FinRL_ensemble_stock_trading_ICAIF_2020.ipynb

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      "name": "FinRL_ensemble_stock_trading_ICAIF_2020.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AI4Finance-LLC/FinRL/blob/master/FinRL_ensemble_stock_trading_ICAIF_2020.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXaoZs2lh1hi"
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      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading 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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPT0ipYE28wL",
        "outputId": "deed382f-2890-4a33-ec55-ed184ff656bb"
      },
      "source": [
        "# ## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "  Downloading lxml-4.6.3-cp37-cp37m-manylinux2014_x86_64.whl (6.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3 MB 39.0 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: finrl, pyfolio, empyrical, yfinance\n",
            "  Building wheel for finrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for finrl: filename=finrl-0.3.0-py3-none-any.whl size=62485 sha256=548f89ba7746371d9fbc7c063450a9d64cac3d3cc24c0cf3d586c8c32346369f\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-a16yk4jq/wheels/17/ff/bd/1bc602a0352762b0b24041b88536d803ae343ed0a711fcf55e\n",
            "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-py3-none-any.whl size=75773 sha256=c246e4d6986a2585399a31bd78f74d3e6de40bb6300dc6c801c9fa651300aa8c\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-a16yk4jq/wheels/ef/09/e5/2c1bf37c050d22557c080deb1be986d06424627c04aeca19b9\n",
            "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for empyrical: filename=empyrical-0.5.5-py3-none-any.whl size=39779 sha256=48520f2d16f17e1892702bd25a2f0fb5ae7f05879ccad90d3e7fb8c262bbd133\n",
            "  Stored in directory: /root/.cache/pip/wheels/d9/91/4b/654fcff57477efcf149eaca236da2fce991526cbab431bf312\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.63-py2.py3-none-any.whl size=23919 sha256=be0aec69566e0157a0dde765e8c91c34ab4796da6cf13b0b4c076fcab4035396\n",
            "  Stored in directory: /root/.cache/pip/wheels/fe/87/8b/7ec24486e001d3926537f5f7801f57a74d181be25b11157983\n",
            "Successfully built finrl pyfolio empyrical yfinance\n",
            "Installing collected packages: lxml, stable-baselines3, int-date, empyrical, yfinance, stockstats, pyfolio, finrl\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed empyrical-0.5.5 finrl-0.3.0 int-date-0.1.8 lxml-4.6.3 pyfolio-0.9.2+75.g4b901f6 stable-baselines3-1.1.0 stockstats-0.3.2 yfinance-0.1.63\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osBHhVysOEzi"
      },
      "source": [
        "\n",
        "<a id='1.2'></a>\n",
        "## 2.2. Check if the additional packages needed are present, if not install them. \n",
        "* Yahoo Finance API\n",
        "* pandas\n",
        "* numpy\n",
        "* matplotlib\n",
        "* stockstats\n",
        "* OpenAI gym\n",
        "* stable-baselines\n",
        "* tensorflow\n",
        "* pyfolio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGv01K8Sh1hn"
      },
      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EeMK7Uentj1V"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPqeTTwoh1hn"
      },
      "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.apps import config\n",
        "from finrl.neo_finrl.preprocessor.yahoodownloader import YahooDownloader\n",
        "from finrl.neo_finrl.preprocessor.preprocessors import FeatureEngineer, data_split\n",
        "from finrl.neo_finrl.env_stock_trading.env_stocktrading import StockTradingEnv\n",
        "from finrl.drl_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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2owTj985RW4"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9A8CN5R5PuZ"
      },
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A289rQWMh1hq"
      },
      "source": [
        "<a id='2'></a>\n",
        "# Part 3. Download Data\n",
        "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
        "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
        "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPeQ7iS-LoMm"
      },
      "source": [
        "\n",
        "\n",
        "-----\n",
        "class YahooDownloader:\n",
        "    Provides methods for retrieving daily stock data from\n",
        "    Yahoo Finance API\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        start_date : str\n",
        "            start date of the data (modified from config.py)\n",
        "        end_date : str\n",
        "            end date of the data (modified from config.py)\n",
        "        ticker_list : list\n",
        "            a list of stock tickers (modified from config.py)\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    fetch_data()\n",
        "        Fetches data from yahoo API\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "h3XJnvrbLp-C",
        "outputId": "68ab1da6-627f-476e-c471-1c655d54a4d7"
      },
      "source": [
        "# from config.py start_date is a string\n",
        "config.START_DATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2009-01-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzqRRTOX6aFu",
        "outputId": "524ed5d8-3b67-461f-ff9a-ae7494909e96"
      },
      "source": [
        "print(config.DOW_30_TICKER)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['AAPL', 'MSFT', 'JPM', 'V', 'RTX', 'PG', 'GS', 'NKE', 'DIS', 'AXP', 'HD', 'INTC', 'WMT', 'IBM', 'MRK', 'UNH', 'KO', 'CAT', 'TRV', 'JNJ', 'CVX', 'MCD', 'VZ', 'CSCO', 'XOM', 'BA', 'MMM', 'PFE', 'WBA', 'DD']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCKm4om-s9kE",
        "outputId": "cad51077-70f6-4e0d-acea-f90b8fc3e3ea"
      },
      "source": [
        "df = YahooDownloader(start_date = '2009-01-01',\n",
        "                     end_date = '2021-07-06',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
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            "[*********************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:  (94410, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "GiRuFOTOtj1Y",
        "outputId": "42241de4-9ab6-4944-a1fd-449672e3c286"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.787006</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.657365</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.655109</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.505757</td>\n",
              "      <td>40980600</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high        low      close     volume   tic  day\n",
              "0  2009-01-02   3.067143   3.251429   3.041429   2.787006  746015200  AAPL    4\n",
              "1  2009-01-02  18.570000  19.520000  18.400000  15.657365   10955700   AXP    4\n",
              "2  2009-01-02  42.799999  45.560001  42.779999  33.941101    7010200    BA    4\n",
              "3  2009-01-02  44.910000  46.980000  44.709999  32.655109    7117200   CAT    4\n",
              "4  2009-01-02  16.410000  17.000000  16.250000  12.505757   40980600  CSCO    4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "DSw4ZEzVtj1Z",
        "outputId": "470c69a4-b526-41eb-9e35-e325793b7529"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>94405</th>\n",
              "      <td>2021-07-02</td>\n",
              "      <td>235.830002</td>\n",
              "      <td>238.779999</td>\n",
              "      <td>235.820007</td>\n",
              "      <td>238.630005</td>\n",
              "      <td>4383700</td>\n",
              "      <td>V</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94406</th>\n",
              "      <td>2021-07-02</td>\n",
              "      <td>56.380001</td>\n",
              "      <td>56.570000</td>\n",
              "      <td>56.259998</td>\n",
              "      <td>55.812996</td>\n",
              "      <td>11387100</td>\n",
              "      <td>VZ</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94407</th>\n",
              "      <td>2021-07-02</td>\n",
              "      <td>49.110001</td>\n",
              "      <td>49.139999</td>\n",
              "      <td>47.570000</td>\n",
              "      <td>48.169998</td>\n",
              "      <td>15863200</td>\n",
              "      <td>WBA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94408</th>\n",
              "      <td>2021-07-02</td>\n",
              "      <td>139.350006</td>\n",
              "      <td>141.080002</td>\n",
              "      <td>139.350006</td>\n",
              "      <td>140.110001</td>\n",
              "      <td>8760200</td>\n",
              "      <td>WMT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94409</th>\n",
              "      <td>2021-07-02</td>\n",
              "      <td>63.009998</td>\n",
              "      <td>63.320000</td>\n",
              "      <td>62.419998</td>\n",
              "      <td>63.169998</td>\n",
              "      <td>14555300</td>\n",
              "      <td>XOM</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             date        open        high  ...    volume  tic  day\n",
              "94405  2021-07-02  235.830002  238.779999  ...   4383700    V    4\n",
              "94406  2021-07-02   56.380001   56.570000  ...  11387100   VZ    4\n",
              "94407  2021-07-02   49.110001   49.139999  ...  15863200  WBA    4\n",
              "94408  2021-07-02  139.350006  141.080002  ...   8760200  WMT    4\n",
              "94409  2021-07-02   63.009998   63.320000  ...  14555300  XOM    4\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV3HrZHLh1hy",
