FinRL_portfolio_allocation.ipynb

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        "<a href=\"https://colab.research.google.com/github/AI4Finance-LLC/FinRL-Library/blob/master/FinRL_portfolio_allocation.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: Portfolio Allocation\n",
        "\n",
        "Tutorials to use OpenAI DRL to perform portfolio allocation 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: \n",
        "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
        "* **Pytorch Version** \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kHCfEiTA80V"
      },
      "source": [
        "# Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vUmLTmoQA7_w"
      },
      "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": {
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      "source": [
        "<a id='0'></a>\r\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L63HKnWvkirx"
      },
      "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.\r\n",
        "\r\n",
        "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\r\n",
        "\r\n",
        "\r\n",
        "* Action: The action space describes the allowed actions that the agent interacts with the\r\n",
        "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\r\n",
        "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\r\n",
        "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\r\n",
        "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\r\n",
        "\r\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\r\n",
        "values at state s′ and s, respectively\r\n",
        "\r\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\r\n",
        "our trading agent observes many different features to better learn in an interactive environment.\r\n",
        "\r\n",
        "* Environment: Dow 30 consituents\r\n",
        "\r\n",
        "\r\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.\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a id='1'></a>\r\n",
        "# Part 2. Getting Started- Load Python Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cVCcCalAknGn"
      },
      "source": [
        "<a id='1.1'></a>\r\n",
        "## 2.1. Install all the packages through FinRL library\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pT8a0fvhA_TW",
        "outputId": "5b4ef030-42d1-4872-a2c2-92641cefae50"
      },
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": null,
      "outputs": [
        {
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.2) (3.1.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.2) (0.4.8)\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-0.0.2-cp36-none-any.whl size=26213 sha256=0e35855829471b82d2286fccaecd633686e6645ed0d94e3b5a4cbd4fbf8b4362\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-15cfulgl/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=79e370e419b6973aa5622498114cedbf2c3c96901d5bbdfc5c8ed6fdb5b75f40\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=87af0c2e1e38cd9a5aeb2fa70fa1c0fd008c8953a9661e1ff57492223e694cd0\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-15cfulgl/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=9a7ee0fe64065b93e921027de1acbbe462beac39848a38f207fcc5fead5de122\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-0.0.2 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": "h2568cp5bU38"
      },
      "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": "code",
      "metadata": {
        "id": "yXKZS6YEbU39"
      },
      "source": [
        "import pkg_resources\n",
        "import pip\n",
        "installedPackages = {pkg.key for pkg in pkg_resources.working_set}\n",
        "required = {'yfinance', 'pandas','numpy', 'matplotlib', 'stockstats','stable-baselines','gym','tensorflow','pyfolio'}\n",
        "missing = required - installedPackages\n",
        "if missing:\n",
        "    !pip install yfinance\n",
        "    !pip install pandas\n",
        "    !pip install numpy\n",
        "    !pip install matplotlib\n",
        "    !pip install stockstats\n",
        "    !pip install gym\n",
        "    !pip install stable-baselines3[extra]\n",
        "    !pip install git+https://github.com/quantopian/pyfolio\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bNmvYN9YbU4B"
      },
      "source": [
        "<a id='1.3'></a>\r\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntfTb0e2bU4C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc012ed7-3aaa-4ac7-b9d2-437b1a9387f6"
      },
      "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.environment import EnvSetup\n",
        "from finrl.env.EnvMultipleStock_train import StockEnvTrain\n",
        "from finrl.env.EnvMultipleStock_trade import StockEnvTrade\n",
        "from finrl.model.models import DRLAgent\n",
        "from finrl.trade.backtest import BackTestStats, BaselineStats, BackTestPlot, backtest_strat, baseline_strat\n",
        "from finrl.trade.backtest import backtest_strat, baseline_strat\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")"
      ],
      "execution_count": null,
      "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": "markdown",
      "metadata": {
        "id": "OlIS2abxkwan"
      },
      "source": [
        "<a id='1.4'></a>\r\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8bBq7nsBCfF"
      },
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slBria_QbU4F"
      },
      "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CPsuy6d9yRPp",
        "outputId": "49811234-e019-4746-a3a7-8acbd6d2b7af"
      },
      "source": [
        "print(config.DOW_30_TICKER)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['AAPL', 'MSFT', 'JPM', 'V', 'RTX', 'PG', 'GS', 'NKE', 'DIS', 'AXP', 'HD', 'INTC', 'WMT', 'IBM', 'MRK', 'UNH', 'KO', 'CAT', 'TRV', 'JNJ', 'CVX', 'MCD', 'VZ', 'CSCO', 'XOM', 'BA', 'MMM', 'PFE', 'WBA', 'DD']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEwzMkFHbU4G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2df90913-4949-464d-bc6c-5e6e87e243f2"
      },
      "source": [
        "df = YahooDownloader(start_date = '2008-01-01',\n",
        "                     end_date = '2020-12-01',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (97507, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1xC-LpbbU4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "61ae4f0b-15f2-4cbd-9216-7d1fb20884d7"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th {\n",
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              "    <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>2008-01-02</td>\n",
              "      <td>7.116786</td>\n",
              "      <td>7.152143</td>\n",
              "      <td>6.876786</td>\n",
              "      <td>5.954069</td>\n",
              "      <td>1.079179e+09</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-01-03</td>\n",
              "      <td>6.978929</td>\n",
              "      <td>7.049643</td>\n",
              "      <td>6.881786</td>\n",
              "      <td>5.956820</td>\n",
              "      <td>8.420664e+08</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-01-04</td>\n",
              "      <td>6.837500</td>\n",
              "      <td>6.892857</td>\n",
              "      <td>6.388928</td>\n",
              "      <td>5.502106</td>\n",
              "      <td>1.455832e+09</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-01-07</td>\n",
              "      <td>6.473214</td>\n",
              "      <td>6.557143</td>\n",
              "      <td>6.079643</td>\n",
              "      <td>5.428458</td>\n",
              "      <td>2.072193e+09</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-01-08</td>\n",
              "      <td>6.433571</td>\n",
              "      <td>6.516428</td>\n",
              "      <td>6.100000</td>\n",
              "      <td>5.233188</td>\n",
              "      <td>1.523816e+09</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date      open      high       low     close        volume   tic\n",
              "0  2008-01-02  7.116786  7.152143  6.876786  5.954069  1.079179e+09  AAPL\n",
              "1  2008-01-03  6.978929  7.049643  6.881786  5.956820  8.420664e+08  AAPL\n",
              "2  2008-01-04  6.837500  6.892857  6.388928  5.502106  1.455832e+09  AAPL\n",
              "3  2008-01-07  6.473214  6.557143  6.079643  5.428458  2.072193e+09  AAPL\n",
              "4  2008-01-08  6.433571  6.516428  6.100000  5.233188  1.523816e+09  AAPL"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mS1-nxRzbU4i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e8cc68d-4888-4154-bb90-ba8649bd93f5"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(97507, 7)"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9UwKwzRbU4l"
      },
      "source": [
        "# Part 4: Preprocess Data\n",
        "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
        "* Add technical indicators. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
        "* Add turbulence index. Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P5h8RbeBHMDQ",
        "outputId": "ec948f24-3a7c-4473-b239-edee98bddd6e"
      },
      "source": [
        "fe = FeatureEngineer(\n",
        "                    use_technical_indicator=True,\n",
        "                    use_turbulence=False,\n",
        "                    user_defined_feature = False)\n",
        "\n",
        "df = fe.preprocess_data(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully added technical indicators\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_zsIW1LAiqCb",
        "outputId": "13ba2f3a-2196-4785-817d-d6ac9c31a366"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90000, 12)"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWB-RoTSbU4s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "2ab841ad-bb7e-4f1b-e370-5ce99e8e216b"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe thead th {\n",
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              "      <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>7.116786</td>\n",
              "      <td>7.152143</td>\n",
              "      <td>6.876786</td>\n",
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              "      <td>1.079179e+09</td>\n",
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              "      <td>-66.666667</td>\n",
              "      <td>100.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-01-03</td>\n",
              "      <td>6.978929</td>\n",
              "      <td>7.049643</td>\n",
              "      <td>6.881786</td>\n",
              "      <td>6.005591</td>\n",
              "      <td>8.420664e+08</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>0.000062</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>-66.666667</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-01-04</td>\n",
              "      <td>6.837500</td>\n",
              "      <td>6.892857</td>\n",
              "      <td>6.388928</td>\n",
              "      <td>5.547154</td>\n",
              "      <td>1.455832e+09</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.014137</td>\n",
              "      <td>0.581478</td>\n",
              "      <td>-100.000000</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-01-07</td>\n",
              "      <td>6.473214</td>\n",
              "      <td>6.557143</td>\n",
              "      <td>6.079643</td>\n",
              "      <td>5.472904</td>\n",
              "      <td>2.072193e+09</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.022929</td>\n",
              "      <td>0.498449</td>\n",
              "      <td>-98.315409</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-01-08</td>\n",
              "      <td>6.433571</td>\n",
              "      <td>6.516428</td>\n",
              "      <td>6.100000</td>\n",
              "      <td>5.276035</td>\n",
              "      <td>1.523816e+09</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.035058</td>\n",
              "      <td>0.358171</td>\n",
              "      <td>-86.839166</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date      open      high  ...      rsi_30      cci_30  dx_30\n",
              "0  2008-01-02  7.116786  7.152143  ...  100.000000  -66.666667  100.0\n",
              "1  2008-01-03  6.978929  7.049643  ...  100.000000  -66.666667  100.0\n",
              "2  2008-01-04  6.837500  6.892857  ...    0.581478 -100.000000  100.0\n",
              "3  2008-01-07  6.473214  6.557143  ...    0.498449  -98.315409  100.0\n",
              "4  2008-01-08  6.433571  6.516428  ...    0.358171  -86.839166  100.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qz9K2vul6RmK"
      },
      "source": [
        "## Add covariance matrix as states"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IhizvNwcrg1n"
      },
      "source": [
        "# add covariance matrix as states\n",
        "df=df.sort_values(['date','tic'],ignore_index=True)\n",
        "df.index = df.date.factorize()[0]\n",
        "\n",
        "cov_list = []\n",
        "# look back is one year\n",
        "lookback=252\n",
        "for i in range(lookback,len(df.index.unique())):\n",
        "  data_lookback = df.loc[i-lookback:i,:]\n",
        "  price_lookback=data_lookback.pivot_table(index = 'date',columns = 'tic', values = 'close')\n",
        "  return_lookback = price_lookback.pct_change().dropna()\n",
        "  covs = return_lookback.cov().values \n",
        "  cov_list.append(covs)\n",
        "  \n",
        "df_cov = pd.DataFrame({'date':df.date.unique()[lookback:],'cov_list':cov_list})\n",
        "df = df.merge(df_cov, on='date')\n",
        "df = df.sort_values(['date','tic']).reset_index(drop=True)\n",
        "        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUPwwa13uBQ-",
        "outputId": "d51944b8-ce83-4ae7-d0cc-fecd56e61b89"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90000, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "wv3jR1zPrg4g",
        "outputId": "76c41b80-fc7b-469d-e1b2-5d199e76b591"
      },
      "source": [
        "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",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>cov_list</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>3.070357</td>\n",
              "      <td>3.133571</td>\n",
              "      <td>3.047857</td>\n",
              "      <td>2.629544</td>\n",
              "      <td>607541200.0</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.084062</td>\n",
              "      <td>42.254783</td>\n",
              "      <td>-80.502280</td>\n",
              "      <td>16.129787</td>\n",
              "      <td>[[0.0014139120472621146, 0.0011800741602556227...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>17.969999</td>\n",
              "      <td>18.750000</td>\n",
              "      <td>17.910000</td>\n",
              "      <td>15.163033</td>\n",
              "      <td>9625600.0</td>\n",
              "      <td>AXP</td>\n",
              "      <td>-0.972946</td>\n",
              "      <td>42.554831</td>\n",
              "      <td>-75.339668</td>\n",
              "      <td>25.776759</td>\n",
              "      <td>[[0.0014139120472621146, 0.0011800741602556227...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>41.590000</td>\n",
              "      <td>43.049999</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>31.760571</td>\n",
              "      <td>5443100.0</td>\n",
              "      <td>BA</td>\n",
              "      <td>-0.277653</td>\n",
              "      <td>47.440272</td>\n",
              "      <td>156.964508</td>\n",
              "      <td>5.366299</td>\n",
              "      <td>[[0.0014139120472621146, 0.0011800741602556227...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>43.700001</td>\n",
              "      <td>45.099998</td>\n",
              "      <td>43.700001</td>\n",
              "      <td>30.961802</td>\n",
              "      <td>6277400.0</td>\n",
              "      <td>CAT</td>\n",
              "      <td>0.685574</td>\n",
              "      <td>51.205307</td>\n",
              "      <td>98.427075</td>\n",
              "      <td>26.331746</td>\n",
              "      <td>[[0.0014139120472621146, 0.0011800741602556227...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>16.180000</td>\n",
              "      <td>16.549999</td>\n",
              "      <td>16.120001</td>\n",
              "      <td>12.288514</td>\n",
              "      <td>37513700.0</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>-0.105032</td>\n",
              "      <td>45.961928</td>\n",
              "      <td>11.948975</td>\n",
              "      <td>13.387087</td>\n",
              "      <td>[[0.0014139120472621146, 0.0011800741602556227...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  ...                                           cov_list\n",
              "0  2008-12-31  ...  [[0.0014139120472621146, 0.0011800741602556227...\n",
              "1  2008-12-31  ...  [[0.0014139120472621146, 0.0011800741602556227...\n",
              "2  2008-12-31  ...  [[0.0014139120472621146, 0.0011800741602556227...\n",
              "3  2008-12-31  ...  [[0.0014139120472621146, 0.0011800741602556227...\n",
              "4  2008-12-31  ...  [[0.0014139120472621146, 0.0011800741602556227...\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UooHj1OgbU4v"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Design Environment\n",
