FinRL_single_stock_trading.ipynb

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        "<a href=\"https://colab.research.google.com/github/AI4Finance-LLC/FinRL-Library/blob/master/FinRL_single_stock_trading.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Single Stock Trading\n",
        "\n",
        "Tutorials to use OpenAI DRL to trade single stock in one Jupyter Notebook | Presented at NeurIPS 2020: Deep RL Workshop\n",
        "\n",
        "* This blog is based on our paper: FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, presented at NeurIPS 2020: Deep RL Workshop.\n",
        "* Check out medium blog for detailed explanations: https://towardsdatascience.com/finrl-for-quantitative-finance-tutorial-for-single-stock-trading-37d6d7c30aac\n",
        "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
        "* **Pytorch Version** \n",
        "\n",
        "\n"
      ]
    },
    {
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      "source": [
        "## Content"
      ]
    },
    {
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      "metadata": {
        "id": "ex2ord116AbP"
      },
      "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)             "
      ]
    },
    {
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      "metadata": {
        "id": "M1eLhMW36cLi"
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      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3eimeRv06YoK"
      },
      "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 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: single stock trading for AAPL\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",
        "\n",
        "We use Apple Inc. stock: AAPL as an example throughout this article, because it is one of the most popular and profitable stocks."
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "<a id='1'></a>\n",
        "# Part 2. Getting Started- Load Python Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UBUcBKap-oII"
      },
      "source": [
        "<a id='1.1'></a>\n",
        "## 2.1. Install all the packages through FinRL library\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "40axJBAP5mer",
        "outputId": "0e61993b-e962-4b70-a178-fe86404dd1ca"
      },
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": 1,
      "outputs": [
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            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (3.4.0)\n",
            "Building wheels for collected packages: finrl, yfinance, pyfolio, empyrical\n",
            "  Building wheel for finrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for finrl: filename=finrl-0.0.3-cp36-none-any.whl size=20652 sha256=7705b37c54de93e6961e5187cdc3f20882b747977acdfbbf6eeeded1996fb093\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-1tg6okn5/wheels/9c/19/bf/c644def96612df1ad42c94d5304966797eaa3221dffc5efe0b\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.55-py2.py3-none-any.whl size=22616 sha256=c38e00353cb119053c2ef2e3d334932b3af371156259cd9cf9d7d48d3291d6f6\n",
            "  Stored in directory: /root/.cache/pip/wheels/04/98/cc/2702a4242d60bdc14f48b4557c427ded1fe92aedf257d4565c\n",
            "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-cp36-none-any.whl size=75764 sha256=534ae04c48ee884ecbbabe0769886e3e2618e657006454546d55fa2e1305f3d0\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-1tg6okn5/wheels/43/ce/d9/6752fb6e03205408773235435205a0519d2c608a94f1976e56\n",
            "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for empyrical: filename=empyrical-0.5.5-cp36-none-any.whl size=39765 sha256=0c5ff9ec963e40d48ff4f12410ced7f975d94b5c077023c48640bb8d8b2649ab\n",
            "  Stored in directory: /root/.cache/pip/wheels/ea/b2/c8/6769d8444d2f2e608fae2641833110668d0ffd1abeb2e9f3fc\n",
            "Successfully built finrl yfinance pyfolio empyrical\n",
            "Installing collected packages: int-date, stockstats, lxml, yfinance, stable-baselines3, empyrical, pyfolio, finrl\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed empyrical-0.5.5 finrl-0.0.3 int-date-0.1.8 lxml-4.6.2 pyfolio-0.9.2+75.g4b901f6 stable-baselines3-0.10.0 stockstats-0.3.2 yfinance-0.1.55\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iXyHD6ir5sxk"
      },
      "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": "QG17M4JwTnSZ"
      },
      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-0bsNMMTnSZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1c336ac-1280-4b16-b1d4-c2971d6667c2"
      },
      "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",
        "from finrl.config import config\n",
        "from finrl.marketdata.yahoodownloader import YahooDownloader\n",
        "from finrl.preprocessing.preprocessors import FeatureEngineer\n",
        "from finrl.preprocessing.data import data_split\n",
        "from finrl.env.env_stocktrading import StockTradingEnv\n",
        "from finrl.model.models import DRLAgent\n",
        "from finrl.trade.backtest import BackTestStats, BaselineStats, BackTestPlot\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pyfolio/pos.py:27: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
            "  'Module \"zipline.assets\" not found; multipliers will not be applied'\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uIPbzYs1TnSd"
      },
      "source": [
        "#Diable the warnings\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pos2IZAL54pp"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zp6lL6dZ53rX"
      },
      "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": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SBPM0sVvTnSg"
      },
      "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": "GCGVmtGzjORf"
      },
      "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": "FBEiH5gOgMOx",
        "outputId": "de388576-0110-46f4-9257-dfe327b3eac5"
      },
      "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": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "sWLMNQ8CgMRx",
        "outputId": "f1864c8b-2b5c-4867-9122-ccf83ebc2902"
      },
      "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": [
              "'2020-09-30'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AmFpuBEZhkF3"
      },
      "source": [
        "ticker_list is a list of stock tickers, in a single stock trading case, the list contains only 1 ticker"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JtIFikNyTnSg",
        "outputId": "4c6a860f-6944-4728-984d-d0dc8d8f2c78"
      },
      "source": [
        "# Download and save the data in a pandas DataFrame:\n",
        "data_df = YahooDownloader(start_date = '2009-01-01',\n",
        "                          end_date = '2021-01-01',\n",
        "                          ticker_list = ['AAPL']).fetch_data()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (3021, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8ZKQmw6TnSl",
        "outputId": "954cbef4-f75b-4d4b-80f2-195eb13954ef"
      },
      "source": [
        "data_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3021, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "Vi6D_ED6TnSs",
        "outputId": "2313cd51-596f-4a58-922b-5ec837bbcbda"
      },
      "source": [
        "data_df.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",
              "    </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>2.795913</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-05</td>\n",
              "      <td>3.327500</td>\n",
              "      <td>3.435000</td>\n",
              "      <td>3.311071</td>\n",
              "      <td>2.913912</td>\n",
              "      <td>1181608400</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-06</td>\n",
              "      <td>3.426786</td>\n",
              "      <td>3.470357</td>\n",
              "      <td>3.299643</td>\n",
              "      <td>2.865849</td>\n",
              "      <td>1289310400</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-07</td>\n",
              "      <td>3.278929</td>\n",
              "      <td>3.303571</td>\n",
              "      <td>3.223572</td>\n",
              "      <td>2.803923</td>\n",
              "      <td>753048800</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-08</td>\n",
              "      <td>3.229643</td>\n",
              "      <td>3.326786</td>\n",
              "      <td>3.215714</td>\n",
              "      <td>2.855991</td>\n",
              "      <td>673500800</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date      open      high       low     close      volume   tic\n",
              "0  2009-01-02  3.067143  3.251429  3.041429  2.795913   746015200  AAPL\n",
              "1  2009-01-05  3.327500  3.435000  3.311071  2.913912  1181608400  AAPL\n",
              "2  2009-01-06  3.426786  3.470357  3.299643  2.865849  1289310400  AAPL\n",
              "3  2009-01-07  3.278929  3.303571  3.223572  2.803923   753048800  AAPL\n",
              "4  2009-01-08  3.229643  3.326786  3.215714  2.855991   673500800  AAPL"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oWiqgpLzTnS3"
      },
      "source": [
        "<a id='3'></a>\n",
        "# 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",
        "* FinRL uses a class **FeatureEngineer** to preprocess the data\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.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rJ9zmxpRks41"
      },
      "source": [
        "class FeatureEngineer:\n",
        "Provides methods for preprocessing the stock price data\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        df: DataFrame\n",
        "            data downloaded from Yahoo API\n",
        "        feature_number : int\n",
        "            number of features we used\n",
        "        use_technical_indicator : boolean\n",
        "            we technical indicator or not\n",
        "        use_turbulence : boolean\n",
        "            use turbulence index or not\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    preprocess_data()\n",
        "        main method to do the feature engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tHu7i-T_wRPc"
      },
      "source": [
        "<a id='3.1'></a>\n",
        "\n",
        "## 4.1 Technical Indicators\n",
        "* FinRL uses stockstats to calcualte technical indicators such as **Moving Average Convergence Divergence (MACD)**, **Relative Strength Index (RSI)**, **Average Directional Index (ADX)**, **Commodity Channel Index (CCI)** and other various indicators and stats.\n",
        "* **stockstats**: supplies a wrapper StockDataFrame based on the **pandas.DataFrame** with inline stock statistics/indicators support.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2RHwY1dHk09N",
        "outputId": "47be0df8-9e74-473e-f78e-9f267bcce3df"
      },
      "source": [
        "## we store the stockstats technical indicator column names in config.py\n",
        "tech_indicator_list=config.TECHNICAL_INDICATORS_LIST\n",
        "print(tech_indicator_list)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['macd', 'rsi_30', 'cci_30', 'dx_30']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rImfGAfCkR8j",
        "outputId": "5b55c2c7-3217-49ad-95b7-d9af0ad91d7a"
      },
      "source": [
        "## user can add more technical indicators\n",
        "## check https://github.com/jealous/stockstats for different names\n",
        "tech_indicator_list=tech_indicator_list+['kdjk','open_2_sma','boll','close_10.0_le_5_c','wr_10','dma','trix']\n",
        "print(tech_indicator_list)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['macd', 'rsi_30', 'cci_30', 'dx_30', 'kdjk', 'open_2_sma', 'boll', 'close_10.0_le_5_c', 'wr_10', 'dma', 'trix']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etvRo2rSwZPg"
      },
      "source": [
        "<a id='3.2'></a>\n",
        "## 4.2 Perform Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SAOsx0m-9u2k",
        "outputId": "6238a0f8-e0bb-4468-ec75-ab3381ea09a9"
      },
      "source": [
        "fe = FeatureEngineer(\n",
        "                    use_technical_indicator=True,\n",
        "                    tech_indicator_list = tech_indicator_list,\n",
        "                    use_turbulence=False,\n",
        "                    user_defined_feature = False)\n",
        "\n",
        "data_df = fe.preprocess_data(data_df)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully added technical indicators\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "wytX_qwWMHP5",
        "outputId": "8113269b-39a0-462b-80b2-b8664eb27a66"
      },
      "source": [
        "data_df.head()"
      ],
      "execution_count": 9,
      "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",
              "        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>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>kdjk</th>\n",
              "      <th>open_2_sma</th>\n",
              "      <th>boll</th>\n",
              "      <th>close_10.0_le_5_c</th>\n",
              "      <th>wr_10</th>\n",
              "      <th>dma</th>\n",
              "      <th>trix</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>2.795913</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>-5.637387</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>2.795913</td>\n",
              "      <td>1.0</td>\n",
              "      <td>216.912162</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.670734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-05</td>\n",
              "      <td>3.327500</td>\n",
              "      <td>3.435000</td>\n",
              "      <td>3.311071</td>\n",
              "      <td>2.913912</td>\n",
              "      <td>1181608400</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>0.002647</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>-14.558226</td>\n",
              "      <td>3.197322</td>\n",
              "      <td>2.854912</td>\n",
              "      <td>2.0</td>\n",
              "      <td>132.399904</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.670734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-06</td>\n",
              "      <td>3.426786</td>\n",
              "      <td>3.470357</td>\n",
              "      <td>3.299643</td>\n",
              "      <td>2.865849</td>\n",
              "      <td>1289310400</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>0.001883</td>\n",
              "      <td>70.355297</td>\n",
              "      <td>46.762875</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>-23.350262</td>\n",
              "      <td>3.377143</td>\n",
              "      <td>2.858558</td>\n",
              "      <td>3.0</td>\n",
