        | 382      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 103      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 3400     |\n",
            "|    time_elapsed       | 197      |\n",
            "|    total_timesteps    | 17000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.00432  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3399     |\n",
            "|    policy_loss        | 755      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 302      |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 3500      |\n",
            "|    time_elapsed       | 203       |\n",
            "|    total_timesteps    | 17500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.8     |\n",
            "|    explained_variance | -0.000238 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3499      |\n",
            "|    policy_loss        | 138       |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 16.6      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 3600     |\n",
            "|    time_elapsed       | 209      |\n",
            "|    total_timesteps    | 18000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3599     |\n",
            "|    policy_loss        | -45.3    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 3.6      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 3700     |\n",
            "|    time_elapsed       | 215      |\n",
            "|    total_timesteps    | 18500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3699     |\n",
            "|    policy_loss        | 92       |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 5.73     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 3800      |\n",
            "|    time_elapsed       | 221       |\n",
            "|    total_timesteps    | 19000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.8     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3799      |\n",
            "|    policy_loss        | 53.9      |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 4.08      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 3900     |\n",
            "|    time_elapsed       | 226      |\n",
            "|    total_timesteps    | 19500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3899     |\n",
            "|    policy_loss        | -38.3    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.74     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 4000     |\n",
            "|    time_elapsed       | 232      |\n",
            "|    total_timesteps    | 20000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3999     |\n",
            "|    policy_loss        | -302     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 52.4     |\n",
            "------------------------------------\n",
            "day: 2892, episode: 20\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4956670.28\n",
            "total_reward: 3956670.28\n",
            "total_cost: 13818.34\n",
            "total_trades: 45527\n",
            "Sharpe: 0.811\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 4100     |\n",
            "|    time_elapsed       | 238      |\n",
            "|    total_timesteps    | 20500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | -0.0226  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4099     |\n",
            "|    policy_loss        | -13.5    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.979    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 4200     |\n",
            "|    time_elapsed       | 244      |\n",
            "|    total_timesteps    | 21000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | -0.123   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4199     |\n",
            "|    policy_loss        | -165     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 17.9     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 4300     |\n",
            "|    time_elapsed       | 249      |\n",
            "|    total_timesteps    | 21500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | -0.233   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4299     |\n",
            "|    policy_loss        | -154     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 15.1     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 4400     |\n",
            "|    time_elapsed       | 255      |\n",
            "|    total_timesteps    | 22000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4399     |\n",
            "|    policy_loss        | -9.65    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 7.64     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 4500     |\n",
            "|    time_elapsed       | 261      |\n",
            "|    total_timesteps    | 22500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4499     |\n",
            "|    policy_loss        | 458      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 138      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 4600     |\n",
            "|    time_elapsed       | 267      |\n",
            "|    total_timesteps    | 23000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4599     |\n",
            "|    policy_loss        | 270      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 45.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 4700     |\n",
            "|    time_elapsed       | 272      |\n",
            "|    total_timesteps    | 23500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 2.38e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4699     |\n",
            "|    policy_loss        | -125     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 14.2     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 4800     |\n",
            "|    time_elapsed       | 278      |\n",
            "|    total_timesteps    | 24000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0.0511   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4799     |\n",
            "|    policy_loss        | -181     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 21       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 4900     |\n",
            "|    time_elapsed       | 284      |\n",
            "|    total_timesteps    | 24500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4899     |\n",
            "|    policy_loss        | -86.4    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 7.87     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 5000     |\n",
            "|    time_elapsed       | 290      |\n",
            "|    total_timesteps    | 25000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4999     |\n",
            "|    policy_loss        | 8.44     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 4.42     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 5100     |\n",
            "|    time_elapsed       | 295      |\n",
            "|    total_timesteps    | 25500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.3    |\n",
            "|    explained_variance | 0.098    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5099     |\n",
            "|    policy_loss        | 11.4     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 4.38     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 5200     |\n",
            "|    time_elapsed       | 301      |\n",
            "|    total_timesteps    | 26000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5199     |\n",
            "|    policy_loss        | -235     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 70.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 5300     |\n",
            "|    time_elapsed       | 307      |\n",
            "|    total_timesteps    | 26500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5299     |\n",
            "|    policy_loss        | 6.38     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 0.132    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 5400     |\n",
            "|    time_elapsed       | 313      |\n",
            "|    total_timesteps    | 27000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5399     |\n",
            "|    policy_loss        | -149     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 13.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 5500     |\n",
            "|    time_elapsed       | 319      |\n",
            "|    total_timesteps    | 27500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.3    |\n",
            "|    explained_variance | 0.135    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5499     |\n",
            "|    policy_loss        | -45.1    |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 6.28     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 5600     |\n",
