FinRL_multiple_stock_trading.ipynb · Spectator/FinRL-Library

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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_multiple_stock_trading.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXaoZs2lh1hi"
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      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading\n",
        "\n",
        "Tutorials to use OpenAI DRL to trade multiple stocks in one Jupyter Notebook | Presented at Deep RL Workshop, NeurIPS 2020.\n",
        "\n",
        "* This blog is based on our paper: FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, presented at NeurIPS 2020: Deep RL Workshop.\n",
        "* Check out medium blog for detailed explanations: https://towardsdatascience.com/finrl-for-quantitative-finance-tutorial-for-multiple-stock-trading-7b00763b7530\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": "lGunVt8oLCVS"
      },
      "source": [
        "# Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOzAKQ-SLGX6"
      },
      "source": [
        "* [1. Problem Definition](#0)\n",
        "* [2. Getting Started - Load Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. Check Additional Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download Data](#2)\n",
        "* [4. Preprocess Data](#3)        \n",
        "    * [4.1. Technical Indicators](#3.1)\n",
        "    * [4.2. Perform Feature Engineering](#3.2)\n",
        "* [5.Build Environment](#4)  \n",
        "    * [5.1. Training & Trade Data Split](#4.1)\n",
        "    * [5.2. User-defined Environment](#4.2)   \n",
        "    * [5.3. Initialize Environment](#4.3)    \n",
        "* [6.Implement DRL Algorithms](#5)  \n",
        "* [7.Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "    * [7.3. Baseline Stats](#6.3)   \n",
        "    * [7.3. Compare to Stock Market Index](#6.4)             "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sApkDlD9LIZv"
      },
      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjLD2TZSLKZ-"
      },
      "source": [
        "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
        "\n",
        "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\n",
        "\n",
        "\n",
        "* Action: The action space describes the allowed actions that the agent interacts with the\n",
        "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
        "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
        "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
        "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
        "\n",
        "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
        "values at state s′ and s, respectively\n",
        "\n",
        "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
        "our trading agent observes many different features to better learn in an interactive environment.\n",
        "\n",
        "* Environment: Dow 30 consituents\n",
        "\n",
        "\n",
        "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ffsre789LY08"
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      "source": [
        "<a id='1'></a>\n",
        "# Part 2. Getting Started- Load Python Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uy5_PTmOh1hj"
      },
      "source": [
        "<a id='1.1'></a>\n",
        "## 2.1. Install all the packages through FinRL library\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mPT0ipYE28wL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36972c15-9228-48a8-825e-4fe399e5a727"
      },
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Building wheels for collected packages: finrl, yfinance, pyfolio, empyrical\n",
            "  Building wheel for finrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for finrl: filename=finrl-0.0.1-cp36-none-any.whl size=26642 sha256=233318e3cb0f70872097d94f8c581dd55771c3818cb54dfa32c46016ccb4b5ac\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-u_jxxdbk/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=22615 sha256=43e2792cf0893dc44a870ced35772bd28c8ff0438cc3ec6103e23612a7e831fd\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=75766 sha256=5212fbe571748143d2faa0ed55be8d471bde3e1aebd32fa99d01d818aa44f056\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-u_jxxdbk/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=39767 sha256=9ba587c4a8b90bc497505db405e654b512e915253879b257382129e3474702bf\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": "osBHhVysOEzi"
      },
      "source": [
        "\n",
        "<a id='1.2'></a>\n",
        "## 2.2. Check if the additional packages needed are present, if not install them. \n",
        "* Yahoo Finance API\n",
        "* pandas\n",
        "* numpy\n",
        "* matplotlib\n",
        "* stockstats\n",
        "* OpenAI gym\n",
        "* stable-baselines\n",
        "* tensorflow\n",
        "* pyfolio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oirvDTJ0h1hk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a09d4271-9ddc-4954-aad5-119f2f3c0922"
      },
      "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": [
        {
          "output_type": "stream",
          "text": [
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            "Collecting git+https://github.com/quantopian/pyfolio\n",
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            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.0->pyfolio==0.9.2+75.g4b901f6) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.0->pyfolio==0.9.2+75.g4b901f6) (1.3.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.16.1->pyfolio==0.9.2+75.g4b901f6) (0.17.0)\n",
            "Requirement already satisfied: pandas-datareader>=0.2 in /usr/local/lib/python3.6/dist-packages (from empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (0.9.0)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython>=3.2.3->pyfolio==0.9.2+75.g4b901f6) (0.2.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython>=3.2.3->pyfolio==0.9.2+75.g4b901f6) (1.15.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=3.2.3->pyfolio==0.9.2+75.g4b901f6) (0.2.5)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython>=3.2.3->pyfolio==0.9.2+75.g4b901f6) (0.6.0)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.6/dist-packages (from pandas-datareader>=0.2->empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (4.6.2)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.6/dist-packages (from pandas-datareader>=0.2->empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (2.23.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader>=0.2->empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader>=0.2->empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader>=0.2->empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader>=0.2->empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (3.0.4)\n",
            "Building wheels for collected packages: pyfolio\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=75766 sha256=aff31d7f24efb52d367180ca51489bb1c309d95d0542706776e8091109f4d19c\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-q_cq26wz/wheels/62/7d/a7/3e462442ba7d63c35414176627c886340521dc3dbc0893ce9f\n",
            "Successfully built pyfolio\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGv01K8Sh1hn"
      },
      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPqeTTwoh1hn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db90f7a1-fecb-45c3-b9c1-6781d93342e2"