        "outputId": "90359d3b-bdcf-4807-87de-843ff729aaa2"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(94410, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "4hYkeaPiICHS",
        "outputId": "7606cbff-735a-4e34-8059-d3a2f2635921"
      },
      "source": [
        "df.sort_values(['date','tic']).head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.787006</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.657365</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.655109</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.505757</td>\n",
              "      <td>40980600</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high        low      close     volume   tic  day\n",
              "0  2009-01-02   3.067143   3.251429   3.041429   2.787006  746015200  AAPL    4\n",
              "1  2009-01-02  18.570000  19.520000  18.400000  15.657365   10955700   AXP    4\n",
              "2  2009-01-02  42.799999  45.560001  42.779999  33.941101    7010200    BA    4\n",
              "3  2009-01-02  44.910000  46.980000  44.709999  32.655109    7117200   CAT    4\n",
              "4  2009-01-02  16.410000  17.000000  16.250000  12.505757   40980600  CSCO    4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2vryMsdNL9H",
        "outputId": "663be644-2580-47d7-b535-8edac5088744"
      },
      "source": [
        "len(df.tic.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XcNyXa7RNPrF",
        "outputId": "7e7d1953-b3e3-4486-ccf4-86c80372b9a6"
      },
      "source": [
        "df.tic.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XOM     3147\n",
              "MSFT    3147\n",
              "WMT     3147\n",
              "DD      3147\n",
              "CAT     3147\n",
              "IBM     3147\n",
              "PFE     3147\n",
              "AXP     3147\n",
              "VZ      3147\n",
              "MMM     3147\n",
              "WBA     3147\n",
              "INTC    3147\n",
              "RTX     3147\n",
              "V       3147\n",
              "UNH     3147\n",
              "JNJ     3147\n",
              "TRV     3147\n",
              "CVX     3147\n",
              "BA      3147\n",
              "DIS     3147\n",
              "MCD     3147\n",
              "JPM     3147\n",
              "AAPL    3147\n",
              "HD      3147\n",
              "CSCO    3147\n",
              "PG      3147\n",
              "GS      3147\n",
              "MRK     3147\n",
              "NKE     3147\n",
              "KO      3147\n",
              "Name: tic, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqC6c40Zh1iH"
      },
      "source": [
        "# Part 4: Preprocess Data\n",
        "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
        "* Add technical indicators. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
        "* Add turbulence index. Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kM5bH9uroCeg"
      },
      "source": [
        "tech_indicators = ['macd',\n",
        " 'rsi_30',\n",
        " 'cci_30',\n",
        " 'dx_30']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jgXfBcjxtj1a",
        "pycharm": {
          "name": "#%%\n"
        },
        "outputId": "1e5c4398-302f-40d9-95eb-08eb91e81f35"
      },
      "source": [
        "fe = FeatureEngineer(\n",
        "                    use_technical_indicator=True,\n",
        "                    tech_indicator_list = tech_indicators,\n",
        "                    use_turbulence=True,\n",
        "                    user_defined_feature = False)\n",
        "\n",
        "processed = fe.preprocess_data(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully added technical indicators\n",
            "Successfully added turbulence index\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "grvhGJJII3Xn",
        "outputId": "dceee58f-faaf-44c8-c516-2ab5b58f506d"
      },
      "source": [
        "processed.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>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>77094</th>\n",
              "      <td>2019-03-20</td>\n",
              "      <td>257.429993</td>\n",
              "      <td>257.690002</td>\n",
              "      <td>249.970001</td>\n",
              "      <td>241.524948</td>\n",
              "      <td>4193500</td>\n",
              "      <td>UNH</td>\n",
              "      <td>2</td>\n",
              "      <td>-1.587316</td>\n",
              "      <td>47.734497</td>\n",
              "      <td>-18.022271</td>\n",
              "      <td>14.007647</td>\n",
              "      <td>27.586209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35558</th>\n",
              "      <td>2013-09-18</td>\n",
              "      <td>166.500000</td>\n",
              "      <td>169.089996</td>\n",
              "      <td>165.639999</td>\n",
              "      <td>148.673782</td>\n",
              "      <td>3422400</td>\n",
              "      <td>GS</td>\n",
              "      <td>2</td>\n",
              "      <td>1.733906</td>\n",
              "      <td>59.056607</td>\n",
              "      <td>134.064628</td>\n",
              "      <td>31.833754</td>\n",
              "      <td>27.052640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14287</th>\n",
              "      <td>2010-11-22</td>\n",
              "      <td>36.880001</td>\n",
              "      <td>37.049999</td>\n",
              "      <td>36.520000</td>\n",
              "      <td>32.184544</td>\n",
              "      <td>6558800</td>\n",
              "      <td>DIS</td>\n",
              "      <td>0</td>\n",
              "      <td>0.512404</td>\n",
              "      <td>57.142069</td>\n",
              "      <td>59.194120</td>\n",
              "      <td>8.262900</td>\n",
              "      <td>21.145445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18055</th>\n",
              "      <td>2011-05-23</td>\n",
              "      <td>19.272499</td>\n",
              "      <td>19.434999</td>\n",
              "      <td>19.150000</td>\n",
              "      <td>17.912233</td>\n",
              "      <td>18258400</td>\n",
              "      <td>V</td>\n",
              "      <td>0</td>\n",
              "      <td>0.141171</td>\n",
              "      <td>50.521848</td>\n",
              "      <td>-74.661662</td>\n",
              "      <td>9.774479</td>\n",
              "      <td>15.165300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59426</th>\n",
              "      <td>2016-11-11</td>\n",
              "      <td>46.700001</td>\n",
              "      <td>46.900002</td>\n",
              "      <td>46.509998</td>\n",
              "      <td>37.822994</td>\n",
              "      <td>16425500</td>\n",
              "      <td>VZ</td>\n",
              "      <td>4</td>\n",
              "      <td>-0.724006</td>\n",
              "      <td>35.162301</td>\n",
              "      <td>-110.407233</td>\n",
              "      <td>51.312178</td>\n",
              "      <td>29.430765</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             date        open        high  ...      cci_30      dx_30  turbulence\n",
              "77094  2019-03-20  257.429993  257.690002  ...  -18.022271  14.007647   27.586209\n",
              "35558  2013-09-18  166.500000  169.089996  ...  134.064628  31.833754   27.052640\n",
              "14287  2010-11-22   36.880001   37.049999  ...   59.194120   8.262900   21.145445\n",
              "18055  2011-05-23   19.272499   19.434999  ...  -74.661662   9.774479   15.165300\n",
              "59426  2016-11-11   46.700001   46.900002  ... -110.407233  51.312178   29.430765\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2zqII8rMIqn",
        "outputId": "5ac63d59-cf39-49a2-cfe7-7fa859a9c96b"
      },
      "source": [
        "stock_dimension = len(processed.tic.unique())\n",
        "state_space = 1 + 2*stock_dimension + len(tech_indicators)*stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stock Dimension: 30, State Space: 181\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWyp84Ltto19"
      },
      "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\": tech_indicators,\n",
        "    \"action_space\": stock_dimension, \n",
        "    \"reward_scaling\": 1e-4,\n",
        "    \"print_verbosity\":5\n",
        "    \n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMNR5nHjh1iz"
      },
      "source": [
        "<a id='5'></a>\n",
        "# Part 6: Implement DRL Algorithms\n",
        "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
        "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
        "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
        "design their own DRL algorithms by adapting these DRL algorithms.\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",
      "metadata": {
        "id": "v-gthCxMtj1d"
      },
      "source": [
        "rebalance_window = 63 # rebalance_window is the number of days to retrain the model\n",
        "validation_window = 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",
        "train_start = '2009-01-01'\n",
        "train_end = '2020-04-01'\n",
        "val_test_start = '2020-04-01'\n",
        "val_test_end = '2021-07-20'\n",
        "\n",
        "ensemble_agent = DRLEnsembleAgent(df=processed,\n",
        "                 train_period=(train_start,train_end),\n",
        "                 val_test_period=(val_test_start,val_test_end),\n",
        "                 rebalance_window=rebalance_window, \n",
        "                 validation_window=validation_window, \n",
        "                 **env_kwargs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsfEHa_Etj1d",
        "scrolled": false
      },
      "source": [
        "A2C_model_kwargs = {\n",
        "                    'n_steps': 5,\n",
