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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        "id": "Yv3IDvrobU37"
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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"
      ]
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    {
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      "metadata": {
        "id": "vUmLTmoQA7_w"
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      "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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      "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": "48d2d23a-d85c-4f52-8ef1-65012d957023"
      },
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": 1,
      "outputs": [
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            "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=23293 sha256=3170f5262376d396a800f14e94731dc49ef8885d101a0edafe7c674468efd94b\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-_1noel28/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=dd05526c7241217e7198228cf0580a206255b70f444085c7e9bd618b64627eff\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=f8d579db3f214f8bee5c9bc6ecd735f8102bb722efd9baa25e7cf681537bd41b\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-_1noel28/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=ac50a40dc5ae3d8b3f4b848ea035186f45ff02aa567319e2e95cdc94009bf884\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": "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": "d268ef40-e425-4671-f1ec-e9e5d6b65659"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "matplotlib.use('Agg')\n",
        "import datetime\n",
        "\n",
        "from finrl.config import config\n",
        "from finrl.marketdata.yahoodownloader import YahooDownloader\n",
        "from finrl.preprocessing.preprocessors import FeatureEngineer\n",
        "from finrl.preprocessing.data import data_split\n",
        "from finrl.env.env_portfolio import StockPortfolioEnv\n",
        "\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": 3,
      "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": 4,
      "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": "fcdd9409-9fb0-4aed-effe-eda7398789e5"
      },
      "source": [
        "print(config.DOW_30_TICKER)"
      ],
      "execution_count": 5,
      "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": "d23d4eb7-36d0-402c-c891-abd547053929"
      },
      "source": [
        "df = YahooDownloader(start_date = '2008-01-01',\n",
        "                     end_date = '2021-01-01',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": 7,
      "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:  (98167, 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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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>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": []
          },
          "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": "b5fba322-ac37-4849-b302-0d1347c61fb4"
      },
      "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": 8,
      "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": "7feea4da-d4e4-4039-868f-cdc1202ddd09"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(98167, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWB-RoTSbU4s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "05f4a953-43c7-4282-f50f-e624f3a1b327"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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",
              "      <td>6.002817</td>\n",
              "      <td>1.079179e+09</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <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": 10
        }
      ]
    },
    {
      "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": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUPwwa13uBQ-",
        "outputId": "f07e6006-a575-4921-bf8e-632dbdf6d84b"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90660, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "wv3jR1zPrg4g",
        "outputId": "ca253fd6-4ed3-45e3-cf49-2a86464c2e53"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 13,
      "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",
              "    .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": 13
        }
      ]
    },
    {
      "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": 14,
      "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",
        "from stable_baselines3.common.vec_env import DummyVecEnv\n",
        "\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=None,\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=-np.inf, 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]\n",
        "\n",
        "    def get_sb_env(self):\n",
        "        e = DummyVecEnv([lambda: self])\n",
        "        obs = e.reset()\n",
        "        return e, obs"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzD06X0CbU43",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ebe2d36-2f98-4c07-b69d-b4cebfd09193"
      },
      "source": [
        "stock_dimension = len(train.tic.unique())\n",
        "state_space = stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stock Dimension: 30, State Space: 30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyg0_ZuVEVQ5"
      },
      "source": [
        "env_kwargs = {\r\n",
        "    \"hmax\": 100, \r\n",
        "    \"initial_amount\": 1000000, \r\n",
        "    \"transaction_cost_pct\": 0.001, \r\n",
        "    \"state_space\": state_space, \r\n",
        "    \"stock_dim\": stock_dimension, \r\n",
        "    \"tech_indicator_list\": config.TECHNICAL_INDICATORS_LIST, \r\n",
        "    \"action_space\": stock_dimension, \r\n",
        "    \"reward_scaling\": 1e-4\r\n",
        "    \r\n",
        "}\r\n",
        "\r\n",
        "e_train_gym = StockPortfolioEnv(df = train, **env_kwargs)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTlOf8SJGdkl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a3da41c-194d-4b94-ba52-713333b58e1a"
      },
      "source": [
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "print(type(env_train))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": 27,
      "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": "781e90cf-175e-4f85-9667-c60a95d6a1dc"
      },
      "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": 28,
      "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": "387049f6-f0b9-4010-b12d-b4e27c182cc4"
      },
      "source": [
        "trained_a2c = agent.train_model(model=model_a2c, \n",
        "                                tb_log_name='a2c',\n",
        "                                total_timesteps=50000)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/a2c/a2c_1\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 139       |\n",
            "|    iterations         | 100       |\n",
            "|    time_elapsed       | 3         |\n",
            "|    total_timesteps    | 500       |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.5     |\n",
            "|    explained_variance | -4.79e+13 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 99        |\n",
            "|    policy_loss        | 1.9e+08   |\n",
            "|    std                | 0.998     |\n",
            "|    value_loss         | 2.51e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 185       |\n",
            "|    iterations         | 200       |\n",
            "|    time_elapsed       | 5         |\n",
            "|    total_timesteps    | 1000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.5     |\n",
            "|    explained_variance | -3.32e+15 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 199       |\n",
            "|    policy_loss        | 2.53e+08  |\n",
            "|    std                | 0.998     |\n",
            "|    value_loss         | 4.2e+13   |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 207       |\n",
            "|    iterations         | 300       |\n",
            "|    time_elapsed       | 7         |\n",
            "|    total_timesteps    | 1500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.5     |\n",
            "|    explained_variance | -2.14e+15 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 299       |\n",