      },
      "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\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pyfolio/pos.py:27: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
            "  'Module \"zipline.assets\" not found; multipliers will not be applied'\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2owTj985RW4"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9A8CN5R5PuZ"
      },
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A289rQWMh1hq"
      },
      "source": [
        "<a id='2'></a>\n",
        "# Part 3. Download Data\n",
        "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
        "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
        "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPeQ7iS-LoMm"
      },
      "source": [
        "\n",
        "\n",
        "-----\n",
        "class YahooDownloader:\n",
        "    Provides methods for retrieving daily stock data from\n",
        "    Yahoo Finance API\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        start_date : str\n",
        "            start date of the data (modified from config.py)\n",
        "        end_date : str\n",
        "            end date of the data (modified from config.py)\n",
        "        ticker_list : list\n",
        "            a list of stock tickers (modified from config.py)\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    fetch_data()\n",
        "        Fetches data from yahoo API\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "h3XJnvrbLp-C",
        "outputId": "26b22b1d-ad69-4483-e310-3f5f523f3ffa"
      },
      "source": [
        "# from config.py start_date is a string\n",
        "config.START_DATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2009-01-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "FUnY8WEfLq3C",
        "outputId": "abcc5cdb-ef15-47ab-c5ac-3bffe5bdbe26"
      },
      "source": [
        "# from config.py end_date is a string\n",
        "config.END_DATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2020-09-30'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzqRRTOX6aFu",
        "outputId": "6103745f-6618-42ac-f3d5-95716bcd5e0f"
      },
      "source": [
        "print(config.DOW_30_TICKER)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['AAPL', 'MSFT', 'JPM', 'V', 'RTX', 'PG', 'GS', 'NKE', 'DIS', 'AXP', 'HD', 'INTC', 'WMT', 'IBM', 'MRK', 'UNH', 'KO', 'CAT', 'TRV', 'JNJ', 'CVX', 'MCD', 'VZ', 'CSCO', 'XOM', 'BA', 'MMM', 'PFE', 'WBA', 'DD']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yCKm4om-s9kE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f9b1fab-5e79-4acf-a054-83e171a0c7ca"
      },
      "source": [
        "df = YahooDownloader(start_date = '2009-01-01',\n",
        "                     end_date = '2020-12-01',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (89970, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CV3HrZHLh1hy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "460516d1-c117-4443-adee-f3391812c9d2"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(89970, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "id": "4hYkeaPiICHS",
        "outputId": "876068ce-8160-434d-8579-5ea9dcecec6b"
      },
      "source": [
        "df.sort_values(['date','tic'],ignore_index=True).head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "    </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>2.773207</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.800624</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>AXP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.680935</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>BA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.514400</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>CAT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.786087</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>CSCO</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high        low      close       volume   tic\n",
              "0  2009-01-02   3.067143   3.251429   3.041429   2.773207  746015200.0  AAPL\n",
              "1  2009-01-02  18.570000  19.520000  18.400000  15.800624   10955700.0   AXP\n",
              "2  2009-01-02  42.799999  45.560001  42.779999  33.680935    7010200.0    BA\n",
              "3  2009-01-02  44.910000  46.980000  44.709999  32.514400    7117200.0   CAT\n",
              "4  2009-01-02  16.410000  17.000000  16.250000  12.786087   40980600.0  CSCO"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqC6c40Zh1iH"
      },
      "source": [
        "# Part 4: Preprocess Data\n",
        "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
        "* Add technical indicators. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
        "* Add turbulence index. Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Le342Hc1h1iI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "789c16d1-4933-4727-d27f-1c0c107b8ff7"
      },
      "source": [
        "df = FeatureEngineer(df.copy(),\n",
        "                    use_technical_indicator=True,\n",
        "                    tech_indicator_list = config.TECHNICAL_INDICATORS_LIST,\n",
        "                    use_turbulence=True,\n",
        "                    user_defined_feature = False).preprocess_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully added technical indicators\n",
            "Successfully added turbulence index\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "grvhGJJII3Xn",
        "outputId": "2667dc49-47a9-4751-cfa1-98ec3a574f0e"
      },
      "source": [
        "df.sort_values(['date','tic'],ignore_index=True).head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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",
              "\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>turbulence</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>2.773207</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.800624</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.680935</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>BA</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.514400</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>CAT</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.786087</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>74.230003</td>\n",
              "      <td>77.300003</td>\n",
              "      <td>73.580002</td>\n",
              "      <td>48.043262</td>\n",
              "      <td>13695900.0</td>\n",
              "      <td>CVX</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>21.605234</td>\n",
              "      <td>22.060680</td>\n",
              "      <td>20.993229</td>\n",
              "      <td>14.527276</td>\n",
              "      <td>13251000.0</td>\n",
              "      <td>DD</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>22.760000</td>\n",
              "      <td>24.030001</td>\n",
              "      <td>22.500000</td>\n",
              "      <td>20.597496</td>\n",
              "      <td>9796600.0</td>\n",
              "      <td>DIS</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>84.019997</td>\n",
              "      <td>87.620003</td>\n",
              "      <td>82.190002</td>\n",
              "      <td>72.844467</td>\n",
              "      <td>14088500.0</td>\n",
              "      <td>GS</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>23.070000</td>\n",