        "                    'ent_coef': 0.01,\n",
        "                    'learning_rate': 0.0005\n",
        "                    }\n",
        "\n",
        "PPO_model_kwargs = {\n",
        "                    \"ent_coef\":0.01,\n",
        "                    \"n_steps\": 2048,\n",
        "                    \"learning_rate\": 0.00025,\n",
        "                    \"batch_size\": 64\n",
        "                    }\n",
        "\n",
        "DDPG_model_kwargs = {\n",
        "                      #\"action_noise\":\"ornstein_uhlenbeck\",\n",
        "                      \"buffer_size\": 100_000,\n",
        "                      \"learning_rate\": 0.000005,\n",
        "                      \"batch_size\": 64\n",
        "                    }\n",
        "\n",
        "timesteps_dict = {'a2c' : 30_000, \n",
        "                 'ppo' : 100_000, \n",
        "                 'ddpg' : 10_000\n",
        "                 }\n",
        "\n",
        "\n",
        "timesteps_dict = {'a2c' : 10_000, \n",
        "                 'ppo' : 10_000, \n",
        "                 'ddpg' : 10_000\n",
        "                 }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_1lyCECstj1e",
        "scrolled": true,
        "outputId": "b82aa928-8212-4238-f176-b9f15bfd73e8"
      },
      "source": [
        "df_summary = ensemble_agent.run_ensemble_strategy(A2C_model_kwargs,\n",
        "                                                 PPO_model_kwargs,\n",
        "                                                 DDPG_model_kwargs,\n",
        "                                                 timesteps_dict)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "============Start Ensemble Strategy============\n",
            "============================================\n",
            "turbulence_threshold:  208.11619437156776\n",
            "======Model training from:  2009-01-01 to  2020-04-02\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/a2c/a2c_126_3\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 5        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0.0748   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | -60.3    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 2.58     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 11       |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -82.9    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 4.07     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 300      |\n",
            "|    time_elapsed       | 17       |\n",
            "|    total_timesteps    | 1500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0.0161   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 299      |\n",
            "|    policy_loss        | -231     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 30.8     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 400      |\n",
            "|    time_elapsed       | 22       |\n",
            "|    total_timesteps    | 2000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0.0509   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 399      |\n",
            "|    policy_loss        | 0.858    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 2.32     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 500      |\n",
            "|    time_elapsed       | 28       |\n",
            "|    total_timesteps    | 2500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -0.0068  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 499      |\n",
            "|    policy_loss        | 282      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 70.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 600      |\n",
            "|    time_elapsed       | 33       |\n",
            "|    total_timesteps    | 3000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -0.02    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 599      |\n",
            "|    policy_loss        | 45.7     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 2.1      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 700      |\n",
            "|    time_elapsed       | 39       |\n",
            "|    total_timesteps    | 3500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0.00494  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 699      |\n",
            "|    policy_loss        | -181     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 23.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 800      |\n",
            "|    time_elapsed       | 45       |\n",
            "|    total_timesteps    | 4000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.183   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 799      |\n",
            "|    policy_loss        | -158     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 15.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 900      |\n",
            "|    time_elapsed       | 51       |\n",
            "|    total_timesteps    | 4500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 899      |\n",
            "|    policy_loss        | -74.4    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 3.74     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 1000     |\n",
            "|    time_elapsed       | 56       |\n",
            "|    total_timesteps    | 5000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 999      |\n",
            "|    policy_loss        | -32.5    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.698    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 1100     |\n",
            "|    time_elapsed       | 62       |\n",
            "|    total_timesteps    | 5500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1099     |\n",
            "|    policy_loss        | -375     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 99.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 1200     |\n",
            "|    time_elapsed       | 68       |\n",
            "|    total_timesteps    | 6000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.06     |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1199     |\n",
            "|    policy_loss        | -88.7    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 5.19     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 88        |\n",
            "|    iterations         | 1300      |\n",
            "|    time_elapsed       | 73        |\n",
            "|    total_timesteps    | 6500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.8     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1299      |\n",
            "|    policy_loss        | -225      |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 33.6      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 1400     |\n",
            "|    time_elapsed       | 79       |\n",
            "|    total_timesteps    | 7000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -0.0747  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1399     |\n",
            "|    policy_loss        | 67.7     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 4.01     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 1500     |\n",
            "|    time_elapsed       | 85       |\n",
            "|    total_timesteps    | 7500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1499     |\n",
            "|    policy_loss        | 0.367    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.02     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 88        |\n",
            "|    iterations         | 1600      |\n",
            "|    time_elapsed       | 90        |\n",
            "|    total_timesteps    | 8000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.8     |\n",
            "|    explained_variance | -5.96e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1599      |\n",
            "|    policy_loss        | 3.49      |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 2.72      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 1700     |\n",
            "|    time_elapsed       | 96       |\n",
            "|    total_timesteps    | 8500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -1.17    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1699     |\n",
            "|    policy_loss        | -643     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 267      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 1800     |\n",
            "|    time_elapsed       | 101      |\n",
            "|    total_timesteps    | 9000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.65     |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1799     |\n",
            "|    policy_loss        | -7.85    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.0362   |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 1900     |\n",
            "|    time_elapsed       | 107      |\n",
            "|    total_timesteps    | 9500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0.0159   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1899     |\n",
            "|    policy_loss        | -96.7    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 6.05     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 2000     |\n",
            "|    time_elapsed       | 113      |\n",
            "|    total_timesteps    | 10000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -0.00985 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1999     |\n",
            "|    policy_loss        | 257      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 36.8     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2020-04-02 to  2020-07-02\n",