        "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
        "\n",
        "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
        "\n",
        "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQnmN1qdk88I"
      },
      "source": [
        "## Training data split: 2009-01-01 to 2018-12-31"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NrPxgv4eBQ_R"
      },
      "source": [
        "train = data_split(df, '2009-01-01','2019-01-01')\n",
        "#trade = data_split(df, '2020-01-01', config.END_DATE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "vU2vXEll0hfk",
        "outputId": "faa8350e-e06a-423b-8e66-93be4cf28765"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>cov_list</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.795913</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.071392</td>\n",
              "      <td>45.440203</td>\n",
              "      <td>-32.036662</td>\n",
              "      <td>2.140159</td>\n",
              "      <td>[[0.0014277103481246368, 0.0011886549864140632...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.800624</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>AXP</td>\n",
              "      <td>-0.865277</td>\n",
              "      <td>43.957557</td>\n",
              "      <td>-42.722340</td>\n",
              "      <td>16.335101</td>\n",
              "      <td>[[0.0014277103481246368, 0.0011886549864140632...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.680935</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>BA</td>\n",
              "      <td>-0.001991</td>\n",
              "      <td>50.822045</td>\n",
              "      <td>272.714919</td>\n",
              "      <td>20.494464</td>\n",
              "      <td>[[0.0014277103481246368, 0.0011886549864140632...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.514400</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>CAT</td>\n",
              "      <td>0.871261</td>\n",
              "      <td>53.661257</td>\n",
              "      <td>129.739408</td>\n",
              "      <td>34.637448</td>\n",
              "      <td>[[0.0014277103481246368, 0.0011886549864140632...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.786087</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>-0.062942</td>\n",
              "      <td>48.322163</td>\n",
              "      <td>54.612483</td>\n",
              "      <td>2.653808</td>\n",
              "      <td>[[0.0014277103481246368, 0.0011886549864140632...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  ...                                           cov_list\n",
              "0  2009-01-02  ...  [[0.0014277103481246368, 0.0011886549864140632...\n",
              "0  2009-01-02  ...  [[0.0014277103481246368, 0.0011886549864140632...\n",
              "0  2009-01-02  ...  [[0.0014277103481246368, 0.0011886549864140632...\n",
              "0  2009-01-02  ...  [[0.0014277103481246368, 0.0011886549864140632...\n",
              "0  2009-01-02  ...  [[0.0014277103481246368, 0.0011886549864140632...\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sxQTNjpblAMN"
      },
      "source": [
        "## Environment for Portfolio Allocation\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlfE-VERbU40"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from gym.utils import seeding\n",
        "import gym\n",
        "from gym import spaces\n",
        "import matplotlib\n",
        "matplotlib.use('Agg')\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "class StockPortfolioEnv(gym.Env):\n",
        "    \"\"\"A single stock trading environment for OpenAI gym\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        df: DataFrame\n",
        "            input data\n",
        "        stock_dim : int\n",
        "            number of unique stocks\n",
        "        hmax : int\n",
        "            maximum number of shares to trade\n",
        "        initial_amount : int\n",
        "            start money\n",
        "        transaction_cost_pct: float\n",
        "            transaction cost percentage per trade\n",
        "        reward_scaling: float\n",
        "            scaling factor for reward, good for training\n",
        "        state_space: int\n",
        "            the dimension of input features\n",
        "        action_space: int\n",
        "            equals stock dimension\n",
        "        tech_indicator_list: list\n",
        "            a list of technical indicator names\n",
        "        turbulence_threshold: int\n",
        "            a threshold to control risk aversion\n",
        "        day: int\n",
        "            an increment number to control date\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    _sell_stock()\n",
        "        perform sell action based on the sign of the action\n",
        "    _buy_stock()\n",
        "        perform buy action based on the sign of the action\n",
        "    step()\n",
        "        at each step the agent will return actions, then \n",
        "        we will calculate the reward, and return the next observation.\n",
        "    reset()\n",
        "        reset the environment\n",
        "    render()\n",
        "        use render to return other functions\n",
        "    save_asset_memory()\n",
        "        return account value at each time step\n",
        "    save_action_memory()\n",
        "        return actions/positions at each time step\n",
        "        \n",
        "\n",
        "    \"\"\"\n",
        "    metadata = {'render.modes': ['human']}\n",
        "\n",
        "    def __init__(self, \n",
        "                df,\n",
        "                stock_dim,\n",
        "                hmax,\n",
        "                initial_amount,\n",
        "                transaction_cost_pct,\n",
        "                reward_scaling,\n",
        "                state_space,\n",
        "                action_space,\n",
        "                tech_indicator_list,\n",
        "                turbulence_threshold,\n",
        "                lookback=252,\n",
        "                day = 0):\n",
        "        #super(StockEnv, self).__init__()\n",
        "        #money = 10 , scope = 1\n",
        "        self.day = day\n",
        "        self.lookback=lookback\n",
        "        self.df = df\n",
        "        self.stock_dim = stock_dim\n",
        "        self.hmax = hmax\n",
        "        self.initial_amount = initial_amount\n",
        "        self.transaction_cost_pct =transaction_cost_pct\n",
        "        self.reward_scaling = reward_scaling\n",
        "        self.state_space = state_space\n",
        "        self.action_space = action_space\n",
        "        self.tech_indicator_list = tech_indicator_list\n",
        "\n",
        "        # action_space normalization and shape is self.stock_dim\n",
        "        self.action_space = spaces.Box(low = 0, high = 1,shape = (self.action_space,)) \n",
        "        # Shape = (34, 30)\n",
        "        # covariance matrix + technical indicators\n",
        "        self.observation_space = spaces.Box(low=0, high=np.inf, shape = (self.state_space+len(self.tech_indicator_list),self.state_space))\n",
        "\n",
        "        # load data from a pandas dataframe\n",
        "        self.data = self.df.loc[self.day,:]\n",
        "        self.covs = self.data['cov_list'].values[0]\n",
        "        self.state =  np.append(np.array(self.covs), [self.data[tech].values.tolist() for tech in self.tech_indicator_list ], axis=0)\n",
        "        self.terminal = False     \n",
        "        self.turbulence_threshold = turbulence_threshold        \n",
        "        # initalize state: inital portfolio return + individual stock return + individual weights\n",
        "        self.portfolio_value = self.initial_amount\n",
        "\n",
        "        # memorize portfolio value each step\n",
        "        self.asset_memory = [self.initial_amount]\n",
        "        # memorize portfolio return each step\n",
        "        self.portfolio_return_memory = [0]\n",
        "        self.actions_memory=[[1/self.stock_dim]*self.stock_dim]\n",
        "        self.date_memory=[self.data.date.unique()[0]]\n",
        "\n",
        "        \n",
        "    def step(self, actions):\n",
        "        # print(self.day)\n",
        "        self.terminal = self.day >= len(self.df.index.unique())-1\n",
        "        # print(actions)\n",
        "\n",
        "        if self.terminal:\n",
        "            df = pd.DataFrame(self.portfolio_return_memory)\n",
        "            df.columns = ['daily_return']\n",
        "            plt.plot(df.daily_return.cumsum(),'r')\n",
        "            plt.savefig('results/cumulative_reward.png')\n",
        "            plt.close()\n",
        "            \n",
        "            plt.plot(self.portfolio_return_memory,'r')\n",
        "            plt.savefig('results/rewards.png')\n",
        "            plt.close()\n",
        "\n",
        "            print(\"=================================\")\n",
        "            print(\"begin_total_asset:{}\".format(self.asset_memory[0]))           \n",
        "            print(\"end_total_asset:{}\".format(self.portfolio_value))\n",
        "\n",
        "            df_daily_return = pd.DataFrame(self.portfolio_return_memory)\n",
        "            df_daily_return.columns = ['daily_return']\n",
        "            if df_daily_return['daily_return'].std() !=0:\n",
        "              sharpe = (252**0.5)*df_daily_return['daily_return'].mean()/ \\\n",
        "                       df_daily_return['daily_return'].std()\n",
        "              print(\"Sharpe: \",sharpe)\n",
        "            print(\"=================================\")\n",
        "            \n",
        "            return self.state, self.reward, self.terminal,{}\n",
        "\n",
        "        else:\n",
        "            #print(\"Model actions: \",actions)\n",
        "            # actions are the portfolio weight\n",
        "            # normalize to sum of 1\n",
        "            #if (np.array(actions) - np.array(actions).min()).sum() != 0:\n",
        "            #  norm_actions = (np.array(actions) - np.array(actions).min()) / (np.array(actions) - np.array(actions).min()).sum()\n",
        "            #else:\n",
        "            #  norm_actions = actions\n",
        "            weights = self.softmax_normalization(actions) \n",
        "            #print(\"Normalized actions: \", weights)\n",
        "            self.actions_memory.append(weights)\n",
        "            last_day_memory = self.data\n",
        "\n",
        "            #load next state\n",
        "            self.day += 1\n",
        "            self.data = self.df.loc[self.day,:]\n",
        "            self.covs = self.data['cov_list'].values[0]\n",
        "            self.state =  np.append(np.array(self.covs), [self.data[tech].values.tolist() for tech in self.tech_indicator_list ], axis=0)\n",
        "            #print(self.state)\n",
        "            # calcualte portfolio return\n",
        "            # individual stocks' return * weight\n",
        "            portfolio_return = sum(((self.data.close.values / last_day_memory.close.values)-1)*weights)\n",
        "            # update portfolio value\n",
        "            new_portfolio_value = self.portfolio_value*(1+portfolio_return)\n",
        "            self.portfolio_value = new_portfolio_value\n",
        "\n",
        "            # save into memory\n",
        "            self.portfolio_return_memory.append(portfolio_return)\n",
        "            self.date_memory.append(self.data.date.unique()[0])            \n",
        "            self.asset_memory.append(new_portfolio_value)\n",
        "\n",
        "            # the reward is the new portfolio value or end portfolo value\n",
        "            self.reward = new_portfolio_value \n",
        "            #print(\"Step reward: \", self.reward)\n",
        "            #self.reward = self.reward*self.reward_scaling\n",
        "\n",
        "        return self.state, self.reward, self.terminal, {}\n",
        "\n",
        "    def reset(self):\n",
        "        self.asset_memory = [self.initial_amount]\n",
        "        self.day = 0\n",
        "        self.data = self.df.loc[self.day,:]\n",
        "        # load states\n",
        "        self.covs = self.data['cov_list'].values[0]\n",
        "        self.state =  np.append(np.array(self.covs), [self.data[tech].values.tolist() for tech in self.tech_indicator_list ], axis=0)\n",
        "        self.portfolio_value = self.initial_amount\n",
        "        #self.cost = 0\n",
        "        #self.trades = 0\n",
        "        self.terminal = False \n",
        "        self.portfolio_return_memory = [0]\n",
        "        self.actions_memory=[[1/self.stock_dim]*self.stock_dim]\n",
        "        self.date_memory=[self.data.date.unique()[0]] \n",
        "        return self.state\n",
        "    \n",
        "    def render(self, mode='human'):\n",
        "        return self.state\n",
        "        \n",
        "    def softmax_normalization(self, actions):\n",
        "        numerator = np.exp(actions)\n",
        "        denominator = np.sum(np.exp(actions))\n",
        "        softmax_output = numerator/denominator\n",
        "        return softmax_output\n",
        "\n",
        "    \n",
        "    def save_asset_memory(self):\n",
        "        date_list = self.date_memory\n",
        "        portfolio_return = self.portfolio_return_memory\n",
        "        #print(len(date_list))\n",
        "        #print(len(asset_list))\n",
        "        df_account_value = pd.DataFrame({'date':date_list,'daily_return':portfolio_return})\n",
        "        return df_account_value\n",
        "\n",
        "    def save_action_memory(self):\n",
        "        # date and close price length must match actions length\n",
        "        date_list = self.date_memory\n",
        "        df_date = pd.DataFrame(date_list)\n",
        "        df_date.columns = ['date']\n",
        "        \n",
        "        action_list = self.actions_memory\n",
        "        df_actions = pd.DataFrame(action_list)\n",
        "        df_actions.columns = self.data.tic.values\n",
        "        df_actions.index = df_date.date\n",
        "        #df_actions = pd.DataFrame({'date':date_list,'actions':action_list})\n",
        "        return df_actions\n",
        "\n",
        "    def _seed(self, seed=None):\n",
        "        self.np_random, seed = seeding.np_random(seed)\n",
        "        return [seed]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzD06X0CbU43"
      },
      "source": [
        "stock_dimension = len(train.tic.unique())\n",
        "state_space = stock_dimension"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_CA2HJeybU45",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36328526-5759-483e-e9ce-e5faa1354c98"
      },
      "source": [
        "stock_dimension"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJc5fax-GbGB"
      },
      "source": [
        "env_setup = EnvSetup(stock_dim = stock_dimension,\n",
        "                        state_space = state_space,\n",
        "                        initial_amount = 1000000,\n",
        "                        tech_indicator_list = config.TECHNICAL_INDICATORS_LIST)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTlOf8SJGdkl"
      },
      "source": [
        "env_train = env_setup.create_env_training(data = train,\n",
        "                                          env_class = StockPortfolioEnv)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2eKIu5UPlPnk"
      },
      "source": [
        "<a id='5'></a>\r\n",
        "# Part 6: Implement DRL Algorithms\r\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.\r\n",
        "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\r\n",
        "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\r\n",
        "design their own DRL algorithms by adapting these DRL algorithms."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDxU0iCEGdnb"
      },
      "source": [
        "# initialize\r\n",
        "agent = DRLAgent(env = env_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hdPe8uzflbXe"
      },
      "source": [
        "### Model 1: **A2C**\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1tORf1fIcQ2",
        "outputId": "fdfbeacc-8ae1-4b9f-de93-628cfe165e73"
      },
      "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": 72,
      "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": "DazEdrMpIdyz",
        "outputId": "aa7a45ec-b520-491c-9713-2bd41f360a51"
      },
      "source": [
        "trained_a2c = agent.train_model(model=model_a2c, \n",
        "                                tb_log_name='a2c',\n",
        "                                total_timesteps=50000)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/a2c/a2c_5\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 291      |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 1        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | -2e+14   |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | 1.82e+08 |\n",
            "|    std                | 0.997    |\n",