            "|    time_elapsed       | 325      |\n",
            "|    total_timesteps    | 28000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.4    |\n",
            "|    explained_variance | 0.311    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5599     |\n",
            "|    policy_loss        | -183     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 22.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 5700     |\n",
            "|    time_elapsed       | 331      |\n",
            "|    total_timesteps    | 28500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.3    |\n",
            "|    explained_variance | -0.266   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5699     |\n",
            "|    policy_loss        | -118     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 15.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 5800     |\n",
            "|    time_elapsed       | 337      |\n",
            "|    total_timesteps    | 29000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.4    |\n",
            "|    explained_variance | -0.102   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5799     |\n",
            "|    policy_loss        | -26.3    |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 4.88     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 5900     |\n",
            "|    time_elapsed       | 343      |\n",
            "|    total_timesteps    | 29500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5899     |\n",
            "|    policy_loss        | -3.35    |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 0.202    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 6000     |\n",
            "|    time_elapsed       | 348      |\n",
            "|    total_timesteps    | 30000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | -0.00693 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5999     |\n",
            "|    policy_loss        | 59.2     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 3.38     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 6100     |\n",
            "|    time_elapsed       | 354      |\n",
            "|    total_timesteps    | 30500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 0.0323   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6099     |\n",
            "|    policy_loss        | 19.3     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 3.48     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 6200     |\n",
            "|    time_elapsed       | 360      |\n",
            "|    total_timesteps    | 31000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6199     |\n",
            "|    policy_loss        | -11.4    |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 0.501    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 6300     |\n",
            "|    time_elapsed       | 366      |\n",
            "|    total_timesteps    | 31500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6299     |\n",
            "|    policy_loss        | 183      |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 39.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 6400     |\n",
            "|    time_elapsed       | 372      |\n",
            "|    total_timesteps    | 32000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6399     |\n",
            "|    policy_loss        | 24.4     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 0.749    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 6500     |\n",
            "|    time_elapsed       | 377      |\n",
            "|    total_timesteps    | 32500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6499     |\n",
            "|    policy_loss        | 46.5     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 6.79     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 6600     |\n",
            "|    time_elapsed       | 383      |\n",
            "|    total_timesteps    | 33000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6599     |\n",
            "|    policy_loss        | -57.5    |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 4.52     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 6700      |\n",
            "|    time_elapsed       | 389       |\n",
            "|    total_timesteps    | 33500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.5     |\n",
            "|    explained_variance | 0.0361    |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 6699      |\n",
            "|    policy_loss        | -1.03e+03 |\n",
            "|    std                | 1.03      |\n",
            "|    value_loss         | 653       |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 6800     |\n",
            "|    time_elapsed       | 395      |\n",
            "|    total_timesteps    | 34000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6799     |\n",
            "|    policy_loss        | -169     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 15.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 6900     |\n",
            "|    time_elapsed       | 401      |\n",
            "|    total_timesteps    | 34500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | -0.0375  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6899     |\n",
            "|    policy_loss        | -468     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 146      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 7000     |\n",
            "|    time_elapsed       | 406      |\n",
            "|    total_timesteps    | 35000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6999     |\n",
            "|    policy_loss        | 31.5     |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 0.647    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 7100     |\n",
            "|    time_elapsed       | 412      |\n",
            "|    total_timesteps    | 35500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7099     |\n",
            "|    policy_loss        | 69.7     |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 3.44     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 7200     |\n",
            "|    time_elapsed       | 418      |\n",
            "|    total_timesteps    | 36000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7199     |\n",
            "|    policy_loss        | -268     |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 42.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 7300     |\n",
            "|    time_elapsed       | 424      |\n",
            "|    total_timesteps    | 36500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7299     |\n",
            "|    policy_loss        | 51.3     |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 5.83     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 7400     |\n",
            "|    time_elapsed       | 429      |\n",
            "|    total_timesteps    | 37000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7399     |\n",
            "|    policy_loss        | 103      |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 7.97     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 7500     |\n",
            "|    time_elapsed       | 435      |\n",
            "|    total_timesteps    | 37500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | -0.223   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7499     |\n",
            "|    policy_loss        | 26.1     |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 0.904    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 7600     |\n",
            "|    time_elapsed       | 441      |\n",
            "|    total_timesteps    | 38000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | 8.34e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7599     |\n",
            "|    policy_loss        | -96      |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 5.42     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 7700     |\n",
            "|    time_elapsed       | 447      |\n",
            "|    total_timesteps    | 38500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7699     |\n",
            "|    policy_loss        | -150     |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 13.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 7800     |\n",
            "|    time_elapsed       | 453      |\n",
            "|    total_timesteps    | 39000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7799     |\n",
            "|    policy_loss        | 5.35     |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 0.633    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 7900     |\n",