            "|    policy_loss        | 4.14e+08  |\n",
            "|    std                | 0.998     |\n",
            "|    value_loss         | 1.02e+14  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 220       |\n",
            "|    iterations         | 400       |\n",
            "|    time_elapsed       | 9         |\n",
            "|    total_timesteps    | 2000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.5     |\n",
            "|    explained_variance | -4.22e+16 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 399       |\n",
            "|    policy_loss        | 4.74e+08  |\n",
            "|    std                | 0.997     |\n",
            "|    value_loss         | 1.38e+14  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 229       |\n",
            "|    iterations         | 500       |\n",
            "|    time_elapsed       | 10        |\n",
            "|    total_timesteps    | 2500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -1.28e+17 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 499       |\n",
            "|    policy_loss        | 6.85e+08  |\n",
            "|    std                | 0.996     |\n",
            "|    value_loss         | 2.67e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4682147.196677087\n",
            "Sharpe:  1.041787542577206\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 230       |\n",
            "|    iterations         | 600       |\n",
            "|    time_elapsed       | 12        |\n",
            "|    total_timesteps    | 3000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -2.77e+16 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 599       |\n",
            "|    policy_loss        | 1.95e+08  |\n",
            "|    std                | 0.995     |\n",
            "|    value_loss         | 2.59e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 236       |\n",
            "|    iterations         | 700       |\n",
            "|    time_elapsed       | 14        |\n",
            "|    total_timesteps    | 3500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -7.87e+16 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 699       |\n",
            "|    policy_loss        | 2.41e+08  |\n",
            "|    std                | 0.994     |\n",
            "|    value_loss         | 4.46e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 240       |\n",
            "|    iterations         | 800       |\n",
            "|    time_elapsed       | 16        |\n",
            "|    total_timesteps    | 4000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -2.05e+20 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 799       |\n",
            "|    policy_loss        | 3.8e+08   |\n",
            "|    std                | 0.994     |\n",
            "|    value_loss         | 1.07e+14  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 244      |\n",
            "|    iterations         | 900      |\n",
            "|    time_elapsed       | 18       |\n",
            "|    total_timesteps    | 4500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.4    |\n",
            "|    explained_variance | -7.9e+17 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 899      |\n",
            "|    policy_loss        | 5.05e+08 |\n",
            "|    std                | 0.994    |\n",
            "|    value_loss         | 1.35e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 246       |\n",
            "|    iterations         | 1000      |\n",
            "|    time_elapsed       | 20        |\n",
            "|    total_timesteps    | 5000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -1.11e+19 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 999       |\n",
            "|    policy_loss        | 6.55e+08  |\n",
            "|    std                | 0.994     |\n",
            "|    value_loss         | 2.91e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4901347.318760229\n",
            "Sharpe:  1.0661821509661518\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 245       |\n",
            "|    iterations         | 1100      |\n",
            "|    time_elapsed       | 22        |\n",
            "|    total_timesteps    | 5500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -3.81e+20 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1099      |\n",
            "|    policy_loss        | 1.78e+08  |\n",
            "|    std                | 0.993     |\n",
            "|    value_loss         | 2.45e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 248       |\n",
            "|    iterations         | 1200      |\n",
            "|    time_elapsed       | 24        |\n",
            "|    total_timesteps    | 6000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -5.03e+18 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1199      |\n",
            "|    policy_loss        | 2.46e+08  |\n",
            "|    std                | 0.993     |\n",
            "|    value_loss         | 4.44e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 249       |\n",
            "|    iterations         | 1300      |\n",
            "|    time_elapsed       | 26        |\n",
            "|    total_timesteps    | 6500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -1.15e+20 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1299      |\n",
            "|    policy_loss        | 4.08e+08  |\n",
            "|    std                | 0.993     |\n",
            "|    value_loss         | 9.59e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 251      |\n",
            "|    iterations         | 1400     |\n",
            "|    time_elapsed       | 27       |\n",
            "|    total_timesteps    | 7000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1399     |\n",
            "|    policy_loss        | 4.32e+08 |\n",
            "|    std                | 0.993    |\n",
            "|    value_loss         | 1.33e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 252       |\n",
            "|    iterations         | 1500      |\n",
            "|    time_elapsed       | 29        |\n",
            "|    total_timesteps    | 7500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.3     |\n",
            "|    explained_variance | -4.32e+20 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1499      |\n",
            "|    policy_loss        | 6.45e+08  |\n",
            "|    std                | 0.993     |\n",
            "|    value_loss         | 2.91e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4991545.376434025\n",
            "Sharpe:  1.0829523611062715\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 251      |\n",
            "|    iterations         | 1600     |\n",
            "|    time_elapsed       | 31       |\n",
            "|    total_timesteps    | 8000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1599     |\n",
            "|    policy_loss        | 1.75e+08 |\n",
            "|    std                | 0.992    |\n",
            "|    value_loss         | 2.17e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 253       |\n",
            "|    iterations         | 1700      |\n",
            "|    time_elapsed       | 33        |\n",
            "|    total_timesteps    | 8500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.3     |\n",
            "|    explained_variance | -1.21e+20 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1699      |\n",
            "|    policy_loss        | 2.39e+08  |\n",
            "|    std                | 0.991     |\n",
            "|    value_loss         | 4.03e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 253       |\n",
            "|    iterations         | 1800      |\n",
            "|    time_elapsed       | 35        |\n",
            "|    total_timesteps    | 9000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.3     |\n",
            "|    explained_variance | -1.49e+20 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1799      |\n",
            "|    policy_loss        | 3.29e+08  |\n",
            "|    std                | 0.991     |\n",
            "|    value_loss         | 8.31e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 254       |\n",
            "|    iterations         | 1900      |\n",
            "|    time_elapsed       | 37        |\n",