              "      <td>24.190001</td>\n",
              "      <td>22.959999</td>\n",
              "      <td>17.909452</td>\n",
              "      <td>14902500.0</td>\n",
              "      <td>HD</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high  ...     cci_30  dx_30  turbulence\n",
              "0  2009-01-02   3.067143   3.251429  ...  66.666667  100.0         0.0\n",
              "1  2009-01-02  18.570000  19.520000  ...  66.666667  100.0         0.0\n",
              "2  2009-01-02  42.799999  45.560001  ...  66.666667  100.0         0.0\n",
              "3  2009-01-02  44.910000  46.980000  ...  66.666667  100.0         0.0\n",
              "4  2009-01-02  16.410000  17.000000  ...  66.666667  100.0         0.0\n",
              "5  2009-01-02  74.230003  77.300003  ...  66.666667  100.0         0.0\n",
              "6  2009-01-02  21.605234  22.060680  ...  66.666667  100.0         0.0\n",
              "7  2009-01-02  22.760000  24.030001  ...  66.666667  100.0         0.0\n",
              "8  2009-01-02  84.019997  87.620003  ...  66.666667  100.0         0.0\n",
              "9  2009-01-02  23.070000  24.190001  ...  66.666667  100.0         0.0\n",
              "\n",
              "[10 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QsYaY0Dh1iw"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Design Environment\n",
        "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
        "\n",
        "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
        "\n",
        "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5TOhcryx44bb"
      },
      "source": [
        "## Training data split: 2009-01-01 to 2018-12-31\n",
        "## Trade data split: 2019-01-01 to 2020-09-30"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0qaVGjLtgbI"
      },
      "source": [
        "train = data_split(df, '2009-01-01','2019-01-01')\n",
        "trade = data_split(df, '2019-01-01','2020-12-01')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p52zNCOhTtLR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "ded764c5-982d-4c2d-9ec4-aaef0cc4d020"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>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>turbulence</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>2.773207</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</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>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</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>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</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>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</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>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high  ...     cci_30  dx_30  turbulence\n",
              "0  2009-01-02   3.067143   3.251429  ...  66.666667  100.0         0.0\n",
              "0  2009-01-02  18.570000  19.520000  ...  66.666667  100.0         0.0\n",
              "0  2009-01-02  42.799999  45.560001  ...  66.666667  100.0         0.0\n",
              "0  2009-01-02  44.910000  46.980000  ...  66.666667  100.0         0.0\n",
              "0  2009-01-02  16.410000  17.000000  ...  66.666667  100.0         0.0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9zU9YaTTvFq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "c10e63ea-f3f9-4bfd-e554-cd671b9d5f23"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>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>turbulence</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>38.249401</td>\n",
              "      <td>148158800.0</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-2.003499</td>\n",
              "      <td>37.867367</td>\n",
              "      <td>-91.588048</td>\n",
              "      <td>42.250808</td>\n",
              "      <td>119.879177</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>119.879177</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>119.879177</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>119.879177</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>119.879177</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date        open        high  ...     cci_30      dx_30  turbulence\n",
              "0  2019-01-02   38.722500   39.712502  ... -91.588048  42.250808  119.879177\n",
              "0  2019-01-02   93.910004   96.269997  ... -97.742269  26.709417  119.879177\n",
              "0  2019-01-02  316.190002  323.950012  ... -21.712382  13.611972  119.879177\n",
              "0  2019-01-02  124.029999  127.879997  ...  -5.091209   0.873482  119.879177\n",
              "0  2019-01-02   42.279999   43.200001  ... -87.496850  29.529377  119.879177\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2zqII8rMIqn"
      },
      "source": [
        "stock_dimension = len(train.tic.unique())\n",
        "state_space = 1 + 2*stock_dimension + len(config.TECHNICAL_INDICATORS_LIST)*stock_dimension"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWyp84Ltto19"
      },
      "source": [
        "env_setup = EnvSetup(stock_dim = stock_dimension,\n",
        "                        state_space = state_space,\n",
        "                        hmax = 100,\n",
        "                        initial_amount = 1000000,\n",
        "                        transaction_cost_pct = 0.001)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64EoqOrQjiVf"
      },
      "source": [
        "## Environment for Training\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xwSvvPjutpqS"
      },
      "source": [
        "env_train = env_setup.create_env_training(data = train,\n",
        "                                          env_class = StockEnvTrain)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4QrezXm3jwu4"
      },
      "source": [
        "## Environment for Trading\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5_2x5PwMtqoN"
      },
      "source": [
        "env_trade, obs_trade = env_setup.create_env_trading(data = trade,\n",
        "                                         env_class = StockEnvTrade) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMNR5nHjh1iz"
      },
      "source": [
        "<a id='5'></a>\n",
        "# Part 6: Implement DRL Algorithms\n",
        "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
        "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
        "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
        "design their own DRL algorithms by adapting these DRL algorithms."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "364PsqckttcQ"
      },
      "source": [
        "agent = DRLAgent(env = env_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDmqOyF9h1iz"
      },
      "source": [
        "### Model Training: 5 models, A2C DDPG, PPO, TD3, SAC\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uijiWgkuh1jB"
      },
      "source": [
        "### Model 1: A2C\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tDLXWsDctyuz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8d885762-c74f-4bb0-ae8f-1dbece1937c5"
      },
      "source": [
        "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':0.0002,\n",
        "\t\t\t  'verbose':0,\n",
        "\t\t\t  'timesteps':150000}\n",