            "A2C Sharpe Ratio:  0.21611230010234098\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ppo/ppo_126_3\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 95   |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 21   |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n",
            "day: 2830, episode: 5\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2604356.22\n",
            "total_reward: 1604356.22\n",
            "total_cost: 334507.67\n",
            "total_trades: 81935\n",
            "Sharpe: 0.595\n",
            "=================================\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 92         |\n",
            "|    iterations           | 2          |\n",
            "|    time_elapsed         | 44         |\n",
            "|    total_timesteps      | 4096       |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02118537 |\n",
            "|    clip_fraction        | 0.258      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -42.6      |\n",
            "|    explained_variance   | -0.0124    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 7.96       |\n",
            "|    n_updates            | 10         |\n",
            "|    policy_gradient_loss | -0.0275    |\n",
            "|    std                  | 1          |\n",
            "|    value_loss           | 13.6       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 92          |\n",
            "|    iterations           | 3           |\n",
            "|    time_elapsed         | 66          |\n",
            "|    total_timesteps      | 6144        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.019661052 |\n",
            "|    clip_fraction        | 0.187       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | 0.0072      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 15.2        |\n",
            "|    n_updates            | 20          |\n",
            "|    policy_gradient_loss | -0.018      |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 53          |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 91          |\n",
            "|    iterations           | 4           |\n",
            "|    time_elapsed         | 89          |\n",
            "|    total_timesteps      | 8192        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.019053053 |\n",
            "|    clip_fraction        | 0.213       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | -0.0174     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 35.7        |\n",
            "|    n_updates            | 30          |\n",
            "|    policy_gradient_loss | -0.0187     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 63.5        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 91          |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 111         |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.025088701 |\n",
            "|    clip_fraction        | 0.297       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | 0.0242      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 9.2         |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0259     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 19.1        |\n",
            "-----------------------------------------\n",
            "======PPO Validation from:  2020-04-02 to  2020-07-02\n",
            "PPO Sharpe Ratio:  0.19780836599213453\n",
            "======DDPG Training========\n",
            "{'buffer_size': 100000, 'learning_rate': 5e-06, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ddpg/ddpg_126_3\n",
            "day: 2830, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2988619.83\n",
            "total_reward: 1988619.83\n",
            "total_cost: 1522.14\n",
            "total_trades: 31976\n",
            "Sharpe: 0.578\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 41       |\n",
            "|    time_elapsed    | 271      |\n",
            "|    total timesteps | 11324    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -56.1    |\n",
            "|    critic_loss     | 33       |\n",
            "|    learning_rate   | 5e-06    |\n",
            "|    n_updates       | 8493     |\n",
            "---------------------------------\n",
            "======DDPG Validation from:  2020-04-02 to  2020-07-02\n",
            "======Best Model Retraining from:  2009-01-01 to  2020-07-02\n",
            "======Trading from:  2020-07-02 to  2020-10-01\n",
            "============================================\n",
            "turbulence_threshold:  208.11619437156776\n",
            "======Model training from:  2009-01-01 to  2020-07-02\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/a2c/a2c_189_3\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 5        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0.503    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | -66.5    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 2.71     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 11       |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -0.498   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -49.2    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.64     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 300      |\n",
            "|    time_elapsed       | 17       |\n",
            "|    total_timesteps    | 1500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.193   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 299      |\n",
            "|    policy_loss        | -147     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 11.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 400      |\n",
            "|    time_elapsed       | 22       |\n",
            "|    total_timesteps    | 2000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 399      |\n",
            "|    policy_loss        | 58.1     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 2.02     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 500      |\n",
            "|    time_elapsed       | 28       |\n",
            "|    total_timesteps    | 2500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 2.38e-07 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 499      |\n",
            "|    policy_loss        | 366      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 114      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 600      |\n",
            "|    time_elapsed       | 34       |\n",
            "|    total_timesteps    | 3000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0.496    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 599      |\n",
            "|    policy_loss        | 85.3     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 4.87     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 700      |\n",
            "|    time_elapsed       | 40       |\n",
            "|    total_timesteps    | 3500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 699      |\n",
            "|    policy_loss        | -86      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 4.58     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 800      |\n",
            "|    time_elapsed       | 45       |\n",
            "|    total_timesteps    | 4000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.00653  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 799      |\n",
            "|    policy_loss        | -35.3    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.78     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 900      |\n",
            "|    time_elapsed       | 51       |\n",
            "|    total_timesteps    | 4500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 899      |\n",
            "|    policy_loss        | 64.5     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 3.51     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 1000     |\n",
            "|    time_elapsed       | 57       |\n",
            "|    total_timesteps    | 5000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 999      |\n",
            "|    policy_loss        | 47.5     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.9      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 1100     |\n",
            "|    time_elapsed       | 62       |\n",
            "|    total_timesteps    | 5500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1099     |\n",
            "|    policy_loss        | -327     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 57       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 1200     |\n",
            "|    time_elapsed       | 68       |\n",
            "|    total_timesteps    | 6000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0.552    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1199     |\n",
            "|    policy_loss        | -144     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 11.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 1300     |\n",
            "|    time_elapsed       | 74       |\n",
            "|    total_timesteps    | 6500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1299     |\n",
            "|    policy_loss        | 153      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 16.2     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 1400     |\n",
            "|    time_elapsed       | 79       |\n",
            "|    total_timesteps    | 7000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1399     |\n",
            "|    policy_loss        | 268      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 49.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 1500     |\n",
            "|    time_elapsed       | 85       |\n",
