FinRL_portfolio_allocation.ipynb · Spectator/FinRL-Library

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FinRL_portfolio_allocation.ipynb 555.62 KB

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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<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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    {
      "cell_type": "markdown",
      "metadata": {
        "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vUmLTmoQA7_w"
      },
      "source": [
        "* [1. Problem Definition](#0)\n",
        "* [2. Getting Started - Load Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. Check Additional Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download Data](#2)\n",
        "* [4. Preprocess Data](#3)        \n",
        "    * [4.1. Technical Indicators](#3.1)\n",
        "    * [4.2. Perform Feature Engineering](#3.2)\n",
        "* [5.Build Environment](#4)  \n",
        "    * [5.1. Training & Trade Data Split](#4.1)\n",
        "    * [5.2. User-defined Environment](#4.2)   \n",
        "    * [5.3. Initialize Environment](#4.3)    \n",
        "* [6.Implement DRL Algorithms](#5)  \n",
        "* [7.Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "    * [7.3. Baseline Stats](#6.3)   \n",
        "    * [7.3. Compare to Stock Market Index](#6.4)             "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12v1i0jVkg48"
      },
      "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": "8a123116-a774-4725-e30f-b94cc634541f"
      },
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.1) (3.4.0)\n",
            "Building wheels for collected packages: finrl, yfinance, pyfolio, empyrical\n",
            "  Building wheel for finrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for finrl: filename=finrl-0.0.1-cp36-none-any.whl size=26655 sha256=f73c1c4f4df5d5d58ae09981c41d4cdbd1f28d5c99872dc6dddc58bc154a1810\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-lo_5_zhy/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=1c6d50b34e147ac2fe8a6feac664472aa213955dbaea1e3d4cfc932bdd61c526\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=553989259cbb0c3cfecd98c0f7f79479ddfc544da09b5d72fcdd9e7a43475e42\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-lo_5_zhy/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=fd0f9756d30181dc98db63683ec9372e30b96bd25f3c9f6ebc725f51c2e360ca\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-0.0.1 int-date-0.1.8 lxml-4.6.2 pyfolio-0.9.2+75.g4b901f6 stable-baselines3-0.10.0 stockstats-0.3.2 yfinance-0.1.55\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2568cp5bU38"
      },
      "source": [
        "\n",
        "<a id='1.2'></a>\n",
        "## 2.2. Check if the additional packages needed are present, if not install them. \n",
        "* Yahoo Finance API\n",
        "* pandas\n",
        "* numpy\n",
        "* matplotlib\n",
        "* stockstats\n",
        "* OpenAI gym\n",
        "* stable-baselines\n",
        "* tensorflow\n",
        "* pyfolio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXKZS6YEbU39"
      },
      "source": [
        "import pkg_resources\n",
        "import pip\n",
        "installedPackages = {pkg.key for pkg in pkg_resources.working_set}\n",
        "required = {'yfinance', 'pandas','numpy', 'matplotlib', 'stockstats','stable-baselines','gym','tensorflow','pyfolio'}\n",
        "missing = required - installedPackages\n",
        "if missing:\n",
        "    !pip install yfinance\n",
        "    !pip install pandas\n",
        "    !pip install numpy\n",
        "    !pip install matplotlib\n",
        "    !pip install stockstats\n",
        "    !pip install gym\n",
        "    !pip install stable-baselines3[extra]\n",
        "    !pip install git+https://github.com/quantopian/pyfolio\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bNmvYN9YbU4B"
      },
      "source": [
        "<a id='1.3'></a>\r\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntfTb0e2bU4C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5330153-e125-4f7b-becd-ccee46f1754d"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "matplotlib.use('Agg')\n",
        "import datetime\n",
        "\n",
        "from finrl.config import config\n",
        "from finrl.marketdata.yahoodownloader import YahooDownloader\n",
        "from finrl.preprocessing.preprocessors import FeatureEngineer\n",
        "from finrl.preprocessing.data import data_split\n",
        "from finrl.env.environment import EnvSetup\n",
        "from finrl.env.EnvMultipleStock_train import StockEnvTrain\n",
        "from finrl.env.EnvMultipleStock_trade import StockEnvTrade\n",
        "from finrl.model.models import DRLAgent\n",
        "from finrl.trade.backtest import BackTestStats, BaselineStats, BackTestPlot, backtest_strat, baseline_strat\n",
        "from finrl.trade.backtest import backtest_strat, baseline_strat\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pyfolio/pos.py:27: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
            "  'Module \"zipline.assets\" not found; multipliers will not be applied'\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "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": 3,
      "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": "08072115-5131-4aa7-c501-8a1a489c0065"
      },
      "source": [
        "print(config.DOW_30_TICKER)"
      ],
      "execution_count": 6,
      "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": "d6f1f71c-4342-4076-ebc9-adc812e1f89e"
      },
      "source": [
        "df = YahooDownloader(start_date = '2008-01-01',\n",
        "                     end_date = '2020-12-01',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (97507, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1xC-LpbbU4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "61ae4f0b-15f2-4cbd-9216-7d1fb20884d7"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <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": {
        "id": "QVN6gRZNbU4m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "654f82ce-169a-4811-fa2d-4019c14be7a5"
      },
      "source": [
        "df = FeatureEngineer(df.copy(),\n",
        "                    use_technical_indicator=True,\n",
        "                    use_turbulence=False).preprocess_data()"
      ],
      "execution_count": 7,
      "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": "e2ab0f0b-f33d-4c54-f91e-cc02ddf0ffbb"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(97507, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWB-RoTSbU4s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "529fc8f1-ff5d-4b2e-9fc1-7a37f81b28d3"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <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",
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              "      <td>-66.666667</td>\n",
              "      <td>100.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-01-03</td>\n",
              "      <td>6.978929</td>\n",
              "      <td>7.049643</td>\n",