        "model_a2c = agent.train_A2C(model_name = \"A2C_{}\".format(now), model_params = a2c_params_tuning)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:191: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:200: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/policies.py:116: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/input.py:25: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/policies.py:561: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.flatten instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/layers/core.py:332: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_layers.py:123: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/distributions.py:418: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/a2c/a2c.py:160: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:449: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:449: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/clip_ops.py:301: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/a2c/a2c.py:184: The name tf.train.RMSPropOptimizer is deprecated. Please use tf.compat.v1.train.RMSPropOptimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/rmsprop.py:119: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/a2c/a2c.py:194: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/a2c/a2c.py:196: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/base_class.py:1169: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.\n",
            "\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2687745.218248674\n",
            "total_reward:1687745.2182486742\n",
            "total_cost:  266522.6134965125\n",
            "total_trades:  73481\n",
            "Sharpe:  0.7375052943758552\n",
            "=================================\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:502: The name tf.Summary is deprecated. Please use tf.compat.v1.Summary instead.\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-47601f041898>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m                           \u001b[0;34m'verbose'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \t\t\t  'timesteps':150000}\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mmodel_a2c\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_A2C\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"A2C_{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma2c_params_tuning\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/finrl/model/models.py\u001b[0m in \u001b[0;36mtrain_A2C\u001b[0;34m(self, model_name, model_params)\u001b[0m\n\u001b[1;32m     51\u001b[0m                     \u001b[0mtensorboard_log\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"{config.TENSORBOARD_LOG_DIR}/{model_name}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m                     )\n\u001b[0;32m---> 53\u001b[0;31m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'timesteps'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_log_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"A2C_run\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/stable_baselines/a2c/a2c.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps)\u001b[0m\n\u001b[1;32m    261\u001b[0m                 \u001b[0mcallback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_rollout_start\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m                 \u001b[0;31m# true_reward is the reward without discount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 263\u001b[0;31m                 \u001b[0mrollout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    264\u001b[0m                 \u001b[0;31m# unpack\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m                 \u001b[0mobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrewards\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mep_infos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrue_reward\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrollout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/stable_baselines/common/runners.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, callback)\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontinue_training\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mabstractmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/stable_baselines/a2c/a2c.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    365\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m                 \u001b[0;31m# Abort training early\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_locals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontinue_training\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/stable_baselines/common/callbacks.py\u001b[0m in \u001b[0;36mupdate_locals\u001b[0;34m(self, locals_)\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0;34m:\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mlocals_\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mcurrent\u001b[0m \u001b[0mlocal\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \"\"\"\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocals_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_init_callback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRiOtrywfAo1"
      },
      "source": [
        "### Model 2: DDPG"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tCDa78rqfO_a",
        "outputId": "4f68dc4d-6034-4db8-9466-700c9108c379"
      },
      "source": [
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "ddpg_params_tuning = {\n",
        "                     'batch_size': 128,\n",
        "\t\t\t               'buffer_size':100000, \n",
        "\t\t\t               'verbose':0,\n",
        "\t\t\t               'timesteps':50000}\n",
        "model_ddpg = agent.train_DDPG(model_name = \"DDPG_{}\".format(now), model_params = ddpg_params_tuning)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ddpg/policies.py:136: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.Dense instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ddpg/ddpg.py:94: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ddpg/ddpg.py:444: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:432: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3876639.240769919\n",
            "total_reward:2876639.240769919\n",
            "total_cost:  9104.342746239017\n",
            "total_trades:  41738\n",
            "Sharpe:  1.0054552314500205\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:7374327.234833065\n",
            "total_reward:6374327.234833065\n",
            "total_cost:  7039.457090340651\n",
            "total_trades:  35912\n",
            "Sharpe:  1.0833990891351075\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5248428.140151841\n",
            "total_reward:4248428.140151841\n",
            "total_cost:  8429.359631345911\n",
            "total_trades:  35402\n",
            "Sharpe:  1.03103504525587\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4718093.052233357\n",
            "total_reward:3718093.052233357\n",
            "total_cost:  10634.70695874195\n",
            "total_trades:  40388\n",
            "Sharpe:  0.9591292955030087\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5706787.595736295\n",
            "total_reward:4706787.595736295\n",
            "total_cost:  5088.324251377151\n",
            "total_trades:  38449\n",
            "Sharpe:  1.046294941645763\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:6114764.380374785\n",
            "total_reward:5114764.380374785\n",