            "|    total_timesteps    | 7500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1499     |\n",
            "|    policy_loss        | -35.7    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 3.02     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 1600     |\n",
            "|    time_elapsed       | 91       |\n",
            "|    total_timesteps    | 8000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 2.38e-07 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1599     |\n",
            "|    policy_loss        | -0.504   |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.38     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 1700     |\n",
            "|    time_elapsed       | 96       |\n",
            "|    total_timesteps    | 8500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0.0178   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1699     |\n",
            "|    policy_loss        | 0.607    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 31.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 1800     |\n",
            "|    time_elapsed       | 102      |\n",
            "|    total_timesteps    | 9000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | -0.487   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1799     |\n",
            "|    policy_loss        | 1.48     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.191    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 1900     |\n",
            "|    time_elapsed       | 108      |\n",
            "|    total_timesteps    | 9500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0.446    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1899     |\n",
            "|    policy_loss        | 62       |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 2.16     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 2000     |\n",
            "|    time_elapsed       | 114      |\n",
            "|    total_timesteps    | 10000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1999     |\n",
            "|    policy_loss        | -16.1    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.693    |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2020-07-02 to  2020-10-01\n",
            "A2C Sharpe Ratio:  0.19728148751783114\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ppo/ppo_189_3\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 93   |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 21   |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n",
            "day: 2893, episode: 5\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2964002.94\n",
            "total_reward: 1964002.94\n",
            "total_cost: 351618.76\n",
            "total_trades: 83925\n",
            "Sharpe: 0.623\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 2           |\n",
            "|    time_elapsed         | 45          |\n",
            "|    total_timesteps      | 4096        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020383948 |\n",
            "|    clip_fraction        | 0.247       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -0.00345    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 7.82        |\n",
            "|    n_updates            | 10          |\n",
            "|    policy_gradient_loss | -0.029      |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 15.4        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 3           |\n",
            "|    time_elapsed         | 68          |\n",
            "|    total_timesteps      | 6144        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020762123 |\n",
            "|    clip_fraction        | 0.248       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | 0.00715     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 18.9        |\n",
            "|    n_updates            | 20          |\n",
            "|    policy_gradient_loss | -0.0212     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 66.9        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 90         |\n",
            "|    iterations           | 4          |\n",
            "|    time_elapsed         | 90         |\n",
            "|    total_timesteps      | 8192       |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02135497 |\n",
            "|    clip_fraction        | 0.23       |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -42.9      |\n",
            "|    explained_variance   | -0.00366   |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 10.6       |\n",
            "|    n_updates            | 30         |\n",
            "|    policy_gradient_loss | -0.0193    |\n",
            "|    std                  | 1.01       |\n",
            "|    value_loss           | 45.2       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 113         |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.025221135 |\n",
            "|    clip_fraction        | 0.286       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43         |\n",
            "|    explained_variance   | -0.0139     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 8.37        |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.024      |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 15.1        |\n",
            "-----------------------------------------\n",
            "======PPO Validation from:  2020-07-02 to  2020-10-01\n",
            "PPO Sharpe Ratio:  0.11966300553732338\n",
            "======DDPG Training========\n",
            "{'buffer_size': 100000, 'learning_rate': 5e-06, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ddpg/ddpg_189_3\n",
            "day: 2893, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2733533.47\n",
            "total_reward: 1733533.47\n",
            "total_cost: 1754.74\n",
            "total_trades: 48328\n",
            "Sharpe: 0.565\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 40       |\n",
            "|    time_elapsed    | 288      |\n",
            "|    total timesteps | 11576    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 29.8     |\n",
            "|    critic_loss     | 48.7     |\n",
            "|    learning_rate   | 5e-06    |\n",
            "|    n_updates       | 8682     |\n",
            "---------------------------------\n",
            "======DDPG Validation from:  2020-07-02 to  2020-10-01\n",
            "======Best Model Retraining from:  2009-01-01 to  2020-10-01\n",
            "======Trading from:  2020-10-01 to  2020-12-31\n",
            "============================================\n",
            "turbulence_threshold:  208.11619437156776\n",
            "======Model training from:  2009-01-01 to  2020-10-01\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/a2c/a2c_252_3\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 5        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.259   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | 15.5     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.236    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 11       |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.0669  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -18.6    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.548    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 300      |\n",
            "|    time_elapsed       | 17       |\n",
            "|    total_timesteps    | 1500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0.0673   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 299      |\n",
            "|    policy_loss        | -313     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 53.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 400      |\n",
            "|    time_elapsed       | 23       |\n",
            "|    total_timesteps    | 2000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.0494  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 399      |\n",
            "|    policy_loss        | 91.6     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 5.8      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 500      |\n",
            "|    time_elapsed       | 28       |\n",
            "|    total_timesteps    | 2500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0.0656   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 499      |\n",
            "|    policy_loss        | 813      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 419      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 600      |\n",
            "|    time_elapsed       | 34       |\n",
            "|    total_timesteps    | 3000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 599      |\n",
            "|    policy_loss        | -30.6    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.673    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 700      |\n",
            "|    time_elapsed       | 40       |\n",
            "|    total_timesteps    | 3500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0.0481   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 699      |\n",
            "|    policy_loss        | -96.5    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 5.75     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 800      |\n",
            "|    time_elapsed       | 45       |\n",
            "|    total_timesteps    | 4000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.368   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 799      |\n",
            "|    policy_loss        | -13.9    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.208    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 900      |\n",
            "|    time_elapsed       | 51       |\n",