            "|    time_elapsed       | 459      |\n",
            "|    total_timesteps    | 39500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7899     |\n",
            "|    policy_loss        | 206      |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 29.2     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 8000      |\n",
            "|    time_elapsed       | 464       |\n",
            "|    total_timesteps    | 40000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.9     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 7999      |\n",
            "|    policy_loss        | -8.47     |\n",
            "|    std                | 1.05      |\n",
            "|    value_loss         | 0.463     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 8100     |\n",
            "|    time_elapsed       | 470      |\n",
            "|    total_timesteps    | 40500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8099     |\n",
            "|    policy_loss        | -83.4    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 5.89     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 8200     |\n",
            "|    time_elapsed       | 476      |\n",
            "|    total_timesteps    | 41000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8199     |\n",
            "|    policy_loss        | -43.9    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 1.42     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 8300     |\n",
            "|    time_elapsed       | 482      |\n",
            "|    total_timesteps    | 41500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 0.0181   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8299     |\n",
            "|    policy_loss        | -4.87    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 0.163    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 8400     |\n",
            "|    time_elapsed       | 488      |\n",
            "|    total_timesteps    | 42000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8399     |\n",
            "|    policy_loss        | -54.5    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 2.66     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 8500     |\n",
            "|    time_elapsed       | 493      |\n",
            "|    total_timesteps    | 42500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8499     |\n",
            "|    policy_loss        | -131     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 9.51     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 8600     |\n",
            "|    time_elapsed       | 499      |\n",
            "|    total_timesteps    | 43000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8599     |\n",
            "|    policy_loss        | -25.2    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 8.14     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 8700     |\n",
            "|    time_elapsed       | 505      |\n",
            "|    total_timesteps    | 43500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.4    |\n",
            "|    explained_variance | -0.0729  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8699     |\n",
            "|    policy_loss        | 27.2     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 1.54     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 8800     |\n",
            "|    time_elapsed       | 511      |\n",
            "|    total_timesteps    | 44000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8799     |\n",
            "|    policy_loss        | -47.4    |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 1.23     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 8900     |\n",
            "|    time_elapsed       | 516      |\n",
            "|    total_timesteps    | 44500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8899     |\n",
            "|    policy_loss        | 40.4     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 1.83     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 9000     |\n",
            "|    time_elapsed       | 522      |\n",
            "|    total_timesteps    | 45000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8999     |\n",
            "|    policy_loss        | 8.5      |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 1.5      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 9100     |\n",
            "|    time_elapsed       | 528      |\n",
            "|    total_timesteps    | 45500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9099     |\n",
            "|    policy_loss        | 47.6     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 1.5      |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 9200      |\n",
            "|    time_elapsed       | 533       |\n",
            "|    total_timesteps    | 46000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.6     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 9199      |\n",
            "|    policy_loss        | 71.6      |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 5.88      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 9300     |\n",
            "|    time_elapsed       | 539      |\n",
            "|    total_timesteps    | 46500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 0.067    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9299     |\n",
            "|    policy_loss        | -172     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 15.2     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 9400     |\n",
            "|    time_elapsed       | 544      |\n",
            "|    total_timesteps    | 47000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9399     |\n",
            "|    policy_loss        | 170      |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 14.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 9500     |\n",
            "|    time_elapsed       | 550      |\n",
            "|    total_timesteps    | 47500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.5    |\n",
            "|    explained_variance | 0.0384   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9499     |\n",
            "|    policy_loss        | 45.9     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 2.11     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 9600     |\n",
            "|    time_elapsed       | 556      |\n",
            "|    total_timesteps    | 48000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9599     |\n",
            "|    policy_loss        | -79.2    |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 3.36     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 9700     |\n",
            "|    time_elapsed       | 562      |\n",
            "|    total_timesteps    | 48500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9699     |\n",
            "|    policy_loss        | 17.8     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 1.62     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 9800     |\n",
            "|    time_elapsed       | 568      |\n",
            "|    total_timesteps    | 49000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9799     |\n",
            "|    policy_loss        | 47.1     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 16       |\n",
            "------------------------------------\n",
            "day: 2892, episode: 30\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2525390.57\n",
            "total_reward: 1525390.57\n",
            "total_cost: 12305.70\n",
            "total_trades: 47215\n",
            "Sharpe: 0.520\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 9900     |\n",
            "|    time_elapsed       | 573      |\n",
            "|    total_timesteps    | 49500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9899     |\n",
            "|    policy_loss        | -5.5     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.235    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 10000    |\n",
            "|    time_elapsed       | 579      |\n",
            "|    total_timesteps    | 50000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9999     |\n",
            "|    policy_loss        | 43.7     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 1.51     |\n",
            "------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRiOtrywfAo1"
      },
      "source": [
        "### Model 2: DDPG"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2YadjfnLwgt",