              "      <td>6.881786</td>\n",
              "      <td>6.005591</td>\n",
              "      <td>8.420664e+08</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>0.000062</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>-66.666667</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-01-04</td>\n",
              "      <td>6.837500</td>\n",
              "      <td>6.892857</td>\n",
              "      <td>6.388928</td>\n",
              "      <td>5.547154</td>\n",
              "      <td>1.455832e+09</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.014137</td>\n",
              "      <td>0.581478</td>\n",
              "      <td>-100.000000</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-01-07</td>\n",
              "      <td>6.473214</td>\n",
              "      <td>6.557143</td>\n",
              "      <td>6.079643</td>\n",
              "      <td>5.472904</td>\n",
              "      <td>2.072193e+09</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.022929</td>\n",
              "      <td>0.498449</td>\n",
              "      <td>-98.315409</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-01-08</td>\n",
              "      <td>6.433571</td>\n",
              "      <td>6.516428</td>\n",
              "      <td>6.100000</td>\n",
              "      <td>5.276035</td>\n",
              "      <td>1.523816e+09</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.035058</td>\n",
              "      <td>0.358171</td>\n",
              "      <td>-86.839166</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date      open      high  ...      rsi_30      cci_30  dx_30\n",
              "0  2008-01-02  7.116786  7.152143  ...  100.000000  -66.666667  100.0\n",
              "1  2008-01-03  6.978929  7.049643  ...  100.000000  -66.666667  100.0\n",
              "2  2008-01-04  6.837500  6.892857  ...    0.581478 -100.000000  100.0\n",
              "3  2008-01-07  6.473214  6.557143  ...    0.498449  -98.315409  100.0\n",
              "4  2008-01-08  6.433571  6.516428  ...    0.358171  -86.839166  100.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qz9K2vul6RmK"
      },
      "source": [
        "## Add covariance matrix as states"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IhizvNwcrg1n"
      },
      "source": [
        "# add covariance matrix as states\n",
        "df=df.sort_values(['date','tic'],ignore_index=True)\n",
        "df.index = df.date.factorize()[0]\n",
        "\n",
        "cov_list = []\n",
        "# look back is one year\n",
        "lookback=252\n",
        "for i in range(lookback,len(df.index.unique())):\n",
        "  data_lookback = df.loc[i-lookback:i,:]\n",
        "  price_lookback=data_lookback.pivot_table(index = 'date',columns = 'tic', values = 'close')\n",
        "  return_lookback = price_lookback.pct_change().dropna()\n",
        "  covs = return_lookback.cov().values \n",
        "  cov_list.append(covs)\n",
        "  \n",
        "df_cov = pd.DataFrame({'date':df.date.unique()[lookback:],'cov_list':cov_list})\n",
        "df = df.merge(df_cov, on='date')\n",
        "df = df.sort_values(['date','tic']).reset_index(drop=True)\n",
        "        "
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUPwwa13uBQ-",
        "outputId": "6d015f51-2559-45c9-84be-d62e0844e2ef"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90000, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "wv3jR1zPrg4g",
        "outputId": "797db2d0-c212-46f0-adf3-40cd6af65e92"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>cov_list</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>3.070357</td>\n",
              "      <td>3.133571</td>\n",
              "      <td>3.047857</td>\n",
              "      <td>2.629544</td>\n",
              "      <td>607541200.0</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.084062</td>\n",
              "      <td>42.254783</td>\n",
              "      <td>-80.502280</td>\n",
              "      <td>16.129787</td>\n",
              "      <td>[[0.0014139120472621146, 0.0011800741602556227...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>17.969999</td>\n",
              "      <td>18.750000</td>\n",
              "      <td>17.910000</td>\n",
              "      <td>15.163033</td>\n",
              "      <td>9625600.0</td>\n",
              "      <td>AXP</td>\n",
              "      <td>-0.972946</td>\n",
              "      <td>42.554831</td>\n",
              "      <td>-75.339668</td>\n",
              "      <td>25.776759</td>\n",
              "      <td>[[0.0014139120472621146, 0.0011800741602556227...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>41.590000</td>\n",
              "      <td>43.049999</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>31.760571</td>\n",
              "      <td>5443100.0</td>\n",
              "      <td>BA</td>\n",
              "      <td>-0.277653</td>\n",
              "      <td>47.440272</td>\n",
              "      <td>156.964508</td>\n",
              "      <td>5.366299</td>\n",
              "      <td>[[0.0014139120472621146, 0.0011800741602556227...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>43.700001</td>\n",
              "      <td>45.099998</td>\n",
              "      <td>43.700001</td>\n",
              "      <td>30.961802</td>\n",
              "      <td>6277400.0</td>\n",
              "      <td>CAT</td>\n",
              "      <td>0.685574</td>\n",
              "      <td>51.205307</td>\n",
              "      <td>98.427075</td>\n",
              "      <td>26.331746</td>\n",
              "      <td>[[0.0014139120472621146, 0.0011800741602556227...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>16.180000</td>\n",
              "      <td>16.549999</td>\n",
              "      <td>16.120001</td>\n",
              "      <td>12.288514</td>\n",
              "      <td>37513700.0</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>-0.105032</td>\n",
              "      <td>45.961928</td>\n",
              "      <td>11.948975</td>\n",
              "      <td>13.387087</td>\n",
              "      <td>[[0.0014139120472621146, 0.0011800741602556227...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  ...                                           cov_list\n",
              "0  2008-12-31  ...  [[0.0014139120472621146, 0.0011800741602556227...\n",
              "1  2008-12-31  ...  [[0.0014139120472621146, 0.0011800741602556227...\n",
              "2  2008-12-31  ...  [[0.0014139120472621146, 0.0011800741602556227...\n",
              "3  2008-12-31  ...  [[0.0014139120472621146, 0.0011800741602556227...\n",
              "4  2008-12-31  ...  [[0.0014139120472621146, 0.0011800741602556227...\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UooHj1OgbU4v"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Design Environment\n",
        "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
        "\n",
        "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
        "\n",