            "|    total_timesteps    | 9500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.3     |\n",
            "|    explained_variance | -5.48e+24 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1899      |\n",
            "|    policy_loss        | 4.03e+08  |\n",
            "|    std                | 0.99      |\n",
            "|    value_loss         | 1.11e+14  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 255       |\n",
            "|    iterations         | 2000      |\n",
            "|    time_elapsed       | 39        |\n",
            "|    total_timesteps    | 10000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.3     |\n",
            "|    explained_variance | -1.32e+22 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1999      |\n",
            "|    policy_loss        | 5.84e+08  |\n",
            "|    std                | 0.99      |\n",
            "|    value_loss         | 2.51e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4375539.302697805\n",
            "Sharpe:  1.001717012260084\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 254      |\n",
            "|    iterations         | 2100     |\n",
            "|    time_elapsed       | 41       |\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        | 1.62e+08 |\n",
            "|    std                | 0.989    |\n",
            "|    value_loss         | 2.15e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 255       |\n",
            "|    iterations         | 2200      |\n",
            "|    time_elapsed       | 43        |\n",
            "|    total_timesteps    | 11000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.2     |\n",
            "|    explained_variance | -1.72e+20 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2199      |\n",
            "|    policy_loss        | 2.47e+08  |\n",
            "|    std                | 0.989     |\n",
            "|    value_loss         | 4.07e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 255      |\n",
            "|    iterations         | 2300     |\n",
            "|    time_elapsed       | 44       |\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        | 3.58e+08 |\n",
            "|    std                | 0.988    |\n",
            "|    value_loss         | 8.34e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 256       |\n",
            "|    iterations         | 2400      |\n",
            "|    time_elapsed       | 46        |\n",
            "|    total_timesteps    | 12000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.2     |\n",
            "|    explained_variance | -8.69e+21 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2399      |\n",
            "|    policy_loss        | 4.31e+08  |\n",
            "|    std                | 0.987     |\n",
            "|    value_loss         | 1.17e+14  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 257      |\n",
            "|    iterations         | 2500     |\n",
            "|    time_elapsed       | 48       |\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        | 6.26e+08 |\n",
            "|    std                | 0.987    |\n",
            "|    value_loss         | 2.37e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4374042.08620406\n",
            "Sharpe:  0.9987195982933951\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 256      |\n",
            "|    iterations         | 2600     |\n",
            "|    time_elapsed       | 50       |\n",
            "|    total_timesteps    | 13000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2599     |\n",
            "|    policy_loss        | 1.67e+08 |\n",
            "|    std                | 0.987    |\n",
            "|    value_loss         | 1.95e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 256      |\n",
            "|    iterations         | 2700     |\n",
            "|    time_elapsed       | 52       |\n",
            "|    total_timesteps    | 13500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2699     |\n",
            "|    policy_loss        | 2.39e+08 |\n",
            "|    std                | 0.986    |\n",
            "|    value_loss         | 4.37e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 257      |\n",
            "|    iterations         | 2800     |\n",
            "|    time_elapsed       | 54       |\n",
            "|    total_timesteps    | 14000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.1    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2799     |\n",
            "|    policy_loss        | 3.74e+08 |\n",
            "|    std                | 0.986    |\n",
            "|    value_loss         | 9.6e+13  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 257      |\n",
            "|    iterations         | 2900     |\n",
            "|    time_elapsed       | 56       |\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        | 4.57e+08 |\n",
            "|    std                | 0.986    |\n",
            "|    value_loss         | 1.38e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 258      |\n",
            "|    iterations         | 3000     |\n",
            "|    time_elapsed       | 58       |\n",
            "|    total_timesteps    | 15000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.1    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2999     |\n",
            "|    policy_loss        | 6.02e+08 |\n",
            "|    std                | 0.985    |\n",
            "|    value_loss         | 2.75e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4828968.521447048\n",
            "Sharpe:  1.0630146219161314\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 257      |\n",
            "|    iterations         | 3100     |\n",
            "|    time_elapsed       | 60       |\n",
            "|    total_timesteps    | 15500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.1    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3099     |\n",
            "|    policy_loss        | 1.86e+08 |\n",
            "|    std                | 0.985    |\n",
            "|    value_loss         | 2.43e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 257      |\n",
            "|    iterations         | 3200     |\n",
            "|    time_elapsed       | 62       |\n",
            "|    total_timesteps    | 16000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.1    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3199     |\n",
            "|    policy_loss        | 2.87e+08 |\n",
            "|    std                | 0.984    |\n",
            "|    value_loss         | 4.53e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 258      |\n",
            "|    iterations         | 3300     |\n",
            "|    time_elapsed       | 63       |\n",
            "|    total_timesteps    | 16500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.1    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3299     |\n",
            "|    policy_loss        | 4.04e+08 |\n",
            "|    std                | 0.984    |\n",
            "|    value_loss         | 1.1e+14  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 258      |\n",
            "|    iterations         | 3400     |\n",
            "|    time_elapsed       | 65       |\n",
            "|    total_timesteps    | 17000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.1    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3399     |\n",
            "|    policy_loss        | 4.67e+08 |\n",
            "|    std                | 0.984    |\n",
            "|    value_loss         | 1.51e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 3500     |\n",
            "|    time_elapsed       | 67       |\n",
            "|    total_timesteps    | 17500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.1    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3499     |\n",
            "|    policy_loss        | 6.17e+08 |\n",
            "|    std                | 0.983    |\n",
            "|    value_loss         | 2.81e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4902665.075647295\n",
            "Sharpe:  1.0673950077944878\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 258      |\n",
            "|    iterations         | 3600     |\n",