              "      <td>140.934334</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.391303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-07</td>\n",
              "      <td>3.278929</td>\n",
              "      <td>3.303571</td>\n",
              "      <td>3.223572</td>\n",
              "      <td>2.803923</td>\n",
              "      <td>753048800</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.000747</td>\n",
              "      <td>50.429222</td>\n",
              "      <td>-29.787322</td>\n",
              "      <td>43.608349</td>\n",
              "      <td>-34.024085</td>\n",
              "      <td>3.352857</td>\n",
              "      <td>2.844899</td>\n",
              "      <td>4.0</td>\n",
              "      <td>155.371731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.195391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-08</td>\n",
              "      <td>3.229643</td>\n",
              "      <td>3.326786</td>\n",
              "      <td>3.215714</td>\n",
              "      <td>2.855991</td>\n",
              "      <td>673500800</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.000088</td>\n",
              "      <td>60.227071</td>\n",
              "      <td>-9.011779</td>\n",
              "      <td>48.358256</td>\n",
              "      <td>-37.093625</td>\n",
              "      <td>3.254286</td>\n",
              "      <td>2.847118</td>\n",
              "      <td>5.0</td>\n",
              "      <td>143.232705</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.125124</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date      open      high  ...       wr_10  dma      trix\n",
              "0  2009-01-02  3.067143  3.251429  ...  216.912162  0.0  0.670734\n",
              "1  2009-01-05  3.327500  3.435000  ...  132.399904  0.0  0.670734\n",
              "2  2009-01-06  3.426786  3.470357  ...  140.934334  0.0  0.391303\n",
              "3  2009-01-07  3.278929  3.303571  ...  155.371731  0.0  0.195391\n",
              "4  2009-01-08  3.229643  3.326786  ...  143.232705  0.0  0.125124\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bwLhXo1cTnTQ"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Build 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": "1D1FlBdOL4b3"
      },
      "source": [
        "<a id='4.1'></a>\n",
        "## 5.1 Training & Trade data split\n",
        "* Training: 2009-01-01 to 2018-12-31\n",
        "* Trade: 2019-01-01 to 2020-09-30"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNOdqfTKL6K-"
      },
      "source": [
        "#train = data_split(data_df, start = config.START_DATE, end = config.START_TRADE_DATE)\n",
        "#trade = data_split(data_df, start = config.START_TRADE_DATE, end = config.END_DATE)\n",
        "train = data_split(data_df, start = '2009-01-01', end = '2019-01-01')\n",
        "trade = data_split(data_df, start = '2019-01-01', end = '2021-01-01')\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUxOshVouLXt"
      },
      "source": [
        "## data normalization, this part is optional, have little impact\n",
        "#feaures_list = list(train.columns)\n",
        "#feaures_list.remove('date')\n",
        "#feaures_list.remove('tic')\n",
        "#feaures_list.remove('close')\n",
        "#print(feaures_list)\n",
        "#from sklearn import preprocessing\n",
        "#data_normaliser = preprocessing.StandardScaler()\n",
        "#train[feaures_list] = data_normaliser.fit_transform(train[feaures_list])\n",
        "#trade[feaures_list] = data_normaliser.transform(trade[feaures_list])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KMHyaSBBDGbe"
      },
      "source": [
        "<a id='4.2'></a>\n",
        "## 5.2 User-defined Environment: a simulation environment class "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "90V3S7cpDcQs"
      },
      "source": [
        "<a id='4.3'></a>\n",
        "## 5.3 Initialize Environment\n",
        "* **stock dimension**: the number of unique stock tickers we use\n",
        "* **hmax**: the maximum amount of shares to buy or sell\n",
        "* **initial amount**: the amount of money we use to trade in the begining\n",
        "* **transaction cost percentage**: a per share rate for every share trade\n",
        "* **tech_indicator_list**: a list of technical indicator names (modified from config.py)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OiH0xO96mGcL",
        "outputId": "490190d9-80dd-4ba0-c15f-2143cdd8341f"
      },
      "source": [
        "## we store the stockstats technical indicator column names in config.py\n",
        "## check https://github.com/jealous/stockstats for different names\n",
        "tech_indicator_list"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['macd',\n",
              " 'rsi_30',\n",
              " 'cci_30',\n",
              " 'dx_30',\n",
              " 'kdjk',\n",
              " 'open_2_sma',\n",
              " 'boll',\n",
              " 'close_10.0_le_5_c',\n",
              " 'wr_10',\n",
              " 'dma',\n",
              " 'trix']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnWOKS7DyGM7",
        "outputId": "09dff84b-19e1-4c4a-c40f-d5280195eaf4"
      },
      "source": [
        "# the stock dimension is 1, because we only use the price data of AAPL.\n",
        "len(train.tic.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ipJcnLvQGAba",
        "outputId": "dc32ebc6-2191-4541-99d2-7e9861da5df6"
      },
      "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": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stock Dimension: 1, State Space: 7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JY5wTwYjGTrg"
      },
      "source": [
        "env_kwargs = {\r\n",
        "    \"hmax\": 100, \r\n",
        "    \"initial_amount\": 100000, \r\n",
        "    \"transaction_cost_pct\": 0.001, \r\n",
        "    \"state_space\": state_space, \r\n",
        "    \"stock_dim\": stock_dimension, \r\n",
        "    \"tech_indicator_list\": config.TECHNICAL_INDICATORS_LIST, \r\n",
        "    \"action_space\": stock_dimension, \r\n",
        "    \"reward_scaling\": 1e-4\r\n",
        "    \r\n",
        "}\r\n",
        "\r\n",
        "e_train_gym = StockTradingEnv(df = train, **env_kwargs)\r\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vmyi2IPnyEkP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a968a96a-a1ee-426e-eed8-ee74d20b9bb8"
      },
      "source": [
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "print(type(env_train))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mdnzYtM1TnTW"
      },
      "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": "BRFIZDw8TnTX"
      },
      "source": [
        "agent = DRLAgent(env = env_train)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zD1NHzGyTnTc"
      },
      "source": [
        "### Model Training: 5 models, A2C DDPG, PPO, TD3, SAC\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y9CM5DeIr9GC"
      },
      "source": [
        "### Model 1: A2C"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rOk-Mr73-EEq",
        "outputId": "1c56a5c6-91ad-4b81-dd07-24ae69e64c23"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "\n",
        "A2C_PARAMS = {\"n_steps\": 5, \"ent_coef\": 0.005, \"learning_rate\": 0.0002}\n",
        "model_a2c = agent.get_model(model_name=\"a2c\",model_kwargs = A2C_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_steps': 5, 'ent_coef': 0.005, 'learning_rate': 0.0002}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXHEidJh-E60",
        "outputId": "9ae1ede1-a8ee-4e56-c4f3-ef809e05d664"
      },
      "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                | 154      |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 3        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.44    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | 0.000127 |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 1.39e-06 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 214      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 4        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.46    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -0.186   |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 0.0327   |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 245      |\n",
            "|    iterations         | 300      |\n",
            "|    time_elapsed       | 6        |\n",
            "|    total_timesteps    | 1500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.46    |\n",
            "|    explained_variance | -17      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 299      |\n",
            "|    policy_loss        | -0.483   |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 0.118    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 266      |\n",
            "|    iterations         | 400      |\n",
            "|    time_elapsed       | 7        |\n",
            "|    total_timesteps    | 2000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.46    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 399      |\n",
            "|    policy_loss        | -0.116   |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 0.00671  |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 280       |\n",
            "|    iterations         | 500       |\n",
            "|    time_elapsed       | 8         |\n",
            "|    total_timesteps    | 2500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.46     |\n",
            "|    explained_variance | -1.76e+14 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 499       |\n",
            "|    policy_loss        | 0.207     |\n",
            "|    std                | 1.05      |\n",
            "|    value_loss         | 0.0266    |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 291      |\n",
            "|    iterations         | 600      |\n",
            "|    time_elapsed       | 10       |\n",
            "|    total_timesteps    | 3000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.47    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 599      |\n",
            "|    policy_loss        | 0.0268   |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 0.000366 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 700      |\n",
            "|    time_elapsed       | 11       |\n",
            "|    total_timesteps    | 3500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.47    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 699      |\n",
            "|    policy_loss        | 0.4      |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 0.0684   |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 303      |\n",
            "|    iterations         | 800      |\n",
            "|    time_elapsed       | 13       |\n",
            "|    total_timesteps    | 4000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.47    |\n",
            "|    explained_variance | -82.8    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 799      |\n",
            "|    policy_loss        | 0.484    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 0.214    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 308       |\n",
            "|    iterations         | 900       |\n",
            "|    time_elapsed       | 14        |\n",
            "|    total_timesteps    | 4500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.48     |\n",
            "|    explained_variance | -2.83e+13 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 899       |\n",
            "|    policy_loss        | -0.128    |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 0.0154    |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 311      |\n",
            "|    iterations         | 1000     |\n",
            "|    time_elapsed       | 16       |\n",
            "|    total_timesteps    | 5000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 999      |\n",
            "|    policy_loss        | -0.172   |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 0.0203   |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 315      |\n",
            "|    iterations         | 1100     |\n",
            "|    time_elapsed       | 17       |\n",
            "|    total_timesteps    | 5500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1099     |\n",
            "|    policy_loss        | 0.0235   |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.000275 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 318      |\n",
            "|    iterations         | 1200     |\n",
            "|    time_elapsed       | 18       |\n",
            "|    total_timesteps    | 6000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | -93.7    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1199     |\n",
            "|    policy_loss        | -0.432   |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.118    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 320      |\n",
            "|    iterations         | 1300     |\n",
            "|    time_elapsed       | 20       |\n",
            "|    total_timesteps    | 6500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -44.1    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1299     |\n",
            "|    policy_loss        | 1.02     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.666    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 322      |\n",
            "|    iterations         | 1400     |\n",
            "|    time_elapsed       | 21       |\n",
            "|    total_timesteps    | 7000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -2.56    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1399     |\n",
            "|    policy_loss        | -0.201   |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.0304   |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 324      |\n",
            "|    iterations         | 1500     |\n",
            "|    time_elapsed       | 23       |\n",
            "|    total_timesteps    | 7500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | -718     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1499     |\n",
            "|    policy_loss        | -1.53    |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 1.03     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 326      |\n",
            "|    iterations         | 1600     |\n",
            "|    time_elapsed       | 24       |\n",
            "|    total_timesteps    | 8000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | -30      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1599     |\n",
            "|    policy_loss        | 0.363    |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.0864   |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 327      |\n",