        "outputId": "3b2a8f89-0561-4083-a015-fbee11693037"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "model_ddpg = agent.get_model(\"ddpg\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 50000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "tCDa78rqfO_a",
        "jupyter": {
          "outputs_hidden": true
        },
        "outputId": "f651f8be-4c93-4b1e-c88a-7e3a09976693"
      },
      "source": [
        "trained_ddpg = agent.train_model(model=model_ddpg, \n",
        "                             tb_log_name='ddpg',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ddpg/ddpg_1\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3761309.8057632465\n",
            "total_reward:2761309.8057632465\n",
            "total_cost:  6807.077776350557\n",
            "total_trades:  39070\n",
            "Sharpe:  1.0173492167488003\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 38       |\n",
            "|    time_elapsed    | 258      |\n",
            "|    total timesteps | 10064    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -2.81    |\n",
            "|    critic_loss     | 272      |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 7548     |\n",
            "---------------------------------\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 33       |\n",
            "|    time_elapsed    | 604      |\n",
            "|    total timesteps | 20128    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -8.32    |\n",
            "|    critic_loss     | 12.8     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 17612    |\n",
            "---------------------------------\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 12       |\n",
            "|    fps             | 31       |\n",
            "|    time_elapsed    | 953      |\n",
            "|    total timesteps | 30192    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -9.46    |\n",
            "|    critic_loss     | 4.31     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 27676    |\n",
            "---------------------------------\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 16       |\n",
            "|    fps             | 30       |\n",
            "|    time_elapsed    | 1309     |\n",
            "|    total timesteps | 40256    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -10.2    |\n",
            "|    critic_loss     | 3.19     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 37740    |\n",
            "---------------------------------\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 20       |\n",
            "|    fps             | 30       |\n",
            "|    time_elapsed    | 1675     |\n",
            "|    total timesteps | 50320    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -11.1    |\n",
            "|    critic_loss     | 2.24     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 47804    |\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gDkU-j-fCmZ"
      },
      "source": [
        "### Model 3: PPO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5D5PFUhMzSV",
        "outputId": "926eeede-2824-4d2d-c9f5-998aa3af8a80"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_steps': 2048, 'ent_coef': 0.01, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Gt8eIQKYM4G3",
        "jupyter": {
          "outputs_hidden": true
        },
        "outputId": "5af2025b-234f-4342-b3a1-26b27f3cbd68"
      },
      "source": [
        "trained_ppo = agent.train_model(model=model_ppo, \n",
        "                             tb_log_name='ppo',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ppo/ppo_2\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 93   |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 21   |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 92        |\n",
            "|    iterations           | 2         |\n",
            "|    time_elapsed         | 44        |\n",
            "|    total_timesteps      | 4096      |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0138289 |\n",
            "|    clip_fraction        | 0.189     |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | -0.00676  |\n",
            "|    learning_rate        | 0.00025   |\n",
            "|    loss                 | 4.45      |\n",
            "|    n_updates            | 10        |\n",
            "|    policy_gradient_loss | -0.0247   |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 11.2      |\n",
            "---------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 91         |\n",
            "|    iterations           | 3          |\n",
            "|    time_elapsed         | 67         |\n",
            "|    total_timesteps      | 6144       |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.01750755 |\n",
            "|    clip_fraction        | 0.188      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -42.6      |\n",
            "|    explained_variance   | -0.000914  |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 18.7       |\n",
            "|    n_updates            | 20         |\n",
            "|    policy_gradient_loss | -0.0203    |\n",
            "|    std                  | 1          |\n",
            "|    value_loss           | 45.3       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 91          |\n",
            "|    iterations           | 4           |\n",
            "|    time_elapsed         | 89          |\n",
            "|    total_timesteps      | 8192        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017273765 |\n",
            "|    clip_fraction        | 0.195       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | 0.00113     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 24          |\n",
            "|    n_updates            | 30          |\n",
            "|    policy_gradient_loss | -0.0229     |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 42.2        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 91          |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 112         |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.016832866 |\n",
            "|    clip_fraction        | 0.2         |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | -0.024      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 8.64        |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0261     |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 17.1        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 91          |\n",
            "|    iterations           | 6           |\n",
            "|    time_elapsed         | 134         |\n",
            "|    total_timesteps      | 12288       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.015978891 |\n",
            "|    clip_fraction        | 0.174       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | -0.00585    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 19.1        |\n",
            "|    n_updates            | 50          |\n",
            "|    policy_gradient_loss | -0.0189     |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 51.6        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 91          |\n",
            "|    iterations           | 7           |\n",
            "|    time_elapsed         | 157         |\n",
            "|    total_timesteps      | 14336       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017250769 |\n",
            "|    clip_fraction        | 0.217       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | 0.00166     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 33.1        |\n",
            "|    n_updates            | 60          |\n",
            "|    policy_gradient_loss | -0.0219     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 48.4        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 91          |\n",
            "|    iterations           | 8           |\n",
            "|    time_elapsed         | 179         |\n",
            "|    total_timesteps      | 16384       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.023984281 |\n",
            "|    clip_fraction        | 0.219       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.8       |\n",
            "|    explained_variance   | -0.0248     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 16.4        |\n",
            "|    n_updates            | 70          |\n",
            "|    policy_gradient_loss | -0.0204     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 31          |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 90         |\n",
            "|    iterations           | 9          |\n",
            "|    time_elapsed         | 202        |\n",