            "|    time_elapsed       | 69       |\n",
            "|    total_timesteps    | 18000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3599     |\n",
            "|    policy_loss        | 1.75e+08 |\n",
            "|    std                | 0.983    |\n",
            "|    value_loss         | 1.94e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 258       |\n",
            "|    iterations         | 3700      |\n",
            "|    time_elapsed       | 71        |\n",
            "|    total_timesteps    | 18500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.1     |\n",
            "|    explained_variance | -4.48e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3699      |\n",
            "|    policy_loss        | 2.21e+08  |\n",
            "|    std                | 0.983     |\n",
            "|    value_loss         | 3.68e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 259       |\n",
            "|    iterations         | 3800      |\n",
            "|    time_elapsed       | 73        |\n",
            "|    total_timesteps    | 19000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42       |\n",
            "|    explained_variance | -1.04e+24 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3799      |\n",
            "|    policy_loss        | 3.57e+08  |\n",
            "|    std                | 0.983     |\n",
            "|    value_loss         | 8.53e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 3900     |\n",
            "|    time_elapsed       | 75       |\n",
            "|    total_timesteps    | 19500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | -1.4e+24 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3899     |\n",
            "|    policy_loss        | 4.07e+08 |\n",
            "|    std                | 0.982    |\n",
            "|    value_loss         | 1.11e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 259       |\n",
            "|    iterations         | 4000      |\n",
            "|    time_elapsed       | 76        |\n",
            "|    total_timesteps    | 20000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42       |\n",
            "|    explained_variance | -2.91e+24 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3999      |\n",
            "|    policy_loss        | 5.99e+08  |\n",
            "|    std                | 0.981     |\n",
            "|    value_loss         | 2.36e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4630313.084156154\n",
            "Sharpe:  1.0367916591214335\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 4100     |\n",
            "|    time_elapsed       | 79       |\n",
            "|    total_timesteps    | 20500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4099     |\n",
            "|    policy_loss        | 1.86e+08 |\n",
            "|    std                | 0.981    |\n",
            "|    value_loss         | 2.11e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 259       |\n",
            "|    iterations         | 4200      |\n",
            "|    time_elapsed       | 80        |\n",
            "|    total_timesteps    | 21000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42       |\n",
            "|    explained_variance | -4.86e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 4199      |\n",
            "|    policy_loss        | 2.23e+08  |\n",
            "|    std                | 0.98      |\n",
            "|    value_loss         | 3.99e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 259       |\n",
            "|    iterations         | 4300      |\n",
            "|    time_elapsed       | 82        |\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        | 3.38e+08  |\n",
            "|    std                | 0.98      |\n",
            "|    value_loss         | 8.91e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 260      |\n",
            "|    iterations         | 4400     |\n",
            "|    time_elapsed       | 84       |\n",
            "|    total_timesteps    | 22000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | -1.4e+24 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4399     |\n",
            "|    policy_loss        | 3.8e+08  |\n",
            "|    std                | 0.98     |\n",
            "|    value_loss         | 1.14e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 260      |\n",
            "|    iterations         | 4500     |\n",
            "|    time_elapsed       | 86       |\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                | 0.98     |\n",
            "|    value_loss         | 2.29e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4637931.115354996\n",
            "Sharpe:  1.0426484883442215\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 4600     |\n",
            "|    time_elapsed       | 88       |\n",
            "|    total_timesteps    | 23000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4599     |\n",
            "|    policy_loss        | 1.83e+08 |\n",
            "|    std                | 0.98     |\n",
            "|    value_loss         | 2.17e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 260       |\n",
            "|    iterations         | 4700      |\n",
            "|    time_elapsed       | 90        |\n",
            "|    total_timesteps    | 23500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.9     |\n",
            "|    explained_variance | -2.05e+20 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 4699      |\n",
            "|    policy_loss        | 2.49e+08  |\n",
            "|    std                | 0.979     |\n",
            "|    value_loss         | 3.92e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 260      |\n",
            "|    iterations         | 4800     |\n",
            "|    time_elapsed       | 92       |\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        | 3.24e+08 |\n",
            "|    std                | 0.978    |\n",
            "|    value_loss         | 8.74e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 4900     |\n",
            "|    time_elapsed       | 94       |\n",
            "|    total_timesteps    | 24500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4899     |\n",
            "|    policy_loss        | 4.32e+08 |\n",
            "|    std                | 0.978    |\n",
            "|    value_loss         | 1.29e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 259       |\n",
            "|    iterations         | 5000      |\n",
            "|    time_elapsed       | 96        |\n",
            "|    total_timesteps    | 25000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.9     |\n",
            "|    explained_variance | -4.83e+21 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 4999      |\n",
            "|    policy_loss        | 5.62e+08  |\n",
            "|    std                | 0.978     |\n",
            "|    value_loss         | 2.61e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5029432.827222277\n",
            "Sharpe:  1.0846580346522545\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 5100     |\n",
            "|    time_elapsed       | 98       |\n",
            "|    total_timesteps    | 25500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5099     |\n",
            "|    policy_loss        | 1.73e+08 |\n",
            "|    std                | 0.978    |\n",
            "|    value_loss         | 2.24e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 5200     |\n",
            "|    time_elapsed       | 100      |\n",
            "|    total_timesteps    | 26000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5199     |\n",
            "|    policy_loss        | 2.44e+08 |\n",
            "|    std                | 0.977    |\n",
            "|    value_loss         | 3.87e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 5300     |\n",
            "|    time_elapsed       | 102      |\n",
            "|    total_timesteps    | 26500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5299     |\n",
            "|    policy_loss        | 3.3e+08  |\n",
            "|    std                | 0.977    |\n",
            "|    value_loss         | 7.86e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 5400     |\n",
