tradingBot.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lb9q2_QZgdNk"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AI4Finance-Foundation/FinRL-Tutorials/blob/master/2-Advance/FinRL_Ensemble_StockTrading_ICAIF_2020.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXaoZs2lh1hi"
      },
      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading Using Ensemble Strategy\n",
        "\n",
        "Tutorials to use OpenAI DRL to trade multiple stocks using ensemble strategy in one Jupyter Notebook | Presented at ICAIF 2020\n",
        "\n",
        "* This notebook is the reimplementation of our paper: Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy, using FinRL.\n",
        "* Check out medium blog for detailed explanations: https://medium.com/@ai4finance/deep-reinforcement-learning-for-automated-stock-trading-f1dad0126a02\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"
      },
      "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",
      "source": [
        "!pip install setuptools==64.0.2\n",
        "!apt-get install swig\n",
        "!pip install wrds\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "metadata": {
        "id": "SOJEXYKl_wyu",
        "outputId": "ec8efb9e-7720-41f2-a551-d265996aa907",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: setuptools==64.0.2 in /usr/local/lib/python3.8/dist-packages (64.0.2)\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "swig is already the newest version (3.0.12-1).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 21 not upgraded.\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: wrds in /usr/local/lib/python3.8/dist-packages (3.1.2)\n",
            "Requirement already satisfied: sqlalchemy in /usr/local/lib/python3.8/dist-packages (from wrds) (1.4.46)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from wrds) (1.3.5)\n",
            "Requirement already satisfied: mock in /usr/local/lib/python3.8/dist-packages (from wrds) (5.0.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from wrds) (1.21.6)\n",
            "Requirement already satisfied: psycopg2-binary in /usr/local/lib/python3.8/dist-packages (from wrds) (2.9.5)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->wrds) (2022.7)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->wrds) (2.8.2)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.8/dist-packages (from sqlalchemy->wrds) (2.0.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->wrds) (1.15.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/AI4Finance-LLC/FinRL-Library.git\n",
            "  Cloning https://github.com/AI4Finance-LLC/FinRL-Library.git to /tmp/pip-req-build-61v64vhl\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/AI4Finance-LLC/FinRL-Library.git /tmp/pip-req-build-61v64vhl\n",
            "  Resolved https://github.com/AI4Finance-LLC/FinRL-Library.git to commit 7a3ff8136bf98a30a588ed587297476929f20cd9\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting elegantrl@ git+https://github.com/AI4Finance-Foundation/ElegantRL.git#egg=elegantrl\n",
            "  Cloning https://github.com/AI4Finance-Foundation/ElegantRL.git to /tmp/pip-install-ex_o5ddw/elegantrl_eb5dd5dc326a403db11c9d90e7058b2a\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/AI4Finance-Foundation/ElegantRL.git /tmp/pip-install-ex_o5ddw/elegantrl_eb5dd5dc326a403db11c9d90e7058b2a\n",
            "  Resolved https://github.com/AI4Finance-Foundation/ElegantRL.git to commit 3a8520bee4e86b9f8683046ae7447b795e0d0849\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2\n",
            "  Cloning https://github.com/quantopian/pyfolio.git to /tmp/pip-install-ex_o5ddw/pyfolio_c7d1b27160874fe78034d8a190c0a9ea\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/quantopian/pyfolio.git /tmp/pip-install-ex_o5ddw/pyfolio_c7d1b27160874fe78034d8a190c0a9ea\n",
            "  Resolved https://github.com/quantopian/pyfolio.git to commit 4b901f6d73aa02ceb6d04b7d83502e5c6f2e81aa\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: jqdatasdk in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (1.8.11)\n",
            "Requirement already satisfied: gym>=0.17 in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (0.21.0)\n",
            "Requirement already satisfied: tensorboardX in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (2.5.1)\n",
            "Requirement already satisfied: scikit-learn>=0.21.0 in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (1.0.2)\n",
            "Requirement already satisfied: exchange_calendars==3.6.3 in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (3.6.3)\n",
            "Requirement already satisfied: gputil in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (1.4.0)\n",
            "Requirement already satisfied: ccxt>=1.66.32 in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (2.6.1)\n",
            "Requirement already satisfied: importlib-metadata==4.13.0 in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (4.13.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (3.2.2)\n",
            "Requirement already satisfied: stockstats>=0.4.0 in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (0.5.1)\n",
            "Requirement already satisfied: pandas>=1.1.5 in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (1.3.5)\n",
            "Requirement already satisfied: stable-baselines3<2.0.0,>=1.6.2 in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (1.7.0)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (1.21.6)\n",
            "Requirement already satisfied: ray[default,tune]==1.3.0 in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (1.3.0)\n",
            "Requirement already satisfied: alpaca_trade_api>=2.1.0 in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (2.3.0)\n",
            "Requirement already satisfied: yfinance in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (0.2.3)\n",
            "Requirement already satisfied: lz4 in /usr/local/lib/python3.8/dist-packages (from finrl==0.3.5) (4.3.2)\n",
            "Requirement already satisfied: korean-lunar-calendar in /usr/local/lib/python3.8/dist-packages (from exchange_calendars==3.6.3->finrl==0.3.5) (0.3.1)\n",
            "Requirement already satisfied: toolz in /usr/local/lib/python3.8/dist-packages (from exchange_calendars==3.6.3->finrl==0.3.5) (0.12.0)\n",
            "Requirement already satisfied: pyluach in /usr/local/lib/python3.8/dist-packages (from exchange_calendars==3.6.3->finrl==0.3.5) (2.0.2)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.8/dist-packages (from exchange_calendars==3.6.3->finrl==0.3.5) (2.8.2)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.8/dist-packages (from exchange_calendars==3.6.3->finrl==0.3.5) (2022.7)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata==4.13.0->finrl==0.3.5) (3.11.0)\n",
            "Requirement already satisfied: redis>=3.5.0 in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (4.4.2)\n",
            "Requirement already satisfied: colorama in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (0.4.6)\n",
            "Requirement already satisfied: prometheus-client>=0.7.1 in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (0.15.0)\n",
            "Requirement already satisfied: msgpack<2.0.0,>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (1.0.3)\n",
            "Requirement already satisfied: opencensus in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (0.11.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (3.9.0)\n",
            "Requirement already satisfied: jsonschema in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (4.3.3)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (3.8.1)\n",
            "Requirement already satisfied: aiohttp-cors in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (0.7.0)\n",
            "Requirement already satisfied: aioredis in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (2.0.1)\n",
            "Requirement already satisfied: grpcio>=1.28.1 in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (1.51.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (2.28.2)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (6.0)\n",
            "Requirement already satisfied: protobuf>=3.15.3 in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (3.19.6)\n",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (7.1.2)\n",
            "Requirement already satisfied: gpustat in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (1.0.0)\n",
            "Requirement already satisfied: py-spy>=0.2.0 in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (0.3.14)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (0.8.10)\n",