            "total_cost:  14639.86218725568\n",
            "total_trades:  38118\n",
            "Sharpe:  1.0978102756473607\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4845266.699600824\n",
            "total_reward:3845266.699600824\n",
            "total_cost:  11057.790948118627\n",
            "total_trades:  40872\n",
            "Sharpe:  0.9254254994857597\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5751061.008915898\n",
            "total_reward:4751061.008915898\n",
            "total_cost:  12918.164268064022\n",
            "total_trades:  41476\n",
            "Sharpe:  1.0214272741940498\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:7274435.433939911\n",
            "total_reward:6274435.433939911\n",
            "total_cost:  5971.728876357337\n",
            "total_trades:  43841\n",
            "Sharpe:  1.0950865802250083\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4615901.514024919\n",
            "total_reward:3615901.514024919\n",
            "total_cost:  10510.182272158909\n",
            "total_trades:  42251\n",
            "Sharpe:  0.9167348239963505\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5304047.0198513195\n",
            "total_reward:4304047.0198513195\n",
            "total_cost:  9589.782395890712\n",
            "total_trades:  41870\n",
            "Sharpe:  0.9245465984803718\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5625461.008441765\n",
            "total_reward:4625461.008441765\n",
            "total_cost:  17341.482320000207\n",
            "total_trades:  42376\n",
            "Sharpe:  0.9406268261325159\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5376035.759855201\n",
            "total_reward:4376035.759855201\n",
            "total_cost:  10974.242491519171\n",
            "total_trades:  42631\n",
            "Sharpe:  0.9597042150192086\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5676323.370232296\n",
            "total_reward:4676323.370232296\n",
            "total_cost:  12975.520036184482\n",
            "total_trades:  37313\n",
            "Sharpe:  0.9099386726375742\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5396300.48576089\n",
            "total_reward:4396300.48576089\n",
            "total_cost:  16881.80250696935\n",
            "total_trades:  42983\n",
            "Sharpe:  0.9337169196345171\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3497906.5797796934\n",
            "total_reward:2497906.5797796934\n",
            "total_cost:  13012.68433630999\n",
            "total_trades:  43816\n",
            "Sharpe:  0.7534791881929837\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:1971547.527567742\n",
            "total_reward:971547.5275677419\n",
            "total_cost:  8164.89994731881\n",
            "total_trades:  39717\n",
            "Sharpe:  0.4443095749118807\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:6060737.70392393\n",
            "total_reward:5060737.70392393\n",
            "total_cost:  21260.86078310216\n",
            "total_trades:  41248\n",
            "Sharpe:  1.0596003374109402\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:6188313.433283304\n",
            "total_reward:5188313.433283304\n",
            "total_cost:  16321.73783524186\n",
            "total_trades:  41381\n",
            "Sharpe:  0.9911460122517576\n",
            "=================================\n",
            "Training time (DDPG):  4.71319918235143  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gDkU-j-fCmZ"
      },
      "source": [
        "### Model 3: PPO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AXo1pwzHfUci",
        "outputId": "e47fa2af-5e4d-4cea-f5e6-552c9387bddb"
      },
      "source": [
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "ppo_params_tuning = {'n_steps':128, \n",
        "                     'batch_size': 64,\n",
        "\t\t\t               'ent_coef':0.005, \n",
        "\t\t\t               'learning_rate':0.00025,\n",
        "\t\t\t              'verbose':0,\n",
        "\t\t\t              'timesteps':50000}\n",
        "model_ppo = agent.train_PPO(model_name = \"PPO_{}\".format(now), model_params = ppo_params_tuning)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ppo2/ppo2.py:198: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ppo2/ppo2.py:206: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
            "\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3048741.3939381745\n",
            "total_reward:2048741.3939381745\n",
            "total_cost:  245568.3302282693\n",
            "total_trades:  72462\n",
            "Sharpe:  0.9202481544831457\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3293240.652184626\n",
            "total_reward:2293240.652184626\n",
            "total_cost:  208401.75301929898\n",
            "total_trades:  69573\n",
            "Sharpe:  0.9548892552085766\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3770063.9598072213\n",
            "total_reward:2770063.9598072213\n",
            "total_cost:  163858.87472231584\n",
            "total_trades:  66022\n",
            "Sharpe:  1.0100154114628663\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3533338.6618839577\n",
            "total_reward:2533338.6618839577\n",
            "total_cost:  140710.91081377305\n",
            "total_trades:  63468\n",
            "Sharpe:  0.8658687183317452\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3624214.190819685\n",
            "total_reward:2624214.190819685\n",
            "total_cost:  154313.0847744043\n",
            "total_trades:  65276\n",
            "Sharpe:  0.8993471111207018\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3202571.663253775\n",
            "total_reward:2202571.663253775\n",
            "total_cost:  178124.36502179946\n",
            "total_trades:  67223\n",
            "Sharpe:  0.7764722688501976\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3071845.7908768407\n",
            "total_reward:2071845.7908768407\n",
            "total_cost:  174568.22859443238\n",
            "total_trades:  66552\n",
            "Sharpe:  0.9040177248410571\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2759716.805770842\n",
            "total_reward:1759716.805770842\n",
            "total_cost:  141406.5384208243\n",
            "total_trades:  63691\n",
            "Sharpe:  0.7943240340782525\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3013214.0920152925\n",
            "total_reward:2013214.0920152925\n",
            "total_cost:  171745.69359744896\n",
            "total_trades:  66307\n",
            "Sharpe:  0.7831482945763572\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2696810.6705320706\n",
            "total_reward:1696810.6705320706\n",
            "total_cost:  159408.83254381173\n",
            "total_trades:  65545\n",
            "Sharpe:  0.7148325207248967\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3092140.8783237417\n",
            "total_reward:2092140.8783237417\n",
            "total_cost:  155293.8358448932\n",
            "total_trades:  64122\n",
            "Sharpe:  0.7113783235078796\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3264985.1486329967\n",
            "total_reward:2264985.1486329967\n",
            "total_cost:  169097.90147489944\n",
            "total_trades:  65095\n",
            "Sharpe:  0.8611881140368268\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3106629.5796447564\n",