            "|    iterations         | 1700     |\n",
            "|    time_elapsed       | 25       |\n",
            "|    total_timesteps    | 8500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | -315     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1699     |\n",
            "|    policy_loss        | -0.257   |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 0.186    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 328       |\n",
            "|    iterations         | 1800      |\n",
            "|    time_elapsed       | 27        |\n",
            "|    total_timesteps    | 9000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.48     |\n",
            "|    explained_variance | -2.33e+05 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1799      |\n",
            "|    policy_loss        | -0.00491  |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 0.144     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 329      |\n",
            "|    iterations         | 1900     |\n",
            "|    time_elapsed       | 28       |\n",
            "|    total_timesteps    | 9500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1899     |\n",
            "|    policy_loss        | -0.232   |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.105    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 330       |\n",
            "|    iterations         | 2000      |\n",
            "|    time_elapsed       | 30        |\n",
            "|    total_timesteps    | 10000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.48     |\n",
            "|    explained_variance | -3.46e+04 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1999      |\n",
            "|    policy_loss        | -1.02     |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 1.39      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 331      |\n",
            "|    iterations         | 2100     |\n",
            "|    time_elapsed       | 31       |\n",
            "|    total_timesteps    | 10500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | -1.34    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2099     |\n",
            "|    policy_loss        | 0.418    |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.18     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 332       |\n",
            "|    iterations         | 2200      |\n",
            "|    time_elapsed       | 33        |\n",
            "|    total_timesteps    | 11000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.48     |\n",
            "|    explained_variance | -1.97e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2199      |\n",
            "|    policy_loss        | -0.255    |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 0.132     |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 333       |\n",
            "|    iterations         | 2300      |\n",
            "|    time_elapsed       | 34        |\n",
            "|    total_timesteps    | 11500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.48     |\n",
            "|    explained_variance | -1.56e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2299      |\n",
            "|    policy_loss        | 1.01      |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 0.536     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 333      |\n",
            "|    iterations         | 2400     |\n",
            "|    time_elapsed       | 35       |\n",
            "|    total_timesteps    | 12000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | -34.4    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2399     |\n",
            "|    policy_loss        | 0.391    |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.116    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 334      |\n",
            "|    iterations         | 2500     |\n",
            "|    time_elapsed       | 37       |\n",
            "|    total_timesteps    | 12500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | -472     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2499     |\n",
            "|    policy_loss        | 2.4      |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 3.92     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 334      |\n",
            "|    iterations         | 2600     |\n",
            "|    time_elapsed       | 38       |\n",
            "|    total_timesteps    | 13000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2599     |\n",
            "|    policy_loss        | -0.194   |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.0448   |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 335      |\n",
            "|    iterations         | 2700     |\n",
            "|    time_elapsed       | 40       |\n",
            "|    total_timesteps    | 13500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | -370     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2699     |\n",
            "|    policy_loss        | 1.7      |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 1.55     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 335      |\n",
            "|    iterations         | 2800     |\n",
            "|    time_elapsed       | 41       |\n",
            "|    total_timesteps    | 14000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | -109     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2799     |\n",
            "|    policy_loss        | 0.942    |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.435    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 336       |\n",
            "|    iterations         | 2900      |\n",
            "|    time_elapsed       | 43        |\n",
            "|    total_timesteps    | 14500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.48     |\n",
            "|    explained_variance | -4.73e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2899      |\n",
            "|    policy_loss        | -0.125    |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 0.0941    |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 337       |\n",
            "|    iterations         | 3000      |\n",
            "|    time_elapsed       | 44        |\n",
            "|    total_timesteps    | 15000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.48     |\n",
            "|    explained_variance | -4.93e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2999      |\n",
            "|    policy_loss        | 2.72      |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 6.84      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 337      |\n",
            "|    iterations         | 3100     |\n",
            "|    time_elapsed       | 45       |\n",
            "|    total_timesteps    | 15500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | -10.3    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3099     |\n",
            "|    policy_loss        | 0.0729   |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.0105   |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 338       |\n",
            "|    iterations         | 3200      |\n",
            "|    time_elapsed       | 47        |\n",
            "|    total_timesteps    | 16000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.48     |\n",
            "|    explained_variance | -3.08e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3199      |\n",
            "|    policy_loss        | -0.769    |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 0.745     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 338      |\n",
            "|    iterations         | 3300     |\n",
            "|    time_elapsed       | 48       |\n",
            "|    total_timesteps    | 16500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | -254     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3299     |\n",
            "|    policy_loss        | 0.777    |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.704    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 338      |\n",
            "|    iterations         | 3400     |\n",
            "|    time_elapsed       | 50       |\n",
            "|    total_timesteps    | 17000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.48    |\n",
            "|    explained_variance | -157     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3399     |\n",
            "|    policy_loss        | 0.495    |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.273    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 338      |\n",
            "|    iterations         | 3500     |\n",
            "|    time_elapsed       | 51       |\n",
            "|    total_timesteps    | 17500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -586     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3499     |\n",
            "|    policy_loss        | 0.275    |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.28     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 338      |\n",
            "|    iterations         | 3600     |\n",
            "|    time_elapsed       | 53       |\n",
            "|    total_timesteps    | 18000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3599     |\n",
            "|    policy_loss        | -0.312   |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.0746   |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 339       |\n",
            "|    iterations         | 3700      |\n",
            "|    time_elapsed       | 54        |\n",
            "|    total_timesteps    | 18500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.49     |\n",
            "|    explained_variance | -6.46e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3699      |\n",
            "|    policy_loss        | 1.51      |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 1.63      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 339       |\n",
            "|    iterations         | 3800      |\n",
            "|    time_elapsed       | 56        |\n",
            "|    total_timesteps    | 19000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.49     |\n",
            "|    explained_variance | -1.77e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3799      |\n",
            "|    policy_loss        | 1.03      |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 0.361     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 339      |\n",
            "|    iterations         | 3900     |\n",
            "|    time_elapsed       | 57       |\n",
            "|    total_timesteps    | 19500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -147     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3899     |\n",
            "|    policy_loss        | -1.46    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 1.84     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 339       |\n",
            "|    iterations         | 4000      |\n",
            "|    time_elapsed       | 58        |\n",
            "|    total_timesteps    | 20000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.49     |\n",
            "|    explained_variance | -3.29e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3999      |\n",
            "|    policy_loss        | -1.55     |\n",
            "|    std                | 1.08      |\n",
            "|    value_loss         | 1.72      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 340       |\n",
            "|    iterations         | 4100      |\n",
            "|    time_elapsed       | 60        |\n",
            "|    total_timesteps    | 20500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.49     |\n",
            "|    explained_variance | -1.86e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 4099      |\n",
            "|    policy_loss        | 0.629     |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 0.335     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 340      |\n",
            "|    iterations         | 4200     |\n",
            "|    time_elapsed       | 61       |\n",
            "|    total_timesteps    | 21000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -256     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4199     |\n",
            "|    policy_loss        | 0.46     |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.103    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 340      |\n",
            "|    iterations         | 4300     |\n",
            "|    time_elapsed       | 63       |\n",
            "|    total_timesteps    | 21500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -375     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4299     |\n",
            "|    policy_loss        | 0.738    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.449    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 340       |\n",
            "|    iterations         | 4400      |\n",
            "|    time_elapsed       | 64        |\n",
            "|    total_timesteps    | 22000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.49     |\n",
            "|    explained_variance | -1.41e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 4399      |\n",
            "|    policy_loss        | -1.05     |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 0.688     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 340      |\n",
            "|    iterations         | 4500     |\n",
            "|    time_elapsed       | 66       |\n",
            "|    total_timesteps    | 22500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -0.338   |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4499     |\n",
            "|    policy_loss        | -0.278   |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.079    |\n",
            "------------------------------------\n",
            "day: 2515, episode: 10\n",
            "begin_total_asset:100000.00\n",
            "end_total_asset:655402.41\n",
            "total_reward:555402.41\n",
            "total_cost: 3067.10\n",
            "total_trades: 2514\n",
            "Sharpe: 0.909\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 340      |\n",
            "|    iterations         | 4600     |\n",
            "|    time_elapsed       | 67       |\n",
            "|    total_timesteps    | 23000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4599     |\n",
            "|    policy_loss        | -0.179   |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.0269   |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 340       |\n",
            "|    iterations         | 4700      |\n",
            "|    time_elapsed       | 68        |\n",
            "|    total_timesteps    | 23500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.49     |\n",