            "|    time_elapsed       | 104      |\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        | 3.88e+08 |\n",
            "|    std                | 0.976    |\n",
            "|    value_loss         | 1.1e+14  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 5500     |\n",
            "|    time_elapsed       | 106      |\n",
            "|    total_timesteps    | 27500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5499     |\n",
            "|    policy_loss        | 5.45e+08 |\n",
            "|    std                | 0.976    |\n",
            "|    value_loss         | 1.98e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4358679.313159029\n",
            "Sharpe:  1.0009594914041635\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 258      |\n",
            "|    iterations         | 5600     |\n",
            "|    time_elapsed       | 108      |\n",
            "|    total_timesteps    | 28000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5599     |\n",
            "|    policy_loss        | 1.88e+08 |\n",
            "|    std                | 0.976    |\n",
            "|    value_loss         | 2.43e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 258      |\n",
            "|    iterations         | 5700     |\n",
            "|    time_elapsed       | 110      |\n",
            "|    total_timesteps    | 28500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5699     |\n",
            "|    policy_loss        | 2.3e+08  |\n",
            "|    std                | 0.975    |\n",
            "|    value_loss         | 4.15e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 258      |\n",
            "|    iterations         | 5800     |\n",
            "|    time_elapsed       | 112      |\n",
            "|    total_timesteps    | 29000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5799     |\n",
            "|    policy_loss        | 3.26e+08 |\n",
            "|    std                | 0.975    |\n",
            "|    value_loss         | 8.38e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 258      |\n",
            "|    iterations         | 5900     |\n",
            "|    time_elapsed       | 114      |\n",
            "|    total_timesteps    | 29500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5899     |\n",
            "|    policy_loss        | 4.58e+08 |\n",
            "|    std                | 0.974    |\n",
            "|    value_loss         | 1.15e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 258      |\n",
            "|    iterations         | 6000     |\n",
            "|    time_elapsed       | 115      |\n",
            "|    total_timesteps    | 30000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5999     |\n",
            "|    policy_loss        | 5.92e+08 |\n",
            "|    std                | 0.974    |\n",
            "|    value_loss         | 2.38e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4744546.637278724\n",
            "Sharpe:  1.0575169659974053\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 258      |\n",
            "|    iterations         | 6100     |\n",
            "|    time_elapsed       | 117      |\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        | 1.71e+08 |\n",
            "|    std                | 0.973    |\n",
            "|    value_loss         | 2.05e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 258       |\n",
            "|    iterations         | 6200      |\n",
            "|    time_elapsed       | 119       |\n",
            "|    total_timesteps    | 31000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.7     |\n",
            "|    explained_variance | -1.09e+21 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 6199      |\n",
            "|    policy_loss        | 2.3e+08   |\n",
            "|    std                | 0.972     |\n",
            "|    value_loss         | 3.59e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 258      |\n",
            "|    iterations         | 6300     |\n",
            "|    time_elapsed       | 121      |\n",
            "|    total_timesteps    | 31500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6299     |\n",
            "|    policy_loss        | 3.07e+08 |\n",
            "|    std                | 0.972    |\n",
            "|    value_loss         | 7.5e+13  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 6400     |\n",
            "|    time_elapsed       | 123      |\n",
            "|    total_timesteps    | 32000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6399     |\n",
            "|    policy_loss        | 3.51e+08 |\n",
            "|    std                | 0.972    |\n",
            "|    value_loss         | 9.64e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 6500     |\n",
            "|    time_elapsed       | 125      |\n",
            "|    total_timesteps    | 32500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6499     |\n",
            "|    policy_loss        | 5.53e+08 |\n",
            "|    std                | 0.972    |\n",
            "|    value_loss         | 2.23e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4538153.298963666\n",
            "Sharpe:  1.0302379732782678\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 258      |\n",
            "|    iterations         | 6600     |\n",
            "|    time_elapsed       | 127      |\n",
            "|    total_timesteps    | 33000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6599     |\n",
            "|    policy_loss        | 1.54e+08 |\n",
            "|    std                | 0.972    |\n",
            "|    value_loss         | 1.92e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 6700     |\n",
            "|    time_elapsed       | 129      |\n",
            "|    total_timesteps    | 33500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6699     |\n",
            "|    policy_loss        | 2.21e+08 |\n",
            "|    std                | 0.971    |\n",
            "|    value_loss         | 3.38e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 6800     |\n",
            "|    time_elapsed       | 131      |\n",
            "|    total_timesteps    | 34000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6799     |\n",
            "|    policy_loss        | 2.99e+08 |\n",
            "|    std                | 0.97     |\n",
            "|    value_loss         | 6.54e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 6900     |\n",
            "|    time_elapsed       | 132      |\n",
            "|    total_timesteps    | 34500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6899     |\n",
            "|    policy_loss        | 3.44e+08 |\n",
            "|    std                | 0.97     |\n",
            "|    value_loss         | 8.18e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 7000     |\n",
            "|    time_elapsed       | 134      |\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        | 5.78e+08 |\n",
            "|    std                | 0.969    |\n",
            "|    value_loss         | 2.12e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4260777.636308199\n",
            "Sharpe:  0.9816559892013299\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 7100     |\n",
            "|    time_elapsed       | 137      |\n",
            "|    total_timesteps    | 35500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7099     |\n",
            "|    policy_loss        | 1.65e+08 |\n",
            "|    std                | 0.969    |\n",
            "|    value_loss         | 2.02e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 7200     |\n",
            "|    time_elapsed       | 138      |\n",
            "|    total_timesteps    | 36000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7199     |\n",
            "|    policy_loss        | 2.18e+08 |\n",
            "|    std                | 0.969    |\n",
            "|    value_loss         | 3.6e+13  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 7300     |\n",
            "|    time_elapsed       | 140      |\n",
            "|    total_timesteps    | 36500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7299     |\n",
            "|    policy_loss        | 3.41e+08 |\n",
            "|    std                | 0.969    |\n",