        "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQnmN1qdk88I"
      },
      "source": [
        "## Training data split: 2009-01-01 to 2018-12-31"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NrPxgv4eBQ_R"
      },
      "source": [
        "train = data_split(df, '2009-01-01','2019-01-01')\n",
        "#trade = data_split(df, '2020-01-01', config.END_DATE)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "vU2vXEll0hfk",
        "outputId": "8c1e442a-820b-42ba-bb17-bab7ed068442"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>cov_list</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.795913</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.071392</td>\n",
              "      <td>45.440203</td>\n",
              "      <td>-32.036662</td>\n",
              "      <td>2.140159</td>\n",
              "      <td>[[0.0014277103481246368, 0.0011886549864140632...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.800624</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>AXP</td>\n",
              "      <td>-0.865277</td>\n",
              "      <td>43.957557</td>\n",
              "      <td>-42.722340</td>\n",
              "      <td>16.335101</td>\n",
              "      <td>[[0.0014277103481246368, 0.0011886549864140632...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.680935</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>BA</td>\n",
              "      <td>-0.001991</td>\n",
              "      <td>50.822045</td>\n",
              "      <td>272.714919</td>\n",
              "      <td>20.494464</td>\n",
              "      <td>[[0.0014277103481246368, 0.0011886549864140632...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.514400</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>CAT</td>\n",
              "      <td>0.871261</td>\n",
              "      <td>53.661257</td>\n",
              "      <td>129.739408</td>\n",
              "      <td>34.637448</td>\n",
              "      <td>[[0.0014277103481246368, 0.0011886549864140632...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.786087</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>-0.062942</td>\n",
              "      <td>48.322163</td>\n",
              "      <td>54.612483</td>\n",
              "      <td>2.653808</td>\n",
              "      <td>[[0.0014277103481246368, 0.0011886549864140632...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  ...                                           cov_list\n",
              "0  2009-01-02  ...  [[0.0014277103481246368, 0.0011886549864140632...\n",
              "0  2009-01-02  ...  [[0.0014277103481246368, 0.0011886549864140632...\n",
              "0  2009-01-02  ...  [[0.0014277103481246368, 0.0011886549864140632...\n",
              "0  2009-01-02  ...  [[0.0014277103481246368, 0.0011886549864140632...\n",
              "0  2009-01-02  ...  [[0.0014277103481246368, 0.0011886549864140632...\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sxQTNjpblAMN"
      },
      "source": [
        "## Environment for Portfolio Allocation\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlfE-VERbU40"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from gym.utils import seeding\n",
        "import gym\n",
        "from gym import spaces\n",
        "import matplotlib\n",
        "matplotlib.use('Agg')\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "class StockPortfolioEnv(gym.Env):\n",
        "    \"\"\"A single stock trading environment for OpenAI gym\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        df: DataFrame\n",
        "            input data\n",
        "        stock_dim : int\n",
        "            number of unique stocks\n",
        "        hmax : int\n",
        "            maximum number of shares to trade\n",
        "        initial_amount : int\n",
        "            start money\n",
        "        transaction_cost_pct: float\n",
        "            transaction cost percentage per trade\n",
        "        reward_scaling: float\n",
        "            scaling factor for reward, good for training\n",
        "        state_space: int\n",
        "            the dimension of input features\n",
        "        action_space: int\n",
        "            equals stock dimension\n",
        "        tech_indicator_list: list\n",
        "            a list of technical indicator names\n",
        "        turbulence_threshold: int\n",
        "            a threshold to control risk aversion\n",
        "        day: int\n",
        "            an increment number to control date\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    _sell_stock()\n",
        "        perform sell action based on the sign of the action\n",
        "    _buy_stock()\n",
        "        perform buy action based on the sign of the action\n",
        "    step()\n",
        "        at each step the agent will return actions, then \n",
        "        we will calculate the reward, and return the next observation.\n",
        "    reset()\n",
        "        reset the environment\n",
        "    render()\n",
        "        use render to return other functions\n",
        "    save_asset_memory()\n",
        "        return account value at each time step\n",
        "    save_action_memory()\n",
        "        return actions/positions at each time step\n",
        "        \n",
        "\n",
        "    \"\"\"\n",
        "    metadata = {'render.modes': ['human']}\n",
        "\n",
        "    def __init__(self, \n",
        "                df,\n",
        "                stock_dim,\n",
        "                hmax,\n",
        "                initial_amount,\n",
        "                transaction_cost_pct,\n",
        "                reward_scaling,\n",
        "                state_space,\n",
        "                action_space,\n",
        "                tech_indicator_list,\n",
        "                turbulence_threshold,\n",
        "                lookback=252,\n",
        "                day = 0):\n",
        "        #super(StockEnv, self).__init__()\n",
        "        #money = 10 , scope = 1\n",
        "        self.day = day\n",
        "        self.lookback=lookback\n",
        "        self.df = df\n",
        "        self.stock_dim = stock_dim\n",
        "        self.hmax = hmax\n",
        "        self.initial_amount = initial_amount\n",
        "        self.transaction_cost_pct =transaction_cost_pct\n",
        "        self.reward_scaling = reward_scaling\n",
        "        self.state_space = state_space\n",
        "        self.action_space = action_space\n",
        "        self.tech_indicator_list = tech_indicator_list\n",
        "\n",
        "        # action_space normalization and shape is self.stock_dim\n",
        "        self.action_space = spaces.Box(low = 0, high = 1,shape = (self.action_space,)) \n",
        "        # Shape = (34, 30)\n",
        "        # covariance matrix + technical indicators\n",
        "        self.observation_space = spaces.Box(low=0, high=np.inf, shape = (self.state_space+len(self.tech_indicator_list),self.state_space))\n",