            "|    explained_variance | -4.05e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 4699      |\n",
            "|    policy_loss        | -1.45     |\n",
            "|    std                | 1.08      |\n",
            "|    value_loss         | 1.81      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 341      |\n",
            "|    iterations         | 4800     |\n",
            "|    time_elapsed       | 70       |\n",
            "|    total_timesteps    | 24000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -356     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4799     |\n",
            "|    policy_loss        | 0.407    |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.0792   |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 341       |\n",
            "|    iterations         | 4900      |\n",
            "|    time_elapsed       | 71        |\n",
            "|    total_timesteps    | 24500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.49     |\n",
            "|    explained_variance | -1.63e+04 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 4899      |\n",
            "|    policy_loss        | -0.447    |\n",
            "|    std                | 1.08      |\n",
            "|    value_loss         | 0.145     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 341      |\n",
            "|    iterations         | 5000     |\n",
            "|    time_elapsed       | 73       |\n",
            "|    total_timesteps    | 25000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -6.5e+04 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4999     |\n",
            "|    policy_loss        | 4.89     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 23.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 341      |\n",
            "|    iterations         | 5100     |\n",
            "|    time_elapsed       | 74       |\n",
            "|    total_timesteps    | 25500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5099     |\n",
            "|    policy_loss        | -0.486   |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.0898   |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 341       |\n",
            "|    iterations         | 5200      |\n",
            "|    time_elapsed       | 76        |\n",
            "|    total_timesteps    | 26000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.49     |\n",
            "|    explained_variance | -2.57e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 5199      |\n",
            "|    policy_loss        | 2.1       |\n",
            "|    std                | 1.08      |\n",
            "|    value_loss         | 2.39      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 341      |\n",
            "|    iterations         | 5300     |\n",
            "|    time_elapsed       | 77       |\n",
            "|    total_timesteps    | 26500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -733     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5299     |\n",
            "|    policy_loss        | 2.94     |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 4.31     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 341       |\n",
            "|    iterations         | 5400      |\n",
            "|    time_elapsed       | 78        |\n",
            "|    total_timesteps    | 27000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.49     |\n",
            "|    explained_variance | -1.22e+04 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 5399      |\n",
            "|    policy_loss        | -3.42     |\n",
            "|    std                | 1.08      |\n",
            "|    value_loss         | 2.64      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 341       |\n",
            "|    iterations         | 5500      |\n",
            "|    time_elapsed       | 80        |\n",
            "|    total_timesteps    | 27500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.49     |\n",
            "|    explained_variance | -2.39e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 5499      |\n",
            "|    policy_loss        | 1.65      |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 1.94      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 342      |\n",
            "|    iterations         | 5600     |\n",
            "|    time_elapsed       | 81       |\n",
            "|    total_timesteps    | 28000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5599     |\n",
            "|    policy_loss        | 0.0289   |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.000913 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 342       |\n",
            "|    iterations         | 5700      |\n",
            "|    time_elapsed       | 83        |\n",
            "|    total_timesteps    | 28500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.49     |\n",
            "|    explained_variance | -1.34e+04 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 5699      |\n",
            "|    policy_loss        | 2.6       |\n",
            "|    std                | 1.08      |\n",
            "|    value_loss         | 1.02      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 342      |\n",
            "|    iterations         | 5800     |\n",
            "|    time_elapsed       | 84       |\n",
            "|    total_timesteps    | 29000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -660     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5799     |\n",
            "|    policy_loss        | 0.035    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.065    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 342      |\n",
            "|    iterations         | 5900     |\n",
            "|    time_elapsed       | 86       |\n",
            "|    total_timesteps    | 29500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -35      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5899     |\n",
            "|    policy_loss        | -0.597   |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.222    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 342       |\n",
            "|    iterations         | 6000      |\n",
            "|    time_elapsed       | 87        |\n",
            "|    total_timesteps    | 30000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.49     |\n",
            "|    explained_variance | -2.74e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 5999      |\n",
            "|    policy_loss        | -4.13     |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 8.29      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 342      |\n",
            "|    iterations         | 6100     |\n",
            "|    time_elapsed       | 88       |\n",
            "|    total_timesteps    | 30500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -0.727   |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6099     |\n",
            "|    policy_loss        | -0.122   |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.0142   |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 343      |\n",
            "|    iterations         | 6200     |\n",
            "|    time_elapsed       | 90       |\n",
            "|    total_timesteps    | 31000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -188     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6199     |\n",
            "|    policy_loss        | 1.19     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.743    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 343      |\n",
            "|    iterations         | 6300     |\n",
            "|    time_elapsed       | 91       |\n",
            "|    total_timesteps    | 31500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -19.6    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6299     |\n",
            "|    policy_loss        | -0.125   |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.00822  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 343      |\n",
            "|    iterations         | 6400     |\n",
            "|    time_elapsed       | 93       |\n",
            "|    total_timesteps    | 32000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -176     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6399     |\n",
            "|    policy_loss        | 1.41     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 2.27     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 343      |\n",
            "|    iterations         | 6500     |\n",
            "|    time_elapsed       | 94       |\n",
            "|    total_timesteps    | 32500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -48.3    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6499     |\n",
            "|    policy_loss        | 3.68     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 6.33     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 343      |\n",
            "|    iterations         | 6600     |\n",
            "|    time_elapsed       | 96       |\n",
            "|    total_timesteps    | 33000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6599     |\n",
            "|    policy_loss        | -0.125   |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.0207   |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 343      |\n",
            "|    iterations         | 6700     |\n",
            "|    time_elapsed       | 97       |\n",
            "|    total_timesteps    | 33500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -694     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6699     |\n",
            "|    policy_loss        | 0.102    |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.0922   |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 343      |\n",
            "|    iterations         | 6800     |\n",
            "|    time_elapsed       | 98       |\n",
            "|    total_timesteps    | 34000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -21      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6799     |\n",
            "|    policy_loss        | 1.45     |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.981    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 343      |\n",
            "|    iterations         | 6900     |\n",
            "|    time_elapsed       | 100      |\n",
            "|    total_timesteps    | 34500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -419     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6899     |\n",
            "|    policy_loss        | -0.128   |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.215    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 344      |\n",
            "|    iterations         | 7000     |\n",
            "|    time_elapsed       | 101      |\n",
            "|    total_timesteps    | 35000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.5     |\n",
            "|    explained_variance | -24.5    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6999     |\n",
            "|    policy_loss        | -3.75    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 11.1     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 344      |\n",
            "|    iterations         | 7100     |\n",
            "|    time_elapsed       | 103      |\n",
            "|    total_timesteps    | 35500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -10.4    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7099     |\n",
            "|    policy_loss        | -0.33    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.142    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 344      |\n",
            "|    iterations         | 7200     |\n",
            "|    time_elapsed       | 104      |\n",
            "|    total_timesteps    | 36000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -37.6    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7199     |\n",
            "|    policy_loss        | 1.5      |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.989    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 344      |\n",
            "|    iterations         | 7300     |\n",
            "|    time_elapsed       | 105      |\n",
            "|    total_timesteps    | 36500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -235     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7299     |\n",
            "|    policy_loss        | 0.099    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.127    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 344      |\n",
            "|    iterations         | 7400     |\n",
            "|    time_elapsed       | 107      |\n",
            "|    total_timesteps    | 37000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -622     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7399     |\n",
            "|    policy_loss        | -0.813   |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.551    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 344      |\n",
            "|    iterations         | 7500     |\n",
            "|    time_elapsed       | 108      |\n",
            "|    total_timesteps    | 37500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -52.6    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7499     |\n",
            "|    policy_loss        | 0.165    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.168    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 7600     |\n",
            "|    time_elapsed       | 110      |\n",
            "|    total_timesteps    | 38000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | 0.672    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7599     |\n",
            "|    policy_loss        | 0.0201   |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.00224  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 7700     |\n",
            "|    time_elapsed       | 111      |\n",
            "|    total_timesteps    | 38500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -3.25    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7699     |\n",
            "|    policy_loss        | -0.0545  |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.0182   |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 7800     |\n",
            "|    time_elapsed       | 112      |\n",
            "|    total_timesteps    | 39000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -3.3e+03 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7799     |\n",
            "|    policy_loss        | 0.0292   |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.288    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 7900     |\n",