            "|    total_timesteps      | 18432      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.01604965 |\n",
            "|    clip_fraction        | 0.165      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -42.9      |\n",
            "|    explained_variance   | 0.00573    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 34.4       |\n",
            "|    n_updates            | 80         |\n",
            "|    policy_gradient_loss | -0.0182    |\n",
            "|    std                  | 1.01       |\n",
            "|    value_loss           | 59.7       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 10          |\n",
            "|    time_elapsed         | 225         |\n",
            "|    total_timesteps      | 20480       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017628487 |\n",
            "|    clip_fraction        | 0.209       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43         |\n",
            "|    explained_variance   | 0.0104      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 32.9        |\n",
            "|    n_updates            | 90          |\n",
            "|    policy_gradient_loss | -0.0145     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 58.3        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 90         |\n",
            "|    iterations           | 11         |\n",
            "|    time_elapsed         | 248        |\n",
            "|    total_timesteps      | 22528      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.01667468 |\n",
            "|    clip_fraction        | 0.156      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43        |\n",
            "|    explained_variance   | -0.00767   |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 16.7       |\n",
            "|    n_updates            | 100        |\n",
            "|    policy_gradient_loss | -0.0132    |\n",
            "|    std                  | 1.02       |\n",
            "|    value_loss           | 47.7       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 12          |\n",
            "|    time_elapsed         | 270         |\n",
            "|    total_timesteps      | 24576       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017743286 |\n",
            "|    clip_fraction        | 0.198       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.1       |\n",
            "|    explained_variance   | -0.0259     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 8.9         |\n",
            "|    n_updates            | 110         |\n",
            "|    policy_gradient_loss | -0.0217     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 21.3        |\n",
            "-----------------------------------------\n",
            "day: 2892, episode: 40\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3278144.88\n",
            "total_reward: 2278144.88\n",
            "total_cost: 329878.36\n",
            "total_trades: 81851\n",
            "Sharpe: 0.666\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 13          |\n",
            "|    time_elapsed         | 293         |\n",
            "|    total_timesteps      | 26624       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020495009 |\n",
            "|    clip_fraction        | 0.193       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.1       |\n",
            "|    explained_variance   | 0.0061      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 25.3        |\n",
            "|    n_updates            | 120         |\n",
            "|    policy_gradient_loss | -0.0204     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 80.1        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 14          |\n",
            "|    time_elapsed         | 316         |\n",
            "|    total_timesteps      | 28672       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.022461332 |\n",
            "|    clip_fraction        | 0.235       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.2       |\n",
            "|    explained_variance   | -0.0052     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 36.1        |\n",
            "|    n_updates            | 130         |\n",
            "|    policy_gradient_loss | -0.0187     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 60.5        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 15          |\n",
            "|    time_elapsed         | 338         |\n",
            "|    total_timesteps      | 30720       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.023339419 |\n",
            "|    clip_fraction        | 0.198       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.2       |\n",
            "|    explained_variance   | -0.00653    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 15.1        |\n",
            "|    n_updates            | 140         |\n",
            "|    policy_gradient_loss | -0.02       |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 24.4        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 16          |\n",
            "|    time_elapsed         | 361         |\n",
            "|    total_timesteps      | 32768       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017588098 |\n",
            "|    clip_fraction        | 0.153       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.3       |\n",
            "|    explained_variance   | 0.0194      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 28.3        |\n",
            "|    n_updates            | 150         |\n",
            "|    policy_gradient_loss | -0.0239     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 54.1        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 17          |\n",
            "|    time_elapsed         | 383         |\n",
            "|    total_timesteps      | 34816       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.015358863 |\n",
            "|    clip_fraction        | 0.118       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.3       |\n",
            "|    explained_variance   | 0.000102    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 232         |\n",
            "|    n_updates            | 160         |\n",
            "|    policy_gradient_loss | -0.0144     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 287         |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 18          |\n",
            "|    time_elapsed         | 405         |\n",
            "|    total_timesteps      | 36864       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.014516832 |\n",
            "|    clip_fraction        | 0.142       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.3       |\n",
            "|    explained_variance   | -0.0177     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 67.8        |\n",
            "|    n_updates            | 170         |\n",
            "|    policy_gradient_loss | -0.0175     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 212         |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 90         |\n",
            "|    iterations           | 19         |\n",
            "|    time_elapsed         | 428        |\n",
            "|    total_timesteps      | 38912      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02127418 |\n",
            "|    clip_fraction        | 0.219      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.4      |\n",
            "|    explained_variance   | -0.0102    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 15         |\n",
            "|    n_updates            | 180        |\n",
            "|    policy_gradient_loss | -0.018     |\n",
            "|    std                  | 1.03       |\n",
            "|    value_loss           | 32.2       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 20          |\n",
            "|    time_elapsed         | 451         |\n",
            "|    total_timesteps      | 40960       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.012819225 |\n",
            "|    clip_fraction        | 0.0916      |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.4       |\n",
            "|    explained_variance   | -0.000161   |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 151         |\n",
            "|    n_updates            | 190         |\n",
            "|    policy_gradient_loss | -0.0111     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 479         |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 21          |\n",
            "|    time_elapsed         | 473         |\n",
            "|    total_timesteps      | 43008       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020413605 |\n",