        "\n",
        "        # load data from a pandas dataframe\n",
        "        self.data = self.df.loc[self.day,:]\n",
        "        self.covs = self.data['cov_list'].values[0]\n",
        "        self.state =  np.append(np.array(self.covs), [self.data[tech].values.tolist() for tech in self.tech_indicator_list ], axis=0)\n",
        "        self.terminal = False     \n",
        "        self.turbulence_threshold = turbulence_threshold        \n",
        "        # initalize state: inital portfolio return + individual stock return + individual weights\n",
        "        self.portfolio_value = self.initial_amount\n",
        "\n",
        "        # memorize portfolio value each step\n",
        "        self.asset_memory = [self.initial_amount]\n",
        "        # memorize portfolio return each step\n",
        "        self.portfolio_return_memory = [0]\n",
        "        self.actions_memory=[[1/self.stock_dim]*self.stock_dim]\n",
        "        self.date_memory=[self.data.date.unique()[0]]\n",
        "\n",
        "        \n",
        "    def step(self, actions):\n",
        "        # print(self.day)\n",
        "        self.terminal = self.day >= len(self.df.index.unique())-1\n",
        "        # print(actions)\n",
        "\n",
        "        if self.terminal:\n",
        "            df = pd.DataFrame(self.portfolio_return_memory)\n",
        "            df.columns = ['daily_return']\n",
        "            plt.plot(df.daily_return.cumsum(),'r')\n",
        "            plt.savefig('results/cumulative_reward.png')\n",
        "            plt.close()\n",
        "            \n",
        "            plt.plot(self.portfolio_return_memory,'r')\n",
        "            plt.savefig('results/rewards.png')\n",
        "            plt.close()\n",
        "\n",
        "            print(\"=================================\")\n",
        "            print(\"begin_total_asset:{}\".format(self.asset_memory[0]))           \n",
        "            print(\"end_total_asset:{}\".format(self.portfolio_value))\n",
        "\n",
        "            df_daily_return = pd.DataFrame(self.portfolio_return_memory)\n",
        "            df_daily_return.columns = ['daily_return']\n",
        "            if df_daily_return['daily_return'].std() !=0:\n",
        "              sharpe = (252**0.5)*df_daily_return['daily_return'].mean()/ \\\n",
        "                       df_daily_return['daily_return'].std()\n",
        "              print(\"Sharpe: \",sharpe)\n",
        "            print(\"=================================\")\n",
        "            \n",
        "            return self.state, self.reward, self.terminal,{}\n",
        "\n",
        "        else:\n",
        "            #print(\"Model actions: \",actions)\n",
        "            # actions are the portfolio weight\n",
        "            # normalize to sum of 1\n",
        "            #if (np.array(actions) - np.array(actions).min()).sum() != 0:\n",
        "            #  norm_actions = (np.array(actions) - np.array(actions).min()) / (np.array(actions) - np.array(actions).min()).sum()\n",
        "            #else:\n",
        "            #  norm_actions = actions\n",
        "            weights = self.softmax_normalization(actions) \n",
        "            #print(\"Normalized actions: \", weights)\n",
        "            self.actions_memory.append(weights)\n",
        "            last_day_memory = self.data\n",
        "\n",
        "            #load next state\n",
        "            self.day += 1\n",
        "            self.data = self.df.loc[self.day,:]\n",
        "            self.covs = self.data['cov_list'].values[0]\n",
        "            self.state =  np.append(np.array(self.covs), [self.data[tech].values.tolist() for tech in self.tech_indicator_list ], axis=0)\n",
        "            #print(self.state)\n",
        "            # calcualte portfolio return\n",
        "            # individual stocks' return * weight\n",
        "            portfolio_return = sum(((self.data.close.values / last_day_memory.close.values)-1)*weights)\n",
        "            # update portfolio value\n",
        "            new_portfolio_value = self.portfolio_value*(1+portfolio_return)\n",
        "            self.portfolio_value = new_portfolio_value\n",
        "\n",
        "            # save into memory\n",
        "            self.portfolio_return_memory.append(portfolio_return)\n",
        "            self.date_memory.append(self.data.date.unique()[0])            \n",
        "            self.asset_memory.append(new_portfolio_value)\n",
        "\n",
        "            # the reward is the new portfolio value or end portfolo value\n",
        "            self.reward = new_portfolio_value \n",
        "            #print(\"Step reward: \", self.reward)\n",
        "            #self.reward = self.reward*self.reward_scaling\n",
        "\n",
        "        return self.state, self.reward, self.terminal, {}\n",
        "\n",
        "    def reset(self):\n",
        "        self.asset_memory = [self.initial_amount]\n",
        "        self.day = 0\n",
        "        self.data = self.df.loc[self.day,:]\n",
        "        # load states\n",
        "        self.covs = self.data['cov_list'].values[0]\n",
        "        self.state =  np.append(np.array(self.covs), [self.data[tech].values.tolist() for tech in self.tech_indicator_list ], axis=0)\n",
        "        self.portfolio_value = self.initial_amount\n",
        "        #self.cost = 0\n",
        "        #self.trades = 0\n",
        "        self.terminal = False \n",
        "        self.portfolio_return_memory = [0]\n",
        "        self.actions_memory=[[1/self.stock_dim]*self.stock_dim]\n",
        "        self.date_memory=[self.data.date.unique()[0]] \n",
        "        return self.state\n",
        "    \n",
        "    def render(self, mode='human'):\n",
        "        return self.state\n",
        "        \n",
        "    def softmax_normalization(self, actions):\n",
        "        numerator = np.exp(actions)\n",
        "        denominator = np.sum(np.exp(actions))\n",
        "        softmax_output = numerator/denominator\n",
        "        return softmax_output\n",
        "\n",
        "    \n",
        "    def save_asset_memory(self):\n",
        "        date_list = self.date_memory\n",
        "        portfolio_return = self.portfolio_return_memory\n",
        "        #print(len(date_list))\n",
        "        #print(len(asset_list))\n",
        "        df_account_value = pd.DataFrame({'date':date_list,'daily_return':portfolio_return})\n",
        "        return df_account_value\n",
        "\n",
        "    def save_action_memory(self):\n",
        "        # date and close price length must match actions length\n",
        "        date_list = self.date_memory\n",
        "        df_date = pd.DataFrame(date_list)\n",
        "        df_date.columns = ['date']\n",
        "        \n",