            "|    value_loss         | 7.77e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 7400     |\n",
            "|    time_elapsed       | 142      |\n",
            "|    total_timesteps    | 37000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7399     |\n",
            "|    policy_loss        | 3.9e+08  |\n",
            "|    std                | 0.968    |\n",
            "|    value_loss         | 9.85e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 259       |\n",
            "|    iterations         | 7500      |\n",
            "|    time_elapsed       | 144       |\n",
            "|    total_timesteps    | 37500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.6     |\n",
            "|    explained_variance | -7.65e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 7499      |\n",
            "|    policy_loss        | 6.56e+08  |\n",
            "|    std                | 0.968     |\n",
            "|    value_loss         | 2.5e+14   |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4892222.4205715535\n",
            "Sharpe:  1.0717110079094554\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 259       |\n",
            "|    iterations         | 7600      |\n",
            "|    time_elapsed       | 146       |\n",
            "|    total_timesteps    | 38000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.6     |\n",
            "|    explained_variance | -6.46e+22 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 7599      |\n",
            "|    policy_loss        | 1.62e+08  |\n",
            "|    std                | 0.967     |\n",
            "|    value_loss         | 2.12e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 259       |\n",
            "|    iterations         | 7700      |\n",
            "|    time_elapsed       | 148       |\n",
            "|    total_timesteps    | 38500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.6     |\n",
            "|    explained_variance | -9.38e+22 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 7699      |\n",
            "|    policy_loss        | 2.12e+08  |\n",
            "|    std                | 0.967     |\n",
            "|    value_loss         | 3.05e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 259       |\n",
            "|    iterations         | 7800      |\n",
            "|    time_elapsed       | 150       |\n",
            "|    total_timesteps    | 39000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.6     |\n",
            "|    explained_variance | -5.65e+21 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 7799      |\n",
            "|    policy_loss        | 3.34e+08  |\n",
            "|    std                | 0.967     |\n",
            "|    value_loss         | 6.69e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 259       |\n",
            "|    iterations         | 7900      |\n",
            "|    time_elapsed       | 152       |\n",
            "|    total_timesteps    | 39500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.5     |\n",
            "|    explained_variance | -2.93e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 7899      |\n",
            "|    policy_loss        | 3.61e+08  |\n",
            "|    std                | 0.966     |\n",
            "|    value_loss         | 9.55e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 8000     |\n",
            "|    time_elapsed       | 154      |\n",
            "|    total_timesteps    | 40000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7999     |\n",
            "|    policy_loss        | 4.88e+08 |\n",
            "|    std                | 0.966    |\n",
            "|    value_loss         | 2.01e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4385301.186352825\n",
            "Sharpe:  1.003519343783517\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 8100     |\n",
            "|    time_elapsed       | 156      |\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        | 1.64e+08 |\n",
            "|    std                | 0.966    |\n",
            "|    value_loss         | 1.88e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 8200     |\n",
            "|    time_elapsed       | 158      |\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        | 2.1e+08  |\n",
            "|    std                | 0.965    |\n",
            "|    value_loss         | 3.03e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 259       |\n",
            "|    iterations         | 8300      |\n",
            "|    time_elapsed       | 159       |\n",
            "|    total_timesteps    | 41500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.5     |\n",
            "|    explained_variance | -2.14e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8299      |\n",
            "|    policy_loss        | 3.06e+08  |\n",
            "|    std                | 0.965     |\n",
            "|    value_loss         | 6.95e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 8400     |\n",
            "|    time_elapsed       | 161      |\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        | 4.14e+08 |\n",
            "|    std                | 0.964    |\n",
            "|    value_loss         | 1.04e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 8500     |\n",
            "|    time_elapsed       | 163      |\n",
            "|    total_timesteps    | 42500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8499     |\n",
            "|    policy_loss        | 5.54e+08 |\n",
            "|    std                | 0.965    |\n",
            "|    value_loss         | 1.99e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4533859.557790691\n",
            "Sharpe:  1.0231980049984988\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 8600     |\n",
            "|    time_elapsed       | 165      |\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        | 1.79e+08 |\n",
            "|    std                | 0.964    |\n",
            "|    value_loss         | 2.04e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 8700     |\n",
            "|    time_elapsed       | 167      |\n",
            "|    total_timesteps    | 43500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8699     |\n",
            "|    policy_loss        | 1.97e+08 |\n",
            "|    std                | 0.963    |\n",
            "|    value_loss         | 3.11e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 259       |\n",
            "|    iterations         | 8800      |\n",
            "|    time_elapsed       | 169       |\n",
            "|    total_timesteps    | 44000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -2.26e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8799      |\n",
            "|    policy_loss        | 3.12e+08  |\n",
            "|    std                | 0.962     |\n",
            "|    value_loss         | 7.42e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 259       |\n",
            "|    iterations         | 8900      |\n",
            "|    time_elapsed       | 171       |\n",
            "|    total_timesteps    | 44500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -3.43e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8899      |\n",
            "|    policy_loss        | 3.98e+08  |\n",
            "|    std                | 0.962     |\n",
            "|    value_loss         | 1.12e+14  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 9000     |\n",
            "|    time_elapsed       | 173      |\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        | 5.29e+08 |\n",
            "|    std                | 0.963    |\n",
            "|    value_loss         | 1.91e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4374719.083403692\n",
            "Sharpe:  0.9973376981198121\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 259       |\n",
            "|    iterations         | 9100      |\n",
            "|    time_elapsed       | 175       |\n",
            "|    total_timesteps    | 45500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -5.39e+22 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 9099      |\n",
            "|    policy_loss        | 1.64e+08  |\n",
            "|    std                | 0.962     |\n",
            "|    value_loss         | 1.73e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 259      |\n",