            "|    value_loss         | 2.35e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 295       |\n",
            "|    iterations         | 200       |\n",
            "|    time_elapsed       | 3         |\n",
            "|    total_timesteps    | 1000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.5     |\n",
            "|    explained_variance | -3.05e+15 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 199       |\n",
            "|    policy_loss        | 2.41e+08  |\n",
            "|    std                | 0.997     |\n",
            "|    value_loss         | 3.84e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 293       |\n",
            "|    iterations         | 300       |\n",
            "|    time_elapsed       | 5         |\n",
            "|    total_timesteps    | 1500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.5     |\n",
            "|    explained_variance | -1.55e+16 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 299       |\n",
            "|    policy_loss        | 3.45e+08  |\n",
            "|    std                | 0.997     |\n",
            "|    value_loss         | 8.64e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 294       |\n",
            "|    iterations         | 400       |\n",
            "|    time_elapsed       | 6         |\n",
            "|    total_timesteps    | 2000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -1.01e+15 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 399       |\n",
            "|    policy_loss        | 4.37e+08  |\n",
            "|    std                | 0.996     |\n",
            "|    value_loss         | 1.18e+14  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 295       |\n",
            "|    iterations         | 500       |\n",
            "|    time_elapsed       | 8         |\n",
            "|    total_timesteps    | 2500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.5     |\n",
            "|    explained_variance | -3.19e+16 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 499       |\n",
            "|    policy_loss        | 5.47e+08  |\n",
            "|    std                | 0.996     |\n",
            "|    value_loss         | 2.14e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4214889.986950536\n",
            "Sharpe:  0.9822348310711878\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 289       |\n",
            "|    iterations         | 600       |\n",
            "|    time_elapsed       | 10        |\n",
            "|    total_timesteps    | 3000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -6.43e+22 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 599       |\n",
            "|    policy_loss        | 1.81e+08  |\n",
            "|    std                | 0.996     |\n",
            "|    value_loss         | 2.55e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 290       |\n",
            "|    iterations         | 700       |\n",
            "|    time_elapsed       | 12        |\n",
            "|    total_timesteps    | 3500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -5.77e+17 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 699       |\n",
            "|    policy_loss        | 2.43e+08  |\n",
            "|    std                | 0.995     |\n",
            "|    value_loss         | 3.97e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 290      |\n",
            "|    iterations         | 800      |\n",
            "|    time_elapsed       | 13       |\n",
            "|    total_timesteps    | 4000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 799      |\n",
            "|    policy_loss        | 3.77e+08 |\n",
            "|    std                | 0.994    |\n",
            "|    value_loss         | 9.34e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 290       |\n",
            "|    iterations         | 900       |\n",
            "|    time_elapsed       | 15        |\n",
            "|    total_timesteps    | 4500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -4.52e+18 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 899       |\n",
            "|    policy_loss        | 4.14e+08  |\n",
            "|    std                | 0.993     |\n",
            "|    value_loss         | 1.14e+14  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 289       |\n",
            "|    iterations         | 1000      |\n",
            "|    time_elapsed       | 17        |\n",
            "|    total_timesteps    | 5000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -5.25e+18 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 999       |\n",
            "|    policy_loss        | 5.61e+08  |\n",
            "|    std                | 0.993     |\n",
            "|    value_loss         | 2.33e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4322651.600862242\n",
            "Sharpe:  0.9978410688494076\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 285      |\n",
            "|    iterations         | 1100     |\n",
            "|    time_elapsed       | 19       |\n",
            "|    total_timesteps    | 5500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1099     |\n",
            "|    policy_loss        | 2e+08    |\n",
            "|    std                | 0.993    |\n",
            "|    value_loss         | 2.45e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 286       |\n",
            "|    iterations         | 1200      |\n",
            "|    time_elapsed       | 20        |\n",
            "|    total_timesteps    | 6000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.3     |\n",
            "|    explained_variance | -2.45e+17 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1199      |\n",
            "|    policy_loss        | 2.37e+08  |\n",
            "|    std                | 0.992     |\n",
            "|    value_loss         | 4.23e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 286       |\n",
            "|    iterations         | 1300      |\n",
            "|    time_elapsed       | 22        |\n",
            "|    total_timesteps    | 6500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.3     |\n",
            "|    explained_variance | -5.16e+19 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1299      |\n",
            "|    policy_loss        | 3.58e+08  |\n",
            "|    std                | 0.992     |\n",
            "|    value_loss         | 8.17e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 287       |\n",
            "|    iterations         | 1400      |\n",
            "|    time_elapsed       | 24        |\n",
            "|    total_timesteps    | 7000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.3     |\n",
            "|    explained_variance | -1.03e+19 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1399      |\n",
            "|    policy_loss        | 4.23e+08  |\n",
            "|    std                | 0.991     |\n",
            "|    value_loss         | 1.12e+14  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 287       |\n",
            "|    iterations         | 1500      |\n",
            "|    time_elapsed       | 26        |\n",
            "|    total_timesteps    | 7500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.3     |\n",
            "|    explained_variance | -3.42e+19 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1499      |\n",
            "|    policy_loss        | 6.26e+08  |\n",
            "|    std                | 0.99      |\n",
            "|    value_loss         | 2.33e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4422096.505241108\n",
            "Sharpe:  1.0113863727131587\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 285       |\n",
            "|    iterations         | 1600      |\n",
            "|    time_elapsed       | 27        |\n",
            "|    total_timesteps    | 8000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.3     |\n",
            "|    explained_variance | -1.07e+24 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1599      |\n",
            "|    policy_loss        | 1.85e+08  |\n",
            "|    std                | 0.99      |\n",
            "|    value_loss         | 2.19e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 286      |\n",
            "|    iterations         | 1700     |\n",
            "|    time_elapsed       | 29       |\n",
            "|    total_timesteps    | 8500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1699     |\n",
            "|    policy_loss        | 2.37e+08 |\n",
            "|    std                | 0.99     |\n",
            "|    value_loss         | 4.03e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 286      |\n",
            "|    iterations         | 1800     |\n",
            "|    time_elapsed       | 31       |\n",
            "|    total_timesteps    | 9000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | -1.7e+22 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1799     |\n",
            "|    policy_loss        | 3.28e+08 |\n",
            "|    std                | 0.989    |\n",
            "|    value_loss         | 8.78e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 287       |\n",
            "|    iterations         | 1900      |\n",
            "|    time_elapsed       | 33        |\n",
            "|    total_timesteps    | 9500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.2     |\n",
            "|    explained_variance | -6.05e+24 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1899      |\n",
            "|    policy_loss        | 4.18e+08  |\n",
            "|    std                | 0.989     |\n",
            "|    value_loss         | 1.23e+14  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 287       |\n",
            "|    iterations         | 2000      |\n",
            "|    time_elapsed       | 34        |\n",
            "|    total_timesteps    | 10000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.2     |\n",
            "|    explained_variance | -3.18e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1999      |\n",
            "|    policy_loss        | 6.29e+08  |\n",
            "|    std                | 0.989     |\n",
            "|    value_loss         | 2.73e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4578285.130012105\n",
            "Sharpe:  1.0325724215094274\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 285      |\n",
            "|    iterations         | 2100     |\n",
            "|    time_elapsed       | 36       |\n",
            "|    total_timesteps    | 10500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2099     |\n",
            "|    policy_loss        | 1.84e+08 |\n",
            "|    std                | 0.989    |\n",
            "|    value_loss         | 2.24e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 285       |\n",
            "|    iterations         | 2200      |\n",
            "|    time_elapsed       | 38        |\n",
            "|    total_timesteps    | 11000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.2     |\n",
            "|    explained_variance | -7.97e+20 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2199      |\n",
            "|    policy_loss        | 2.41e+08  |\n",
            "|    std                | 0.989     |\n",
            "|    value_loss         | 4.48e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 285      |\n",
            "|    iterations         | 2300     |\n",
            "|    time_elapsed       | 40       |\n",
            "|    total_timesteps    | 11500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2299     |\n",
            "|    policy_loss        | 3.87e+08 |\n",
            "|    std                | 0.988    |\n",
            "|    value_loss         | 9.85e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 286      |\n",
            "|    iterations         | 2400     |\n",
            "|    time_elapsed       | 41       |\n",
            "|    total_timesteps    | 12000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | -5.8e+20 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2399     |\n",
            "|    policy_loss        | 4.41e+08 |\n",
            "|    std                | 0.988    |\n",
            "|    value_loss         | 1.32e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 286      |\n",
            "|    iterations         | 2500     |\n",
            "|    time_elapsed       | 43       |\n",
            "|    total_timesteps    | 12500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2499     |\n",
            "|    policy_loss        | 5.84e+08 |\n",
            "|    std                | 0.988    |\n",
            "|    value_loss         | 2.78e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4728296.351798115\n",
            "Sharpe:  1.0532256581833759\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 283       |\n",
            "|    iterations         | 2600      |\n",
            "|    time_elapsed       | 45        |\n",
            "|    total_timesteps    | 13000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.2     |\n",
            "|    explained_variance | -3.28e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2599      |\n",
            "|    policy_loss        | 1.73e+08  |\n",
            "|    std                | 0.987     |\n",
            "|    value_loss         | 2.12e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 283       |\n",
            "|    iterations         | 2700      |\n",
            "|    time_elapsed       | 47        |\n",
            "|    total_timesteps    | 13500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.2     |\n",
            "|    explained_variance | -3.51e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2699      |\n",
            "|    policy_loss        | 2.74e+08  |\n",
            "|    std                | 0.986     |\n",
            "|    value_loss         | 4.6e+13   |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 284       |\n",
            "|    iterations         | 2800      |\n",
            "|    time_elapsed       | 49        |\n",
            "|    total_timesteps    | 14000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.1     |\n",
            "|    explained_variance | -3.49e+21 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2799      |\n",
            "|    policy_loss        | 3.68e+08  |\n",
            "|    std                | 0.986     |\n",
            "|    value_loss         | 9.72e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 284      |\n",
            "|    iterations         | 2900     |\n",
            "|    time_elapsed       | 50       |\n",
            "|    total_timesteps    | 14500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.1    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2899     |\n",
            "|    policy_loss        | 4.74e+08 |\n",
            "|    std                | 0.985    |\n",
            "|    value_loss         | 1.4e+14  |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 283       |\n",
            "|    iterations         | 3000      |\n",
            "|    time_elapsed       | 52        |\n",
            "|    total_timesteps    | 15000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.1     |\n",
            "|    explained_variance | -2.14e+24 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2999      |\n",
            "|    policy_loss        | 5.98e+08  |\n",
            "|    std                | 0.985     |\n",
            "|    value_loss         | 2.81e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4816460.725794997\n",
            "Sharpe:  1.0642776119449358\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 3100     |\n",
            "|    time_elapsed       | 54       |\n",
            "|    total_timesteps    | 15500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.1    |\n",
            "|    explained_variance | -5.1e+22 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3099     |\n",
            "|    policy_loss        | 1.77e+08 |\n",
            "|    std                | 0.984    |\n",
            "|    value_loss         | 2.1e+13  |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 282       |\n",
            "|    iterations         | 3200      |\n",
            "|    time_elapsed       | 56        |\n",
            "|    total_timesteps    | 16000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.1     |\n",
            "|    explained_variance | -3.29e+20 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3199      |\n",
            "|    policy_loss        | 2.19e+08  |\n",
            "|    std                | 0.984     |\n",
            "|    value_loss         | 3.82e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 3300      |\n",
            "|    time_elapsed       | 58        |\n",
            "|    total_timesteps    | 16500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.1     |\n",
            "|    explained_variance | -2.28e+20 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3299      |\n",
            "|    policy_loss        | 3.6e+08   |\n",
            "|    std                | 0.983     |\n",
            "|    value_loss         | 9.14e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 3400     |\n",
            "|    time_elapsed       | 60       |\n",
            "|    total_timesteps    | 17000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3399     |\n",