            "|    time_elapsed       | 114      |\n",
            "|    total_timesteps    | 39500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -188     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7899     |\n",
            "|    policy_loss        | -0.641   |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.915    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 8000     |\n",
            "|    time_elapsed       | 115      |\n",
            "|    total_timesteps    | 40000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.49    |\n",
            "|    explained_variance | -65.2    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7999     |\n",
            "|    policy_loss        | 1.43     |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 1.01     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 8100     |\n",
            "|    time_elapsed       | 117      |\n",
            "|    total_timesteps    | 40500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.5     |\n",
            "|    explained_variance | -1.57    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8099     |\n",
            "|    policy_loss        | 0.487    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.14     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 8200     |\n",
            "|    time_elapsed       | 118      |\n",
            "|    total_timesteps    | 41000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.5     |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8199     |\n",
            "|    policy_loss        | -0.121   |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.0371   |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 8300     |\n",
            "|    time_elapsed       | 120      |\n",
            "|    total_timesteps    | 41500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.5     |\n",
            "|    explained_variance | -598     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8299     |\n",
            "|    policy_loss        | 1.31     |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.949    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 345       |\n",
            "|    iterations         | 8400      |\n",
            "|    time_elapsed       | 121       |\n",
            "|    total_timesteps    | 42000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.5      |\n",
            "|    explained_variance | -1.23e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8399      |\n",
            "|    policy_loss        | -0.213    |\n",
            "|    std                | 1.08      |\n",
            "|    value_loss         | 0.105     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 8500     |\n",
            "|    time_elapsed       | 122      |\n",
            "|    total_timesteps    | 42500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.5     |\n",
            "|    explained_variance | -161     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8499     |\n",
            "|    policy_loss        | 2.54     |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 3.13     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 8600     |\n",
            "|    time_elapsed       | 124      |\n",
            "|    total_timesteps    | 43000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.5     |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8599     |\n",
            "|    policy_loss        | -0.544   |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.12     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 345       |\n",
            "|    iterations         | 8700      |\n",
            "|    time_elapsed       | 125       |\n",
            "|    total_timesteps    | 43500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.5      |\n",
            "|    explained_variance | -8.74e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8699      |\n",
            "|    policy_loss        | -2.35     |\n",
            "|    std                | 1.08      |\n",
            "|    value_loss         | 1.35      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 8800     |\n",
            "|    time_elapsed       | 127      |\n",
            "|    total_timesteps    | 44000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.5     |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8799     |\n",
            "|    policy_loss        | -0.204   |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.113    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 345       |\n",
            "|    iterations         | 8900      |\n",
            "|    time_elapsed       | 128       |\n",
            "|    total_timesteps    | 44500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.5      |\n",
            "|    explained_variance | -2.62e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8899      |\n",
            "|    policy_loss        | 0.0545    |\n",
            "|    std                | 1.08      |\n",
            "|    value_loss         | 0.217     |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 345       |\n",
            "|    iterations         | 9000      |\n",
            "|    time_elapsed       | 130       |\n",
            "|    total_timesteps    | 45000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.5      |\n",
            "|    explained_variance | -4.07e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8999      |\n",
            "|    policy_loss        | 5.84      |\n",
            "|    std                | 1.09      |\n",
            "|    value_loss         | 20.4      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 9100     |\n",
            "|    time_elapsed       | 131      |\n",
            "|    total_timesteps    | 45500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.5     |\n",
            "|    explained_variance | 0.672    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9099     |\n",
            "|    policy_loss        | -0.417   |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 0.145    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 9200     |\n",
            "|    time_elapsed       | 133      |\n",
            "|    total_timesteps    | 46000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.5     |\n",
            "|    explained_variance | -594     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9199     |\n",
            "|    policy_loss        | -3.07    |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 2.11     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 9300     |\n",
            "|    time_elapsed       | 134      |\n",
            "|    total_timesteps    | 46500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.5     |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9299     |\n",
            "|    policy_loss        | 1.23     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 0.311    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 9400     |\n",
            "|    time_elapsed       | 135      |\n",
            "|    total_timesteps    | 47000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.51    |\n",
            "|    explained_variance | -260     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9399     |\n",
            "|    policy_loss        | -1.07    |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 0.535    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 9500     |\n",
            "|    time_elapsed       | 137      |\n",
            "|    total_timesteps    | 47500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.51    |\n",
            "|    explained_variance | -913     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9499     |\n",
            "|    policy_loss        | 0.836    |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 0.352    |\n",
            "------------------------------------\n",
            "day: 2515, episode: 20\n",
            "begin_total_asset:100000.00\n",
            "end_total_asset:769375.58\n",
            "total_reward:669375.58\n",
            "total_cost: 3661.18\n",
            "total_trades: 2515\n",
            "Sharpe: 0.967\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 9600     |\n",
            "|    time_elapsed       | 138      |\n",
            "|    total_timesteps    | 48000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.5     |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9599     |\n",
            "|    policy_loss        | 0.196    |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 0.0405   |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 345       |\n",
            "|    iterations         | 9700      |\n",
            "|    time_elapsed       | 140       |\n",
            "|    total_timesteps    | 48500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.5      |\n",
            "|    explained_variance | -5.05e+04 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 9699      |\n",
            "|    policy_loss        | -1.31     |\n",
            "|    std                | 1.09      |\n",
            "|    value_loss         | 0.572     |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 345       |\n",
            "|    iterations         | 9800      |\n",
            "|    time_elapsed       | 141       |\n",
            "|    total_timesteps    | 49000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -1.5      |\n",
            "|    explained_variance | -1.19e+03 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 9799      |\n",
            "|    policy_loss        | 0.276     |\n",
            "|    std                | 1.09      |\n",
            "|    value_loss         | 0.348     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 9900     |\n",
            "|    time_elapsed       | 143      |\n",
            "|    total_timesteps    | 49500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.5     |\n",
            "|    explained_variance | -37.6    |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9899     |\n",
            "|    policy_loss        | -1.5     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 0.926    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 345      |\n",
            "|    iterations         | 10000    |\n",
            "|    time_elapsed       | 144      |\n",
            "|    total_timesteps    | 50000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -1.5     |\n",
            "|    explained_variance | -293     |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9999     |\n",
            "|    policy_loss        | -0.151   |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 0.441    |\n",
            "------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9upN8FI2r_X1"
      },
      "source": [
        "### Model 2: DDPG"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gUFJlHsi-Ka-",
        "outputId": "1b7db70f-f740-401c-ab55-a567b4235e18"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "DDPG_PARAMS = {\"batch_size\": 64, \"buffer_size\": 500000, \"learning_rate\": 0.0001}\n",
        "\n",
        "\n",
        "model_ddpg = agent.get_model(\"ddpg\",model_kwargs = DDPG_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 64, 'buffer_size': 500000, 'learning_rate': 0.0001}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I5J_Scwp-Nis",
        "outputId": "a538962d-33c8-44d3-94af-6ef61fc0d230"
      },
      "source": [
        "trained_ddpg = agent.train_model(model=model_ddpg, \n",
        "                             tb_log_name='ddpg',\n",
        "                             total_timesteps=30000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ddpg/ddpg_3\n",
            "day: 2515, episode: 40\n",
            "begin_total_asset:100000.00\n",
            "end_total_asset:882157.61\n",
            "total_reward:782157.61\n",
            "total_cost: 99.90\n",
            "total_trades: 2515\n",
            "Sharpe: 1.001\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 71       |\n",
            "|    time_elapsed    | 140      |\n",
            "|    total timesteps | 10064    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 1.3e+03  |\n",
            "|    critic_loss     | 1.4e+03  |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 7548     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 61       |\n",
            "|    time_elapsed    | 324      |\n",
            "|    total timesteps | 20128    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 602      |\n",
            "|    critic_loss     | 914      |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 17612    |\n",
            "---------------------------------\n",
            "day: 2515, episode: 50\n",
            "begin_total_asset:100000.00\n",
            "end_total_asset:882157.61\n",
            "total_reward:782157.61\n",
            "total_cost: 99.90\n",
            "total_trades: 2515\n",
            "Sharpe: 1.001\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 12       |\n",
            "|    fps             | 59       |\n",
            "|    time_elapsed    | 510      |\n",
            "|    total timesteps | 30192    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 307      |\n",
            "|    critic_loss     | 540      |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 27676    |\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1sve9WGvsC__"
      },
      "source": [
        "### Model 3: PPO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOjuycpS-Qvn",
        "outputId": "13ab0019-419b-4e6e-949d-c2617ae296b3"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "PPO_PARAMS = {\n",
        "    \"n_steps\": 2048,\n",
        "    \"ent_coef\": 0.005,\n",
        "    \"learning_rate\": 0.0001,\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': 0.005, 'learning_rate': 0.0001, 'batch_size': 128}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G9hCHA8A-RSy",
        "outputId": "df865883-f3a8-42b2-951f-b129b44bd248"
      },
      "source": [
        "trained_ppo = agent.train_model(model=model_ppo, \n",
        "                             tb_log_name='ppo',\n",
        "                             total_timesteps=80000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ppo/ppo_1\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 719  |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 2    |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:154201.6784307579\n",
            "total_reward:54201.67843075789\n",
            "total_cost:  6229.798879286764\n",
            "total_trades:  2456\n",
            "Sharpe:  0.5097435961979868\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 643           |\n",
            "|    iterations           | 2             |\n",