            "|    clip_fraction        | 0.212       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.5       |\n",
            "|    explained_variance   | -0.00631    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 61.9        |\n",
            "|    n_updates            | 200         |\n",
            "|    policy_gradient_loss | -0.0166     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 235         |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 22          |\n",
            "|    time_elapsed         | 496         |\n",
            "|    total_timesteps      | 45056       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.026887551 |\n",
            "|    clip_fraction        | 0.243       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.5       |\n",
            "|    explained_variance   | -0.0938     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 31.7        |\n",
            "|    n_updates            | 210         |\n",
            "|    policy_gradient_loss | -0.0157     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 55.5        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 23          |\n",
            "|    time_elapsed         | 518         |\n",
            "|    total_timesteps      | 47104       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017504608 |\n",
            "|    clip_fraction        | 0.162       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.5       |\n",
            "|    explained_variance   | -0.00543    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 93.7        |\n",
            "|    n_updates            | 220         |\n",
            "|    policy_gradient_loss | -0.0155     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 180         |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 24          |\n",
            "|    time_elapsed         | 541         |\n",
            "|    total_timesteps      | 49152       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.014675225 |\n",
            "|    clip_fraction        | 0.149       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.6       |\n",
            "|    explained_variance   | 0.000218    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 474         |\n",
            "|    n_updates            | 230         |\n",
            "|    policy_gradient_loss | -0.0118     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 653         |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 25          |\n",
            "|    time_elapsed         | 564         |\n",
            "|    total_timesteps      | 51200       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.018881265 |\n",
            "|    clip_fraction        | 0.201       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.6       |\n",
            "|    explained_variance   | -0.00896    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 110         |\n",
            "|    n_updates            | 240         |\n",
            "|    policy_gradient_loss | -0.0131     |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 183         |\n",
            "-----------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Zpv4S0-fDBv"
      },
      "source": [
        "### Model 4: TD3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSAHhV4Xc-bh",
        "outputId": "24fe279b-fc4e-4768-dd94-871a4921aa84"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 100, 'buffer_size': 1000000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSRxNYAxdKpU",
        "outputId": "d12df119-6d25-42e2-e40b-be7754669731"
      },
      "source": [
        "trained_td3 = agent.train_model(model=model_td3, \n",
        "                             tb_log_name='td3',\n",
        "                             total_timesteps=30000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/td3/td3_1\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 8        |\n",
            "|    time_elapsed    | 1321     |\n",
            "|    total timesteps | 11572    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 12.1     |\n",
            "|    critic_loss     | 950      |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 8679     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 7        |\n",
            "|    time_elapsed    | 3058     |\n",
            "|    total timesteps | 23144    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 12.5     |\n",
            "|    critic_loss     | 368      |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 20251    |\n",
            "---------------------------------\n",
            "day: 2892, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3243762.14\n",
            "total_reward: 2243762.14\n",
            "total_cost: 1384.89\n",
            "total_trades: 28982\n",
            "Sharpe: 0.561\n",
            "=================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dr49PotrfG01"
      },
      "source": [
        "### Model 5: SAC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwOhVjqRkCdM",
        "outputId": "a45ea9df-f1b5-415d-e483-018ecb36c687"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 1000000, 'learning_rate': 0.0001, 'learning_starts': 100, 'ent_coef': 'auto_0.1'}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8RSdKCckJyH",
        "outputId": "cc75b58b-bfa1-42ed-958f-51f5b1b4c1ca"
      },
      "source": [
        "trained_sac = agent.train_model(model=model_sac, \n",
        "                             tb_log_name='sac',\n",
        "                             total_timesteps=60000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/sac/sac_1\n",
            "day: 2892, episode: 50\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 5214770.00\n",
            "total_reward: 4214770.00\n",
            "total_cost: 9813.93\n",
            "total_trades: 48910\n",
            "Sharpe: 0.893\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 26       |\n",
            "|    time_elapsed    | 438      |\n",
            "|    total timesteps | 11572    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 998      |\n",
            "|    critic_loss     | 5.15e+03 |\n",
            "|    ent_coef        | 0.171    |\n",
            "|    ent_coef_loss   | -59      |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 11471    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 26       |\n",
            "|    time_elapsed    | 885      |\n",
            "|    total timesteps | 23144    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 497      |\n",
            "|    critic_loss     | 84.8     |\n",
            "|    ent_coef        | 0.0529   |\n",
            "|    ent_coef_loss   | -119     |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 23043    |\n",
            "---------------------------------\n",
            "day: 2892, episode: 60\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3966805.61\n",
            "total_reward: 2966805.61\n",
            "total_cost: 7790.49\n",
            "total_trades: 58155\n",
            "Sharpe: 0.652\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 12       |\n",
            "|    fps             | 26       |\n",
            "|    time_elapsed    | 1335     |\n",
            "|    total timesteps | 34716    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 265      |\n",
            "|    critic_loss     | 40.4     |\n",
            "|    ent_coef        | 0.0171   |\n",
            "|    ent_coef_loss   | -119     |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 34615    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 16       |\n",
            "|    fps             | 25       |\n",
            "|    time_elapsed    | 1791     |\n",
            "|    total timesteps | 46288    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 147      |\n",
            "|    critic_loss     | 16.3     |\n",
            "|    ent_coef        | 0.00574  |\n",
            "|    ent_coef_loss   | -81      |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 46187    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 20       |\n",
            "|    fps             | 25       |\n",
            "|    time_elapsed    | 2250     |\n",
            "|    total timesteps | 57860    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 85.1     |\n",
            "|    critic_loss     | 6.02     |\n",
            "|    ent_coef        | 0.00223  |\n",
            "|    ent_coef_loss   | -12.8    |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 57759    |\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2wZgkQXh1jE"
      },
      "source": [
        "## Trading\n",