            "|    policy_loss        | 4.04e+08 |\n",
            "|    std                | 0.982    |\n",
            "|    value_loss         | 1.26e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 3500      |\n",
            "|    time_elapsed       | 62        |\n",
            "|    total_timesteps    | 17500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42       |\n",
            "|    explained_variance | -7.39e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3499      |\n",
            "|    policy_loss        | 6.27e+08  |\n",
            "|    std                | 0.982     |\n",
            "|    value_loss         | 2.54e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4717775.4157805685\n",
            "Sharpe:  1.0534769697955462\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 3600      |\n",
            "|    time_elapsed       | 64        |\n",
            "|    total_timesteps    | 18000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42       |\n",
            "|    explained_variance | -1.05e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3599      |\n",
            "|    policy_loss        | 1.79e+08  |\n",
            "|    std                | 0.981     |\n",
            "|    value_loss         | 1.92e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 3700      |\n",
            "|    time_elapsed       | 65        |\n",
            "|    total_timesteps    | 18500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42       |\n",
            "|    explained_variance | -1.66e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3699      |\n",
            "|    policy_loss        | 2.36e+08  |\n",
            "|    std                | 0.981     |\n",
            "|    value_loss         | 3.82e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 3800     |\n",
            "|    time_elapsed       | 67       |\n",
            "|    total_timesteps    | 19000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3799     |\n",
            "|    policy_loss        | 3.53e+08 |\n",
            "|    std                | 0.981    |\n",
            "|    value_loss         | 8.81e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 3900      |\n",
            "|    time_elapsed       | 69        |\n",
            "|    total_timesteps    | 19500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42       |\n",
            "|    explained_variance | -2.35e+21 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3899      |\n",
            "|    policy_loss        | 3.88e+08  |\n",
            "|    std                | 0.981     |\n",
            "|    value_loss         | 1.15e+14  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 4000      |\n",
            "|    time_elapsed       | 70        |\n",
            "|    total_timesteps    | 20000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42       |\n",
            "|    explained_variance | -2.82e+24 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3999      |\n",
            "|    policy_loss        | 5.68e+08  |\n",
            "|    std                | 0.98      |\n",
            "|    value_loss         | 2.28e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4557939.365582631\n",
            "Sharpe:  1.0307280653381903\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 4100     |\n",
            "|    time_elapsed       | 72       |\n",
            "|    total_timesteps    | 20500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | -2.6e+23 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4099     |\n",
            "|    policy_loss        | 1.65e+08 |\n",
            "|    std                | 0.98     |\n",
            "|    value_loss         | 2.14e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 4200     |\n",
            "|    time_elapsed       | 74       |\n",
            "|    total_timesteps    | 21000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4199     |\n",
            "|    policy_loss        | 2.3e+08  |\n",
            "|    std                | 0.979    |\n",
            "|    value_loss         | 3.57e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 4300      |\n",
            "|    time_elapsed       | 76        |\n",
            "|    total_timesteps    | 21500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.9     |\n",
            "|    explained_variance | -1.09e+24 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 4299      |\n",
            "|    policy_loss        | 3.69e+08  |\n",
            "|    std                | 0.979     |\n",
            "|    value_loss         | 8.91e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 4400     |\n",
            "|    time_elapsed       | 78       |\n",
            "|    total_timesteps    | 22000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4399     |\n",
            "|    policy_loss        | 4.14e+08 |\n",
            "|    std                | 0.978    |\n",
            "|    value_loss         | 1.16e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 4500     |\n",
            "|    time_elapsed       | 79       |\n",
            "|    total_timesteps    | 22500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4499     |\n",
            "|    policy_loss        | 5.85e+08 |\n",
            "|    std                | 0.978    |\n",
            "|    value_loss         | 2.26e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4573035.639333263\n",
            "Sharpe:  1.0330265835378454\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 4600     |\n",
            "|    time_elapsed       | 81       |\n",
            "|    total_timesteps    | 23000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4599     |\n",
            "|    policy_loss        | 1.47e+08 |\n",
            "|    std                | 0.978    |\n",
            "|    value_loss         | 1.8e+13  |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 4700      |\n",
            "|    time_elapsed       | 83        |\n",
            "|    total_timesteps    | 23500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.9     |\n",
            "|    explained_variance | -1.04e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 4699      |\n",
            "|    policy_loss        | 2.36e+08  |\n",
            "|    std                | 0.978     |\n",
            "|    value_loss         | 3.36e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 4800     |\n",
            "|    time_elapsed       | 85       |\n",
            "|    total_timesteps    | 24000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4799     |\n",
            "|    policy_loss        | 3.52e+08 |\n",
            "|    std                | 0.978    |\n",
            "|    value_loss         | 8.02e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 4900     |\n",
            "|    time_elapsed       | 86       |\n",
            "|    total_timesteps    | 24500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4899     |\n",
            "|    policy_loss        | 4.06e+08 |\n",
            "|    std                | 0.976    |\n",
            "|    value_loss         | 1.1e+14  |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 282       |\n",
            "|    iterations         | 5000      |\n",
            "|    time_elapsed       | 88        |\n",
            "|    total_timesteps    | 25000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.8     |\n",
            "|    explained_variance | -1.08e+22 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 4999      |\n",
            "|    policy_loss        | 5.12e+08  |\n",
            "|    std                | 0.976     |\n",
            "|    value_loss         | 2.06e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4483838.92907349\n",
            "Sharpe:  1.0209787874390623\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 5100      |\n",
            "|    time_elapsed       | 90        |\n",
            "|    total_timesteps    | 25500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.8     |\n",
            "|    explained_variance | -3.47e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 5099      |\n",
            "|    policy_loss        | 1.77e+08  |\n",
            "|    std                | 0.975     |\n",
            "|    value_loss         | 2.25e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 5200     |\n",
            "|    time_elapsed       | 92       |\n",
            "|    total_timesteps    | 26000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5199     |\n",
            "|    policy_loss        | 2.28e+08 |\n",
            "|    std                | 0.975    |\n",
            "|    value_loss         | 3.85e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 5300     |\n",
            "|    time_elapsed       | 93       |\n",
            "|    total_timesteps    | 26500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5299     |\n",
            "|    policy_loss        | 3.49e+08 |\n",
            "|    std                | 0.974    |\n",
            "|    value_loss         | 7.97e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 5400     |\n",
            "|    time_elapsed       | 95       |\n",
            "|    total_timesteps    | 27000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5399     |\n",
            "|    policy_loss        | 3.98e+08 |\n",
            "|    std                | 0.973    |\n",
            "|    value_loss         | 1.13e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 5500     |\n",
            "|    time_elapsed       | 97       |\n",
            "|    total_timesteps    | 27500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5499     |\n",
            "|    policy_loss        | 5.14e+08 |\n",
            "|    std                | 0.973    |\n",
            "|    value_loss         | 2.09e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4481315.1199709335\n",
            "Sharpe:  1.0199771156344852\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 5600     |\n",
            "|    time_elapsed       | 99       |\n",
            "|    total_timesteps    | 28000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5599     |\n",
            "|    policy_loss        | 1.89e+08 |\n",
            "|    std                | 0.973    |\n",
            "|    value_loss         | 2.4e+13  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 5700     |\n",
            "|    time_elapsed       | 101      |\n",
            "|    total_timesteps    | 28500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5699     |\n",
            "|    policy_loss        | 2.49e+08 |\n",
            "|    std                | 0.972    |\n",
            "|    value_loss         | 4.48e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 282       |\n",
            "|    iterations         | 5800      |\n",
            "|    time_elapsed       | 102       |\n",
            "|    total_timesteps    | 29000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.7     |\n",
            "|    explained_variance | -1.99e+21 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 5799      |\n",
            "|    policy_loss        | 3.7e+08   |\n",
            "|    std                | 0.972     |\n",
            "|    value_loss         | 8.6e+13   |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 5900     |\n",
            "|    time_elapsed       | 104      |\n",
            "|    total_timesteps    | 29500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5899     |\n",
            "|    policy_loss        | 4.06e+08 |\n",
            "|    std                | 0.972    |\n",
            "|    value_loss         | 1.23e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 6000     |\n",
            "|    time_elapsed       | 106      |\n",
            "|    total_timesteps    | 30000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5999     |\n",
            "|    policy_loss        | 6.13e+08 |\n",
            "|    std                | 0.971    |\n",
            "|    value_loss         | 2.46e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4886043.474900911\n",
            "Sharpe:  1.0725124404022184\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 6100     |\n",
            "|    time_elapsed       | 108      |\n",
            "|    total_timesteps    | 30500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6099     |\n",
            "|    policy_loss        | 1.86e+08 |\n",
            "|    std                | 0.97     |\n",
            "|    value_loss         | 2.2e+13  |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 282       |\n",
            "|    iterations         | 6200      |\n",
            "|    time_elapsed       | 109       |\n",
            "|    total_timesteps    | 31000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.6     |\n",
            "|    explained_variance | -9.36e+20 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 6199      |\n",
            "|    policy_loss        | 2.34e+08  |\n",
            "|    std                | 0.97      |\n",
            "|    value_loss         | 3.58e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 6300     |\n",
            "|    time_elapsed       | 111      |\n",
            "|    total_timesteps    | 31500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6299     |\n",
            "|    policy_loss        | 3.28e+08 |\n",
            "|    std                | 0.97     |\n",
            "|    value_loss         | 7.53e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 6400     |\n",
            "|    time_elapsed       | 113      |\n",
            "|    total_timesteps    | 32000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6399     |\n",
            "|    policy_loss        | 3.61e+08 |\n",
            "|    std                | 0.969    |\n",
            "|    value_loss         | 9.35e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 6500     |\n",
            "|    time_elapsed       | 115      |\n",
            "|    total_timesteps    | 32500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6499     |\n",
            "|    policy_loss        | 5.59e+08 |\n",
            "|    std                | 0.969    |\n",
            "|    value_loss         | 2.17e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4527612.162111566\n",
            "Sharpe:  1.0252821280039681\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 6600      |\n",
            "|    time_elapsed       | 117       |\n",
            "|    total_timesteps    | 33000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.6     |\n",
            "|    explained_variance | -2.31e+22 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 6599      |\n",
            "|    policy_loss        | 1.71e+08  |\n",
            "|    std                | 0.969     |\n",
            "|    value_loss         | 1.96e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 6700     |\n",
            "|    time_elapsed       | 118      |\n",
            "|    total_timesteps    | 33500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6699     |\n",
            "|    policy_loss        | 2.19e+08 |\n",
            "|    std                | 0.968    |\n",
            "|    value_loss         | 3.64e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 6800     |\n",
            "|    time_elapsed       | 120      |\n",
            "|    total_timesteps    | 34000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6799     |\n",
            "|    policy_loss        | 3.27e+08 |\n",
            "|    std                | 0.968    |\n",
            "|    value_loss         | 7.42e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 282       |\n",
            "|    iterations         | 6900      |\n",
            "|    time_elapsed       | 122       |\n",
            "|    total_timesteps    | 34500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.6     |\n",
            "|    explained_variance | -2.83e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 6899      |\n",
            "|    policy_loss        | 3.49e+08  |\n",
            "|    std                | 0.967     |\n",
            "|    value_loss         | 9.14e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 7000     |\n",
            "|    time_elapsed       | 123      |\n",
            "|    total_timesteps    | 35000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6999     |\n",
            "|    policy_loss        | 5.65e+08 |\n",
            "|    std                | 0.967    |\n",
            "|    value_loss         | 2.34e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4421057.094141287\n",
            "Sharpe:  1.0107989875267063\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 7100     |\n",
            "|    time_elapsed       | 125      |\n",
            "|    total_timesteps    | 35500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7099     |\n",
            "|    policy_loss        | 1.76e+08 |\n",
            "|    std                | 0.966    |\n",
            "|    value_loss         | 2.25e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 7200     |\n",
            "|    time_elapsed       | 127      |\n",
            "|    total_timesteps    | 36000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7199     |\n",
            "|    policy_loss        | 2.58e+08 |\n",
            "|    std                | 0.966    |\n",