            "|    total_timesteps    | 4500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 899      |\n",
            "|    policy_loss        | 53.2     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 2.05     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 1000     |\n",
            "|    time_elapsed       | 57       |\n",
            "|    total_timesteps    | 5000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.406   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 999      |\n",
            "|    policy_loss        | -52.7    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 4.05     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 1100     |\n",
            "|    time_elapsed       | 63       |\n",
            "|    total_timesteps    | 5500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0.0247   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1099     |\n",
            "|    policy_loss        | 187      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 32.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 1200     |\n",
            "|    time_elapsed       | 69       |\n",
            "|    total_timesteps    | 6000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1199     |\n",
            "|    policy_loss        | 77.8     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 4.76     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 1300     |\n",
            "|    time_elapsed       | 74       |\n",
            "|    total_timesteps    | 6500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1299     |\n",
            "|    policy_loss        | 99.7     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 8.72     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 1400     |\n",
            "|    time_elapsed       | 80       |\n",
            "|    total_timesteps    | 7000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1399     |\n",
            "|    policy_loss        | -59      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 9.2      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 1500     |\n",
            "|    time_elapsed       | 86       |\n",
            "|    total_timesteps    | 7500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.155   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1499     |\n",
            "|    policy_loss        | 55.3     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 3.35     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 1600     |\n",
            "|    time_elapsed       | 92       |\n",
            "|    total_timesteps    | 8000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.0239  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1599     |\n",
            "|    policy_loss        | 5.55     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.404    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 1700     |\n",
            "|    time_elapsed       | 97       |\n",
            "|    total_timesteps    | 8500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1699     |\n",
            "|    policy_loss        | 171      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 20.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 1800     |\n",
            "|    time_elapsed       | 103      |\n",
            "|    total_timesteps    | 9000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.409   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1799     |\n",
            "|    policy_loss        | 28.1     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.07     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 1900     |\n",
            "|    time_elapsed       | 109      |\n",
            "|    total_timesteps    | 9500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1899     |\n",
            "|    policy_loss        | -1.48    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 3.36     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 2000     |\n",
            "|    time_elapsed       | 115      |\n",
            "|    total_timesteps    | 10000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 2.71e-05 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1999     |\n",
            "|    policy_loss        | -178     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 23.3     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2020-10-01 to  2020-12-31\n",
            "A2C Sharpe Ratio:  0.2614387542464737\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ppo/ppo_252_3\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 93   |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 21   |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n",
            "day: 2956, episode: 5\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3208541.25\n",
            "total_reward: 2208541.25\n",
            "total_cost: 358079.38\n",
            "total_trades: 85756\n",
            "Sharpe: 0.672\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 2           |\n",
            "|    time_elapsed         | 45          |\n",
            "|    total_timesteps      | 4096        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020903485 |\n",
            "|    clip_fraction        | 0.227       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -0.0158     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 4.19        |\n",
            "|    n_updates            | 10          |\n",
            "|    policy_gradient_loss | -0.0291     |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 12          |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 89          |\n",
            "|    iterations           | 3           |\n",
            "|    time_elapsed         | 68          |\n",
            "|    total_timesteps      | 6144        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.022098038 |\n",
            "|    clip_fraction        | 0.221       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | -0.0143     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 52.9        |\n",
            "|    n_updates            | 20          |\n",
            "|    policy_gradient_loss | -0.021      |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 49.4        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 89         |\n",
            "|    iterations           | 4          |\n",
            "|    time_elapsed         | 91         |\n",
            "|    total_timesteps      | 8192       |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.01743197 |\n",
            "|    clip_fraction        | 0.205      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -42.8      |\n",
            "|    explained_variance   | 0.00124    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 35.5       |\n",
            "|    n_updates            | 30         |\n",
            "|    policy_gradient_loss | -0.022     |\n",
            "|    std                  | 1.01       |\n",
            "|    value_loss           | 74.4       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 89          |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 114         |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.026599832 |\n",
            "|    clip_fraction        | 0.248       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.9       |\n",
            "|    explained_variance   | -0.0274     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3.15        |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0258     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 13.6        |\n",
            "-----------------------------------------\n",
            "======PPO Validation from:  2020-10-01 to  2020-12-31\n",
            "PPO Sharpe Ratio:  0.22569366831080892\n",
            "======DDPG Training========\n",
            "{'buffer_size': 100000, 'learning_rate': 5e-06, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ddpg/ddpg_252_3\n",
            "day: 2956, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 6574831.87\n",
            "total_reward: 5574831.87\n",
            "total_cost: 2901.04\n",
            "total_trades: 38698\n",
            "Sharpe: 0.907\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 42       |\n",
            "|    time_elapsed    | 280      |\n",
            "|    total timesteps | 11828    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -62.9    |\n",
            "|    critic_loss     | 62.5     |\n",
            "|    learning_rate   | 5e-06    |\n",
            "|    n_updates       | 8871     |\n",
            "---------------------------------\n",
            "======DDPG Validation from:  2020-10-01 to  2020-12-31\n",
            "======Best Model Retraining from:  2009-01-01 to  2020-12-31\n",
            "======Trading from:  2020-12-31 to  2021-04-05\n",
            "============================================\n",
            "turbulence_threshold:  208.11619437156776\n",
            "======Model training from:  2009-01-01 to  2020-12-31\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/a2c/a2c_315_3\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 5        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -0.146   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | -86.9    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 3.97     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 11       |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | 0.0915   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -96.7    |\n",
            "|    std                | 0.998    |\n",
            "|    value_loss         | 4.99     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 300      |\n",
            "|    time_elapsed       | 17       |\n",