        "Assume that we have $1,000,000 initial capital at 2019-01-01. 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",
      "metadata": {
        "id": "efwBi84ch1jE"
      },
      "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'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VHZMBpSqh1jG",
        "outputId": "af33c1ef-72cc-406d-a47c-9b93edf2734f"
      },
      "source": [
        "insample_risk_indicator.vix.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2893.000000\n",
              "mean       18.824245\n",
              "std         8.489311\n",
              "min         9.140000\n",
              "25%        13.330000\n",
              "50%        16.139999\n",
              "75%        21.309999\n",
              "max        82.690002\n",
              "Name: vix, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BDkszkMloRWT",
        "outputId": "9a65400e-5177-4377-d449-bb6920faf447"
      },
      "source": [
        "insample_risk_indicator.vix.quantile(0.996)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "57.40400183105453"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AL7hs7svnNWT",
        "outputId": "58d46dad-36f8-4c5d-bc4f-9d94cc54f6cf"
      },
      "source": [
        "insample_risk_indicator.turbulence.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2893.000000\n",
              "mean       36.304942\n",
              "std        46.455663\n",
              "min         0.000000\n",
              "25%        15.656261\n",
              "50%        25.706689\n",
              "75%        40.421435\n",
              "max       768.533870\n",
              "Name: turbulence, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N78hfHckoqJ9",
        "outputId": "7fd92cc4-822f-4f27-9887-056501da0544"
      },
      "source": [
        "insample_risk_indicator.turbulence.quantile(0.996)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "311.3218398208453"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "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 2018-12 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",
      "metadata": {
        "id": "cIqoV0GSI52v"
      },
      "source": [
        "#trade = data_split(processed_full, '2020-07-01','2021-07-06')\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()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "W_XNgGsBMeVw",
        "outputId": "0d397ea6-5f59-4f24-d858-88898cfdb3f6"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>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>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</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>90.418251</td>\n",
              "      <td>110737200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.031827</td>\n",
              "      <td>62.807148</td>\n",
              "      <td>107.482905</td>\n",
              "      <td>29.730532</td>\n",
              "      <td>28.620001</td>\n",
              "      <td>23.958856</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>92.809692</td>\n",
              "      <td>3301000.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.392220</td>\n",
              "      <td>48.504814</td>\n",
              "      <td>-66.344801</td>\n",
              "      <td>3.142448</td>\n",
              "      <td>28.620001</td>\n",
              "      <td>23.958856</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>50.925771</td>\n",
              "      <td>24.220608</td>\n",
              "      <td>15.932920</td>\n",
              "      <td>28.620001</td>\n",
              "      <td>23.958856</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>122.491104</td>\n",
              "      <td>2807800.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.292031</td>\n",
              "      <td>52.865415</td>\n",
              "      <td>35.517990</td>\n",
              "      <td>14.457404</td>\n",
              "      <td>28.620001</td>\n",
              "      <td>23.958856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>46.540001</td>\n",
              "      <td>46.720001</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>44.289536</td>\n",
              "      <td>17129500.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.277462</td>\n",
              "      <td>53.160557</td>\n",
              "      <td>18.936292</td>\n",
              "      <td>13.380576</td>\n",
              "      <td>28.620001</td>\n",
              "      <td>23.958856</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date   tic        open  ...      dx_30        vix  turbulence\n",
              "0  2020-07-01  AAPL   91.279999  ...  29.730532  28.620001   23.958856\n",
              "0  2020-07-01   AXP   95.250000  ...   3.142448  28.620001   23.958856\n",
              "0  2020-07-01    BA  185.880005  ...  15.932920  28.620001   23.958856\n",
              "0  2020-07-01   CAT  129.380005  ...  14.457404  28.620001   23.958856\n",
              "0  2020-07-01  CSCO   46.540001  ...  13.380576  28.620001   23.958856\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLOnL5eYh1jR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1078909-8502-4bb4-f1e7-913f54209512"
      },
      "source": [
        "df_account_value, df_actions = DRLAgent.DRL_prediction(\n",
        "    model=trained_sac, \n",
        "    environment = e_trade_gym)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hit end!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERxw3KqLkcP4",
        "outputId": "91b5053c-5a83-4e83-860c-f580f988e0ab"
      },
      "source": [
        "df_account_value.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(253, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "2yRkNguY5yvp",
        "outputId": "54a56a8e-6dbc-4de3-d1c8-6804f3ce19f9"
      },
      "source": [
        "df_account_value.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\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>account_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>2021-06-25</td>\n",
              "      <td>1.389157e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>2021-06-28</td>\n",
              "      <td>1.372579e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>2021-06-29</td>\n",
              "      <td>1.368576e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>2021-06-30</td>\n",
              "      <td>1.378171e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>2021-07-01</td>\n",
              "      <td>1.386487e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           date  account_value\n",
              "248  2021-06-25   1.389157e+06\n",
              "249  2021-06-28   1.372579e+06\n",
              "250  2021-06-29   1.368576e+06\n",
              "251  2021-06-30   1.378171e+06\n",
              "252  2021-07-01   1.386487e+06"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 223
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "nFlK5hNbWVFk",
        "outputId": "35e8786a-be73-4f54-ab21-957d5dc15b44"
      },
      "source": [
        "df_actions.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>AXP</th>\n",
              "      <th>BA</th>\n",
              "      <th>CAT</th>\n",
              "      <th>CSCO</th>\n",
              "      <th>CVX</th>\n",
              "      <th>DD</th>\n",
              "      <th>DIS</th>\n",
              "      <th>GS</th>\n",
              "      <th>HD</th>\n",
              "      <th>IBM</th>\n",
              "      <th>INTC</th>\n",
              "      <th>JNJ</th>\n",
              "      <th>JPM</th>\n",
              "      <th>KO</th>\n",
              "      <th>MCD</th>\n",
              "      <th>MMM</th>\n",
              "      <th>MRK</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NKE</th>\n",
              "      <th>PFE</th>\n",
              "      <th>PG</th>\n",
              "      <th>RTX</th>\n",
              "      <th>TRV</th>\n",
              "      <th>UNH</th>\n",
              "      <th>V</th>\n",
              "      <th>VZ</th>\n",
              "      <th>WBA</th>\n",
              "      <th>WMT</th>\n",
              "      <th>XOM</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-07-01</th>\n",
              "      <td>0</td>\n",
              "      <td>91</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "      <td>80</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "      <td>96</td>\n",
              "      <td>88</td>\n",
              "      <td>92</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "      <td>97</td>\n",
              "      <td>51</td>\n",
              "      <td>75</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>63</td>\n",