            "Requirement already satisfied: colorful in /usr/local/lib/python3.8/dist-packages (from ray[default,tune]==1.3.0->finrl==0.3.5) (0.5.5)\n",
            "Requirement already satisfied: websocket-client<2,>=0.56.0 in /usr/local/lib/python3.8/dist-packages (from alpaca_trade_api>=2.1.0->finrl==0.3.5) (1.4.2)\n",
            "Requirement already satisfied: deprecation==2.1.0 in /usr/local/lib/python3.8/dist-packages (from alpaca_trade_api>=2.1.0->finrl==0.3.5) (2.1.0)\n",
            "Requirement already satisfied: urllib3<2,>1.24 in /usr/local/lib/python3.8/dist-packages (from alpaca_trade_api>=2.1.0->finrl==0.3.5) (1.24.3)\n",
            "Requirement already satisfied: websockets<11,>=9.0 in /usr/local/lib/python3.8/dist-packages (from alpaca_trade_api>=2.1.0->finrl==0.3.5) (10.4)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.8/dist-packages (from aiohttp->ray[default,tune]==1.3.0->finrl==0.3.5) (6.0.4)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->ray[default,tune]==1.3.0->finrl==0.3.5) (22.2.0)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->ray[default,tune]==1.3.0->finrl==0.3.5) (1.8.2)\n",
            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->ray[default,tune]==1.3.0->finrl==0.3.5) (2.1.1)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->ray[default,tune]==1.3.0->finrl==0.3.5) (1.3.1)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->ray[default,tune]==1.3.0->finrl==0.3.5) (4.0.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from aiohttp->ray[default,tune]==1.3.0->finrl==0.3.5) (1.3.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from deprecation==2.1.0->alpaca_trade_api>=2.1.0->finrl==0.3.5) (21.3)\n",
            "Requirement already satisfied: cryptography>=2.6.1 in /usr/local/lib/python3.8/dist-packages (from ccxt>=1.66.32->finrl==0.3.5) (39.0.0)\n",
            "Requirement already satisfied: aiodns>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from ccxt>=1.66.32->finrl==0.3.5) (3.0.0)\n",
            "Requirement already satisfied: setuptools>=60.9.0 in /usr/local/lib/python3.8/dist-packages (from ccxt>=1.66.32->finrl==0.3.5) (64.0.2)\n",
            "Requirement already satisfied: certifi>=2018.1.18 in /usr/local/lib/python3.8/dist-packages (from ccxt>=1.66.32->finrl==0.3.5) (2022.12.7)\n",
            "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from gym>=0.17->finrl==0.3.5) (2.2.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.0->finrl==0.3.5) (1.7.3)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.0->finrl==0.3.5) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.0->finrl==0.3.5) (1.2.0)\n",
            "Requirement already satisfied: torch>=1.11 in /usr/local/lib/python3.8/dist-packages (from stable-baselines3<2.0.0,>=1.6.2->finrl==0.3.5) (1.13.0+cu116)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from jqdatasdk->finrl==0.3.5) (1.15.0)\n",
            "Requirement already satisfied: thriftpy2>=0.3.9 in /usr/local/lib/python3.8/dist-packages (from jqdatasdk->finrl==0.3.5) (0.4.16)\n",
            "Requirement already satisfied: SQLAlchemy>=1.2.8 in /usr/local/lib/python3.8/dist-packages (from jqdatasdk->finrl==0.3.5) (1.4.46)\n",
            "Requirement already satisfied: pymysql>=0.7.6 in /usr/local/lib/python3.8/dist-packages (from jqdatasdk->finrl==0.3.5) (1.0.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->finrl==0.3.5) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->finrl==0.3.5) (1.4.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib->finrl==0.3.5) (0.11.0)\n",
            "Requirement already satisfied: ipython>=3.2.3 in /usr/local/lib/python3.8/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (7.9.0)\n",
            "Requirement already satisfied: seaborn>=0.7.1 in /usr/local/lib/python3.8/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (0.11.2)\n",
            "Requirement already satisfied: empyrical>=0.5.0 in /usr/local/lib/python3.8/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (0.5.5)\n",
            "Requirement already satisfied: appdirs>=1.4.4 in /usr/local/lib/python3.8/dist-packages (from yfinance->finrl==0.3.5) (1.4.4)\n",
            "Requirement already satisfied: html5lib>=1.1 in /usr/local/lib/python3.8/dist-packages (from yfinance->finrl==0.3.5) (1.1)\n",
            "Requirement already satisfied: lxml>=4.9.1 in /usr/local/lib/python3.8/dist-packages (from yfinance->finrl==0.3.5) (4.9.2)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.8/dist-packages (from yfinance->finrl==0.3.5) (0.0.11)\n",
            "Requirement already satisfied: beautifulsoup4>=4.11.1 in /usr/local/lib/python3.8/dist-packages (from yfinance->finrl==0.3.5) (4.11.1)\n",
            "Requirement already satisfied: frozendict>=2.3.4 in /usr/local/lib/python3.8/dist-packages (from yfinance->finrl==0.3.5) (2.3.4)\n",
            "Requirement already satisfied: pycares>=4.0.0 in /usr/local/lib/python3.8/dist-packages (from aiodns>=1.1.1->ccxt>=1.66.32->finrl==0.3.5) (4.3.0)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.8/dist-packages (from beautifulsoup4>=4.11.1->yfinance->finrl==0.3.5) (2.3.2.post1)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.8/dist-packages (from cryptography>=2.6.1->ccxt>=1.66.32->finrl==0.3.5) (1.15.1)\n",
            "Requirement already satisfied: pandas-datareader>=0.2 in /usr/local/lib/python3.8/dist-packages (from empyrical>=0.5.0->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (0.9.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.8/dist-packages (from html5lib>=1.1->yfinance->finrl==0.3.5) (0.5.1)\n",
            "Requirement already satisfied: jedi>=0.10 in /usr/local/lib/python3.8/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (0.18.2)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.8/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (2.6.1)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.8/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (0.2.0)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.8/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (0.7.5)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.8/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (5.7.1)\n",
            "Requirement already satisfied: pexpect in /usr/local/lib/python3.8/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (4.8.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.8/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (4.4.2)\n",
            "Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (2.0.10)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->ray[default,tune]==1.3.0->finrl==0.3.5) (2.10)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.8/dist-packages (from SQLAlchemy>=1.2.8->jqdatasdk->finrl==0.3.5) (2.0.1)\n",
            "Requirement already satisfied: ply<4.0,>=3.4 in /usr/local/lib/python3.8/dist-packages (from thriftpy2>=0.3.9->jqdatasdk->finrl==0.3.5) (3.11)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch>=1.11->stable-baselines3<2.0.0,>=1.6.2->finrl==0.3.5) (4.4.0)\n",
            "Requirement already satisfied: blessed>=1.17.1 in /usr/local/lib/python3.8/dist-packages (from gpustat->ray[default,tune]==1.3.0->finrl==0.3.5) (1.19.1)\n",
            "Requirement already satisfied: psutil>=5.6.0 in /usr/local/lib/python3.8/dist-packages (from gpustat->ray[default,tune]==1.3.0->finrl==0.3.5) (5.9.4)\n",
            "Requirement already satisfied: nvidia-ml-py<=11.495.46,>=11.450.129 in /usr/local/lib/python3.8/dist-packages (from gpustat->ray[default,tune]==1.3.0->finrl==0.3.5) (11.495.46)\n",
            "Requirement already satisfied: box2d-py==2.3.5 in /usr/local/lib/python3.8/dist-packages (from gym>=0.17->finrl==0.3.5) (2.3.5)\n",
            "Requirement already satisfied: pyglet>=1.4.0 in /usr/local/lib/python3.8/dist-packages (from gym>=0.17->finrl==0.3.5) (2.0.3)\n",
            "Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema->ray[default,tune]==1.3.0->finrl==0.3.5) (5.10.2)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema->ray[default,tune]==1.3.0->finrl==0.3.5) (0.19.3)\n",
            "Requirement already satisfied: google-api-core<3.0.0,>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from opencensus->ray[default,tune]==1.3.0->finrl==0.3.5) (2.11.0)\n",
            "Requirement already satisfied: opencensus-context>=0.1.3 in /usr/local/lib/python3.8/dist-packages (from opencensus->ray[default,tune]==1.3.0->finrl==0.3.5) (0.1.3)\n",
            "Requirement already satisfied: wcwidth>=0.1.4 in /usr/local/lib/python3.8/dist-packages (from blessed>=1.17.1->gpustat->ray[default,tune]==1.3.0->finrl==0.3.5) (0.2.5)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.8/dist-packages (from cffi>=1.12->cryptography>=2.6.1->ccxt>=1.66.32->finrl==0.3.5) (2.21)\n",