        "        action_list = self.actions_memory\n",
        "        df_actions = pd.DataFrame(action_list)\n",
        "        df_actions.columns = self.data.tic.values\n",
        "        df_actions.index = df_date.date\n",
        "        #df_actions = pd.DataFrame({'date':date_list,'actions':action_list})\n",
        "        return df_actions\n",
        "\n",
        "    def _seed(self, seed=None):\n",
        "        self.np_random, seed = seeding.np_random(seed)\n",
        "        return [seed]"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzD06X0CbU43"
      },
      "source": [
        "stock_dimension = len(train.tic.unique())\n",
        "state_space = stock_dimension"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_CA2HJeybU45",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebddedb7-57b0-4c83-e3d0-a91821bbe2cd"
      },
      "source": [
        "stock_dimension"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJc5fax-GbGB"
      },
      "source": [
        "env_setup = EnvSetup(stock_dim = stock_dimension,\n",
        "                        state_space = state_space,\n",
        "                        initial_amount = 1000000,\n",
        "                        tech_indicator_list = config.TECHNICAL_INDICATORS_LIST)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTlOf8SJGdkl"
      },
      "source": [
        "env_train = env_setup.create_env_training(data = train,\n",
        "                                          env_class = StockPortfolioEnv)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2eKIu5UPlPnk"
      },
      "source": [
        "<a id='5'></a>\r\n",
        "# Part 6: Implement DRL Algorithms\r\n",
        "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\r\n",
        "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\r\n",
        "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\r\n",
        "design their own DRL algorithms by adapting these DRL algorithms."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDxU0iCEGdnb"
      },
      "source": [
        "# initialize\r\n",
        "agent = DRLAgent(env = env_train)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hdPe8uzflbXe"
      },
      "source": [
        "### Model 1: **A2C**\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GM1LN89fGdqT",
        "outputId": "88fd7a24-ba59-468d-e996-ea6e290fef59"
      },
      "source": [
        "env_train = env_setup.create_env_training(data = train,\n",
        "                                          env_class = StockPortfolioEnv)\n",
        "agent = DRLAgent(env = env_train)\n",
        "\n",
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "a2c_params_tuning = {'n_steps':5, \n",
        "\t\t\t  'ent_coef':0.005, \n",
        "\t\t\t  'learning_rate':0.0003,\n",
        "\t\t\t  'verbose':0,\n",
        "\t\t\t  'timesteps':50000}\n",
        "model_a2c = agent.train_A2C(model_name = \"A2C_{}\".format(now), model_params = a2c_params_tuning) "
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4752573.285890826\n",
            "Sharpe:  1.056793649060926\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4434865.853810067\n",
            "Sharpe:  1.01453647941275\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5170350.629283343\n",
            "Sharpe:  1.1044800489344546\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4442643.782339922\n",
            "Sharpe:  1.0070311291684941\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4827057.092573336\n",
            "Sharpe:  1.0580478201706944\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4413951.914986481\n",
            "Sharpe:  1.0011698329568703\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4690316.1485578\n",
            "Sharpe:  1.0437519257576284\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5340824.029481623\n",
            "Sharpe:  1.1265733861967495\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4824043.426627462\n",
            "Sharpe:  1.05534478888532\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4671362.184461779\n",
            "Sharpe:  1.0372164002364217\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5308218.494015194\n",
            "Sharpe:  1.1147390236875365\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4963877.687887304\n",
            "Sharpe:  1.0774579465466188\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4610594.092859265\n",
            "Sharpe:  1.0289969868569555\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4437549.763856726\n",
            "Sharpe:  1.0091349477859528\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4512915.328724293\n",
            "Sharpe:  1.0255784289150842\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4608163.552727206\n",
            "Sharpe:  1.0429922855595979\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4760251.985094509\n",
            "Sharpe:  1.0608264496141786\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4437444.003215053\n",
            "Sharpe:  1.013427124074686\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4558999.415768689\n",
            "Sharpe:  1.0362008064729102\n",
            "=================================\n",
            "Training time (A2C):  3.4864849130312603  minutes\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": "tRoOL4AMLeCH",
        "outputId": "d846d3a0-f35d-467d-9dc7-6594c30230ac"
      },
      "source": [
        "env_train = env_setup.create_env_training(data = train,\r\n",
        "                                          env_class = StockPortfolioEnv)\r\n",
        "agent = DRLAgent(env = env_train)\r\n",
        "\r\n",
        "print(\"==============Model Training===========\")\r\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\r\n",
        "ppo_params_tuning = {'n_steps':128, \r\n",
        "                     'batch_size': 64,\r\n",
        "\t\t\t               'ent_coef':0.005, \r\n",
        "\t\t\t               'learning_rate':0.00025,\r\n",
        "\t\t\t              'verbose':0,\r\n",
        "\t\t\t              'timesteps':50000}\r\n",
        "model_ppo = agent.train_PPO(model_name = \"PPO_{}\".format(now), model_params = ppo_params_tuning)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4938338.204481581\n",
            "Sharpe:  1.0776292652607975\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4379143.172096554\n",
            "Sharpe:  1.0039253398945611\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4765891.205441805\n",
            "Sharpe:  1.0606110444590757\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4664266.219935015\n",
            "Sharpe:  1.040812866455088\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4742705.414964842\n",
            "Sharpe:  1.0579510110171575\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4699601.775067548\n",
            "Sharpe:  1.0487861239205885\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4789889.986873262\n",
            "Sharpe:  1.062594309725888\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4599652.015306297\n",