              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-02</th>\n",
              "      <td>0</td>\n",
              "      <td>94</td>\n",
              "      <td>98</td>\n",
              "      <td>0</td>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>-71</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "      <td>95</td>\n",
              "      <td>99</td>\n",
              "      <td>66</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "      <td>97</td>\n",
              "      <td>63</td>\n",
              "      <td>-46</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>98</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-63</td>\n",
              "      <td>76</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-06</th>\n",
              "      <td>0</td>\n",
              "      <td>93</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>95</td>\n",
              "      <td>0</td>\n",
              "      <td>-9</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "      <td>97</td>\n",
              "      <td>98</td>\n",
              "      <td>98</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "      <td>97</td>\n",
              "      <td>81</td>\n",
              "      <td>-29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>92</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>-59</td>\n",
              "      <td>0</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-07</th>\n",
              "      <td>0</td>\n",
              "      <td>94</td>\n",
              "      <td>89</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "      <td>96</td>\n",
              "      <td>99</td>\n",
              "      <td>11</td>\n",
              "      <td>93</td>\n",
              "      <td>0</td>\n",
              "      <td>92</td>\n",
              "      <td>91</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>-26</td>\n",
              "      <td>0</td>\n",
              "      <td>89</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>97</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-08</th>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>98</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>57</td>\n",
              "      <td>98</td>\n",
              "      <td>0</td>\n",
              "      <td>98</td>\n",
              "      <td>97</td>\n",
              "      <td>51</td>\n",
              "      <td>97</td>\n",
              "      <td>0</td>\n",
              "      <td>98</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>88</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>97</td>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            AAPL  AXP  BA  CAT  CSCO  CVX  DD  ...  TRV  UNH   V  VZ  WBA  WMT  XOM\n",
              "date                                           ...                                 \n",
              "2020-07-01     0   91  96    0    96    0  80  ...    0    0  63  69    0    0   99\n",
              "2020-07-02     0   94  98    0    57    0 -71  ...    0    0 -63  76   64    0   92\n",
              "2020-07-06     0   93  90    0    95    0  -9  ...    0    0   0  78  -59    0   93\n",
              "2020-07-07     0   94  89    0    53    0   0  ...    0    0   0  97   39    0   98\n",
              "2020-07-08     0   90  98    0    53    0  57  ...    0    0  31  97   58    0   98\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 224
        }
      ]
    },
    {
      "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nzkr9yv-AdV_",
        "outputId": "05d324cf-c804-4fe2-c910-a1f2e12d57e3"
      },
      "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')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Backtest Results===========\n",
            "Annual return          0.384697\n",
            "Cumulative returns     0.386487\n",
            "Annual volatility      0.167256\n",
            "Sharpe ratio           2.038126\n",
            "Calmar ratio           3.938909\n",
            "Stability              0.931964\n",
            "Max drawdown          -0.097666\n",
            "Omega ratio            1.412164\n",
            "Sortino ratio          3.244631\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.408697\n",
            "Daily value at risk   -0.019719\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QkV-LB66iwhD",
        "outputId": "8b3467c9-1ace-4dfd-881c-c8f5709feea1"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (252, 8)\n",
            "Annual return          0.340686\n",
            "Cumulative returns     0.340686\n",
            "Annual volatility      0.145825\n",
            "Sharpe ratio           2.092401\n",
            "Calmar ratio           3.814740\n",
            "Stability              0.945610\n",
            "Max drawdown          -0.089308\n",
            "Omega ratio            1.416513\n",
            "Sortino ratio          3.133042\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.080042\n",
            "Daily value at risk   -0.017161\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "qg1kvfemrrQH",
        "outputId": "9da72600-2ddf-4832-8285-92d80e6b31d4"
      },
      "source": [
        "df_account_value.loc[0,'date']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2020-07-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tt1bzL5OrsTa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b628de9b-762d-40a3-f791-7be24e05f23b"
      },
      "source": [
        "df_account_value.loc[len(df_account_value)-1,'date']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2021-07-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U6Suru3h1jc"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lKRGftSS7pNM",
        "outputId": "6c25fc75-f91a-49c0-fce2-3a4cb1935eeb"
      },
      "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'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Compare to DJIA===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (252, 8)\n"
          ],
          "name": "stdout"
        },
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          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2020-07-01</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2021-06-30</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>12</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>37.817%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>37.817%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>16.752%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>2.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>3.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-9.767%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>3.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>1.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-1.977%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>1.04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
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            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Worst drawdown periods</th>\n",
              "      <th>Net drawdown in %</th>\n",
              "      <th>Peak date</th>\n",
              "      <th>Valley date</th>\n",
              "      <th>Recovery date</th>\n",
              "      <th>Duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.77</td>\n",
              "      <td>2020-09-02</td>\n",
              "      <td>2020-10-28</td>\n",
              "      <td>2020-11-10</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.30</td>\n",
              "      <td>2021-01-20</td>\n",
              "      <td>2021-01-29</td>\n",
              "      <td>2021-02-04</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.29</td>\n",
              "      <td>2021-05-10</td>\n",
              "      <td>2021-05-19</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.89</td>\n",
              "      <td>2020-07-22</td>\n",
              "      <td>2020-07-31</td>\n",
              "      <td>2020-08-07</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.64</td>\n",
              "      <td>2021-02-24</td>\n",
              "      <td>2021-02-26</td>\n",
              "      <td>2021-03-08</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
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          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyfolio/tears.py:907: UserWarning: Passed returns do not overlap with anyinteresting times.\n",
            "  'interesting times.', UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BzBaE63H3RLc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYeOjax-7H_5"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}