            "Requirement already satisfied: google-auth<3.0dev,>=2.14.1 in /usr/local/lib/python3.8/dist-packages (from google-api-core<3.0.0,>=1.0.0->opencensus->ray[default,tune]==1.3.0->finrl==0.3.5) (2.15.0)\n",
            "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.56.2 in /usr/local/lib/python3.8/dist-packages (from google-api-core<3.0.0,>=1.0.0->opencensus->ray[default,tune]==1.3.0->finrl==0.3.5) (1.57.1)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.8/dist-packages (from jedi>=0.10->ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (0.8.3)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.8/dist-packages (from pexpect->ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (0.7.0)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from google-auth<3.0dev,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default,tune]==1.3.0->finrl==0.3.5) (5.2.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.8/dist-packages (from google-auth<3.0dev,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default,tune]==1.3.0->finrl==0.3.5) (4.9)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.8/dist-packages (from google-auth<3.0dev,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default,tune]==1.3.0->finrl==0.3.5) (0.2.8)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.8/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3.0dev,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default,tune]==1.3.0->finrl==0.3.5) (0.4.8)\n"
          ]
        }
      ]
    },
    {
      "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": "markdown",
      "metadata": {
        "id": "nGv01K8Sh1hn"
      },
      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EeMK7Uentj1V"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lPqeTTwoh1hn"
      },
      "outputs": [],
      "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",
        "%matplotlib inline\n",
        "from finrl.config_tickers import DOW_30_TICKER\n",
        "from finrl.meta.preprocessor.yahoodownloader import YahooDownloader\n",
        "from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
        "from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
        "from finrl.agents.stablebaselines3.models import DRLAgent,DRLEnsembleAgent\n",
        "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
        "\n",
        "from pprint import pprint\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")\n",
        "\n",
        "import itertools"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2owTj985RW4"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w9A8CN5R5PuZ"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from finrl.main import check_and_make_directories\n",
        "from finrl.config import (\n",
        "    DATA_SAVE_DIR,\n",
        "    TRAINED_MODEL_DIR,\n",
        "    TENSORBOARD_LOG_DIR,\n",
        "    RESULTS_DIR,\n",
        "    INDICATORS,\n",
        "    TRAIN_START_DATE,\n",
        "    TRAIN_END_DATE,\n",
        "    TEST_START_DATE,\n",
        "    TEST_END_DATE,\n",
        "    TRADE_START_DATE,\n",
        "    TRADE_END_DATE,\n",
        ")\n",
        "\n",
        "check_and_make_directories([DATA_SAVE_DIR, TRAINED_MODEL_DIR, TENSORBOARD_LOG_DIR, RESULTS_DIR])"
      ]
    },
    {
      "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",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzqRRTOX6aFu",
        "outputId": "a927dc8b-61c2-45bc-9d59-48cf0f655b97"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['AXP', 'AMGN', 'AAPL', 'BA', 'CAT', 'CSCO', 'CVX', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'KO', 'JPM', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PG', 'TRV', 'UNH', 'CRM', 'VZ', 'V', 'WBA', 'WMT', 'DIS', 'DOW']\n"
          ]
        }
      ],
      "source": [
        "print(DOW_30_TICKER)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCKm4om-s9kE",
        "outputId": "deeef56c-fbba-42b7-ec25-3264de649245"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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:  (96942, 8)\n"
          ]
        }
      ],
      "source": [
        "TRAIN_START_DATE = '2009-04-01'\n",
        "TRAIN_END_DATE = '2021-01-01'\n",
        "TEST_START_DATE = '2021-01-01'\n",
        "TEST_END_DATE = '2022-06-01'\n",
        "\n",
        "df = YahooDownloader(start_date = TRAIN_START_DATE,\n",
        "                     end_date = TEST_END_DATE,\n",
        "                     ticker_list = DOW_30_TICKER).fetch_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GiRuFOTOtj1Y",
        "outputId": "dea34e9f-c452-4327-bfce-878c9dc5a9ee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date       open       high        low      close     volume   tic  \\\n",
              "0  2009-04-01   3.717500   3.892857   3.710357   3.308904  589372000  AAPL   \n",
              "1  2009-04-01  48.779999  48.930000  47.099998  36.228397   10850100  AMGN   \n",
              "2  2009-04-01  13.340000  14.640000  13.080000  11.732111   27701800   AXP   \n",
              "3  2009-04-01  34.520000  35.599998  34.209999  26.850748    9288800    BA   \n",
              "4  2009-04-01  27.500000  29.520000  27.440001  19.820395   15308300   CAT   \n",
              "\n",
              "   day  \n",
              "0    2  \n",
              "1    2  \n",
              "2    2  \n",
              "3    2  \n",
              "4    2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a9343f51-d849-4d7d-a57d-6b37d2e5fc60\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-04-01</td>\n",
              "      <td>3.717500</td>\n",
              "      <td>3.892857</td>\n",
              "      <td>3.710357</td>\n",
              "      <td>3.308904</td>\n",
              "      <td>589372000</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-04-01</td>\n",
              "      <td>48.779999</td>\n",
              "      <td>48.930000</td>\n",
              "      <td>47.099998</td>\n",
              "      <td>36.228397</td>\n",
              "      <td>10850100</td>\n",
              "      <td>AMGN</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-04-01</td>\n",
              "      <td>13.340000</td>\n",
              "      <td>14.640000</td>\n",
              "      <td>13.080000</td>\n",
              "      <td>11.732111</td>\n",
              "      <td>27701800</td>\n",
              "      <td>AXP</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-04-01</td>\n",
              "      <td>34.520000</td>\n",
              "      <td>35.599998</td>\n",
              "      <td>34.209999</td>\n",
              "      <td>26.850748</td>\n",
              "      <td>9288800</td>\n",
              "      <td>BA</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-04-01</td>\n",
              "      <td>27.500000</td>\n",
              "      <td>29.520000</td>\n",
              "      <td>27.440001</td>\n",
              "      <td>19.820395</td>\n",
              "      <td>15308300</td>\n",
              "      <td>CAT</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9343f51-d849-4d7d-a57d-6b37d2e5fc60')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a9343f51-d849-4d7d-a57d-6b37d2e5fc60 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a9343f51-d849-4d7d-a57d-6b37d2e5fc60');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "DSw4ZEzVtj1Z",
        "outputId": "08340d7a-f995-446d-84c1-0054e7aa9500"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             date        open        high         low       close    volume  \\\n",
              "96937  2022-05-31  503.619995  504.109985  495.660004  491.949829   4003100   \n",
              "96938  2022-05-31  210.380005  214.350006  209.110001  211.322525   9586400   \n",
              "96939  2022-05-31   51.259998   51.560001   50.849998   49.042248  25016600   \n",
              "96940  2022-05-31   43.480000   44.270000   43.049999   42.811329   8192000   \n",
              "96941  2022-05-31  127.459999  129.899994  127.419998  127.591217  12304100   \n",
              "\n",
              "       tic  day  \n",
              "96937  UNH    1  \n",
              "96938    V    1  \n",
              "96939   VZ    1  \n",
              "96940  WBA    1  \n",
              "96941  WMT    1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b5932b4c-b233-454e-ac1a-d1475e35b408\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>96937</th>\n",
              "      <td>2022-05-31</td>\n",
              "      <td>503.619995</td>\n",
              "      <td>504.109985</td>\n",
              "      <td>495.660004</td>\n",
              "      <td>491.949829</td>\n",
              "      <td>4003100</td>\n",