            "|    time_elapsed         | 6             |\n",
            "|    total_timesteps      | 4096          |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 0.00047010495 |\n",
            "|    clip_fraction        | 0.00688       |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.42         |\n",
            "|    explained_variance   | -0.797        |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 0.00326       |\n",
            "|    n_updates            | 10            |\n",
            "|    policy_gradient_loss | -0.00325      |\n",
            "|    std                  | 0.995         |\n",
            "|    value_loss           | 0.0544        |\n",
            "-------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:208474.5414265936\n",
            "total_reward:108474.54142659361\n",
            "total_cost:  6595.42972978121\n",
            "total_trades:  2466\n",
            "Sharpe:  0.7961560092919648\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 617           |\n",
            "|    iterations           | 3             |\n",
            "|    time_elapsed         | 9             |\n",
            "|    total_timesteps      | 6144          |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 0.00029668477 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.41         |\n",
            "|    explained_variance   | -11.2         |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 0.00154       |\n",
            "|    n_updates            | 20            |\n",
            "|    policy_gradient_loss | -0.00037      |\n",
            "|    std                  | 0.992         |\n",
            "|    value_loss           | 0.0267        |\n",
            "-------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:226623.22692253595\n",
            "total_reward:126623.22692253595\n",
            "total_cost:  6662.477544789074\n",
            "total_trades:  2486\n",
            "Sharpe:  0.6118778493241752\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 608          |\n",
            "|    iterations           | 4            |\n",
            "|    time_elapsed         | 13           |\n",
            "|    total_timesteps      | 8192         |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0017147474 |\n",
            "|    clip_fraction        | 0.0106       |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.41        |\n",
            "|    explained_variance   | -18.7        |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 0.0406       |\n",
            "|    n_updates            | 30           |\n",
            "|    policy_gradient_loss | -0.00139     |\n",
            "|    std                  | 0.988        |\n",
            "|    value_loss           | 0.0987       |\n",
            "------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:320411.7910579003\n",
            "total_reward:220411.7910579003\n",
            "total_cost:  6595.420933233377\n",
            "total_trades:  2510\n",
            "Sharpe:  0.726873276762527\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 603            |\n",
            "|    iterations           | 5              |\n",
            "|    time_elapsed         | 16             |\n",
            "|    total_timesteps      | 10240          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -0.00034709403 |\n",
            "|    clip_fraction        | 9.77e-05       |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -1.41          |\n",
            "|    explained_variance   | -17            |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 0.156          |\n",
            "|    n_updates            | 40             |\n",
            "|    policy_gradient_loss | -0.000743      |\n",
            "|    std                  | 0.985          |\n",
            "|    value_loss           | 0.413          |\n",
            "--------------------------------------------\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 594           |\n",
            "|    iterations           | 6             |\n",
            "|    time_elapsed         | 20            |\n",
            "|    total_timesteps      | 12288         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | -0.0008853029 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.4          |\n",
            "|    explained_variance   | -48.2         |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 0.421         |\n",
            "|    n_updates            | 50            |\n",
            "|    policy_gradient_loss | -0.000165     |\n",
            "|    std                  | 0.985         |\n",
            "|    value_loss           | 1.02          |\n",
            "-------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:364791.16926864977\n",
            "total_reward:264791.16926864977\n",
            "total_cost:  6459.485391873797\n",
            "total_trades:  2514\n",
            "Sharpe:  0.7751585130144075\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 584          |\n",
            "|    iterations           | 7            |\n",
            "|    time_elapsed         | 24           |\n",
            "|    total_timesteps      | 14336        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0024066083 |\n",
            "|    clip_fraction        | 0.000146     |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.4         |\n",
            "|    explained_variance   | -14          |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 0.261        |\n",
            "|    n_updates            | 60           |\n",
            "|    policy_gradient_loss | -0.000178    |\n",
            "|    std                  | 0.987        |\n",
            "|    value_loss           | 0.633        |\n",
            "------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:386029.3211038883\n",
            "total_reward:286029.3211038883\n",
            "total_cost:  6686.318669397613\n",
            "total_trades:  2514\n",
            "Sharpe:  0.864197994307941\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 583          |\n",
            "|    iterations           | 8            |\n",
            "|    time_elapsed         | 28           |\n",
            "|    total_timesteps      | 16384        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0044404184 |\n",
            "|    clip_fraction        | 0.00937      |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.41        |\n",
            "|    explained_variance   | -43.1        |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 0.59         |\n",
            "|    n_updates            | 70           |\n",
            "|    policy_gradient_loss | -0.000688    |\n",
            "|    std                  | 0.987        |\n",
            "|    value_loss           | 1.04         |\n",
            "------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:367652.02145418554\n",
            "total_reward:267652.02145418554\n",
            "total_cost:  6563.71718565929\n",
            "total_trades:  2514\n",
            "Sharpe:  0.8576480650161772\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 582           |\n",
            "|    iterations           | 9             |\n",
            "|    time_elapsed         | 31            |\n",
            "|    total_timesteps      | 18432         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 0.00033915893 |\n",
            "|    clip_fraction        | 0.00464       |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.41         |\n",
            "|    explained_variance   | -164          |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 0.485         |\n",
            "|    n_updates            | 80            |\n",
            "|    policy_gradient_loss | -0.000485     |\n",
            "|    std                  | 0.988         |\n",
            "|    value_loss           | 0.893         |\n",
            "-------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:404021.36766140145\n",
            "total_reward:304021.36766140145\n",
            "total_cost:  6733.062301228537\n",
            "total_trades:  2514\n",
            "Sharpe:  0.878678154319762\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 581         |\n",
            "|    iterations           | 10          |\n",
            "|    time_elapsed         | 35          |\n",
            "|    total_timesteps      | 20480       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.004277601 |\n",
            "|    clip_fraction        | 0.0364      |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -1.41       |\n",
            "|    explained_variance   | -24.7       |\n",
            "|    learning_rate        | 0.0001      |\n",
            "|    loss                 | 0.397       |\n",
            "|    n_updates            | 90          |\n",
            "|    policy_gradient_loss | -0.00232    |\n",
            "|    std                  | 0.987       |\n",
            "|    value_loss           | 0.908       |\n",
            "-----------------------------------------\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 578           |\n",
            "|    iterations           | 11            |\n",
            "|    time_elapsed         | 38            |\n",
            "|    total_timesteps      | 22528         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 0.00073247147 |\n",
            "|    clip_fraction        | 0.0336        |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.41         |\n",
            "|    explained_variance   | -25           |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 0.572         |\n",
            "|    n_updates            | 100           |\n",
            "|    policy_gradient_loss | -0.00311      |\n",
            "|    std                  | 0.985         |\n",
            "|    value_loss           | 1.26          |\n",
            "-------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:397410.98459220416\n",
            "total_reward:297410.98459220416\n",
            "total_cost:  6624.640401721625\n",
            "total_trades:  2515\n",
            "Sharpe:  0.8064228580435082\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 576          |\n",
            "|    iterations           | 12           |\n",
            "|    time_elapsed         | 42           |\n",
            "|    total_timesteps      | 24576        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0025355744 |\n",
            "|    clip_fraction        | 0.00815      |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.4         |\n",
            "|    explained_variance   | -20          |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 0.582        |\n",
            "|    n_updates            | 110          |\n",
            "|    policy_gradient_loss | -0.000497    |\n",
            "|    std                  | 0.985        |\n",
            "|    value_loss           | 0.936        |\n",
            "------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:435471.36903033056\n",
            "total_reward:335471.36903033056\n",
            "total_cost:  6649.137584532092\n",
            "total_trades:  2515\n",
            "Sharpe:  0.8464205040380157\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 576         |\n",
            "|    iterations           | 13          |\n",
            "|    time_elapsed         | 46          |\n",
            "|    total_timesteps      | 26624       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.003859992 |\n",
            "|    clip_fraction        | 0.0109      |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -1.4        |\n",
            "|    explained_variance   | -43.4       |\n",
            "|    learning_rate        | 0.0001      |\n",
            "|    loss                 | 0.627       |\n",
            "|    n_updates            | 120         |\n",
            "|    policy_gradient_loss | -0.00183    |\n",
            "|    std                  | 0.983       |\n",
            "|    value_loss           | 1.42        |\n",
            "-----------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:407207.4827603418\n",
            "total_reward:307207.4827603418\n",
            "total_cost:  6763.380354823194\n",
            "total_trades:  2510\n",
            "Sharpe:  0.8443762816476841\n",
            "=================================\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 576        |\n",
            "|    iterations           | 14         |\n",
            "|    time_elapsed         | 49         |\n",
            "|    total_timesteps      | 28672      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.00445222 |\n",
            "|    clip_fraction        | 0.0231     |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -1.4       |\n",
            "|    explained_variance   | -37.1      |\n",
            "|    learning_rate        | 0.0001     |\n",
            "|    loss                 | 1.07       |\n",
            "|    n_updates            | 130        |\n",
            "|    policy_gradient_loss | -0.00134   |\n",
            "|    std                  | 0.98       |\n",
            "|    value_loss           | 1.55       |\n",
            "----------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:509305.9047923183\n",
            "total_reward:409305.9047923183\n",
            "total_cost:  6651.384115466746\n",
            "total_trades:  2515\n",
            "Sharpe:  0.9409978962165385\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 576          |\n",
            "|    iterations           | 15           |\n",
            "|    time_elapsed         | 53           |\n",
            "|    total_timesteps      | 30720        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0055885077 |\n",
            "|    clip_fraction        | 0.0357       |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.4         |\n",
            "|    explained_variance   | -63.7        |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 0.625        |\n",
            "|    n_updates            | 140          |\n",
            "|    policy_gradient_loss | 0.00057      |\n",
            "|    std                  | 0.979        |\n",
            "|    value_loss           | 1.5          |\n",
            "------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:459898.21614673093\n",
            "total_reward:359898.21614673093\n",
            "total_cost:  6596.732766210887\n",
            "total_trades:  2515\n",
            "Sharpe:  0.9113808701068679\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 575          |\n",
            "|    iterations           | 16           |\n",
            "|    time_elapsed         | 56           |\n",