            "|    value_loss         | 4.21e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 7300     |\n",
            "|    time_elapsed       | 129      |\n",
            "|    total_timesteps    | 36500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7299     |\n",
            "|    policy_loss        | 3.62e+08 |\n",
            "|    std                | 0.966    |\n",
            "|    value_loss         | 9.34e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 282      |\n",
            "|    iterations         | 7400     |\n",
            "|    time_elapsed       | 131      |\n",
            "|    total_timesteps    | 37000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7399     |\n",
            "|    policy_loss        | 4.28e+08 |\n",
            "|    std                | 0.966    |\n",
            "|    value_loss         | 1.13e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 282       |\n",
            "|    iterations         | 7500      |\n",
            "|    time_elapsed       | 132       |\n",
            "|    total_timesteps    | 37500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.5     |\n",
            "|    explained_variance | -8.86e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 7499      |\n",
            "|    policy_loss        | 6.17e+08  |\n",
            "|    std                | 0.965     |\n",
            "|    value_loss         | 2.9e+14   |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5149420.259396572\n",
            "Sharpe:  1.1054735965838662\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 7600      |\n",
            "|    time_elapsed       | 134       |\n",
            "|    total_timesteps    | 38000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.5     |\n",
            "|    explained_variance | -6.36e+22 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 7599      |\n",
            "|    policy_loss        | 1.75e+08  |\n",
            "|    std                | 0.965     |\n",
            "|    value_loss         | 2.09e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 7700      |\n",
            "|    time_elapsed       | 136       |\n",
            "|    total_timesteps    | 38500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.5     |\n",
            "|    explained_variance | -9.42e+22 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 7699      |\n",
            "|    policy_loss        | 2.22e+08  |\n",
            "|    std                | 0.965     |\n",
            "|    value_loss         | 3.07e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 7800      |\n",
            "|    time_elapsed       | 138       |\n",
            "|    total_timesteps    | 39000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.5     |\n",
            "|    explained_variance | -1.29e+22 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 7799      |\n",
            "|    policy_loss        | 3.44e+08  |\n",
            "|    std                | 0.965     |\n",
            "|    value_loss         | 7.22e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 7900      |\n",
            "|    time_elapsed       | 140       |\n",
            "|    total_timesteps    | 39500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.5     |\n",
            "|    explained_variance | -3.31e+21 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 7899      |\n",
            "|    policy_loss        | 3.95e+08  |\n",
            "|    std                | 0.965     |\n",
            "|    value_loss         | 1.05e+14  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 8000      |\n",
            "|    time_elapsed       | 141       |\n",
            "|    total_timesteps    | 40000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.5     |\n",
            "|    explained_variance | -6.87e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 7999      |\n",
            "|    policy_loss        | 5.57e+08  |\n",
            "|    std                | 0.965     |\n",
            "|    value_loss         | 2.25e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4671399.665730687\n",
            "Sharpe:  1.04306357312528\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 8100     |\n",
            "|    time_elapsed       | 143      |\n",
            "|    total_timesteps    | 40500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8099     |\n",
            "|    policy_loss        | 1.57e+08 |\n",
            "|    std                | 0.965    |\n",
            "|    value_loss         | 1.96e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 8200     |\n",
            "|    time_elapsed       | 145      |\n",
            "|    total_timesteps    | 41000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8199     |\n",
            "|    policy_loss        | 2.17e+08 |\n",
            "|    std                | 0.965    |\n",
            "|    value_loss         | 3.05e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 8300     |\n",
            "|    time_elapsed       | 147      |\n",
            "|    total_timesteps    | 41500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8299     |\n",
            "|    policy_loss        | 3.08e+08 |\n",
            "|    std                | 0.965    |\n",
            "|    value_loss         | 7.42e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 8400     |\n",
            "|    time_elapsed       | 149      |\n",
            "|    total_timesteps    | 42000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8399     |\n",
            "|    policy_loss        | 3.66e+08 |\n",
            "|    std                | 0.964    |\n",
            "|    value_loss         | 1.11e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 8500      |\n",
            "|    time_elapsed       | 150       |\n",
            "|    total_timesteps    | 42500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.5     |\n",
            "|    explained_variance | -6.69e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8499      |\n",
            "|    policy_loss        | 5.38e+08  |\n",
            "|    std                | 0.964     |\n",
            "|    value_loss         | 2.19e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4780411.166230081\n",
            "Sharpe:  1.060902163930051\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 8600     |\n",
            "|    time_elapsed       | 152      |\n",
            "|    total_timesteps    | 43000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8599     |\n",
            "|    policy_loss        | 1.6e+08  |\n",
            "|    std                | 0.964    |\n",
            "|    value_loss         | 2.04e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 8700      |\n",
            "|    time_elapsed       | 154       |\n",
            "|    total_timesteps    | 43500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.5     |\n",
            "|    explained_variance | -9.69e+22 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8699      |\n",
            "|    policy_loss        | 2.02e+08  |\n",
            "|    std                | 0.964     |\n",
            "|    value_loss         | 3.19e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 281       |\n",
            "|    iterations         | 8800      |\n",
            "|    time_elapsed       | 156       |\n",
            "|    total_timesteps    | 44000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.5     |\n",
            "|    explained_variance | -1.16e+24 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8799      |\n",
            "|    policy_loss        | 3.13e+08  |\n",
            "|    std                | 0.964     |\n",
            "|    value_loss         | 7.6e+13   |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 8900     |\n",
            "|    time_elapsed       | 158      |\n",
            "|    total_timesteps    | 44500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8899     |\n",
            "|    policy_loss        | 4.13e+08 |\n",
            "|    std                | 0.964    |\n",
            "|    value_loss         | 1.16e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 281      |\n",
            "|    iterations         | 9000     |\n",
            "|    time_elapsed       | 160      |\n",
            "|    total_timesteps    | 45000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8999     |\n",
            "|    policy_loss        | 5.57e+08 |\n",
            "|    std                | 0.964    |\n",
            "|    value_loss         | 2.21e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4788546.581298462\n",
            "Sharpe:  1.0592662476362555\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 280      |\n",
            "|    iterations         | 9100     |\n",
            "|    time_elapsed       | 162      |\n",
            "|    total_timesteps    | 45500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9099     |\n",
            "|    policy_loss        | 1.63e+08 |\n",
            "|    std                | 0.963    |\n",
            "|    value_loss         | 1.85e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 280      |\n",
            "|    iterations         | 9200     |\n",
            "|    time_elapsed       | 163      |\n",
            "|    total_timesteps    | 46000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9199     |\n",
            "|    policy_loss        | 2.06e+08 |\n",
            "|    std                | 0.963    |\n",
            "|    value_loss         | 2.94e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 280      |\n",
            "|    iterations         | 9300     |\n",
            "|    time_elapsed       | 165      |\n",
            "|    total_timesteps    | 46500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9299     |\n",
            "|    policy_loss        | 3.21e+08 |\n",
            "|    std                | 0.962    |\n",
            "|    value_loss         | 6.9e+13  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 280      |\n",
            "|    iterations         | 9400     |\n",
            "|    time_elapsed       | 167      |\n",
            "|    total_timesteps    | 47000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9399     |\n",
            "|    policy_loss        | 3.94e+08 |\n",
            "|    std                | 0.962    |\n",
            "|    value_loss         | 1.09e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 280      |\n",
            "|    iterations         | 9500     |\n",
            "|    time_elapsed       | 169      |\n",
            "|    total_timesteps    | 47500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9499     |\n",
            "|    policy_loss        | 5.45e+08 |\n",
            "|    std                | 0.961    |\n",
            "|    value_loss         | 2.21e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4952996.978617552\n",
            "Sharpe:  1.070999308885424\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 280      |\n",
            "|    iterations         | 9600     |\n",
            "|    time_elapsed       | 171      |\n",
            "|    total_timesteps    | 48000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9599     |\n",
            "|    policy_loss        | 1.63e+08 |\n",
            "|    std                | 0.962    |\n",
            "|    value_loss         | 1.76e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 280       |\n",
            "|    iterations         | 9700      |\n",
            "|    time_elapsed       | 172       |\n",
            "|    total_timesteps    | 48500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -3.45e+21 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 9699      |\n",
            "|    policy_loss        | 1.99e+08  |\n",
            "|    std                | 0.961     |\n",
            "|    value_loss         | 2.89e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 280      |\n",
            "|    iterations         | 9800     |\n",
            "|    time_elapsed       | 174      |\n",
            "|    total_timesteps    | 49000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9799     |\n",
            "|    policy_loss        | 3.15e+08 |\n",
            "|    std                | 0.96     |\n",
            "|    value_loss         | 7.15e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 280      |\n",
            "|    iterations         | 9900     |\n",
            "|    time_elapsed       | 176      |\n",
            "|    total_timesteps    | 49500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9899     |\n",
            "|    policy_loss        | 4.15e+08 |\n",
            "|    std                | 0.96     |\n",
            "|    value_loss         | 1.05e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 280      |\n",
            "|    iterations         | 10000    |\n",
            "|    time_elapsed       | 178      |\n",
            "|    total_timesteps    | 50000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9999     |\n",
            "|    policy_loss        | 5.57e+08 |\n",
            "|    std                | 0.96     |\n",
            "|    value_loss         | 2.07e+14 |\n",
            "------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lvrqTro3lhAh"
      },
      "source": [
        "### Model 2: **PPO**\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kVXta7jVKMhV",
        "outputId": "4cb82ab6-914d-4d2b-a4ee-e47c8557dad3"
      },
      "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": 87,
      "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": "z5XlUIszKUGx",
        "outputId": "3989711a-cbed-4649-e71c-566d52917478"
      },
      "source": [
        "trained_ppo = agent.train_model(model=model_ppo, \n",
        "                             tb_log_name='ppo',\n",
        "                             total_timesteps=80000)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ppo/ppo_3\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 458  |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 4    |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4917364.6278486075\n",
            "Sharpe:  1.074414829116363\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 391            |\n",
            "|    iterations           | 2              |\n",
            "|    time_elapsed         | 10             |\n",
            "|    total_timesteps      | 4096           |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -7.8231096e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -3.71e+14      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 7.78e+14       |\n",
            "|    n_updates            | 10             |\n",
            "|    policy_gradient_loss | -6.16e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 1.57e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4996331.100586685\n",
            "Sharpe:  1.0890927964884638\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 373            |\n",
            "|    iterations           | 3              |\n",
            "|    time_elapsed         | 16             |\n",
            "|    total_timesteps      | 6144           |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -3.5390258e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -8.76e+14      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.1e+15        |\n",
            "|    n_updates            | 20             |\n",
            "|    policy_gradient_loss | -4.29e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.33e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4751039.2878817525\n",
            "Sharpe:  1.0560179406423764\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 365            |\n",
            "|    iterations           | 4              |\n",
            "|    time_elapsed         | 22             |\n",
            "|    total_timesteps      | 8192           |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.6763806e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -8.01e+15      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.25e+15       |\n",
            "|    n_updates            | 30             |\n",
            "|    policy_gradient_loss | -5.58e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.59e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4769059.347696523\n",
            "Sharpe:  1.056814654380227\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 360            |\n",
            "|    iterations           | 5              |\n",
            "|    time_elapsed         | 28             |\n",
            "|    total_timesteps      | 10240          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -5.5879354e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -2.55e+16      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.24e+15       |\n",
            "|    n_updates            | 40             |\n",
            "|    policy_gradient_loss | -4.9e-07       |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.7e+15        |\n",
            "--------------------------------------------\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 358            |\n",
            "|    iterations           | 6              |\n",
            "|    time_elapsed         | 34             |\n",