              "      <td>UNH</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96938</th>\n",
              "      <td>2022-05-31</td>\n",
              "      <td>210.380005</td>\n",
              "      <td>214.350006</td>\n",
              "      <td>209.110001</td>\n",
              "      <td>211.322525</td>\n",
              "      <td>9586400</td>\n",
              "      <td>V</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96939</th>\n",
              "      <td>2022-05-31</td>\n",
              "      <td>51.259998</td>\n",
              "      <td>51.560001</td>\n",
              "      <td>50.849998</td>\n",
              "      <td>49.042248</td>\n",
              "      <td>25016600</td>\n",
              "      <td>VZ</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96940</th>\n",
              "      <td>2022-05-31</td>\n",
              "      <td>43.480000</td>\n",
              "      <td>44.270000</td>\n",
              "      <td>43.049999</td>\n",
              "      <td>42.811329</td>\n",
              "      <td>8192000</td>\n",
              "      <td>WBA</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96941</th>\n",
              "      <td>2022-05-31</td>\n",
              "      <td>127.459999</td>\n",
              "      <td>129.899994</td>\n",
              "      <td>127.419998</td>\n",
              "      <td>127.591217</td>\n",
              "      <td>12304100</td>\n",
              "      <td>WMT</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5932b4c-b233-454e-ac1a-d1475e35b408')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b5932b4c-b233-454e-ac1a-d1475e35b408 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b5932b4c-b233-454e-ac1a-d1475e35b408');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV3HrZHLh1hy",
        "outputId": "0988b9fe-9ab4-4d34-881d-f29b10e65c81"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(96942, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4hYkeaPiICHS",
        "outputId": "c20f2da5-3224-4e6e-96a2-7b72b8dce6d8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date       open       high        low      close     volume   tic  \\\n",
              "0  2009-04-01   3.717500   3.892857   3.710357   3.308904  589372000  AAPL   \n",
              "1  2009-04-01  48.779999  48.930000  47.099998  36.228397   10850100  AMGN   \n",
              "2  2009-04-01  13.340000  14.640000  13.080000  11.732111   27701800   AXP   \n",
              "3  2009-04-01  34.520000  35.599998  34.209999  26.850748    9288800    BA   \n",
              "4  2009-04-01  27.500000  29.520000  27.440001  19.820395   15308300   CAT   \n",
              "\n",
              "   day  \n",
              "0    2  \n",
              "1    2  \n",
              "2    2  \n",
              "3    2  \n",
              "4    2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b10b5ba3-bbca-49c2-8a40-90b2f294c2d1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-04-01</td>\n",
              "      <td>3.717500</td>\n",
              "      <td>3.892857</td>\n",
              "      <td>3.710357</td>\n",
              "      <td>3.308904</td>\n",
              "      <td>589372000</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-04-01</td>\n",
              "      <td>48.779999</td>\n",
              "      <td>48.930000</td>\n",
              "      <td>47.099998</td>\n",
              "      <td>36.228397</td>\n",
              "      <td>10850100</td>\n",
              "      <td>AMGN</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-04-01</td>\n",
              "      <td>13.340000</td>\n",
              "      <td>14.640000</td>\n",
              "      <td>13.080000</td>\n",
              "      <td>11.732111</td>\n",
              "      <td>27701800</td>\n",
              "      <td>AXP</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-04-01</td>\n",
              "      <td>34.520000</td>\n",
              "      <td>35.599998</td>\n",
              "      <td>34.209999</td>\n",
              "      <td>26.850748</td>\n",
              "      <td>9288800</td>\n",
              "      <td>BA</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-04-01</td>\n",
              "      <td>27.500000</td>\n",
              "      <td>29.520000</td>\n",
              "      <td>27.440001</td>\n",
              "      <td>19.820395</td>\n",
              "      <td>15308300</td>\n",
              "      <td>CAT</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b10b5ba3-bbca-49c2-8a40-90b2f294c2d1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b10b5ba3-bbca-49c2-8a40-90b2f294c2d1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b10b5ba3-bbca-49c2-8a40-90b2f294c2d1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df.sort_values(['date','tic']).head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2vryMsdNL9H",
        "outputId": "fdacf756-c028-4fad-8f44-790b602666c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "len(df.tic.unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XcNyXa7RNPrF",
        "outputId": "a4a7501f-a378-478d-c51c-285a568d2c6b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AAPL    3315\n",
              "AMGN    3315\n",
              "WMT     3315\n",
              "WBA     3315\n",
              "VZ      3315\n",
              "V       3315\n",
              "UNH     3315\n",
              "TRV     3315\n",
              "PG      3315\n",
              "NKE     3315\n",
              "MSFT    3315\n",
              "MRK     3315\n",
              "MMM     3315\n",
              "MCD     3315\n",
              "KO      3315\n",
              "JPM     3315\n",
              "JNJ     3315\n",
              "INTC    3315\n",
              "IBM     3315\n",
              "HON     3315\n",
              "HD      3315\n",
              "GS      3315\n",
              "DIS     3315\n",
              "CVX     3315\n",
              "CSCO    3315\n",
              "CRM     3315\n",
              "CAT     3315\n",
              "BA      3315\n",
              "AXP     3315\n",
              "DOW      807\n",
              "Name: tic, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df.tic.value_counts()"
      ]
    },
    {
      "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",
      "execution_count": null,
      "metadata": {
        "id": "kM5bH9uroCeg"
      },
      "outputs": [],
      "source": [
        " INDICATORS = ['macd',\n",
        "               'rsi_30',\n",
        "               'cci_30',\n",
        "               'dx_30']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jgXfBcjxtj1a",
        "outputId": "35328f99-9056-4447-b253-fc3a4b670c62",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Successfully added technical indicators\n",
            "Successfully added turbulence index\n"
          ]
        }
      ],
      "source": [
        "fe = FeatureEngineer(use_technical_indicator=True,\n",
        "                     tech_indicator_list = INDICATORS,\n",
        "                     use_turbulence=True,\n",
        "                     user_defined_feature = False)\n",
        "\n",
        "processed = fe.preprocess_data(df)\n",
        "processed = processed.copy()\n",
        "processed = processed.fillna(0)\n",
        "processed = processed.replace(np.inf,0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "grvhGJJII3Xn",
        "outputId": "fde7339a-4717-4faf-f2a3-991a1813ba93"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             date        open        high         low       close     volume  \\\n",
              "2821   2009-08-19   25.000000   25.600000   25.000000   21.940813   11145500   \n",
              "62596  2017-10-25  141.160004  143.229996  140.960007  123.970779    6563600   \n",
              "39546  2014-08-29   57.652672   57.652672   57.089695   43.994267    5990158   \n",
              "46313  2015-08-05   28.237499   29.360001   28.025000   26.180571  397250400   \n",
              "57423  2017-02-10  165.250000  166.449997  164.470001  155.444748    2689700   \n",
              "\n",
              "        tic  day      macd     rsi_30      cci_30      dx_30  turbulence  \n",
              "2821    DIS    2  0.225982  54.098345    9.212596   1.571742    0.000000  \n",
              "62596   JNJ    2  2.379369  65.243357  136.265128  48.407780   51.810794  \n",
              "39546   MRK    4  0.485724  58.604924  129.609437  46.128168   13.778847  \n",
              "46313  AAPL    2 -0.561262  37.814633 -230.416492  53.244568  204.257634  \n",