            "|    total_timesteps      | 32768        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0031307968 |\n",
            "|    clip_fraction        | 0.000928     |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.4         |\n",
            "|    explained_variance   | -22.5        |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 1.44         |\n",
            "|    n_updates            | 150          |\n",
            "|    policy_gradient_loss | -0.000283    |\n",
            "|    std                  | 0.976        |\n",
            "|    value_loss           | 2.3          |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 576          |\n",
            "|    iterations           | 17           |\n",
            "|    time_elapsed         | 60           |\n",
            "|    total_timesteps      | 34816        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0006956297 |\n",
            "|    clip_fraction        | 0.00278      |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.4         |\n",
            "|    explained_variance   | -257         |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 0.878        |\n",
            "|    n_updates            | 160          |\n",
            "|    policy_gradient_loss | -0.0005      |\n",
            "|    std                  | 0.978        |\n",
            "|    value_loss           | 2.04         |\n",
            "------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:393533.6497418458\n",
            "total_reward:293533.6497418458\n",
            "total_cost:  6681.590494590975\n",
            "total_trades:  2515\n",
            "Sharpe:  0.8518320188401365\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 576          |\n",
            "|    iterations           | 18           |\n",
            "|    time_elapsed         | 63           |\n",
            "|    total_timesteps      | 36864        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0031630935 |\n",
            "|    clip_fraction        | 0.0041       |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.4         |\n",
            "|    explained_variance   | -31.9        |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 0.33         |\n",
            "|    n_updates            | 170          |\n",
            "|    policy_gradient_loss | -0.00149     |\n",
            "|    std                  | 0.981        |\n",
            "|    value_loss           | 0.592        |\n",
            "------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:408871.7170712545\n",
            "total_reward:308871.7170712545\n",
            "total_cost:  6603.797727765146\n",
            "total_trades:  2513\n",
            "Sharpe:  0.8421582751951677\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 576         |\n",
            "|    iterations           | 19          |\n",
            "|    time_elapsed         | 67          |\n",
            "|    total_timesteps      | 38912       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.004779038 |\n",
            "|    clip_fraction        | 0.00396     |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -1.4        |\n",
            "|    explained_variance   | -131        |\n",
            "|    learning_rate        | 0.0001      |\n",
            "|    loss                 | 0.968       |\n",
            "|    n_updates            | 180         |\n",
            "|    policy_gradient_loss | -0.000368   |\n",
            "|    std                  | 0.987       |\n",
            "|    value_loss           | 1.19        |\n",
            "-----------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:504000.8123993165\n",
            "total_reward:404000.8123993165\n",
            "total_cost:  6532.99519724017\n",
            "total_trades:  2515\n",
            "Sharpe:  0.9054715733132074\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 575          |\n",
            "|    iterations           | 20           |\n",
            "|    time_elapsed         | 71           |\n",
            "|    total_timesteps      | 40960        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0039343373 |\n",
            "|    clip_fraction        | 0.0118       |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.41        |\n",
            "|    explained_variance   | -116         |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 0.777        |\n",
            "|    n_updates            | 190          |\n",
            "|    policy_gradient_loss | -0.00175     |\n",
            "|    std                  | 0.984        |\n",
            "|    value_loss           | 1.71         |\n",
            "------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:493973.7566072885\n",
            "total_reward:393973.7566072885\n",
            "total_cost:  6570.434457393137\n",
            "total_trades:  2515\n",
            "Sharpe:  0.8792837035559181\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 575          |\n",
            "|    iterations           | 21           |\n",
            "|    time_elapsed         | 74           |\n",
            "|    total_timesteps      | 43008        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0038417936 |\n",
            "|    clip_fraction        | 0.0168       |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.4         |\n",
            "|    explained_variance   | -37.7        |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 1.04         |\n",
            "|    n_updates            | 200          |\n",
            "|    policy_gradient_loss | -0.000976    |\n",
            "|    std                  | 0.983        |\n",
            "|    value_loss           | 2.35         |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 574          |\n",
            "|    iterations           | 22           |\n",
            "|    time_elapsed         | 78           |\n",
            "|    total_timesteps      | 45056        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0026280978 |\n",
            "|    clip_fraction        | 0.00947      |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.4         |\n",
            "|    explained_variance   | -190         |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 1.15         |\n",
            "|    n_updates            | 210          |\n",
            "|    policy_gradient_loss | -0.000627    |\n",
            "|    std                  | 0.984        |\n",
            "|    value_loss           | 2.5          |\n",
            "------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:458212.9979555064\n",
            "total_reward:358212.9979555064\n",
            "total_cost:  6677.85432126429\n",
            "total_trades:  2513\n",
            "Sharpe:  0.9070571534874813\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 574          |\n",
            "|    iterations           | 23           |\n",
            "|    time_elapsed         | 81           |\n",
            "|    total_timesteps      | 47104        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0032378745 |\n",
            "|    clip_fraction        | 0.0179       |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.4         |\n",
            "|    explained_variance   | -89.7        |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 0.451        |\n",
            "|    n_updates            | 220          |\n",
            "|    policy_gradient_loss | -0.00196     |\n",
            "|    std                  | 0.985        |\n",
            "|    value_loss           | 0.908        |\n",
            "------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:520679.2495774107\n",
            "total_reward:420679.2495774107\n",
            "total_cost:  6464.60742463431\n",
            "total_trades:  2512\n",
            "Sharpe:  0.9168742761976522\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 574          |\n",
            "|    iterations           | 24           |\n",
            "|    time_elapsed         | 85           |\n",
            "|    total_timesteps      | 49152        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0014177399 |\n",
            "|    clip_fraction        | 0.0178       |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.4         |\n",
            "|    explained_variance   | -38.3        |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 0.86         |\n",
            "|    n_updates            | 230          |\n",
            "|    policy_gradient_loss | -0.000988    |\n",
            "|    std                  | 0.984        |\n",
            "|    value_loss           | 1.88         |\n",
            "------------------------------------------\n",
            "begin_total_asset:100000\n",
            "end_total_asset:449063.871983055\n",
            "total_reward:349063.871983055\n",
            "total_cost:  6303.219492919385\n",
            "total_trades:  2513\n",
            "Sharpe:  0.8225755015832578\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 574         |\n",
            "|    iterations           | 25          |\n",
            "|    time_elapsed         | 89          |\n",
            "|    total_timesteps      | 51200       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.002376902 |\n",
            "|    clip_fraction        | 0.0062      |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -1.4        |\n",
            "|    explained_variance   | -21.4       |\n",
            "|    learning_rate        | 0.0001      |\n",
            "|    loss                 | 1.11        |\n",
            "|    n_updates            | 240         |\n",
            "|    policy_gradient_loss | -0.000614   |\n",
            "|    std                  | 0.984       |\n",
            "|    value_loss           | 2.49        |\n",
            "-----------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CiBG2ZknsG73"
      },
      "source": [
        "### Model 4: TD3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UcRdoBc8-Xze",
        "outputId": "cc0a65bf-f79d-48ce-c173-6ae08747b0fa"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "TD3_PARAMS = {\"batch_size\": 128, \n",
        "              \"buffer_size\": 1000000, \n",
        "              \"learning_rate\": 0.0003}\n",
        "\n",
        "model_td3 = agent.get_model(\"td3\",model_kwargs = TD3_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 1000000, 'learning_rate': 0.0003}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CB5cAAio-ZSs",
        "outputId": "741a1ed9-9bb0-4763-f237-5cd461ac100e"
      },
      "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             | 56       |\n",
            "|    time_elapsed    | 177      |\n",
            "|    total timesteps | 10064    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 1.16e+03 |\n",
            "|    critic_loss     | 2.82e+03 |\n",
            "|    learning_rate   | 0.0003   |\n",
            "|    n_updates       | 7548     |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 48       |\n",
            "|    time_elapsed    | 414      |\n",
            "|    total timesteps | 20128    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 1.05e+03 |\n",
            "|    critic_loss     | 1.26e+03 |\n",
            "|    learning_rate   | 0.0003   |\n",
            "|    n_updates       | 17612    |\n",
            "---------------------------------\n",
            "day: 2515, episode: 30\n",
            "begin_total_asset:100000.00\n",
            "end_total_asset:882157.61\n",
            "total_reward:782157.61\n",
            "total_cost: 99.90\n",
            "total_trades: 2515\n",
            "Sharpe: 1.001\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 12       |\n",
            "|    fps             | 45       |\n",
            "|    time_elapsed    | 656      |\n",
            "|    total timesteps | 30192    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 841      |\n",
            "|    critic_loss     | 606      |\n",
            "|    learning_rate   | 0.0003   |\n",
            "|    n_updates       | 27676    |\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oDk2qrlTLZCp"
      },
      "source": [
        "### Model 4: SAC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "el8sK4fo-dl1",
        "outputId": "20c5793c-397b-4dc3-a702-857178e61813"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "SAC_PARAMS = {\n",
        "    \"batch_size\": 128,\n",
        "    \"buffer_size\": 100000,\n",
        "    \"learning_rate\": 0.00003,\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": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 100000, 'learning_rate': 3e-05, '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": "gaF80PR0-d_d",
        "outputId": "b26b6435-ca69-4000-b2b3-22596677d8ff"
      },
      "source": [
        "trained_sac = agent.train_model(model=model_sac, \n",
        "                             tb_log_name='sac',\n",
        "                             total_timesteps=30000)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/sac/sac_5\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 43       |\n",
            "|    time_elapsed    | 232      |\n",
            "|    total timesteps | 10064    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -410     |\n",
            "|    critic_loss     | 9.99     |\n",
            "|    ent_coef        | 0.132    |\n",
            "|    ent_coef_loss   | 25.1     |\n",
            "|    learning_rate   | 3e-05    |\n",
            "|    n_updates       | 9963     |\n",
            "---------------------------------\n",
            "day: 2515, episode: 30\n",
            "begin_total_asset:100000.00\n",
            "end_total_asset:882157.61\n",
            "total_reward:782157.61\n",
            "total_cost: 99.90\n",
            "total_trades: 2515\n",
            "Sharpe: 1.001\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 43       |\n",
            "|    time_elapsed    | 464      |\n",
            "|    total timesteps | 20128    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -155     |\n",
            "|    critic_loss     | 9.36     |\n",
            "|    ent_coef        | 0.178    |\n",
            "|    ent_coef_loss   | 17.3     |\n",
            "|    learning_rate   | 3e-05    |\n",
            "|    n_updates       | 20027    |\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KE1Xm9N9TnTn"
      },
      "source": [
        "### Trading\n",
        "* we use the environment class we initialized at 5.3 to create a stock trading environment\n",
        "* Assume that we have $100,000 initial capital at 2019-01-01. \n",
        "* We use the trained model of PPO to trade AAPL."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "kLFUwxhCoHN_",