            "Sharpe:  1.0388310773241345\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4885834.133428683\n",
            "Sharpe:  1.0723380630470452\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4642184.400446007\n",
            "Sharpe:  1.042826715068999\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4735640.177902391\n",
            "Sharpe:  1.054286212374529\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5101143.242436538\n",
            "Sharpe:  1.1036907436196504\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4891313.902036869\n",
            "Sharpe:  1.074450865663112\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4415015.01864699\n",
            "Sharpe:  1.0056058005004287\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4702893.300209483\n",
            "Sharpe:  1.051819507016535\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4992268.090325537\n",
            "Sharpe:  1.0844766257102236\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4544235.426298249\n",
            "Sharpe:  1.0310610427162208\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4642969.345907846\n",
            "Sharpe:  1.043805028932407\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4994699.361216589\n",
            "Sharpe:  1.0831529570304148\n",
            "=================================\n",
            "Training time (PPO):  2.749698475996653  minutes\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": "MJB_ZEAdxUuZ",
        "outputId": "ad3a8a5f-8319-4d92-fdd3-ad39502979f6"
      },
      "source": [
        "env_train = env_setup.create_env_training(data = train,\r\n",
        "                                          env_class = StockPortfolioEnv)\r\n",
        "agent = DRLAgent(env = env_train)\r\n",
        "\r\n",
        "print(\"==============Model Training===========\")\r\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\r\n",
        "ddpg_params_tuning = {\r\n",
        "                     'batch_size': 128,\r\n",
        "\t\t\t               'buffer_size':100000, \r\n",
        "\t\t\t               'verbose':0,\r\n",
        "\t\t\t               'timesteps':50000}\r\n",
        "model_ddpg = agent.train_DDPG(model_name = \"DDPG_{}\".format(now), model_params = ddpg_params_tuning)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4700528.981482282\n",
            "Sharpe:  1.0397165739595422\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4858875.858547951\n",
            "Sharpe:  1.057937480675237\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4860642.8317080885\n",
            "Sharpe:  1.0585305473306752\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4864562.36256084\n",
            "Sharpe:  1.0594136095652191\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4884670.345054097\n",
            "Sharpe:  1.0611423631169552\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4902366.357733155\n",
            "Sharpe:  1.0634348816099244\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4873449.512429855\n",
            "Sharpe:  1.0596503208645291\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4868323.9008494085\n",
            "Sharpe:  1.0598858714070958\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4880059.272680283\n",
            "Sharpe:  1.0608095713413894\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4833812.982856953\n",
            "Sharpe:  1.0542802749827405\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4892850.450189528\n",
            "Sharpe:  1.0624574230999182\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4844684.189174783\n",
            "Sharpe:  1.0558629335431\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4841134.203409045\n",
            "Sharpe:  1.0561647975675852\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4862285.251465485\n",
            "Sharpe:  1.0582673275289776\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4838613.819409638\n",
            "Sharpe:  1.0552401148292396\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4884939.923113089\n",
            "Sharpe:  1.0611962452714467\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4902801.46801668\n",
            "Sharpe:  1.0634433939693428\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4924647.112495918\n",
            "Sharpe:  1.0668206718816247\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4876654.321951141\n",
            "Sharpe:  1.0600456942648098\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4910166.285830842\n",
            "Sharpe:  1.0646306640812377\n",
            "=================================\n",
            "Training time (DDPG):  45.182397135098775  minutes\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": "S-8NPKPVDD5m",
        "outputId": "45b962ce-6a21-41d0-cab5-d10bd4a05285"
      },
      "source": [
        "env_train = env_setup.create_env_training(data = train,\n",
        "                                          env_class = StockPortfolioEnv)\n",
        "agent = DRLAgent(env = env_train)\n",
        "\n",
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "sac_params_tuning={\n",
        "  'batch_size': 256,\n",
        " 'buffer_size': 1000000,\n",
        "  'ent_coef':'auto_0.1',\n",
        " 'learning_rate': 0.0003,\n",
        " 'learning_starts':200,\n",
        " 'timesteps': 50000,\n",
        " 'verbose': 0}\n",
        "model_sac = agent.train_SAC(model_name = \"SAC_{}\".format(now), model_params = sac_params_tuning)"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4938128.91512742\n",
            "Sharpe:  1.0940453350490542\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008839.192881948\n",
            "Sharpe:  1.1159475540725028\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008831.293953379\n",
            "Sharpe:  1.1159470692564635\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008827.68945152\n",
            "Sharpe:  1.1159466070795048\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008822.396448965\n",
            "Sharpe:  1.1159460100099994\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008823.013147674\n",
            "Sharpe:  1.1159460588807268\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008830.669306872\n",
            "Sharpe:  1.11594703064177\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008816.308569373\n",
            "Sharpe:  1.115945198015815\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008828.576985155\n",
            "Sharpe:  1.1159466884339966\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008825.974073107\n",
            "Sharpe:  1.1159463508750787\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008826.994093881\n",
            "Sharpe:  1.11594636953811\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008806.473254383\n",
            "Sharpe:  1.1159437459873636\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008824.509319155\n",
            "Sharpe:  1.1159460089269644\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008829.06807048\n",