            "|    total_timesteps    | 1500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | -0.11    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 299      |\n",
            "|    policy_loss        | -239     |\n",
            "|    std                | 0.998    |\n",
            "|    value_loss         | 31.8     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 400       |\n",
            "|    time_elapsed       | 23        |\n",
            "|    total_timesteps    | 2000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.5     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 399       |\n",
            "|    policy_loss        | -22.5     |\n",
            "|    std                | 0.999     |\n",
            "|    value_loss         | 1.46      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 500      |\n",
            "|    time_elapsed       | 29       |\n",
            "|    total_timesteps    | 2500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 499      |\n",
            "|    policy_loss        | 309      |\n",
            "|    std                | 0.999    |\n",
            "|    value_loss         | 76       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 600      |\n",
            "|    time_elapsed       | 35       |\n",
            "|    total_timesteps    | 3000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 599      |\n",
            "|    policy_loss        | -17.1    |\n",
            "|    std                | 0.998    |\n",
            "|    value_loss         | 46.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 700      |\n",
            "|    time_elapsed       | 41       |\n",
            "|    total_timesteps    | 3500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | 0.0538   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 699      |\n",
            "|    policy_loss        | 22.3     |\n",
            "|    std                | 0.999    |\n",
            "|    value_loss         | 2.22     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 800      |\n",
            "|    time_elapsed       | 47       |\n",
            "|    total_timesteps    | 4000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 799      |\n",
            "|    policy_loss        | -54.8    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 2.32     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 900      |\n",
            "|    time_elapsed       | 53       |\n",
            "|    total_timesteps    | 4500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 899      |\n",
            "|    policy_loss        | 89.5     |\n",
            "|    std                | 0.999    |\n",
            "|    value_loss         | 6.52     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 1000     |\n",
            "|    time_elapsed       | 58       |\n",
            "|    total_timesteps    | 5000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -0.0476  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 999      |\n",
            "|    policy_loss        | -114     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 9.5      |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 84        |\n",
            "|    iterations         | 1100      |\n",
            "|    time_elapsed       | 64        |\n",
            "|    total_timesteps    | 5500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.5     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1099      |\n",
            "|    policy_loss        | 238       |\n",
            "|    std                | 0.999     |\n",
            "|    value_loss         | 38        |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 1200     |\n",
            "|    time_elapsed       | 70       |\n",
            "|    total_timesteps    | 6000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1199     |\n",
            "|    policy_loss        | 19.9     |\n",
            "|    std                | 0.998    |\n",
            "|    value_loss         | 13.8     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 1300     |\n",
            "|    time_elapsed       | 76       |\n",
            "|    total_timesteps    | 6500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1299     |\n",
            "|    policy_loss        | 14.1     |\n",
            "|    std                | 0.996    |\n",
            "|    value_loss         | 0.969    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 1400     |\n",
            "|    time_elapsed       | 82       |\n",
            "|    total_timesteps    | 7000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1399     |\n",
            "|    policy_loss        | 86       |\n",
            "|    std                | 0.997    |\n",
            "|    value_loss         | 5.16     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 84        |\n",
            "|    iterations         | 1500      |\n",
            "|    time_elapsed       | 88        |\n",
            "|    total_timesteps    | 7500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.5     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1499      |\n",
            "|    policy_loss        | -191      |\n",
            "|    std                | 0.999     |\n",
            "|    value_loss         | 22.8      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 1600     |\n",
            "|    time_elapsed       | 94       |\n",
            "|    total_timesteps    | 8000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1599     |\n",
            "|    policy_loss        | 6.73     |\n",
            "|    std                | 0.999    |\n",
            "|    value_loss         | 0.382    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 1700     |\n",
            "|    time_elapsed       | 100      |\n",
            "|    total_timesteps    | 8500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1699     |\n",
            "|    policy_loss        | 271      |\n",
            "|    std                | 0.999    |\n",
            "|    value_loss         | 50.5     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 84        |\n",
            "|    iterations         | 1800      |\n",
            "|    time_elapsed       | 105       |\n",
            "|    total_timesteps    | 9000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.5     |\n",
            "|    explained_variance | -0.000778 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1799      |\n",
            "|    policy_loss        | 605       |\n",
            "|    std                | 0.999     |\n",
            "|    value_loss         | 342       |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 1900     |\n",
            "|    time_elapsed       | 111      |\n",
            "|    total_timesteps    | 9500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1899     |\n",
            "|    policy_loss        | 99.3     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 6.04     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 2000     |\n",
            "|    time_elapsed       | 117      |\n",
            "|    total_timesteps    | 10000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1999     |\n",
            "|    policy_loss        | 101      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 9.05     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2020-12-31 to  2021-04-05\n",
            "A2C Sharpe Ratio:  0.2323908322650918\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ppo/ppo_315_3\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 90   |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 22   |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n",
            "day: 3019, episode: 5\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3377394.33\n",
            "total_reward: 2377394.33\n",
            "total_cost: 375949.56\n",
            "total_trades: 87383\n",
            "Sharpe: 0.692\n",
            "=================================\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 88         |\n",
            "|    iterations           | 2          |\n",
            "|    time_elapsed         | 46         |\n",
            "|    total_timesteps      | 4096       |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02141723 |\n",
            "|    clip_fraction        | 0.272      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -42.6      |\n",
            "|    explained_variance   | -0.0369    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 4.1        |\n",
            "|    n_updates            | 10         |\n",
            "|    policy_gradient_loss | -0.0227    |\n",
            "|    std                  | 1          |\n",
            "|    value_loss           | 10.5       |\n",
            "----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 87         |\n",
            "|    iterations           | 3          |\n",
            "|    time_elapsed         | 70         |\n",
            "|    total_timesteps      | 6144       |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02257656 |\n",
            "|    clip_fraction        | 0.233      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -42.8      |\n",
            "|    explained_variance   | 0.00117    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 16.9       |\n",
            "|    n_updates            | 20         |\n",
            "|    policy_gradient_loss | -0.0255    |\n",
            "|    std                  | 1.01       |\n",
            "|    value_loss           | 38.2       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 87          |\n",
            "|    iterations           | 4           |\n",
            "|    time_elapsed         | 93          |\n",