        "outputId": "22540921-fc5e-460b-f9ef-1438131b45d7"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": 23,
      "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>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>kdjk</th>\n",
              "      <th>open_2_sma</th>\n",
              "      <th>boll</th>\n",
              "      <th>close_10.0_le_5_c</th>\n",
              "      <th>wr_10</th>\n",
              "      <th>dma</th>\n",
              "      <th>trix</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>38.722500</td>\n",
              "      <td>39.712502</td>\n",
              "      <td>38.557499</td>\n",
              "      <td>38.562561</td>\n",
              "      <td>148158800</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-2.019903</td>\n",
              "      <td>37.867349</td>\n",
              "      <td>-91.567852</td>\n",
              "      <td>42.250808</td>\n",
              "      <td>27.327775</td>\n",
              "      <td>39.177500</td>\n",
              "      <td>40.034790</td>\n",
              "      <td>0.0</td>\n",
              "      <td>63.418114</td>\n",
              "      <td>-6.886014</td>\n",
              "      <td>-0.761652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>35.994999</td>\n",
              "      <td>36.430000</td>\n",
              "      <td>35.500000</td>\n",
              "      <td>34.721451</td>\n",
              "      <td>365248800</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-2.203028</td>\n",
              "      <td>32.751919</td>\n",
              "      <td>-177.961195</td>\n",
              "      <td>55.246973</td>\n",
              "      <td>13.056580</td>\n",
              "      <td>37.358749</td>\n",
              "      <td>39.514298</td>\n",
              "      <td>0.0</td>\n",
              "      <td>112.236531</td>\n",
              "      <td>-7.096225</td>\n",
              "      <td>-0.763466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>36.132500</td>\n",
              "      <td>37.137501</td>\n",
              "      <td>35.950001</td>\n",
              "      <td>36.203678</td>\n",
              "      <td>234428400</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-2.203157</td>\n",
              "      <td>36.192789</td>\n",
              "      <td>-139.712389</td>\n",
              "      <td>47.060632</td>\n",
              "      <td>14.108980</td>\n",
              "      <td>36.063749</td>\n",
              "      <td>39.167181</td>\n",
              "      <td>0.0</td>\n",
              "      <td>86.003419</td>\n",
              "      <td>-7.054847</td>\n",
              "      <td>-0.766086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-07</td>\n",
              "      <td>37.174999</td>\n",
              "      <td>37.207500</td>\n",
              "      <td>36.474998</td>\n",
              "      <td>36.123104</td>\n",
              "      <td>219111200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-2.184578</td>\n",
              "      <td>36.088949</td>\n",
              "      <td>-122.741121</td>\n",
              "      <td>46.245025</td>\n",
              "      <td>14.191732</td>\n",
              "      <td>36.653749</td>\n",
              "      <td>38.840088</td>\n",
              "      <td>0.0</td>\n",
              "      <td>85.642763</td>\n",
              "      <td>-6.910649</td>\n",
              "      <td>-0.767321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-08</td>\n",
              "      <td>37.389999</td>\n",
              "      <td>37.955002</td>\n",
              "      <td>37.130001</td>\n",
              "      <td>36.811718</td>\n",
              "      <td>164101200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-2.090194</td>\n",
              "      <td>37.669998</td>\n",
              "      <td>-95.012445</td>\n",
              "      <td>37.537680</td>\n",
              "      <td>19.535793</td>\n",
              "      <td>37.282499</td>\n",
              "      <td>38.623490</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69.776084</td>\n",
              "      <td>-6.599589</td>\n",
              "      <td>-0.759067</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high  ...       wr_10       dma      trix\n",
              "0  2019-01-02  38.722500  39.712502  ...   63.418114 -6.886014 -0.761652\n",
              "1  2019-01-03  35.994999  36.430000  ...  112.236531 -7.096225 -0.763466\n",
              "2  2019-01-04  36.132500  37.137501  ...   86.003419 -7.054847 -0.766086\n",
              "3  2019-01-07  37.174999  37.207500  ...   85.642763 -6.910649 -0.767321\n",
              "4  2019-01-08  37.389999  37.955002  ...   69.776084 -6.599589 -0.759067\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLHl6V7eqV6_"
      },
      "source": [
        "## make a prediction and get the account value change\n",
        "trade = data_split(data_df, start = '2019-01-01', end = '2021-01-01')\n",
        "\n",
        "e_trade_gym = StockTradingEnv(df = trade, **env_kwargs)\n",
        "env_trade, obs_trade = e_trade_gym.get_sb_env()\n",
        "\n",
        "df_account_value, df_actions = DRLAgent.DRL_prediction(model=trained_sac,\n",
        "                                           test_data = trade,\n",
        "                                           test_env = env_trade,\n",
        "                                           test_obs = obs_trade)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYXxFzD5TnTw"
      },
      "source": [
        "<a id='6'></a>\n",
        "# Part 7: Backtesting Performance\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": "8GwqOO-v1NVz"
      },
      "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": "C9cpPm9YYxHC",
        "outputId": "7c7a8e34-7ed3-441d-9036-829240430843"
      },
      "source": [
        "print(\"==============Get Backtest Results===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "\n",
        "perf_stats_all = BackTestStats(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": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Backtest Results===========\n",
            "annual return:  98.50882005689301\n",
            "sharpe ratio:  1.8721562790475017\n",
            "Annual return          0.854161\n",
            "Cumulative returns     2.446346\n",
            "Annual volatility      0.366741\n",
            "Sharpe ratio           1.872156\n",
            "Calmar ratio           2.718396\n",
            "Stability              0.938197\n",
            "Max drawdown          -0.314215\n",
            "Omega ratio            1.425156\n",
            "Sortino ratio          2.833458\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.101078\n",
            "Daily value at risk   -0.043480\n",
            "Alpha                  0.000000\n",
            "Beta                   1.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-Tenjb0hcNr"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y4Gw3HNr1TDU"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MA_8LuZE1J3X",
        "outputId": "3d2c46b2-2b6e-4cfd-9648-68ae7d37d89c"
      },
      "source": [
        "print(\"==============Compare to AAPL itself buy-and-hold===========\")\n",
        "%matplotlib inline\n",
        "BackTestPlot(account_value=df_account_value, \n",
        "             baseline_ticker = 'AAPL',\n",
        "             baseline_start = '2019-01-01',\n",
        "             baseline_end = '2021-01-01')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Compare to AAPL itself buy-and-hold===========\n",
            "annual return:  98.50882005689301\n",
            "sharpe ratio:  1.8721562790475017\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (505, 7)\n"
          ],
          "name": "stdout"
        },
        {
          "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>2019-01-03</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-12-31</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>24</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>85.643%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>244.635%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>36.674%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>1.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>2.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-31.422%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>2.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>-0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>6.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>1.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-4.348%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "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>31.42</td>\n",
              "      <td>2020-02-12</td>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>2020-06-05</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.37</td>\n",
              "      <td>2020-09-01</td>\n",
              "      <td>2020-09-18</td>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.84</td>\n",
              "      <td>2019-05-03</td>\n",
              "      <td>2019-06-03</td>\n",
              "      <td>2019-07-31</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.24</td>\n",
              "      <td>2019-07-31</td>\n",
              "      <td>2019-08-05</td>\n",
              "      <td>2019-08-21</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.84</td>\n",
              "      <td>2020-07-20</td>\n",
              "      <td>2020-07-24</td>\n",
              "      <td>2020-07-31</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "qSFMdgCJE4O-"
      },
      "source": [
        "<a id='6.3'></a>\n",
        "## 7.3 Baseline Stats"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MpR7aQIwqdC4",
        "outputId": "24029a71-3596-4090-d7bc-7f44dd6000fc"
      },
      "source": [
        "print(\"==============Get Baseline Stats===========\")\n",
        "baesline_perf_stats=BaselineStats('AAPL')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (483, 7)\n",
            "Annual return          0.800630\n",
            "Cumulative returns     2.087191\n",
            "Annual volatility      0.382201\n",
            "Sharpe ratio           1.735128\n",
            "Calmar ratio           2.547565\n",
            "Stability              0.930285\n",
            "Max drawdown          -0.314273\n",
            "Omega ratio            1.387241\n",
            "Sortino ratio          2.572655\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.081315\n",
            "Daily value at risk   -0.045521\n",
            "Alpha                  0.000000\n",
            "Beta                   1.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vSFRXQfYFTQf",
        "outputId": "a5951bab-aa47-4fa8-9cd9-9fc62b5b02f5"
      },
      "source": [
        "print(\"==============Get Baseline Stats===========\")\n",
        "baesline_perf_stats=BaselineStats('^GSPC')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (483, 7)\n",
            "Annual return          0.210804\n",
            "Cumulative returns     0.442863\n",
            "Annual volatility      0.264385\n",
            "Sharpe ratio           0.858377\n",
            "Calmar ratio           0.621384\n",
            "Stability              0.467075\n",
            "Max drawdown          -0.339250\n",
            "Omega ratio            1.200929\n",
            "Sortino ratio          1.183069\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.726400\n",
            "Daily value at risk   -0.032409\n",
            "Alpha                  0.000000\n",
            "Beta                   1.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tunrA4mjE9la"
      },
      "source": [
        "<a id='6.4'></a>\n",
        "## 7.4 Compare to Stock Market Index"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5Ca2gHxi1gzX",
        "outputId": "68de7e8c-882a-4e8a-c9a9-cea8ea6ca212"
      },
      "source": [
        "print(\"==============Compare to S&P 500===========\")\n",
        "%matplotlib inline\n",
        "# S&P 500: ^GSPC\n",
        "# Dow Jones Index: ^DJI\n",
        "# NASDAQ 100: ^NDX\n",
        "BackTestPlot(df_account_value, baseline_ticker = '^GSPC')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Compare to S&P 500===========\n",
            "annual return:  98.50882005689301\n",
            "sharpe ratio:  1.8721562790475017\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (483, 7)\n"
          ],
          "name": "stdout"
        },
        {
          "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>2019-01-03</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-11-30</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>22</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>80.433%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>209.212%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>37.022%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>1.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>2.56</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>-31.422%</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>2.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>-0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>6.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>1.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-4.403%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>1.13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
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          }
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          "output_type": "display_data",
          "data": {
            "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>31.42</td>\n",
              "      <td>2020-02-12</td>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>2020-06-05</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.37</td>\n",
              "      <td>2020-09-01</td>\n",
              "      <td>2020-09-18</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.84</td>\n",
              "      <td>2019-05-03</td>\n",
              "      <td>2019-06-03</td>\n",
              "      <td>2019-07-31</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.24</td>\n",
              "      <td>2019-07-31</td>\n",
              "      <td>2019-08-05</td>\n",
              "      <td>2019-08-21</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.84</td>\n",
              "      <td>2020-07-20</td>\n",
              "      <td>2020-07-24</td>\n",
              "      <td>2020-07-31</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3_aHGEOTCluG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}