            "Sharpe:  1.1159466169405305\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008824.625014838\n",
            "Sharpe:  1.1159456294327126\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008816.1021542335\n",
            "Sharpe:  1.1159447400702325\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008810.814830466\n",
            "Sharpe:  1.1159433576431526\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008785.951531317\n",
            "Sharpe:  1.1159406638359677\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5008833.515504516\n",
            "Sharpe:  1.1159463360474509\n",
            "=================================\n",
            "Training time (SAC):  61.66397470633189  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E2Ma6YpTlnuZ"
      },
      "source": [
        "## Trading\r\n",
        "Assume that we have $1,000,000 initial capital at 2019-01-01. We use the DDPG model to trade Dow jones 30 stocks."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uas8U6k455sI"
      },
      "source": [
        "trade = data_split(df,'2019-01-01', '2020-12-01')\n",
        "#trade = data_split(df,config.START_TRADE_DATE,config.END_DATE)"
      ],
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcGYlhyal205",
        "outputId": "caf16af9-c9bd-4fb5-b679-c4e44b13e5d7"
      },
      "source": [
        "trade.shape"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14490, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "4udD8hGlrj7L",
        "outputId": "ba927e45-37b4-4fca-a517-d7dc4bd35447"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": 131,
      "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>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": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qq4W9FbSstT7",
        "outputId": "ef0479a3-71cf-40eb-b929-bd945a92b377"
      },
      "source": [
        "env_trade, obs_trade = env_setup.create_env_trading(data = trade,\r\n",
        "                                         env_class = StockPortfolioEnv) \r\n",
        "\r\n",
        "df_daily_return, df_actions = DRLAgent.DRL_prediction(model=model_sac,\r\n",
        "                        test_data = trade,\r\n",
        "                        test_env = env_trade,\r\n",
        "                        test_obs = obs_trade)"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:1335068.6302893583\n",
            "Sharpe:  0.6977379887919614\n",
            "=================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "uJvj3pXt_Ukg",
        "outputId": "266e826c-5052-494e-b733-e8fa6311ba36"
      },
      "source": [
        "df_daily_return.head()"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>daily_return</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>-0.026035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>0.030938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-07</td>\n",
              "      <td>0.002980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-08</td>\n",
              "      <td>0.010119</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     -0.026035\n",
              "2  2019-01-04      0.030938\n",
              "3  2019-01-07      0.002980\n",
              "4  2019-01-08      0.010119"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "tByVcZ2L9TAJ",
        "outputId": "aa488abd-b56b-4d53-833f-2e012c72306f"
      },
      "source": [
        "df_actions.head()"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>AXP</th>\n",
              "      <th>BA</th>\n",
              "      <th>CAT</th>\n",
              "      <th>CSCO</th>\n",
              "      <th>CVX</th>\n",
              "      <th>DD</th>\n",
              "      <th>DIS</th>\n",
              "      <th>GS</th>\n",
              "      <th>HD</th>\n",
              "      <th>IBM</th>\n",
              "      <th>INTC</th>\n",
              "      <th>JNJ</th>\n",
              "      <th>JPM</th>\n",
              "      <th>KO</th>\n",
              "      <th>MCD</th>\n",
              "      <th>MMM</th>\n",
              "      <th>MRK</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NKE</th>\n",
              "      <th>PFE</th>\n",
              "      <th>PG</th>\n",
              "      <th>RTX</th>\n",
              "      <th>TRV</th>\n",
              "      <th>UNH</th>\n",
              "      <th>V</th>\n",
              "      <th>VZ</th>\n",
              "      <th>WBA</th>\n",
              "      <th>WMT</th>\n",
              "      <th>XOM</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-01-02</th>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-03</th>\n",
              "      <td>0.050287</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.050281</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.050285</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.050286</td>\n",
              "      <td>0.050286</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.018503</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.050285</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.050284</td>\n",
              "      <td>0.050286</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.050286</td>\n",
              "      <td>0.050284</td>\n",
              "      <td>0.050286</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.050285</td>\n",
              "      <td>0.050286</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.050286</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.018500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-04</th>\n",
              "      <td>0.050286</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.050286</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.050282</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.050285</td>\n",
              "      <td>0.050287</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.050284</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.050288</td>\n",
              "      <td>0.050286</td>\n",
              "      <td>0.018504</td>\n",
              "      <td>0.050280</td>\n",
              "      <td>0.050286</td>\n",
              "      <td>0.050285</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.050285</td>\n",
              "      <td>0.050285</td>\n",
              "      <td>0.018503</td>\n",
              "      <td>0.050283</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.018500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-07</th>\n",
              "      <td>0.050285</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.050287</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.050283</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.050284</td>\n",
              "      <td>0.050286</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.050285</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.050285</td>\n",
              "      <td>0.050287</td>\n",
              "      <td>0.018502</td>\n",
              "      <td>0.050280</td>\n",
              "      <td>0.050287</td>\n",