            "|    total_timesteps      | 8192        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.022311995 |\n",
            "|    clip_fraction        | 0.24        |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.8       |\n",
            "|    explained_variance   | 0.0067      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 28.5        |\n",
            "|    n_updates            | 30          |\n",
            "|    policy_gradient_loss | -0.0233     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 53.4        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 87          |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 117         |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.026932936 |\n",
            "|    clip_fraction        | 0.258       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.9       |\n",
            "|    explained_variance   | -0.0495     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 7           |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0214     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 11.7        |\n",
            "-----------------------------------------\n",
            "======PPO Validation from:  2020-12-31 to  2021-04-05\n",
            "PPO Sharpe Ratio:  0.21488509972314795\n",
            "======DDPG Training========\n",
            "{'buffer_size': 100000, 'learning_rate': 5e-06, 'batch_size': 64}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ddpg/ddpg_315_3\n",
            "day: 3019, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4859680.76\n",
            "total_reward: 3859680.76\n",
            "total_cost: 2039.96\n",
            "total_trades: 55064\n",
            "Sharpe: 0.754\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 41       |\n",
            "|    time_elapsed    | 287      |\n",
            "|    total timesteps | 12080    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -37.7    |\n",
            "|    critic_loss     | 102      |\n",
            "|    learning_rate   | 5e-06    |\n",
            "|    n_updates       | 9060     |\n",
            "---------------------------------\n",
            "======DDPG Validation from:  2020-12-31 to  2021-04-05\n",
            "======Best Model Retraining from:  2009-01-01 to  2021-04-05\n",
            "======Trading from:  2021-04-05 to  2021-07-02\n",
            "Ensemble Strategy took:  37.87242200771968  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "-0qd8acMtj1f",
        "outputId": "c168ea80-66e1-4d3d-9fc9-dbcf0aea76f1"
      },
      "source": [
        "df_summary"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-78e5cde76b38>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_summary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'df_summary' is not defined"
          ]
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "X4JKB--8tj1g"
      },
      "source": [
        "unique_trade_date = processed[(processed.date > val_test_start)&(processed.date <= val_test_end)].date.unique()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9mKF7GGtj1g",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b348adf6-a0bf-423e-ca83-71472ba6fcf4"
      },
      "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))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sharpe Ratio:  0.675414196177082\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyosyW7_tj1g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "0fd6eb22-86b7-4067-9271-ca8cb7806c17"
      },
      "source": [
        "df_account_value.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>2016-01-04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.000182e+06</td>\n",
              "      <td>2016-01-05</td>\n",
              "      <td>0.000182</td>\n",
              "      <td>2016-01-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.980331e+05</td>\n",
              "      <td>2016-01-06</td>\n",
              "      <td>-0.002149</td>\n",
              "      <td>2016-01-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.931734e+05</td>\n",
              "      <td>2016-01-07</td>\n",
              "      <td>-0.004869</td>\n",
              "      <td>2016-01-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.901513e+05</td>\n",
              "      <td>2016-01-08</td>\n",
              "      <td>-0.003043</td>\n",
              "      <td>2016-01-08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   account_value        date  daily_return    datadate\n",
              "0   1.000000e+06  2016-01-04           NaN  2016-01-04\n",
              "1   1.000182e+06  2016-01-05      0.000182  2016-01-05\n",
              "2   9.980331e+05  2016-01-06     -0.002149  2016-01-06\n",
              "3   9.931734e+05  2016-01-07     -0.004869  2016-01-07\n",
              "4   9.901513e+05  2016-01-08     -0.003043  2016-01-08"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wLsRdw2Ctj1h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "0837a717-6007-4e61-80a2-319273a56b9b"
      },
      "source": [
        "%matplotlib inline\n",
        "df_account_value.account_value.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3533217110>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lr2zX7ZxNyFQ"
      },
      "source": [
        "<a id='6.1'></a>\n",
        "## 7.1 BackTestStats\n",
        "pass in df_account_value, this information is stored in env class\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nzkr9yv-AdV_",
        "scrolled": true,
        "outputId": "3cdac2b4-999f-4add-9ea7-62e40401b51b"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Backtest Results===========\n",
            "Annual return          0.084635\n",
            "Cumulative returns     0.531933\n",
            "Annual volatility      0.133682\n",
            "Sharpe ratio           0.675414\n",
            "Calmar ratio           0.351787\n",
            "Stability              0.593141\n",
            "Max drawdown          -0.240587\n",
            "Omega ratio            1.140421\n",
            "Sortino ratio          0.925874\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.027047\n",
            "Daily value at risk   -0.016484\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DiHhM1YkoCel",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98980249-804a-40c2-8469-eee53ed6345d"
      },
      "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')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (1322, 8)\n",
            "Annual return          0.136321\n",
            "Cumulative returns     0.955059\n",
            "Annual volatility      0.198830\n",
            "Sharpe ratio           0.743460\n",
            "Calmar ratio           0.367579\n",
            "Stability              0.823306\n",
            "Max drawdown          -0.370862\n",
            "Omega ratio            1.175499\n",
            "Sortino ratio          1.025462\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.888069\n",
            "Daily value at risk   -0.024464\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U6Suru3h1jc"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HggausPRoCem",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2d6febda-99b5-472c-abc6-e779585bcc6f"
      },
      "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'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Compare to DJIA===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (1322, 8)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2016-01-04</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2021-04-05</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>62</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>8.583%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>54.032%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>13.371%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>0.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>0.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-24.059%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>0.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>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>1.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-1.648%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.44</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Worst drawdown periods</th>\n",
              "      <th>Net drawdown in %</th>\n",
              "      <th>Peak date</th>\n",
              "      <th>Valley date</th>\n",
              "      <th>Recovery date</th>\n",
              "      <th>Duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24.06</td>\n",
              "      <td>2018-10-03</td>\n",
              "      <td>2020-05-13</td>\n",
              "      <td>2021-01-20</td>\n",
              "      <td>601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.28</td>\n",
              "      <td>2018-01-17</td>\n",
              "      <td>2018-03-23</td>\n",
              "      <td>2018-09-12</td>\n",
              "      <td>171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.54</td>\n",
              "      <td>2016-04-19</td>\n",
              "      <td>2016-06-27</td>\n",
              "      <td>2016-07-14</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.97</td>\n",
              "      <td>2021-01-21</td>\n",
              "      <td>2021-01-29</td>\n",
              "      <td>2021-02-10</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.82</td>\n",
              "      <td>2016-07-20</td>\n",
              "      <td>2016-11-04</td>\n",
              "      <td>2016-11-22</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkdWMUWKoCem"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}