              "57423    BA    4  2.131971  64.513104   96.060896  28.170135   33.538351  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-617bd384-b937-48a7-98c5-f912ea98518d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>day</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>2821</th>\n",
              "      <td>2009-08-19</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>25.600000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>21.940813</td>\n",
              "      <td>11145500</td>\n",
              "      <td>DIS</td>\n",
              "      <td>2</td>\n",
              "      <td>0.225982</td>\n",
              "      <td>54.098345</td>\n",
              "      <td>9.212596</td>\n",
              "      <td>1.571742</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62596</th>\n",
              "      <td>2017-10-25</td>\n",
              "      <td>141.160004</td>\n",
              "      <td>143.229996</td>\n",
              "      <td>140.960007</td>\n",
              "      <td>123.970779</td>\n",
              "      <td>6563600</td>\n",
              "      <td>JNJ</td>\n",
              "      <td>2</td>\n",
              "      <td>2.379369</td>\n",
              "      <td>65.243357</td>\n",
              "      <td>136.265128</td>\n",
              "      <td>48.407780</td>\n",
              "      <td>51.810794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39546</th>\n",
              "      <td>2014-08-29</td>\n",
              "      <td>57.652672</td>\n",
              "      <td>57.652672</td>\n",
              "      <td>57.089695</td>\n",
              "      <td>43.994267</td>\n",
              "      <td>5990158</td>\n",
              "      <td>MRK</td>\n",
              "      <td>4</td>\n",
              "      <td>0.485724</td>\n",
              "      <td>58.604924</td>\n",
              "      <td>129.609437</td>\n",
              "      <td>46.128168</td>\n",
              "      <td>13.778847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46313</th>\n",
              "      <td>2015-08-05</td>\n",
              "      <td>28.237499</td>\n",
              "      <td>29.360001</td>\n",
              "      <td>28.025000</td>\n",
              "      <td>26.180571</td>\n",
              "      <td>397250400</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.561262</td>\n",
              "      <td>37.814633</td>\n",
              "      <td>-230.416492</td>\n",
              "      <td>53.244568</td>\n",
              "      <td>204.257634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57423</th>\n",
              "      <td>2017-02-10</td>\n",
              "      <td>165.250000</td>\n",
              "      <td>166.449997</td>\n",
              "      <td>164.470001</td>\n",
              "      <td>155.444748</td>\n",
              "      <td>2689700</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "      <td>2.131971</td>\n",
              "      <td>64.513104</td>\n",
              "      <td>96.060896</td>\n",
              "      <td>28.170135</td>\n",
              "      <td>33.538351</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-617bd384-b937-48a7-98c5-f912ea98518d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-617bd384-b937-48a7-98c5-f912ea98518d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-617bd384-b937-48a7-98c5-f912ea98518d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "processed.sample(5)"
      ]
    },
    {
      "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": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2zqII8rMIqn",
        "outputId": "c8dcd10d-2841-4355-bf27-05634e875fe3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stock Dimension: 29, State Space: 175\n"
          ]
        }
      ],
      "source": [
        "stock_dimension = len(processed.tic.unique())\n",
        "state_space = 1 + 2*stock_dimension + len(INDICATORS)*stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AWyp84Ltto19"
      },
      "outputs": [],
      "source": [
        "env_kwargs = {\n",
        "    \"hmax\": 100, \n",
        "    \"initial_amount\": 1000000, \n",
        "    \"buy_cost_pct\": 0.001, \n",
        "    \"sell_cost_pct\": 0.001, \n",
        "    \"state_space\": state_space, \n",
        "    \"stock_dim\": stock_dimension, \n",
        "    \"tech_indicator_list\": INDICATORS,\n",
        "    \"action_space\": stock_dimension, \n",
        "    \"reward_scaling\": 1e-4,\n",
        "    \"print_verbosity\":5\n",
        "    \n",
        "}"
      ]
    },
    {
      "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.\n",
        "\n",
        "* In this notebook, we are training and validating 3 agents (A2C, PPO, DDPG) using Rolling-window Ensemble Method ([reference code](https://github.com/AI4Finance-LLC/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020/blob/80415db8fa7b2179df6bd7e81ce4fe8dbf913806/model/models.py#L92))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v-gthCxMtj1d"
      },
      "outputs": [],
      "source": [
        "rebalance_window = 63 #63 # rebalance_window is the number of days to retrain the model\n",
        "validation_window = 63 #63 # validation_window is the number of days to do validation and trading (e.g. if validation_window=63, then both validation and trading period will be 63 days)\n",
        "\n",
        "ensemble_agent = DRLEnsembleAgent(df=processed,\n",
        "                 train_period=(TRAIN_START_DATE,TRAIN_END_DATE),\n",
        "                 val_test_period=(TEST_START_DATE,TEST_END_DATE),\n",
        "                 rebalance_window=rebalance_window, \n",
        "                 validation_window=validation_window, \n",
        "                 **env_kwargs)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KsfEHa_Etj1d",
        "scrolled": false
      },
      "outputs": [],
      "source": [
        "A2C_model_kwargs = {\n",
        "                    'n_steps': 5,\n",
        "                    'ent_coef': 0.005,\n",
        "                    'learning_rate': 0.0007\n",
        "                    }\n",
        "\n",
        "PPO_model_kwargs = {\n",
        "                    \"ent_coef\":0.01,\n",
        "                    \"n_steps\": 2, #2048\n",
        "                    \"learning_rate\": 0.00025,\n",
        "                    \"batch_size\": 128\n",
        "                    }\n",
        "\n",
        "DDPG_model_kwargs = {\n",
        "                      #\"action_noise\":\"ornstein_uhlenbeck\",\n",
        "                      \"buffer_size\": 1, #10_000\n",
        "                      \"learning_rate\": 0.0005,\n",
        "                      \"batch_size\": 64\n",
        "                    }\n",
        "\n",
        "timesteps_dict = {'a2c' : 1, #10_000 each\n",
        "                 'ppo' : 1, \n",
        "                 'ddpg' : 1\n",
        "                 }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_1lyCECstj1e",
        "outputId": "1b8c4fd8-0a68-43a9-9353-2638e60c980a",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============Start Ensemble Strategy============\n",
            "============================================\n",
            "turbulence_threshold:  203.4025492950057\n",
            "======Model training from:  2009-04-01 to  2021-01-04\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.005, 'learning_rate': 0.0007}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/a2c/a2c_126_2\n",
            "======A2C Validation from:  2021-01-04 to  2021-04-06\n",
            "A2C Sharpe Ratio:  0.14436838779143474\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ppo/ppo_126_1\n",
            "------------------------------------\n",
            "| time/              |             |\n",
            "|    fps             | 47          |\n",
            "|    iterations      | 1           |\n",
            "|    time_elapsed    | 0           |\n",
            "|    total_timesteps | 2           |\n",
            "| train/             |             |\n",
            "|    reward          | 0.019164504 |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2021-01-04 to  2021-04-06\n",
            "PPO Sharpe Ratio:  0.25605306569795894\n",
            "======DDPG Training========\n",
            "{'buffer_size': 1, 'learning_rate': 0.0005, 'batch_size': 64}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ddpg/ddpg_126_1\n",
            "======DDPG Validation from:  2021-01-04 to  2021-04-06\n",
            "======Best Model Retraining from:  2009-04-01 to  2021-04-06\n",
            "======Trading from:  2021-04-06 to  2021-07-06\n",
            "============================================\n",
            "turbulence_threshold:  203.4025492950057\n",