            "|    total_timesteps      | 12288          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | 1.13621354e-07 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -9.17e+16      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.35e+15       |\n",
            "|    n_updates            | 50             |\n",
            "|    policy_gradient_loss | -4.28e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.77e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4816491.86007194\n",
            "Sharpe:  1.0636199939613733\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 356           |\n",
            "|    iterations           | 7             |\n",
            "|    time_elapsed         | 40            |\n",
            "|    total_timesteps      | 14336         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.5390258e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.42e+17     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.03e+15      |\n",
            "|    n_updates            | 60            |\n",
            "|    policy_gradient_loss | -6.52e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.94e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4631919.83090099\n",
            "Sharpe:  1.0396504731290799\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 354           |\n",
            "|    iterations           | 8             |\n",
            "|    time_elapsed         | 46            |\n",
            "|    total_timesteps      | 16384         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.7508864e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -6.93e+17     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 9.83e+14      |\n",
            "|    n_updates            | 70            |\n",
            "|    policy_gradient_loss | -5.78e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.06e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4728763.286321457\n",
            "Sharpe:  1.052390302374202\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 353           |\n",
            "|    iterations           | 9             |\n",
            "|    time_elapsed         | 52            |\n",
            "|    total_timesteps      | 18432         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.4703484e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.72e+18     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.25e+15      |\n",
            "|    n_updates            | 80            |\n",
            "|    policy_gradient_loss | -4.84e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.33e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4439983.024798136\n",
            "Sharpe:  1.013829383303325\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 352            |\n",
            "|    iterations           | 10             |\n",
            "|    time_elapsed         | 58             |\n",
            "|    total_timesteps      | 20480          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.3038516e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.7e+18       |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.17e+15       |\n",
            "|    n_updates            | 90             |\n",
            "|    policy_gradient_loss | -4.82e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.58e+15       |\n",
            "--------------------------------------------\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 352           |\n",
            "|    iterations           | 11            |\n",
            "|    time_elapsed         | 63            |\n",
            "|    total_timesteps      | 22528         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | -9.313226e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.85e+18     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.2e+15       |\n",
            "|    n_updates            | 100           |\n",
            "|    policy_gradient_loss | -5.2e-07      |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.51e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5048884.524536961\n",
            "Sharpe:  1.0963911876706685\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 351           |\n",
            "|    iterations           | 12            |\n",
            "|    time_elapsed         | 69            |\n",
            "|    total_timesteps      | 24576         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.7252903e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -2.67e+18     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.44e+15      |\n",
            "|    n_updates            | 110           |\n",
            "|    policy_gradient_loss | -4.53e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.8e+15       |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4824229.456193555\n",
            "Sharpe:  1.0648549464252506\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 351           |\n",
            "|    iterations           | 13            |\n",
            "|    time_elapsed         | 75            |\n",
            "|    total_timesteps      | 26624         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.3527613e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -3.38e+18     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 7.89e+14      |\n",
            "|    n_updates            | 120           |\n",
            "|    policy_gradient_loss | -6.06e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.76e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4602974.615591427\n",
            "Sharpe:  1.034753433280377\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 350           |\n",
            "|    iterations           | 14            |\n",
            "|    time_elapsed         | 81            |\n",
            "|    total_timesteps      | 28672         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 8.8475645e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.75e+19     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.23e+15      |\n",
            "|    n_updates            | 130           |\n",
            "|    policy_gradient_loss | -5.8e-07      |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.27e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4608422.583401322\n",
            "Sharpe:  1.035300880612428\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 349           |\n",
            "|    iterations           | 15            |\n",
            "|    time_elapsed         | 87            |\n",
            "|    total_timesteps      | 30720         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.3038516e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -7.71e+18     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.22e+15      |\n",
            "|    n_updates            | 140           |\n",
            "|    policy_gradient_loss | -5.63e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.39e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4826869.636472441\n",
            "Sharpe:  1.0676330284861433\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 348            |\n",
            "|    iterations           | 16             |\n",
            "|    time_elapsed         | 94             |\n",
            "|    total_timesteps      | 32768          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.4901161e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.51e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.22e+15       |\n",
            "|    n_updates            | 150            |\n",
            "|    policy_gradient_loss | -5.78e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.7e+15        |\n",
            "--------------------------------------------\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 346           |\n",
            "|    iterations           | 17            |\n",
            "|    time_elapsed         | 100           |\n",
            "|    total_timesteps      | 34816         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | -5.401671e-08 |\n",
            "|    clip_fraction        | 0             |\n",
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            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.48e+19     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.48e+15      |\n",
            "|    n_updates            | 160           |\n",
            "|    policy_gradient_loss | -3.96e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.81e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4364006.929301854\n",
            "Sharpe:  1.002176631256902\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 345            |\n",
            "|    iterations           | 18             |\n",
            "|    time_elapsed         | 106            |\n",
            "|    total_timesteps      | 36864          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.0803342e-07 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.15e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 8.41e+14       |\n",
            "|    n_updates            | 170            |\n",
            "|    policy_gradient_loss | -4.91e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 1.58e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4796634.5596691\n",
            "Sharpe:  1.0678319491053092\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 344            |\n",
            "|    iterations           | 19             |\n",
            "|    time_elapsed         | 112            |\n",
            "|    total_timesteps      | 38912          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.3038516e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -4.21e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.03e+15       |\n",
            "|    n_updates            | 180            |\n",
            "|    policy_gradient_loss | -5.6e-07       |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.02e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4969786.413399254\n",
            "Sharpe:  1.0823021486710163\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 344            |\n",
            "|    iterations           | 20             |\n",
            "|    time_elapsed         | 118            |\n",
            "|    total_timesteps      | 40960          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -6.7055225e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -6.41e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.22e+15       |\n",
            "|    n_updates            | 190            |\n",
            "|    policy_gradient_loss | -2.87e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.4e+15        |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4885480.801922398\n",
            "Sharpe:  1.0729451877791811\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 343            |\n",
            "|    iterations           | 21             |\n",
            "|    time_elapsed         | 125            |\n",
            "|    total_timesteps      | 43008          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -5.5879354e-09 |\n",
            "|    clip_fraction        | 0              |\n",
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            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -6.85e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.62e+15       |\n",
            "|    n_updates            | 200            |\n",
            "|    policy_gradient_loss | -5.24e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.95e+15       |\n",
            "--------------------------------------------\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 343           |\n",
            "|    iterations           | 22            |\n",
            "|    time_elapsed         | 131           |\n",
            "|    total_timesteps      | 45056         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.8067658e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -7.01e+19     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.34e+15      |\n",
            "|    n_updates            | 210           |\n",
            "|    policy_gradient_loss | -4.62e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.93e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5613709.009268909\n",
            "Sharpe:  1.1673870008513114\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 342            |\n",
            "|    iterations           | 23             |\n",
            "|    time_elapsed         | 137            |\n",
            "|    total_timesteps      | 47104          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -2.0489097e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -6.72e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.41e+15       |\n",
            "|    n_updates            | 220            |\n",
            "|    policy_gradient_loss | -4.78e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.71e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5043800.590470289\n",
            "Sharpe:  1.0953673306850924\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 342           |\n",
            "|    iterations           | 24            |\n",
            "|    time_elapsed         | 143           |\n",
            "|    total_timesteps      | 49152         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.4214387e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.37e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.01e+15      |\n",
            "|    n_updates            | 230           |\n",
            "|    policy_gradient_loss | -5.28e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.26e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4776576.852863929\n",
            "Sharpe:  1.0593811754233755\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 342           |\n",
            "|    iterations           | 25            |\n",
            "|    time_elapsed         | 149           |\n",
            "|    total_timesteps      | 51200         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.4703484e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -3.27e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.21e+15      |\n",
            "|    n_updates            | 240           |\n",
            "|    policy_gradient_loss | -4.82e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.46e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4468393.200157898\n",
            "Sharpe:  1.0192746589767419\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 341           |\n",
            "|    iterations           | 26            |\n",
            "|    time_elapsed         | 156           |\n",
            "|    total_timesteps      | 53248         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.6077032e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.96e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.31e+15      |\n",
            "|    n_updates            | 250           |\n",
            "|    policy_gradient_loss | -5.36e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.59e+15      |\n",
            "-------------------------------------------\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 341            |\n",
            "|    iterations           | 27             |\n",
            "|    time_elapsed         | 162            |\n",
            "|    total_timesteps      | 55296          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.3038516e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.68e+20      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.33e+15       |\n",
            "|    n_updates            | 260            |\n",
            "|    policy_gradient_loss | -3.77e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.51e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4875234.39450474\n",
            "Sharpe:  1.0721137742534572\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 340            |\n",
            "|    iterations           | 28             |\n",
            "|    time_elapsed         | 168            |\n",
            "|    total_timesteps      | 57344          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.2479722e-07 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.66e+20      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.59e+15       |\n",
            "|    n_updates            | 270            |\n",
            "|    policy_gradient_loss | -4.61e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.8e+15        |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4600459.210918712\n",
            "Sharpe:  1.034756153745345\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 340           |\n",
            "|    iterations           | 29            |\n",
            "|    time_elapsed         | 174           |\n",
            "|    total_timesteps      | 59392         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | -4.284084e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.26e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 8.07e+14      |\n",
            "|    n_updates            | 280           |\n",
            "|    policy_gradient_loss | -5.44e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.62e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4526188.381438201\n",
            "Sharpe:  1.0293846869900876\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 339            |\n",
            "|    iterations           | 30             |\n",