            "|    iterations         | 9200     |\n",
            "|    time_elapsed       | 177      |\n",
            "|    total_timesteps    | 46000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | -8.5e+22 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9199     |\n",
            "|    policy_loss        | 2.03e+08 |\n",
            "|    std                | 0.962    |\n",
            "|    value_loss         | 2.82e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 259       |\n",
            "|    iterations         | 9300      |\n",
            "|    time_elapsed       | 178       |\n",
            "|    total_timesteps    | 46500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -1.98e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 9299      |\n",
            "|    policy_loss        | 3.02e+08  |\n",
            "|    std                | 0.962     |\n",
            "|    value_loss         | 6.51e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 260       |\n",
            "|    iterations         | 9400      |\n",
            "|    time_elapsed       | 180       |\n",
            "|    total_timesteps    | 47000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -8.29e+21 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 9399      |\n",
            "|    policy_loss        | 3.8e+08   |\n",
            "|    std                | 0.962     |\n",
            "|    value_loss         | 9.7e+13   |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 260       |\n",
            "|    iterations         | 9500      |\n",
            "|    time_elapsed       | 182       |\n",
            "|    total_timesteps    | 47500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -5.55e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 9499      |\n",
            "|    policy_loss        | 4.81e+08  |\n",
            "|    std                | 0.961     |\n",
            "|    value_loss         | 1.81e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4484473.003268213\n",
            "Sharpe:  1.0199955076119884\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 260      |\n",
            "|    iterations         | 9600     |\n",
            "|    time_elapsed       | 184      |\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        | 1.36e+08 |\n",
            "|    std                | 0.961    |\n",
            "|    value_loss         | 1.5e+13  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 260      |\n",
            "|    iterations         | 9700     |\n",
            "|    time_elapsed       | 186      |\n",
            "|    total_timesteps    | 48500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9699     |\n",
            "|    policy_loss        | 1.85e+08 |\n",
            "|    std                | 0.961    |\n",
            "|    value_loss         | 2.35e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 260      |\n",
            "|    iterations         | 9800     |\n",
            "|    time_elapsed       | 188      |\n",
            "|    total_timesteps    | 49000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9799     |\n",
            "|    policy_loss        | 3e+08    |\n",
            "|    std                | 0.961    |\n",
            "|    value_loss         | 6.38e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 260      |\n",
            "|    iterations         | 9900     |\n",
            "|    time_elapsed       | 190      |\n",
            "|    total_timesteps    | 49500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9899     |\n",
            "|    policy_loss        | 3.83e+08 |\n",
            "|    std                | 0.961    |\n",
            "|    value_loss         | 9.43e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 260      |\n",
            "|    iterations         | 10000    |\n",
            "|    time_elapsed       | 192      |\n",
            "|    total_timesteps    | 50000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9999     |\n",
            "|    policy_loss        | 5.38e+08 |\n",
            "|    std                | 0.961    |\n",
            "|    value_loss         | 1.87e+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": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_steps': 2048, 'ent_coef': 0.005, 'learning_rate': 0.0001, 'batch_size': 128}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "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": null,
      "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",
            "|    clip_range           | 0.2           |\n",
            "|    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",
            "|    clip_range           | 0.2            |\n",
            "|    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', '2021-01-01')\n",
        "#trade = data_split(df,config.START_TRADE_DATE,config.END_DATE)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcGYlhyal205",
        "outputId": "7413582f-dc5c-4b5f-cd05-f96a081be63c"
      },
      "source": [
        "trade.shape"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15150, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "4udD8hGlrj7L",
        "outputId": "fc246366-e884-4ab3-92c4-f0d0015bd580"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <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": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qq4W9FbSstT7",
        "outputId": "d92eb564-0231-45e9-cd3e-44b618537786"
      },
      "source": [
        "e_trade_gym = StockPortfolioEnv(df = trade, **env_kwargs)\r\n",
        "env_trade, obs_trade = e_trade_gym.get_sb_env()\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": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:1420871.8717832256\n",
            "Sharpe:  0.7824418490922643\n",
            "=================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "uJvj3pXt_Ukg",
        "outputId": "106b2f89-525c-4597-aa49-2a6739a612fe"
      },
      "source": [
        "df_daily_return.head()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>daily_return</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>-0.025758</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>0.034749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-07</td>\n",
              "      <td>0.003744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-08</td>\n",
              "      <td>0.009476</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     -0.025758\n",
              "2  2019-01-04      0.034749\n",
              "3  2019-01-07      0.003744\n",
              "4  2019-01-08      0.009476"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
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        "id": "tByVcZ2L9TAJ",
        "outputId": "1ee02b27-3a29-45ed-a223-c6401245f976"
      },
      "source": [
        "df_actions.head()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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",
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              "    <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>0.023235</td>\n",
              "      <td>0.034676</td>\n",
              "      <td>0.023235</td>\n",
              "      <td>0.023235</td>\n",
              "      <td>0.043493</td>\n",
              "      <td>0.023235</td>\n",
              "      <td>0.023235</td>\n",
              "      <td>0.033294</td>\n",
              "      <td>0.023235</td>\n",
              "      <td>0.025560</td>\n",
              "      <td>0.023235</td>\n",
              "      <td>0.037088</td>\n",
              "      <td>0.023235</td>\n",
              "      <td>0.036206</td>\n",
              "      <td>0.023235</td>\n",
              "      <td>0.039682</td>\n",
              "      <td>0.063160</td>\n",
              "      <td>0.063160</td>\n",
              "      <td>0.033178</td>\n",
              "      <td>0.023235</td>\n",
              "      <td>0.023235</td>\n",
              "      <td>0.023235</td>\n",
              "      <td>0.053148</td>\n",
              "      <td>0.042635</td>\n",
              "      <td>0.059792</td>\n",
              "      <td>0.023235</td>\n",
              "      <td>0.023235</td>\n",
              "      <td>0.023235</td>\n",
              "      <td>0.063160</td>\n",