            "======Model training from:  2009-04-01 to  2021-04-06\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.005, 'learning_rate': 0.0007}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/a2c/a2c_189_1\n",
            "======A2C Validation from:  2021-04-06 to  2021-07-06\n",
            "A2C Sharpe Ratio:  0.24211477519802077\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ppo/ppo_189_1\n",
            "-------------------------------------\n",
            "| time/              |              |\n",
            "|    fps             | 62           |\n",
            "|    iterations      | 1            |\n",
            "|    time_elapsed    | 0            |\n",
            "|    total_timesteps | 2            |\n",
            "| train/             |              |\n",
            "|    reward          | 0.0028279088 |\n",
            "-------------------------------------\n",
            "======PPO Validation from:  2021-04-06 to  2021-07-06\n",
            "PPO Sharpe Ratio:  0.16966525300375485\n",
            "======DDPG Training========\n",
            "{'buffer_size': 1, 'learning_rate': 0.0005, 'batch_size': 64}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ddpg/ddpg_189_1\n",
            "======DDPG Validation from:  2021-04-06 to  2021-07-06\n",
            "======Best Model Retraining from:  2009-04-01 to  2021-07-06\n",
            "======Trading from:  2021-07-06 to  2021-10-04\n",
            "============================================\n",
            "turbulence_threshold:  203.4025492950057\n",
            "======Model training from:  2009-04-01 to  2021-07-06\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.005, 'learning_rate': 0.0007}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/a2c/a2c_252_1\n",
            "======A2C Validation from:  2021-07-06 to  2021-10-04\n",
            "A2C Sharpe Ratio:  0.0047296329718654806\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ppo/ppo_252_1\n",
            "------------------------------------\n",
            "| time/              |             |\n",
            "|    fps             | 59          |\n",
            "|    iterations      | 1           |\n",
            "|    time_elapsed    | 0           |\n",
            "|    total_timesteps | 2           |\n",
            "| train/             |             |\n",
            "|    reward          | 0.037194405 |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2021-07-06 to  2021-10-04\n",
            "PPO Sharpe Ratio:  -0.02644333693082262\n",
            "======DDPG Training========\n",
            "{'buffer_size': 1, 'learning_rate': 0.0005, 'batch_size': 64}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ddpg/ddpg_252_1\n",
            "======DDPG Validation from:  2021-07-06 to  2021-10-04\n",
            "======Best Model Retraining from:  2009-04-01 to  2021-10-04\n",
            "======Trading from:  2021-10-04 to  2022-01-03\n",
            "============================================\n",
            "turbulence_threshold:  203.4025492950057\n",
            "======Model training from:  2009-04-01 to  2021-10-04\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.005, 'learning_rate': 0.0007}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/a2c/a2c_315_1\n",
            "======A2C Validation from:  2021-10-04 to  2022-01-03\n",
            "A2C Sharpe Ratio:  0.22080259787185566\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ppo/ppo_315_1\n",
            "------------------------------------\n",
            "| time/              |             |\n",
            "|    fps             | 59          |\n",
            "|    iterations      | 1           |\n",
            "|    time_elapsed    | 0           |\n",
            "|    total_timesteps | 2           |\n",
            "| train/             |             |\n",
            "|    reward          | 0.012130291 |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2021-10-04 to  2022-01-03\n",
            "PPO Sharpe Ratio:  0.16656545953109805\n",
            "======DDPG Training========\n",
            "{'buffer_size': 1, 'learning_rate': 0.0005, 'batch_size': 64}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ddpg/ddpg_315_1\n",
            "======DDPG Validation from:  2021-10-04 to  2022-01-03\n",
            "======Best Model Retraining from:  2009-04-01 to  2022-01-03\n",
            "======Trading from:  2022-01-03 to  2022-04-04\n",
            "Ensemble Strategy took:  3.726950216293335  minutes\n"
          ]
        }
      ],
      "source": [
        "df_summary = ensemble_agent.run_ensemble_strategy(A2C_model_kwargs,\n",
        "                                                 PPO_model_kwargs,\n",
        "                                                 DDPG_model_kwargs,\n",
        "                                                 timesteps_dict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "-0qd8acMtj1f",
        "outputId": "04c91955-ad35-433a-96d0-a777846b3485"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Iter   Val Start     Val End Model Used A2C Sharpe PPO Sharpe DDPG Sharpe\n",
              "0  126  2021-01-04  2021-04-06       DDPG   0.144368   0.256053    0.299737\n",
              "1  189  2021-04-06  2021-07-06        A2C   0.242115   0.169665    0.239078\n",
              "2  252  2021-07-06  2021-10-04        A2C    0.00473  -0.026443   -0.026299\n",
              "3  315  2021-10-04  2022-01-03        A2C   0.220803   0.166565    0.113222"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-182a8e2a-8660-4cd9-9b1c-6fdf1c6ece7b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Iter</th>\n",
              "      <th>Val Start</th>\n",
              "      <th>Val End</th>\n",
              "      <th>Model Used</th>\n",
              "      <th>A2C Sharpe</th>\n",
              "      <th>PPO Sharpe</th>\n",
              "      <th>DDPG Sharpe</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>126</td>\n",
              "      <td>2021-01-04</td>\n",
              "      <td>2021-04-06</td>\n",
              "      <td>DDPG</td>\n",
              "      <td>0.144368</td>\n",
              "      <td>0.256053</td>\n",
              "      <td>0.299737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>189</td>\n",
              "      <td>2021-04-06</td>\n",
              "      <td>2021-07-06</td>\n",
              "      <td>A2C</td>\n",
              "      <td>0.242115</td>\n",
              "      <td>0.169665</td>\n",
              "      <td>0.239078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>252</td>\n",
              "      <td>2021-07-06</td>\n",
              "      <td>2021-10-04</td>\n",
              "      <td>A2C</td>\n",
              "      <td>0.00473</td>\n",
              "      <td>-0.026443</td>\n",
              "      <td>-0.026299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>315</td>\n",
              "      <td>2021-10-04</td>\n",
              "      <td>2022-01-03</td>\n",
              "      <td>A2C</td>\n",
              "      <td>0.220803</td>\n",
              "      <td>0.166565</td>\n",
              "      <td>0.113222</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-182a8e2a-8660-4cd9-9b1c-6fdf1c6ece7b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-182a8e2a-8660-4cd9-9b1c-6fdf1c6ece7b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-182a8e2a-8660-4cd9-9b1c-6fdf1c6ece7b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "df_summary"
      ]
    },
    {
      "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": "code",
      "execution_count": null,
      "metadata": {
        "id": "X4JKB--8tj1g"
      },
      "outputs": [],
      "source": [
        "unique_trade_date = processed[(processed.date > TEST_START_DATE)&(processed.date <= TEST_END_DATE)].date.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q9mKF7GGtj1g",
        "outputId": "4bdb56ca-72aa-4a05-c165-382e59800dcd",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sharpe Ratio:  0.09608769419577266\n"
          ]
        }
      ],
      "source": [
        "df_trade_date = pd.DataFrame({'datadate':unique_trade_date})\n",
        "\n",
        "df_account_value=pd.DataFrame()\n",
        "for i in range(rebalance_window+validation_window, len(unique_trade_date)+1,rebalance_window):\n",
        "    temp = pd.read_csv('results/account_value_trade_{}_{}.csv'.format('ensemble',i))\n",
        "    df_account_value = df_account_value.append(temp,ignore_index=True)\n",
        "sharpe=(252**0.5)*df_account_value.account_value.pct_change(1).mean()/df_account_value.account_value.pct_change(1).std()\n",
        "print('Sharpe Ratio: ',sharpe)\n",