            "total_reward:2106629.5796447564\n",
            "total_cost:  156059.88216477341\n",
            "total_trades:  64735\n",
            "Sharpe:  0.8337757600671555\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3101765.563581535\n",
            "total_reward:2101765.563581535\n",
            "total_cost:  141704.11478872094\n",
            "total_trades:  63020\n",
            "Sharpe:  0.7868378968332507\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3870851.8138185116\n",
            "total_reward:2870851.8138185116\n",
            "total_cost:  139163.3939960446\n",
            "total_trades:  62628\n",
            "Sharpe:  1.0398952470182161\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3971468.1489379085\n",
            "total_reward:2971468.1489379085\n",
            "total_cost:  96543.31543344291\n",
            "total_trades:  56808\n",
            "Sharpe:  1.074727317652792\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3862850.6640476706\n",
            "total_reward:2862850.6640476706\n",
            "total_cost:  68077.1690398825\n",
            "total_trades:  52321\n",
            "Sharpe:  1.0505756197667369\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4750937.677483732\n",
            "total_reward:3750937.677483732\n",
            "total_cost:  77272.76184075551\n",
            "total_trades:  54465\n",
            "Sharpe:  1.1094458223439796\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4404566.248673428\n",
            "total_reward:3404566.248673428\n",
            "total_cost:  78227.07486048219\n",
            "total_trades:  53813\n",
            "Sharpe:  1.0844266786058263\n",
            "=================================\n",
            "Training time (PPO):  2.3141635179519655  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Zpv4S0-fDBv"
      },
      "source": [
        "### Model 4: TD3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0S5WIHJffWdP",
        "outputId": "750ba07b-d0b1-483c-c654-95fefe5562a4"
      },
      "source": [
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "td3_params_tuning = {\n",
        "                     'batch_size': 128,\n",
        "\t\t\t               'buffer_size':200000, \n",
        "                     'learning_rate': 0.0002,\n",
        "\t\t\t               'verbose':0,\n",
        "\t\t\t               'timesteps':50000}\n",
        "model_td3 = agent.train_TD3(model_name = \"TD3_{}\".format(now), model_params = td3_params_tuning)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2662114.6849694927\n",
            "total_reward:1662114.6849694927\n",
            "total_cost:  5154.707305372425\n",
            "total_trades:  35274\n",
            "Sharpe:  0.6952087010828358\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2988142.5784285073\n",
            "total_reward:1988142.5784285073\n",
            "total_cost:  1632.59511942303\n",
            "total_trades:  40163\n",
            "Sharpe:  0.7289450168804903\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2883484.519447818\n",
            "total_reward:1883484.519447818\n",
            "total_cost:  1214.8873761913726\n",
            "total_trades:  37755\n",
            "Sharpe:  0.7095756761323406\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2873489.4430507906\n",
            "total_reward:1873489.4430507906\n",
            "total_cost:  1209.6024044447397\n",
            "total_trades:  37753\n",
            "Sharpe:  0.7123425504445975\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2915279.9369537304\n",
            "total_reward:1915279.9369537304\n",
            "total_cost:  1228.91289347425\n",
            "total_trades:  37752\n",
            "Sharpe:  0.7163200645078105\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2895154.510926656\n",
            "total_reward:1895154.510926656\n",
            "total_cost:  1263.9253499270492\n",
            "total_trades:  35674\n",
            "Sharpe:  0.7150207708720131\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3019681.6740699634\n",
            "total_reward:2019681.6740699634\n",
            "total_cost:  999.0000961172603\n",
            "total_trades:  35210\n",
            "Sharpe:  0.751148686794223\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3013520.945701581\n",
            "total_reward:2013520.945701581\n",
            "total_cost:  999.0009760471949\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7469440146690564\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3042938.2329919036\n",
            "total_reward:2042938.2329919036\n",
            "total_cost:  998.9997361943996\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7578211044729053\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3022953.1741263247\n",
            "total_reward:2022953.1741263247\n",
            "total_cost:  998.9986360614382\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7499871433174924\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3034794.836248101\n",
            "total_reward:2034794.8362481012\n",
            "total_cost:  999.0002705945211\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7524134952479796\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3035611.0598908253\n",
            "total_reward:2035611.0598908253\n",
            "total_cost:  998.9994517822719\n",
            "total_trades:  35210\n",
            "Sharpe:  0.751224298946767\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3025457.1671319176\n",
            "total_reward:2025457.1671319176\n",
            "total_cost:  998.9986633752917\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7517964545172184\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3041055.656330986\n",
            "total_reward:2041055.656330986\n",
            "total_cost:  998.9988048372863\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7551908443838147\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3034913.564139066\n",
            "total_reward:2034913.5641390658\n",
            "total_cost:  999.0009547227162\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7522526241481273\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3031574.2165369885\n",
            "total_reward:2031574.2165369885\n",
            "total_cost:  999.0005162070407\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7514584870083608\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3041294.4768735766\n",
            "total_reward:2041294.4768735766\n",
            "total_cost:  999.000755579339\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7542438320343516\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3045522.0506182173\n",
            "total_reward:2045522.0506182173\n",
            "total_cost:  999.0007476681062\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7544841897685759\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3024340.0694520893\n",
            "total_reward:2024340.0694520893\n",
            "total_cost:  999.0004214439606\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7505835079244929\n",
            "=================================\n",
            "Training time (DDPG):  6.593421852588653  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dr49PotrfG01"
      },
      "source": [
        "### Model 5: SAC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jr6sX5pCfkVo",