              "      <td>0.050286</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.050287</td>\n",
              "      <td>0.050287</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.050284</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-08</th>\n",
              "      <td>0.050285</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.050286</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.050288</td>\n",
              "      <td>0.018502</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018508</td>\n",
              "      <td>0.050284</td>\n",
              "      <td>0.050288</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.018503</td>\n",
              "      <td>0.018502</td>\n",
              "      <td>0.050270</td>\n",
              "      <td>0.018502</td>\n",
              "      <td>0.050288</td>\n",
              "      <td>0.050273</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.050283</td>\n",
              "      <td>0.050285</td>\n",
              "      <td>0.050281</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.050288</td>\n",
              "      <td>0.050287</td>\n",
              "      <td>0.018502</td>\n",
              "      <td>0.050287</td>\n",
              "      <td>0.018503</td>\n",
              "      <td>0.018501</td>\n",
              "      <td>0.018502</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  0.050287  0.018500  0.050281  ...  0.018500  0.018501  0.018500\n",
              "2019-01-04  0.050286  0.018500  0.050286  ...  0.018500  0.018501  0.018500\n",
              "2019-01-07  0.050285  0.018501  0.050287  ...  0.018501  0.018500  0.018500\n",
              "2019-01-08  0.050285  0.018501  0.050286  ...  0.018503  0.018501  0.018502\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBX3Y68o1vRG"
      },
      "source": [
        "df_actions.to_csv('df_actions.csv')"
      ],
      "execution_count": 122,
      "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": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hqvwr6SY8l9A",
        "outputId": "cb239b06-ed60-43b8-bafb-bc0bf9275a82"
      },
      "source": [
        "print(\"==============DRL Strategy Stats===========\")\r\n",
        "perf_stats_all"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============DRL Strategy Stats===========\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Annual return           0.162733\n",
              "Cumulative returns      0.335069\n",
              "Annual volatility       0.267785\n",
              "Sharpe ratio            0.697738\n",
              "Calmar ratio            0.469616\n",
              "Stability               0.205802\n",
              "Max drawdown           -0.346525\n",
              "Omega ratio             1.165019\n",
              "Sortino ratio           0.977028\n",
              "Skew                   -0.467037\n",
              "Kurtosis               13.625965\n",
              "Tail ratio              0.814860\n",
              "Daily value at risk    -0.032996\n",
              "Alpha                   0.000000\n",
              "Beta                    1.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fqSEF5PfjjT",
        "outputId": "ee3b1b68-e7d0-4b15-a733-73e8919e37f0"
      },
      "source": [
        "print(\"==============Get Index Stats===========\")\r\n",
        "baesline_perf_stats=BaselineStats('^DJI',\r\n",
        "                                  baseline_start = '2019-01-01',\r\n",
        "                                  baseline_end = '2020-12-01')"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Index Stats===========\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (483, 7)\n",
            "Annual return          0.132593\n",
            "Cumulative returns     0.269525\n",
            "Annual volatility      0.280347\n",
            "Sharpe ratio           0.586312\n",
            "Calmar ratio           0.357526\n",
            "Stability              0.060828\n",
            "Max drawdown          -0.370862\n",
            "Omega ratio            1.137261\n",
            "Sortino ratio          0.807330\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.844803\n",
            "Daily value at risk   -0.034668\n",
            "Alpha                  0.000000\n",
            "Beta                   1.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lVVCMVSAmcrI"
      },
      "source": [
        "<a id='6.2'></a>\r\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89oxLnMHyboh",
        "outputId": "e9ffa759-500f-439c-8f11-0977ffe31972"
      },
      "source": [
        "dji, dow_strat = baseline_strat('^DJI','2019-01-01','2020-12-01')"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (483, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "87xaEUFvyqe2",
        "outputId": "750a89bf-65eb-4cbc-b720-c4f4d21ebc3f"
      },
      "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": 151,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2019-01-02</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-11-30</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>23</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>16.273%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>33.507%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>26.779%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>0.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>0.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-34.652%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>0.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>-0.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>13.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>0.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-3.3%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Worst drawdown periods</th>\n",
              "      <th>Net drawdown in %</th>\n",
              "      <th>Peak date</th>\n",
              "      <th>Valley date</th>\n",
              "      <th>Recovery date</th>\n",
              "      <th>Duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>34.65</td>\n",
              "      <td>2020-01-17</td>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>2020-11-09</td>\n",
              "      <td>212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.62</td>\n",
              "      <td>2019-07-15</td>\n",
              "      <td>2019-08-14</td>\n",
              "      <td>2019-10-30</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.88</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>2.60</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.13</td>\n",
              "      <td>2019-02-25</td>\n",
              "      <td>2019-03-08</td>\n",
              "      <td>2019-03-15</td>\n",
              "      <td>15</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": []
    }
  ]
}

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提交于 2020-12-20 13:54 +08:00 . Created using Colaboratory

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