        "df_account_value=df_account_value.join(df_trade_date[validation_window:].reset_index(drop=True))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "oyosyW7_tj1g",
        "outputId": "75009e74-5074-4e5f-9bfe-9d94e8a96c8b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   account_value        date  daily_return    datadate\n",
              "0   1.000000e+06  2021-04-06           NaN  2021-04-06\n",
              "1   9.999415e+05  2021-04-07     -0.000058  2021-04-07\n",
              "2   1.000887e+06  2021-04-08      0.000945  2021-04-08\n",
              "3   1.008799e+06  2021-04-09      0.007905  2021-04-09\n",
              "4   1.007317e+06  2021-04-12     -0.001470  2021-04-12"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c2873be0-5cb4-4433-8266-743255826389\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>account_value</th>\n",
              "      <th>date</th>\n",
              "      <th>daily_return</th>\n",
              "      <th>datadate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>2021-04-06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2021-04-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.999415e+05</td>\n",
              "      <td>2021-04-07</td>\n",
              "      <td>-0.000058</td>\n",
              "      <td>2021-04-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.000887e+06</td>\n",
              "      <td>2021-04-08</td>\n",
              "      <td>0.000945</td>\n",
              "      <td>2021-04-08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.008799e+06</td>\n",
              "      <td>2021-04-09</td>\n",
              "      <td>0.007905</td>\n",
              "      <td>2021-04-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.007317e+06</td>\n",
              "      <td>2021-04-12</td>\n",
              "      <td>-0.001470</td>\n",
              "      <td>2021-04-12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2873be0-5cb4-4433-8266-743255826389')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c2873be0-5cb4-4433-8266-743255826389 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c2873be0-5cb4-4433-8266-743255826389');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "df_account_value.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "wLsRdw2Ctj1h",
        "outputId": "f30f7a8c-f03d-4a77-91df-a0a11f95e3b1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a5471d9a0>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "df_account_value.account_value.plot()"
      ]
    },
    {
      "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",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nzkr9yv-AdV_",
        "outputId": "d02bf082-7116-4db2-883c-7d8685fdea95",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============Get Backtest Results===========\n",
            "Annual return          0.003927\n",
            "Cumulative returns     0.003927\n",
            "Annual volatility      0.133999\n",
            "Sharpe ratio           0.096088\n",
            "Calmar ratio           0.028174\n",
            "Stability              0.082392\n",
            "Max drawdown          -0.139385\n",
            "Omega ratio            1.016089\n",
            "Sortino ratio          0.137167\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.945446\n",
            "Daily value at risk   -0.016831\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(\"==============Get Backtest Results===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "\n",
        "perf_stats_all = backtest_stats(account_value=df_account_value)\n",
        "perf_stats_all = pd.DataFrame(perf_stats_all)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DiHhM1YkoCel",
        "outputId": "fe49c038-c7a5-4cf4-afa4-a8ad2c302ab9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============Get Baseline Stats===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 8)\n",
            "Annual return          0.037486\n",
            "Cumulative returns     0.037335\n",
            "Annual volatility      0.134331\n",
            "Sharpe ratio           0.342028\n",
            "Calmar ratio           0.331049\n",
            "Stability              0.066383\n",
            "Max drawdown          -0.113235\n",
            "Omega ratio            1.058031\n",
            "Sortino ratio          0.480831\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.970301\n",
            "Daily value at risk   -0.016742\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "#baseline stats\n",
        "print(\"==============Get Baseline Stats===========\")\n",
        "baseline_df = get_baseline(\n",
        "        ticker=\"^DJI\", \n",
        "        start = df_account_value.loc[0,'date'],\n",
        "        end = df_account_value.loc[len(df_account_value)-1,'date'])\n",
        "\n",
        "stats = backtest_stats(baseline_df, value_col_name = 'close')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U6Suru3h1jc"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HggausPRoCem",
        "outputId": "90dbf37a-bd09-42e0-b4a3-3360e37c4bbf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============Compare to DJIA===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 8)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2021-04-06</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2022-04-01</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>12</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>0.393%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>0.393%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>13.4%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-13.938%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-1.683%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>-0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "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>13.94</td>\n",
              "      <td>2021-11-05</td>\n",
              "      <td>2022-02-23</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.21</td>\n",
              "      <td>2021-08-16</td>\n",
              "      <td>2021-09-30</td>\n",
              "      <td>2021-11-04</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.90</td>\n",
              "      <td>2021-05-07</td>\n",
              "      <td>2021-06-18</td>\n",
              "      <td>2021-06-29</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.59</td>\n",
              "      <td>2021-07-15</td>\n",
              "      <td>2021-07-19</td>\n",
              "      <td>2021-07-23</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.77</td>\n",
              "      <td>2021-04-23</td>\n",
              "      <td>2021-04-28</td>\n",
              "      <td>2021-05-06</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Stress Events</th>\n",
              "      <th>mean</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>New Normal</th>\n",
              "      <td>0.01%</td>\n",
              "      <td>-2.33%</td>\n",
              "      <td>2.72%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "print(\"==============Compare to DJIA===========\")\n",
        "%matplotlib inline\n",
        "# S&P 500: ^GSPC\n",
        "# Dow Jones Index: ^DJI\n",
        "# NASDAQ 100: ^NDX\n",
        "backtest_plot(df_account_value, \n",
        "              baseline_ticker = '^DJI', \n",
        "              baseline_start = df_account_value.loc[0,'date'],\n",
        "              baseline_end = df_account_value.loc[len(df_account_value)-1,'date'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "oBQx4bVQFi-a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from alpaca.data.historical import CryptoHistoricalDataClient\n",
        "from alpaca.data.requests import CryptoBarsRequest\n",
        "from alpaca.data.timeframe import TimeFrame\n",
        "import datetime as datetime\n",
        "from alpaca.data.live import StockDataStream\n",
        "from alpaca.trading.client import TradingClient\n",
        "from alpaca.trading.requests import MarketOrderRequest\n",
        "from alpaca.trading.enums import OrderSide, TimeInForce\n",
        "from alpaca.trading.requests import LimitOrderRequest\n",
        "# #I make a cryptocurrency trading bot and hand it a $1000 dollars and leave it to trade that unsupervised for a week.\n",
        "\n",
        "# # paper=True enables paper trading\n",
        "trading_client = TradingClient('AKBX22ZJIYXAAD7YPOB0', 'dfHX3SjDlg85SDtDAVQQqrfPTqD2npPKQj0ifMSf', paper=False)\n",
        "\n",
        "\n",
        "##Market Order \n",
        "# preparing orders\n",
        "market_order_data = MarketOrderRequest(\n",
        "                    symbol=\"NVDA\",\n",
        "                    qty=1,\n",