        "outputId": "10f3c493-2a01-4fea-ac55-592247e35434"
      },
      "source": [
        "env_train = env_setup.create_env_training(data = train,\n",
        "                                          env_class = StockEnvTrain)\n",
        "agent = DRLAgent(env = env_train)\n",
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "sac_params_tuning={\n",
        "  'batch_size': 128,\n",
        " 'buffer_size': 100000,\n",
        "  'ent_coef':'auto_0.1',\n",
        " 'learning_rate': 0.0001,\n",
        " 'learning_starts':200,\n",
        " 'timesteps': 40000,\n",
        " 'verbose': 0}\n",
        "model_sac = agent.train_SAC(model_name = \"SAC_{}\".format(now), model_params = sac_params_tuning)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4001906.86040679\n",
            "total_reward:3001906.86040679\n",
            "total_cost:  12317.681658001982\n",
            "total_trades:  44080\n",
            "Sharpe:  0.8633524547314674\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5226111.002580503\n",
            "total_reward:4226111.002580503\n",
            "total_cost:  11987.87492935015\n",
            "total_trades:  44397\n",
            "Sharpe:  0.9730132680026102\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3912877.560624407\n",
            "total_reward:2912877.560624407\n",
            "total_cost:  60250.529116420184\n",
            "total_trades:  45040\n",
            "Sharpe:  0.8470575417279771\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4065245.564295219\n",
            "total_reward:3065245.564295219\n",
            "total_cost:  138976.3179850487\n",
            "total_trades:  51908\n",
            "Sharpe:  0.8604008933319752\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4525960.6934151715\n",
            "total_reward:3525960.6934151715\n",
            "total_cost:  191736.44967067274\n",
            "total_trades:  65164\n",
            "Sharpe:  0.927772025235702\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2686280.775143401\n",
            "total_reward:1686280.7751434012\n",
            "total_cost:  111850.51186024824\n",
            "total_trades:  61797\n",
            "Sharpe:  0.6327187916912932\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3415904.2586198333\n",
            "total_reward:2415904.2586198333\n",
            "total_cost:  72638.44856734877\n",
            "total_trades:  55660\n",
            "Sharpe:  0.7426975770410519\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5674732.2607731875\n",
            "total_reward:4674732.2607731875\n",
            "total_cost:  52141.572943951585\n",
            "total_trades:  52546\n",
            "Sharpe:  1.1339516063200115\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5653187.113997827\n",
            "total_reward:4653187.113997827\n",
            "total_cost:  28393.183897107734\n",
            "total_trades:  48620\n",
            "Sharpe:  1.0703768063389338\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:6086155.7603387935\n",
            "total_reward:5086155.7603387935\n",
            "total_cost:  16825.5977310799\n",
            "total_trades:  46240\n",
            "Sharpe:  1.112135959769754\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5996551.691611615\n",
            "total_reward:4996551.691611615\n",
            "total_cost:  10821.86237775878\n",
            "total_trades:  44500\n",
            "Sharpe:  1.1038873149339885\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5791728.481688333\n",
            "total_reward:4791728.481688333\n",
            "total_cost:  7868.013655355516\n",
            "total_trades:  43608\n",
            "Sharpe:  1.062389142435331\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5750921.487752676\n",
            "total_reward:4750921.487752676\n",
            "total_cost:  6154.9106223757535\n",
            "total_trades:  42757\n",
            "Sharpe:  1.0680758738660567\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5687970.985934678\n",
            "total_reward:4687970.985934678\n",
            "total_cost:  5896.736782837331\n",
            "total_trades:  42224\n",
            "Sharpe:  1.0743284266532789\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5345120.994680668\n",
            "total_reward:4345120.994680668\n",
            "total_cost:  4400.642574448798\n",
            "total_trades:  41625\n",
            "Sharpe:  1.070842516526519\n",
            "=================================\n",
            "Training time (SAC):  21.955962300300598  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2wZgkQXh1jE"
      },
      "source": [
        "## Trading\n",
        "Assume that we have $1,000,000 initial capital at 2019-01-01. We use the DDPG model to trade Dow jones 30 stocks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bEv5KGC8h1jE"
      },
      "source": [
        "### Set turbulence threshold\n",
        "Set the turbulence threshold to be the 99% quantile of insample turbulence data, if current turbulence index is greater than the threshold, then we assume that the current market is volatile"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efwBi84ch1jE"
      },
      "source": [
        "data_turbulence = df[(df.date<'2019-01-01') & (df.date>='2009-01-01')]\n",
        "insample_turbulence = data_turbulence.drop_duplicates(subset=['date'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHZMBpSqh1jG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ab772a0-2905-48da-82ac-1bd0ee9bab63"
      },
      "source": [
        "insample_turbulence.turbulence.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2516.000000\n",
              "mean       75.936148\n",
              "std        38.178007\n",
              "min         0.000000\n",
              "25%        58.034371\n",
              "50%        74.015096\n",
              "75%        96.948850\n",
              "max       230.256631\n",
              "Name: turbulence, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuwDPkV9h1jL"
      },
      "source": [
        "turbulence_threshold = np.quantile(insample_turbulence.turbulence.values,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwoz_7VSh1jO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "677d9b2e-9b5f-4e84-b42a-e0e5f29371ad"
      },
      "source": [
        "turbulence_threshold"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "230.25663125087942"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5mmgQF_h1jQ"
      },
      "source": [
        "### Trade\n",
        "\n",
        "DRL model needs to update periodically in order to take full advantage of the data, ideally we need to retrain our model yearly, quarterly, or monthly. We also need to tune the parameters along the way, in this notebook I only use the in-sample data from 2009-01 to 2018-12 to tune the parameters once, so there is some alpha decay here as the length of trade date extends. \n",
        "\n",
        "Numerous hyperparameters – e.g. the learning rate, the total number of samples to train on – influence the learning process and are usually determined by testing some variations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLOnL5eYh1jR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ec74736-cf55-4d88-d57c-694c135e3159"
      },
      "source": [
        "env_trade, obs_trade = env_setup.create_env_trading(data = trade,\n",