            "|    time_elapsed         | 180            |\n",
            "|    total_timesteps      | 61440          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -2.4214387e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -6.44e+20      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.12e+15       |\n",
            "|    n_updates            | 290            |\n",
            "|    policy_gradient_loss | -5.65e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.1e+15        |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4487836.803716703\n",
            "Sharpe:  1.010974660894394\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 339            |\n",
            "|    iterations           | 31             |\n",
            "|    time_elapsed         | 187            |\n",
            "|    total_timesteps      | 63488          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -2.6077032e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -4.47e+20      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.14e+15       |\n",
            "|    n_updates            | 300            |\n",
            "|    policy_gradient_loss | -4.8e-07       |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.25e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4480729.650671386\n",
            "Sharpe:  1.0219085518652522\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 339            |\n",
            "|    iterations           | 32             |\n",
            "|    time_elapsed         | 193            |\n",
            "|    total_timesteps      | 65536          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -2.0302832e-07 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -3.87e+20      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.28e+15       |\n",
            "|    n_updates            | 310            |\n",
            "|    policy_gradient_loss | -4.4e-07       |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.51e+15       |\n",
            "--------------------------------------------\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 339          |\n",
            "|    iterations           | 33           |\n",
            "|    time_elapsed         | 199          |\n",
            "|    total_timesteps      | 67584        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 1.359731e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -42.6        |\n",
            "|    explained_variance   | -3.68e+20    |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 1.24e+15     |\n",
            "|    n_updates            | 320          |\n",
            "|    policy_gradient_loss | -4.51e-07    |\n",
            "|    std                  | 1            |\n",
            "|    value_loss           | 2.66e+15     |\n",
            "------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4399373.734699048\n",
            "Sharpe:  1.005407087483561\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 338           |\n",
            "|    iterations           | 34            |\n",
            "|    time_elapsed         | 205           |\n",
            "|    total_timesteps      | 69632         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.2351742e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -2.29e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 8.5e+14       |\n",
            "|    n_updates            | 330           |\n",
            "|    policy_gradient_loss | -5.56e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.64e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4305742.921261859\n",
            "Sharpe:  0.9945061913961891\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 338           |\n",
            "|    iterations           | 35            |\n",
            "|    time_elapsed         | 211           |\n",
            "|    total_timesteps      | 71680         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.3411045e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -7.11e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 7.97e+14      |\n",
            "|    n_updates            | 340           |\n",
            "|    policy_gradient_loss | -6.48e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.8e+15       |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4794175.629957249\n",
            "Sharpe:  1.0611635246548963\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 338            |\n",
            "|    iterations           | 36             |\n",
            "|    time_elapsed         | 217            |\n",
            "|    total_timesteps      | 73728          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -3.3527613e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.16e+21      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.07e+15       |\n",
            "|    n_updates            | 350            |\n",
            "|    policy_gradient_loss | -4.82e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.06e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4467487.416264421\n",
            "Sharpe:  1.021012208464475\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 338          |\n",
            "|    iterations           | 37           |\n",
            "|    time_elapsed         | 224          |\n",
            "|    total_timesteps      | 75776        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 5.401671e-08 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -42.6        |\n",
            "|    explained_variance   | -9.89e+20    |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 1.46e+15     |\n",
            "|    n_updates            | 360          |\n",
            "|    policy_gradient_loss | -4.78e-07    |\n",
            "|    std                  | 1            |\n",
            "|    value_loss           | 2.75e+15     |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 338           |\n",
            "|    iterations           | 38            |\n",
            "|    time_elapsed         | 229           |\n",
            "|    total_timesteps      | 77824         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.6763806e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -7.64e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.25e+15      |\n",
            "|    n_updates            | 370           |\n",
            "|    policy_gradient_loss | -4.54e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.57e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4806649.219027834\n",
            "Sharpe:  1.0604486398186765\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 338          |\n",
            "|    iterations           | 39           |\n",
            "|    time_elapsed         | 236          |\n",
            "|    total_timesteps      | 79872        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 4.284084e-08 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -42.6        |\n",
            "|    explained_variance   | -6.96e+20    |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 1.28e+15     |\n",
            "|    n_updates            | 380          |\n",
            "|    policy_gradient_loss | -5.9e-07     |\n",
            "|    std                  | 1            |\n",
            "|    value_loss           | 2.44e+15     |\n",
            "------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4653147.508966551\n",
            "Sharpe:  1.043189911078732\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 338           |\n",
            "|    iterations           | 40            |\n",
            "|    time_elapsed         | 242           |\n",
            "|    total_timesteps      | 81920         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 6.3329935e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.04e+21     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.01e+15      |\n",
            "|    n_updates            | 390           |\n",
            "|    policy_gradient_loss | -5.33e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.82e+15      |\n",
            "-------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a3Iuv554xYFH"
      },
      "source": [
        "### Model 3: **DDPG**\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ojmppgo4LPLz",
        "outputId": "bb2459a2-3231-481e-9628-4ace4256f9e1"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "DDPG_PARAMS = {\"batch_size\": 128, \"buffer_size\": 50000, \"learning_rate\": 0.001}\n",
        "\n",
        "\n",
        "model_ddpg = agent.get_model(\"ddpg\",model_kwargs = DDPG_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 50000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bWt6BIR0LT25",
        "outputId": "d523d157-f2c7-4667-9971-214ddc51fcb1"
      },
      "source": [
        "trained_ddpg = agent.train_model(model=model_ddpg, \n",
        "                             tb_log_name='ddpg',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ddpg/ddpg_2\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4625995.900359718\n",
            "Sharpe:  1.040202670783119\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 4         |\n",
            "|    fps             | 22        |\n",
            "|    time_elapsed    | 439       |\n",
            "|    total timesteps | 10064     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -6.99e+07 |\n",
            "|    critic_loss     | 7.27e+12  |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 7548      |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 8         |\n",
            "|    fps             | 20        |\n",
            "|    time_elapsed    | 980       |\n",
            "|    total timesteps | 20128     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.44e+08 |\n",
            "|    critic_loss     | 1.81e+13  |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 17612     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 12        |\n",
            "|    fps             | 19        |\n",
            "|    time_elapsed    | 1542      |\n",
            "|    total timesteps | 30192     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.88e+08 |\n",
            "|    critic_loss     | 2.72e+13  |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 27676     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 16        |\n",
            "|    fps             | 18        |\n",
            "|    time_elapsed    | 2133      |\n",
            "|    total timesteps | 40256     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -2.15e+08 |\n",
            "|    critic_loss     | 3.45e+13  |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 37740     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 20       |\n",
            "|    fps             | 17       |\n",
            "|    time_elapsed    | 2874     |\n",
            "|    total timesteps | 50320    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -2.3e+08 |\n",
            "|    critic_loss     | 4.05e+13 |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 47804    |\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SPEXBcm-uBJo"
      },
      "source": [
        "### Model 4: **SAC**\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaWf2QeiLqyO",
        "outputId": "f74b3a46-6195-43f3-d321-512a3318f767"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "SAC_PARAMS = {\n",
        "    \"batch_size\": 128,\n",
        "    \"buffer_size\": 100000,\n",
        "    \"learning_rate\": 0.0003,\n",
        "    \"learning_starts\": 100,\n",
        "    \"ent_coef\": \"auto_0.1\",\n",
        "}\n",
        "\n",
        "model_sac = agent.get_model(\"sac\",model_kwargs = SAC_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 100000, 'learning_rate': 0.0003, '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": "ZgYVPqtKLvi3",
        "outputId": "d633cfb8-2379-485c-9c2b-5737f0fa2b23"
      },
      "source": [
        "trained_sac = agent.train_model(model=model_sac, \n",
        "                             tb_log_name='sac',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/sac/sac_1\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4449463.498168942\n",
            "Sharpe:  1.01245667390232\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418643.239765096\n",
            "Sharpe:  1.0135796594260282\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418644.1960784905\n",
            "Sharpe:  1.0135797537524718\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418659.429680678\n",
            "Sharpe:  1.013581852537709\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 4         |\n",
            "|    fps             | 12        |\n",
            "|    time_elapsed    | 783       |\n",
            "|    total timesteps | 10064     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -8.83e+07 |\n",
            "|    critic_loss     | 6.57e+12  |\n",
            "|    ent_coef        | 2.24      |\n",
            "|    ent_coef_loss   | -205      |\n",
            "|    learning_rate   | 0.0003    |\n",
            "|    n_updates       | 9963      |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418651.576406099\n",
            "Sharpe:  1.013581224026754\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418670.948269031\n",
            "Sharpe:  1.0135838030234754\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418682.278829884\n",
            "Sharpe:  1.013585596968056\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418791.911955293\n",
            "Sharpe:  1.0136007328171013\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 8         |\n",
            "|    fps             | 12        |\n",
            "|    time_elapsed    | 1585      |\n",
            "|    total timesteps | 20128     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.51e+08 |\n",
            "|    critic_loss     | 1.12e+13  |\n",
            "|    ent_coef        | 41.7      |\n",
            "|    ent_coef_loss   | -670      |\n",
            "|    learning_rate   | 0.0003    |\n",
            "|    n_updates       | 20027     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418737.365107464\n",
            "Sharpe:  1.0135970410224868\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418754.895735274\n",
            "Sharpe:  1.0135965589029627\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4419325.814567342\n",
            "Sharpe:  1.0136807224228588\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418142.473513333\n",
            "Sharpe:  1.0135234795926031\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 12        |\n",
            "|    fps             | 12        |\n",
            "|    time_elapsed    | 2400      |\n",
            "|    total timesteps | 30192     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.85e+08 |\n",
            "|    critic_loss     | 1.87e+13  |\n",
            "|    ent_coef        | 725       |\n",
            "|    ent_coef_loss   | -673      |\n",
            "|    learning_rate   | 0.0003    |\n",
            "|    n_updates       | 30091     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4422046.188863339\n",
            "Sharpe:  1.0140936726052256\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4424919.463828854\n",
            "Sharpe:  1.014521127041106\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4427483.152494239\n",
            "Sharpe:  1.0148626804754584\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4460697.650185859\n",
            "Sharpe:  1.019852362102548\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 16        |\n",
            "|    fps             | 12        |\n",
            "|    time_elapsed    | 3210      |\n",
            "|    total timesteps | 40256     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.93e+08 |\n",
            "|    critic_loss     | 1.62e+13  |\n",
            "|    ent_coef        | 1.01e+04  |\n",
            "|    ent_coef_loss   | -238      |\n",
            "|    learning_rate   | 0.0003    |\n",
            "|    n_updates       | 40155     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4434035.982803257\n",
            "Sharpe:  1.0161512551319891\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4454728.906041551\n",
            "Sharpe:  1.018484863448905\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4475667.120269234\n",
            "Sharpe:  1.0215545521682856\n",
            "=================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E2Ma6YpTlnuZ"
      },
      "source": [
        "## Trading\r\n",
        "Assume that we have $1,000,000 initial capital at 2019-01-01. We use the DDPG model to trade Dow jones 30 stocks."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uas8U6k455sI"
      },
      "source": [
        "trade = data_split(df,'2019-01-01', '2020-12-01')\n",
        "#trade = data_split(df,config.START_TRADE_DATE,config.END_DATE)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcGYlhyal205",
        "outputId": "46d3c11b-3714-49b5-a5e7-45ee0251c500"
      },
      "source": [
        "trade.shape"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14490, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "4udD8hGlrj7L",
        "outputId": "28a892a8-f5f5-4402-ccaa-b07d96043157"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>cov_list</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>148158800.0</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>[[0.00032652138848500076, 0.000135816615033450...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>93.910004</td>\n",