        "                    side=OrderSide.SELL,\n",
        "                    time_in_force=TimeInForce.DAY\n",
        "                    )\n",
        "\n",
        "# Market order\n",
        "market_order = trading_client.submit_order(\n",
        "                order_data=market_order_data\n",
        "               )\n",
        "\n",
        "# if(DRLEnsembleAGent.Sharpe > .4)\n",
        "\n",
        "# # Market order\n",
        "# market_order = trading_client.submit_order(fndsdsdsfdsdsf\n",
        "#                 order_data=market_order_data\n",
        "#                )\n",
        "\n",
        "# else\n",
        "\n",
        "# print(no trades for today)\n",
        "\n"
      ],
      "metadata": {
        "id": "vU91ottmE1tu"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install alpaca-py\n"
      ],
      "metadata": {
        "id": "sihEYckxItYW",
        "outputId": "3ed0adc1-0ed6-4434-d3e4-79c988cfd100",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting alpaca-py\n",
            "  Downloading alpaca_py-0.7.0-py3-none-any.whl (95 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m95.3/95.3 KB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting sseclient-py<2.0.0,>=1.7.2\n",
            "  Downloading sseclient_py-1.7.2-py2.py3-none-any.whl (8.4 kB)\n",
            "Requirement already satisfied: pandas<2.0.0,>=1.3.5 in /usr/local/lib/python3.8/dist-packages (from alpaca-py) (1.3.5)\n",
            "Collecting websockets<11.0,>=10.2\n",
            "  Downloading websockets-10.4-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (106 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.0/107.0 KB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: msgpack<2.0.0,>=1.0.3 in /usr/local/lib/python3.8/dist-packages (from alpaca-py) (1.0.4)\n",
            "Collecting requests<3.0.0,>=2.27.1\n",
            "  Downloading requests-2.28.2-py3-none-any.whl (62 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.8/62.8 KB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pydantic<2.0.0,>=1.9.0 in /usr/local/lib/python3.8/dist-packages (from alpaca-py) (1.10.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas<2.0.0,>=1.3.5->alpaca-py) (2.8.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas<2.0.0,>=1.3.5->alpaca-py) (1.21.6)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas<2.0.0,>=1.3.5->alpaca-py) (2022.7)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.8/dist-packages (from pydantic<2.0.0,>=1.9.0->alpaca-py) (4.4.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0,>=2.27.1->alpaca-py) (2022.12.7)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0,>=2.27.1->alpaca-py) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0,>=2.27.1->alpaca-py) (1.24.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0,>=2.27.1->alpaca-py) (2.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas<2.0.0,>=1.3.5->alpaca-py) (1.15.0)\n",
            "Installing collected packages: sseclient-py, websockets, requests, alpaca-py\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.25.1\n",
            "    Uninstalling requests-2.25.1:\n",
            "      Successfully uninstalled requests-2.25.1\n",
            "Successfully installed alpaca-py-0.7.0 requests-2.28.2 sseclient-py-1.7.2 websockets-10.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install alpaca"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mvUk15fGqgYk",
        "outputId": "c964a5fa-dc21-469e-8483-0f4e887a2b17"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting alpaca\n",
            "  Downloading alpaca-1.0.0.tar.gz (10 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: alpaca\n",
            "  Building wheel for alpaca (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for alpaca: filename=alpaca-1.0.0-py3-none-any.whl size=14089 sha256=ec2399ff8786717f3f1d8e7f7c8c6e77e3693cf1a8e09b651ce2afbdb2112560\n",
            "  Stored in directory: /root/.cache/pip/wheels/37/c0/67/09e87b64a3d712bead9cfccf3ac045a2926ab94714f4f9ea66\n",
            "Successfully built alpaca\n",
            "Installing collected packages: alpaca\n",
            "Successfully installed alpaca-1.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HQEPSXi3qgjP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from alpaca.data.historical import CryptoHistoricalDataClient\n",
        "from alpaca.data.requests import CryptoBarsRequest\n",
        "from alpaca.data.timeframe import TimeFrame\n",
        "import datetime as datetime\n",
        "from alpaca.data.live import StockDataStream\n",
        "from alpaca.trading.client import TradingClient\n",
        "from alpaca.trading.requests import MarketOrderRequest\n",
        "from alpaca.trading.enums import OrderSide, TimeInForce\n",
        "from alpaca.trading.requests import LimitOrderRequest\n",
        "\n",
        "# #I make a cryptocurrency trading bot and hand it a $1000 dollars and leave it to trade that unsupervised for a week.\n",
        "\n",
        "# # paper=True enables paper trading\n",
        "trading_client = TradingClient('PKJFG13OGRFGBCNGW757', 'xgUhyrffFUxOmuSchcONqHcbFc6kMjMHOWodxwH9', paper=True)\n",
        "\n",
        "\n",
        "##Market Order \n",
        "# preparing orders\n",
        "market_order_data = MarketOrderRequest(\n",
        "                    symbol=\"SPY\",\n",
        "                    qty=10,\n",
        "                    side=OrderSide.BUY,\n",
        "                    time_in_force=TimeInForce.DAY\n",
        "                    )\n",
        "\n",
        "# Market order\n",
        "market_order = trading_client.submit_order(\n",
        "                order_data=market_order_data\n",
        "               )\n",
        "\n",
        "\n",
        "## Twitter Sentiment Analysis \n",
        "\n",
        "# limit_order_data = LimitOrderRequest(\n",
        "#                     symbol=\"BTC/USD\",\n",
        "#                     limit_price=17000,\n",
        "#                     notional=4000,\n",
        "#                     side=OrderSide.SELL,\n",
        "#                     time_in_force=TimeInForce.FOK\n",
        "#                    )\n",
        "\n",
        "# # Limit order\n",
        "# limit_order = trading_client.submit_order(\n",
        "#                 order_data=limit_order_data\n",
        "#               )\n",
        "\n",
        "# wss_client = StockDataStream('AKIQ3ALAC0B2HOM1PPNE', 'dMekSYsqKqyoF6WM4lJ8YnE92ERDxeQVrhvrjAxa')\n",
        "\n",
        "# # async handler\n",
        "# async def quote_data_handler(data):\n",
        "#     # quote data will arrive here\n",
        "#     print(data)\n",
        "\n",
        "\n",
        "# wss_client.subscribe_quotes(quote_data_handler, \"AAPL\")\n",
        "\n",
        "# wss_client.run()\n",
        "\n",
        "# # no keys required for crypto data\n",
        "# client = CryptoHistoricalDataClient()\n",
        "\n",
        "# request_params = CryptoBarsRequest(\n",
        "#                         symbol_or_symbols=[\"BTC/USD\", \"ETH/USD\"],\n",
        "#                         timeframe=TimeFrame.Day,\n",
        "#                         start=datetime.datetime(2022, 12, 20, 14, 30)\n",
        "#                  )\n",
        "\n",
        "# bars = client.get_crypto_bars(request_params)\n",
        "# bars.df\n",
        "# print(bars)"
      ],
      "metadata": {
        "id": "pUYnw76WSQyk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "outputId": "3cb5737c-d989-4e85-cb5e-33a2a8676af6"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-0c7000bbaf13>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0malpaca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistorical\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCryptoHistoricalDataClient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0malpaca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequests\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCryptoBarsRequest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0malpaca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeframe\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTimeFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0malpaca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlive\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStockDataStream\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'alpaca'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.10"
    },
    "pycharm": {
      "stem_cell": {
        "cell_type": "raw",
        "metadata": {
          "collapsed": false
        },
        "source": []
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}