              "      <td>0.023235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-04</th>\n",
              "      <td>0.060499</td>\n",
              "      <td>0.060499</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.060499</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.026594</td>\n",
              "      <td>0.035480</td>\n",
              "      <td>0.049006</td>\n",
              "      <td>0.060499</td>\n",
              "      <td>0.060499</td>\n",
              "      <td>0.060499</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.034468</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.025367</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.027233</td>\n",
              "      <td>0.022256</td>\n",
              "      <td>0.060499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-07</th>\n",
              "      <td>0.023563</td>\n",
              "      <td>0.036157</td>\n",
              "      <td>0.023563</td>\n",
              "      <td>0.024554</td>\n",
              "      <td>0.023563</td>\n",
              "      <td>0.023563</td>\n",
              "      <td>0.023563</td>\n",
              "      <td>0.064051</td>\n",
              "      <td>0.032970</td>\n",
              "      <td>0.023563</td>\n",
              "      <td>0.031224</td>\n",
              "      <td>0.026265</td>\n",
              "      <td>0.023563</td>\n",
              "      <td>0.033548</td>\n",
              "      <td>0.033222</td>\n",
              "      <td>0.023563</td>\n",
              "      <td>0.023563</td>\n",
              "      <td>0.064051</td>\n",
              "      <td>0.023563</td>\n",
              "      <td>0.023563</td>\n",
              "      <td>0.023563</td>\n",
              "      <td>0.064051</td>\n",
              "      <td>0.023563</td>\n",
              "      <td>0.023563</td>\n",
              "      <td>0.029752</td>\n",
              "      <td>0.064051</td>\n",
              "      <td>0.038208</td>\n",
              "      <td>0.023563</td>\n",
              "      <td>0.040404</td>\n",
              "      <td>0.064051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-08</th>\n",
              "      <td>0.036447</td>\n",
              "      <td>0.019203</td>\n",
              "      <td>0.019203</td>\n",
              "      <td>0.052199</td>\n",
              "      <td>0.052199</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>0.019707</td>\n",
              "      <td>0.019203</td>\n",
              "      <td>0.019203</td>\n",
              "      <td>0.047345</td>\n",
              "      <td>0.027229</td>\n",
              "      <td>0.019203</td>\n",
              "      <td>0.019203</td>\n",
              "      <td>0.019203</td>\n",
              "      <td>0.052199</td>\n",
              "      <td>0.019203</td>\n",
              "      <td>0.052199</td>\n",
              "      <td>0.052199</td>\n",
              "      <td>0.052199</td>\n",
              "      <td>0.051098</td>\n",
              "      <td>0.052199</td>\n",
              "      <td>0.036401</td>\n",
              "      <td>0.019203</td>\n",
              "      <td>0.036034</td>\n",
              "      <td>0.019203</td>\n",
              "      <td>0.019203</td>\n",
              "      <td>0.052199</td>\n",
              "      <td>0.024620</td>\n",
              "      <td>0.019203</td>\n",
              "      <td>0.033091</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  0.023235  0.034676  0.023235  ...  0.023235  0.063160  0.023235\n",
              "2019-01-04  0.060499  0.060499  0.022256  ...  0.027233  0.022256  0.060499\n",
              "2019-01-07  0.023563  0.036157  0.023563  ...  0.023563  0.040404  0.064051\n",
              "2019-01-08  0.036447  0.019203  0.019203  ...  0.024620  0.019203  0.033091\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "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": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hqvwr6SY8l9A",
        "outputId": "fc6b65ba-4993-4cb6-e7b7-71e186f5e264"
      },
      "source": [
        "print(\"==============DRL Strategy Stats===========\")\r\n",
        "perf_stats_all"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============DRL Strategy Stats===========\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Annual return           0.191589\n",
              "Cumulative returns      0.420872\n",
              "Annual volatility       0.271215\n",
              "Sharpe ratio            0.782442\n",
              "Calmar ratio            0.551532\n",
              "Stability               0.345040\n",
              "Max drawdown           -0.347376\n",
              "Omega ratio             1.184896\n",
              "Sortino ratio           1.112929\n",
              "Skew                   -0.236884\n",
              "Kurtosis               13.701781\n",
              "Tail ratio              0.844140\n",
              "Daily value at risk    -0.033328\n",
              "Alpha                   0.000000\n",
              "Beta                    1.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fqSEF5PfjjT",
        "outputId": "a22f341c-6f43-4359-81a9-37ccf3bddcf0"
      },
      "source": [
        "print(\"==============Get Index Stats===========\")\r\n",
        "baesline_perf_stats=BaselineStats('^DJI',\r\n",
        "                                  baseline_start = '2019-01-01',\r\n",
        "                                  baseline_end = '2021-01-01')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Index Stats===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (505, 7)\n",
            "Annual return          0.144674\n",
            "Cumulative returns     0.310981\n",
            "Annual volatility      0.274619\n",
            "Sharpe ratio           0.631418\n",
            "Calmar ratio           0.390102\n",
            "Stability              0.116677\n",
            "Max drawdown          -0.370862\n",
            "Omega ratio            1.149365\n",
            "Sortino ratio          0.870084\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.860710\n",
            "Daily value at risk   -0.033911\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": "8378330e-f1c5-4439-c235-a03b30e6fa35"
      },
      "source": [
        "dji, dow_strat = baseline_strat('^DJI','2019-01-01','2021-01-01')"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (505, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "87xaEUFvyqe2",
        "outputId": "0aa5197e-1d94-47f1-cc15-2083adea9bbd"
      },
      "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": 42,
      "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>2020-12-31</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>24</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>19.159%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>42.087%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>27.122%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>0.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>0.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-34.738%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>1.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>-0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>13.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-3.333%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.98</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>34.74</td>\n",
              "      <td>2020-01-17</td>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>2020-09-02</td>\n",
              "      <td>164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.72</td>\n",
              "      <td>2020-09-02</td>\n",
              "      <td>2020-10-28</td>\n",
              "      <td>2020-11-09</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.51</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>6.43</td>\n",
              "      <td>2019-07-15</td>\n",
              "      <td>2019-08-14</td>\n",
              "      <td>2019-09-10</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.52</td>\n",
              "      <td>2019-09-13</td>\n",
              "      <td>2019-10-02</td>\n",
              "      <td>2019-11-01</td>\n",
              "      <td>36</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": []
    }
  ]
}