        "                                         env_class = StockEnvTrade,\n",
        "                                         turbulence_threshold=250) \n",
        "\n",
        "df_account_value, df_actions = DRLAgent.DRL_prediction(model=model_sac,\n",
        "                        test_data = trade,\n",
        "                        test_env = env_trade,\n",
        "                        test_obs = obs_trade)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "previous_total_asset:1000000\n",
            "end_total_asset:1421886.4120278372\n",
            "total_reward:421886.41202783724\n",
            "total_cost:  8583.701293266118\n",
            "total trades:  7946\n",
            "Sharpe:  1.0204666740575243\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERxw3KqLkcP4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a0652e7-4d95-4a15-aac9-4789dd5abf8c"
      },
      "source": [
        "df_account_value.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(483, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2yRkNguY5yvp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "e668cb5e-d63f-4c2b-a0a0-c2a72b4ac47d"
      },
      "source": [
        "df_account_value.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>account_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>1.000000e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>9.969635e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>1.003808e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-07</td>\n",
              "      <td>1.006161e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-08</td>\n",
              "      <td>1.008881e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  account_value\n",
              "0  2019-01-02   1.000000e+06\n",
              "1  2019-01-03   9.969635e+05\n",
              "2  2019-01-04   1.003808e+06\n",
              "3  2019-01-07   1.006161e+06\n",
              "4  2019-01-08   1.008881e+06"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6vvNSC6h1jZ"
      },
      "source": [
        "<a id='6'></a>\n",
        "# Part 7: Backtest Our Strategy\n",
        "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lr2zX7ZxNyFQ"
      },
      "source": [
        "<a id='6.1'></a>\n",
        "## 7.1 BackTestStats\n",
        "pass in df_account_value, this information is stored in env class\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nzkr9yv-AdV_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90bea8f7-fa0e-43fe-8e87-eeda1cee4724"
      },
      "source": [
        "print(\"==============Get Backtest Results===========\")\n",
        "perf_stats_all = BackTestStats(account_value=df_account_value)\n",
        "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
        "perf_stats_all.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Backtest Results===========\n",
            "annual return:  22.639255033981232\n",
            "sharpe ratio:  1.0204666740575243\n",
            "Annual return          0.201588\n",
            "Cumulative returns     0.421886\n",
            "Annual volatility      0.200065\n",
            "Sharpe ratio           1.020467\n",
            "Calmar ratio           0.923314\n",
            "Stability              0.676354\n",
            "Max drawdown          -0.218331\n",
            "Omega ratio            1.216931\n",
            "Sortino ratio          1.425862\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.862870\n",
            "Daily value at risk   -0.024396\n",
            "Alpha                  0.000000\n",
            "Beta                   1.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U6Suru3h1jc"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lKRGftSS7pNM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d7613aac-003e-461d-98f4-2b358105a9e9"
      },
      "source": [
        "print(\"==============Compare to DJIA===========\")\n",
        "%matplotlib inline\n",
        "# S&P 500: ^GSPC\n",
        "# Dow Jones Index: ^DJI\n",
        "# NASDAQ 100: ^NDX\n",
        "BackTestPlot(df_account_value, \n",
        "             baseline_ticker = '^DJI', \n",
        "             baseline_start = '2019-01-01',\n",
        "             baseline_end = '2020-12-01')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Compare to DJIA===========\n",
            "annual return:  22.639255033981232\n",
            "sharpe ratio:  1.0204666740575243\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (483, 7)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2019-01-03</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-11-30</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>22</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>20.205%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>42.189%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>20.006%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>1.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>0.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-21.833%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>1.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>-0.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>8.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>0.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-2.44%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.54</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>21.83</td>\n",
              "      <td>2020-02-19</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>2020-06-08</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.27</td>\n",
              "      <td>2020-06-08</td>\n",
              "      <td>2020-06-26</td>\n",
              "      <td>2020-08-12</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.29</td>\n",
              "      <td>2020-09-02</td>\n",
              "      <td>2020-10-28</td>\n",
              "      <td>2020-11-09</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.44</td>\n",
              "      <td>2019-07-29</td>\n",
              "      <td>2019-08-14</td>\n",
              "      <td>2019-10-21</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.76</td>\n",
              "      <td>2019-04-23</td>\n",
              "      <td>2019-06-03</td>\n",
              "      <td>2019-06-18</td>\n",
              "      <td>41</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": "markdown",
      "metadata": {
        "id": "SlLT9_5WN478"
      },
      "source": [
        "<a id='6.3'></a>\n",
        "## 7.3 Baseline Stats"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YktexHcqh1jc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25a45cd2-f155-40ed-d40a-639683d229b4"
      },
      "source": [
        "print(\"==============Get Baseline Stats===========\")\n",
        "baesline_perf_stats=BaselineStats('^DJI',\n",
        "                                  baseline_start = '2019-01-01',\n",
        "                                  baseline_end = '2020-12-01')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (483, 7)\n",
            "Annual return          0.132593\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": "code",
      "metadata": {
        "id": "A6W2J57ch1j9"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}

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authored 2020-12-23 08:05 +08:00 . Created using Colaboratory

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