              "      <td>96.269997</td>\n",
              "      <td>93.769997</td>\n",
              "      <td>92.643311</td>\n",
              "      <td>4175400.0</td>\n",
              "      <td>AXP</td>\n",
              "      <td>-3.426008</td>\n",
              "      <td>41.204982</td>\n",
              "      <td>-97.742269</td>\n",
              "      <td>26.709417</td>\n",
              "      <td>[[0.00032652138848500076, 0.000135816615033450...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>316.190002</td>\n",
              "      <td>323.950012</td>\n",
              "      <td>313.709991</td>\n",
              "      <td>314.645142</td>\n",
              "      <td>3292200.0</td>\n",
              "      <td>BA</td>\n",
              "      <td>-5.550592</td>\n",
              "      <td>47.010000</td>\n",
              "      <td>-21.712382</td>\n",
              "      <td>13.611972</td>\n",
              "      <td>[[0.00032652138848500076, 0.000135816615033450...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>124.029999</td>\n",
              "      <td>127.879997</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>119.302582</td>\n",
              "      <td>4783200.0</td>\n",
              "      <td>CAT</td>\n",
              "      <td>-0.686759</td>\n",
              "      <td>48.229089</td>\n",
              "      <td>-5.091209</td>\n",
              "      <td>0.873482</td>\n",
              "      <td>[[0.00032652138848500076, 0.000135816615033450...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>42.279999</td>\n",
              "      <td>43.200001</td>\n",
              "      <td>42.209999</td>\n",
              "      <td>40.382099</td>\n",
              "      <td>23833500.0</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>-0.960061</td>\n",
              "      <td>44.872557</td>\n",
              "      <td>-87.496850</td>\n",
              "      <td>29.529377</td>\n",
              "      <td>[[0.00032652138848500076, 0.000135816615033450...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  ...                                           cov_list\n",
              "0  2019-01-02  ...  [[0.00032652138848500076, 0.000135816615033450...\n",
              "0  2019-01-02  ...  [[0.00032652138848500076, 0.000135816615033450...\n",
              "0  2019-01-02  ...  [[0.00032652138848500076, 0.000135816615033450...\n",
              "0  2019-01-02  ...  [[0.00032652138848500076, 0.000135816615033450...\n",
              "0  2019-01-02  ...  [[0.00032652138848500076, 0.000135816615033450...\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qq4W9FbSstT7",
        "outputId": "fcf7688c-1885-4719-c1c0-9d35d43518cc"
      },
      "source": [
        "env_trade, obs_trade = env_setup.create_env_trading(data = trade,\r\n",
        "                                         env_class = StockPortfolioEnv) \r\n",
        "\r\n",
        "df_daily_return, df_actions = DRLAgent.DRL_prediction(model=trained_a2c,\r\n",
        "                        test_data = trade,\r\n",
        "                        test_env = env_trade,\r\n",
        "                        test_obs = obs_trade)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:1343903.7557656527\n",
            "Sharpe:  0.7020751011786319\n",
            "=================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "uJvj3pXt_Ukg",
        "outputId": "220ca159-42a9-44cb-bc75-c45ce2581265"
      },
      "source": [
        "df_daily_return.head()"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>daily_return</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>-0.026553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>0.034149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-07</td>\n",
              "      <td>0.004528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-08</td>\n",
              "      <td>0.009527</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  daily_return\n",
              "0  2019-01-02      0.000000\n",
              "1  2019-01-03     -0.026553\n",
              "2  2019-01-04      0.034149\n",
              "3  2019-01-07      0.004528\n",
              "4  2019-01-08      0.009527"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "tByVcZ2L9TAJ",
        "outputId": "f07d68cd-4026-4ede-d2cf-e8107d43c48c"
      },
      "source": [
        "df_actions.head()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>AXP</th>\n",
              "      <th>BA</th>\n",
              "      <th>CAT</th>\n",
              "      <th>CSCO</th>\n",
              "      <th>CVX</th>\n",
              "      <th>DD</th>\n",
              "      <th>DIS</th>\n",
              "      <th>GS</th>\n",
              "      <th>HD</th>\n",
              "      <th>IBM</th>\n",
              "      <th>INTC</th>\n",
              "      <th>JNJ</th>\n",
              "      <th>JPM</th>\n",
              "      <th>KO</th>\n",
              "      <th>MCD</th>\n",
              "      <th>MMM</th>\n",
              "      <th>MRK</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NKE</th>\n",
              "      <th>PFE</th>\n",
              "      <th>PG</th>\n",
              "      <th>RTX</th>\n",
              "      <th>TRV</th>\n",
              "      <th>UNH</th>\n",
              "      <th>V</th>\n",
              "      <th>VZ</th>\n",
              "      <th>WBA</th>\n",
              "      <th>WMT</th>\n",
              "      <th>XOM</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-01-02</th>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-03</th>\n",
              "      <td>0.044185</td>\n",
              "      <td>0.042191</td>\n",
              "      <td>0.060286</td>\n",
              "      <td>0.022178</td>\n",
              "      <td>0.023759</td>\n",
              "      <td>0.022178</td>\n",
              "      <td>0.022178</td>\n",
              "      <td>0.038266</td>\n",
              "      <td>0.056008</td>\n",
              "      <td>0.022178</td>\n",
              "      <td>0.024225</td>\n",
              "      <td>0.060286</td>\n",
              "      <td>0.035443</td>\n",
              "      <td>0.022178</td>\n",
              "      <td>0.060286</td>\n",
              "      <td>0.022178</td>\n",
              "      <td>0.022178</td>\n",
              "      <td>0.022178</td>\n",
              "      <td>0.022178</td>\n",
              "      <td>0.022178</td>\n",
              "      <td>0.022178</td>\n",
              "      <td>0.047411</td>\n",
              "      <td>0.032231</td>\n",
              "      <td>0.022178</td>\n",
              "      <td>0.060286</td>\n",
              "      <td>0.022178</td>\n",
              "      <td>0.022178</td>\n",
              "      <td>0.022178</td>\n",
              "      <td>0.060286</td>\n",
              "      <td>0.022178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-04</th>\n",
              "      <td>0.038420</td>\n",
              "      <td>0.021994</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.025684</td>\n",
              "      <td>0.056489</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.056489</td>\n",
              "      <td>0.056489</td>\n",
              "      <td>0.042223</td>\n",
              "      <td>0.056489</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.056489</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.056489</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.041126</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.056489</td>\n",
              "      <td>0.024378</td>\n",
              "      <td>0.025732</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.056489</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.037594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-07</th>\n",
              "      <td>0.021340</td>\n",
              "      <td>0.021340</td>\n",
              "      <td>0.058008</td>\n",
              "      <td>0.050729</td>\n",
              "      <td>0.046202</td>\n",
              "      <td>0.050280</td>\n",
              "      <td>0.033245</td>\n",
              "      <td>0.025786</td>\n",
              "      <td>0.021340</td>\n",
              "      <td>0.029563</td>\n",
              "      <td>0.027136</td>\n",
              "      <td>0.021340</td>\n",
              "      <td>0.021340</td>\n",
              "      <td>0.024039</td>\n",
              "      <td>0.021340</td>\n",
              "      <td>0.021340</td>\n",
              "      <td>0.042887</td>\n",
              "      <td>0.030118</td>\n",
              "      <td>0.021663</td>\n",
              "      <td>0.057325</td>\n",
              "      <td>0.021683</td>\n",
              "      <td>0.037073</td>\n",
              "      <td>0.021481</td>\n",
              "      <td>0.058008</td>\n",
              "      <td>0.021340</td>\n",
              "      <td>0.058008</td>\n",
              "      <td>0.033600</td>\n",
              "      <td>0.057440</td>\n",
              "      <td>0.023670</td>\n",
              "      <td>0.021340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-08</th>\n",
              "      <td>0.053896</td>\n",
              "      <td>0.019827</td>\n",
              "      <td>0.030132</td>\n",
              "      <td>0.046383</td>\n",
              "      <td>0.019827</td>\n",
              "      <td>0.044589</td>\n",
              "      <td>0.019827</td>\n",
              "      <td>0.026917</td>\n",
              "      <td>0.019827</td>\n",
              "      <td>0.020397</td>\n",
              "      <td>0.019827</td>\n",
              "      <td>0.019827</td>\n",
              "      <td>0.053896</td>\n",
              "      <td>0.053896</td>\n",
              "      <td>0.019827</td>\n",
              "      <td>0.053896</td>\n",
              "      <td>0.019827</td>\n",
              "      <td>0.019827</td>\n",
              "      <td>0.026934</td>\n",
              "      <td>0.053896</td>\n",
              "      <td>0.031253</td>\n",
              "      <td>0.025987</td>\n",
              "      <td>0.019827</td>\n",
              "      <td>0.053896</td>\n",
              "      <td>0.019827</td>\n",
              "      <td>0.053896</td>\n",
              "      <td>0.023377</td>\n",
              "      <td>0.053896</td>\n",
              "      <td>0.053896</td>\n",
              "      <td>0.020871</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                AAPL       AXP        BA  ...       WBA       WMT       XOM\n",
              "date                                      ...                              \n",
              "2019-01-02  0.033333  0.033333  0.033333  ...  0.033333  0.033333  0.033333\n",
              "2019-01-03  0.044185  0.042191  0.060286  ...  0.022178  0.060286  0.022178\n",
              "2019-01-04  0.038420  0.021994  0.020781  ...  0.020781  0.020781  0.037594\n",
              "2019-01-07  0.021340  0.021340  0.058008  ...  0.057440  0.023670  0.021340\n",
              "2019-01-08  0.053896  0.019827  0.030132  ...  0.053896  0.053896  0.020871\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBX3Y68o1vRG"
      },
      "source": [
        "df_actions.to_csv('df_actions.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qFO42LcomPUT"
      },
      "source": [
        "<a id='6'></a>\r\n",
        "# Part 7: Backtest Our Strategy\r\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": "fAvxipWFmUe8"
      },
      "source": [
        "<a id='6.1'></a>\r\n",
        "## 7.1 BackTestStats\r\n",
        "pass in df_account_value, this information is stored in env class\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oGu3PCa8l6L"
      },
      "source": [
        "from pyfolio import timeseries\n",
        "DRL_strat = backtest_strat(df_daily_return)\n",
        "perf_func = timeseries.perf_stats \n",
        "perf_stats_all = perf_func( returns=DRL_strat, \n",
        "                              factor_returns=DRL_strat, \n",
        "                                positions=None, transactions=None, turnover_denom=\"AGB\")"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hqvwr6SY8l9A",
        "outputId": "ca1d019d-7a9a-40f7-ba57-f8a68c1b7e1c"
      },
      "source": [
        "print(\"==============DRL Strategy Stats===========\")\r\n",
        "perf_stats_all"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============DRL Strategy Stats===========\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Annual return           0.157274\n",
              "Cumulative returns      0.323080\n",
              "Annual volatility       0.275750\n",
              "Sharpe ratio            0.668227\n",
              "Calmar ratio            0.445329\n",
              "Stability               0.112416\n",
              "Max drawdown           -0.353164\n",
              "Omega ratio             1.156700\n",
              "Sortino ratio           0.941239\n",
              "Skew                   -0.338198\n",
              "Kurtosis               13.513578\n",
              "Tail ratio              0.804657\n",
              "Daily value at risk    -0.034010\n",
              "Alpha                   0.000000\n",
              "Beta                    1.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fqSEF5PfjjT",
        "outputId": "dcae7358-296b-4ea7-f909-69341c1d01ec"
      },
      "source": [
        "print(\"==============Get Index Stats===========\")\r\n",
        "baesline_perf_stats=BaselineStats('^DJI',\r\n",
        "                                  baseline_start = '2019-01-01',\r\n",
        "                                  baseline_end = '2020-12-01')"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Index Stats===========\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (483, 7)\n",
            "Annual return          0.132593\n",
            "Cumulative returns     0.269525\n",
            "Annual volatility      0.280347\n",
            "Sharpe ratio           0.586312\n",
            "Calmar ratio           0.357526\n",
            "Stability              0.060828\n",
            "Max drawdown          -0.370862\n",
            "Omega ratio            1.137261\n",
            "Sortino ratio          0.807330\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.844803\n",
            "Daily value at risk   -0.034668\n",
            "Alpha                  0.000000\n",
            "Beta                   1.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lVVCMVSAmcrI"
      },
      "source": [
        "<a id='6.2'></a>\r\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89oxLnMHyboh",
        "outputId": "213814d4-8ea8-43a6-965b-40121e931375"
      },
      "source": [
        "dji, dow_strat = baseline_strat('^DJI','2019-01-01','2020-12-01')"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (483, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "87xaEUFvyqe2",
        "outputId": "001d1699-48af-4c5c-ff2b-0c39150f4483"
      },
      "source": [
        "import pyfolio\n",
        "%matplotlib inline\n",
        "with pyfolio.plotting.plotting_context(font_scale=1.1):\n",
        "        pyfolio.create_full_tear_sheet(returns = DRL_strat,\n",
        "                                       benchmark_rets=dow_strat, set_context=False)\n",
        "\n"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "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>2019-01-02</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>23</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>15.727%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>32.308%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>27.575%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>0.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>0.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-35.316%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>0.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>-0.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>13.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>0.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-3.401%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.97</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>35.32</td>\n",
              "      <td>2020-01-17</td>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>2020-11-10</td>\n",
              "      <td>213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.35</td>\n",
              "      <td>2019-07-15</td>\n",
              "      <td>2019-08-14</td>\n",
              "      <td>2019-09-11</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.33</td>\n",
              "      <td>2019-04-30</td>\n",
              "      <td>2019-05-31</td>\n",
              "      <td>2019-06-18</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.52</td>\n",
              "      <td>2019-09-13</td>\n",
              "      <td>2019-10-02</td>\n",
              "      <td>2019-10-30</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.46</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pyfolio/tears.py:907: UserWarning: Passed returns do not overlap with anyinteresting times.\n",
            "  'interesting times.', UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2DgsIW-fh0s"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}