FinRL_ensemble_stock_trading_ICAIF_2020.ipynb · yutiansut/FinRL-Library

加入 Gitee

与超过 1200万开发者一起发现、参与优秀开源项目，私有仓库也完全免费：）

文件

克隆/下载

FinRL_ensemble_stock_trading_ICAIF_2020.ipynb 703.62 KB

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FinRL_ensemble_stock_trading_ICAIF_2020.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.10"
    },
    "pycharm": {
      "stem_cell": {
        "cell_type": "raw",
        "metadata": {
          "collapsed": false
        },
        "source": []
      }
    }
  },
  "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>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXaoZs2lh1hi"
      },
      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading Using Ensemble Strategy\n",
        "\n",
        "Tutorials to use OpenAI DRL to trade multiple stocks using ensemble strategy in one Jupyter Notebook | Presented at ICAIF 2020\n",
        "\n",
        "* This notebook is the reimplementation of our paper: Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy, using FinRL.\n",
        "* Check out medium blog for detailed explanations: https://medium.com/@ai4finance/deep-reinforcement-learning-for-automated-stock-trading-f1dad0126a02\n",
        "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
        "* **Pytorch Version** \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGunVt8oLCVS"
      },
      "source": [
        "# Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOzAKQ-SLGX6"
      },
      "source": [
        "* [1. Problem Definition](#0)\n",
        "* [2. Getting Started - Load Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. Check Additional Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download Data](#2)\n",
        "* [4. Preprocess Data](#3)        \n",
        "    * [4.1. Technical Indicators](#3.1)\n",
        "    * [4.2. Perform Feature Engineering](#3.2)\n",
        "* [5.Build Environment](#4)  \n",
        "    * [5.1. Training & Trade Data Split](#4.1)\n",
        "    * [5.2. User-defined Environment](#4.2)   \n",
        "    * [5.3. Initialize Environment](#4.3)    \n",
        "* [6.Implement DRL Algorithms](#5)  \n",
        "* [7.Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "    * [7.3. Baseline Stats](#6.3)   \n",
        "    * [7.3. Compare to Stock Market Index](#6.4)             "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sApkDlD9LIZv"
      },
      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjLD2TZSLKZ-"
      },
      "source": [
        "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
        "\n",
        "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\n",
        "\n",
        "\n",
        "* Action: The action space describes the allowed actions that the agent interacts with the\n",
        "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
        "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
        "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
        "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
        "\n",
        "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
        "values at state s′ and s, respectively\n",
        "\n",
        "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
        "our trading agent observes many different features to better learn in an interactive environment.\n",
        "\n",
        "* Environment: Dow 30 consituents\n",
        "\n",
        "\n",
        "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ffsre789LY08"
      },
      "source": [
        "<a id='1'></a>\n",
        "# Part 2. Getting Started- Load Python Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uy5_PTmOh1hj"
      },
      "source": [
        "<a id='1.1'></a>\n",
        "## 2.1. Install all the packages through FinRL library\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPT0ipYE28wL",
        "outputId": "dcf73e63-fece-409e-e887-2d33774cdf94"
      },
      "source": [
        "# ## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/AI4Finance-LLC/FinRL-Library.git\n",
            "  Cloning https://github.com/AI4Finance-LLC/FinRL-Library.git to /tmp/pip-req-build-41gdi4py\n",
            "  Running command git clone -q https://github.com/AI4Finance-LLC/FinRL-Library.git /tmp/pip-req-build-41gdi4py\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.0) (1.19.5)\n",
            "Requirement already satisfied: pandas>=1.1.5 in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.0) (1.1.5)\n",
            "Collecting stockstats\n",
            "  Downloading https://files.pythonhosted.org/packages/32/41/d3828c5bc0a262cb3112a4024108a3b019c183fa3b3078bff34bf25abf91/stockstats-0.3.2-py2.py3-none-any.whl\n",
            "Collecting yfinance\n",
            "  Downloading https://files.pythonhosted.org/packages/a7/ee/315752b9ef281ba83c62aa7ec2e2074f85223da6e7e74efb4d3e11c0f510/yfinance-0.1.59.tar.gz\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.0) (3.2.2)\n",
            "Requirement already satisfied: scikit-learn>=0.21.0 in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.0) (0.22.2.post1)\n",
            "Requirement already satisfied: gym>=0.17 in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.0) (0.17.3)\n",
            "Collecting stable-baselines3[extra]\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/18/d3/6ae6e774ac6cf8f5eeca1c30b9125231db901b75f72da7d81e939f293f69/stable_baselines3-1.0-py3-none-any.whl (152kB)\n",
            "\u001b[K     |████████████████████████████████| 153kB 25.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: pytest in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.0) (3.6.4)\n",
            "Requirement already satisfied: setuptools>=41.4.0 in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.0) (57.0.0)\n",
            "Requirement already satisfied: wheel>=0.33.6 in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.0) (0.36.2)\n",
            "Collecting pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2\n",
            "  Cloning https://github.com/quantopian/pyfolio.git to /tmp/pip-install-l_3lra45/pyfolio\n",
            "  Running command git clone -q https://github.com/quantopian/pyfolio.git /tmp/pip-install-l_3lra45/pyfolio\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.1.5->finrl==0.3.0) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.1.5->finrl==0.3.0) (2.8.1)\n",
            "Collecting int-date>=0.1.7\n",
            "  Downloading https://files.pythonhosted.org/packages/43/27/31803df15173ab341fe7548c14154b54227dfd8f630daa09a1c6e7db52f7/int_date-0.1.8-py2.py3-none-any.whl\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.7/dist-packages (from yfinance->finrl==0.3.0) (2.23.0)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance->finrl==0.3.0) (0.0.9)\n",
            "Collecting lxml>=4.5.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/30/c0/d0526314971fc661b083ab135747dc68446a3022686da8c16d25fcf6ef07/lxml-4.6.3-cp37-cp37m-manylinux2014_x86_64.whl (6.3MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3MB 27.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->finrl==0.3.0) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->finrl==0.3.0) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->finrl==0.3.0) (0.10.0)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.0->finrl==0.3.0) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.0->finrl==0.3.0) (1.0.1)\n",
            "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from gym>=0.17->finrl==0.3.0) (1.3.0)\n",
            "Requirement already satisfied: pyglet<=1.5.0,>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from gym>=0.17->finrl==0.3.0) (1.5.0)\n",
            "Requirement already satisfied: torch>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.3.0) (1.8.1+cu101)\n",
            "Requirement already satisfied: opencv-python; extra == \"extra\" in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.3.0) (4.1.2.30)\n",
            "Requirement already satisfied: pillow; extra == \"extra\" in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.3.0) (7.1.2)\n",
            "Requirement already satisfied: tensorboard>=2.2.0; extra == \"extra\" in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.3.0) (2.5.0)\n",
            "Requirement already satisfied: atari-py~=0.2.0; extra == \"extra\" in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.3.0) (0.2.9)\n",
            "Requirement already satisfied: psutil; extra == \"extra\" in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.3.0) (5.4.8)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.3.0) (1.15.0)\n",
            "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.3.0) (1.10.0)\n",
            "Requirement already satisfied: pluggy<0.8,>=0.5 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.3.0) (0.7.1)\n",
            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.3.0) (8.7.0)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.3.0) (21.2.0)\n",
            "Requirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.3.0) (1.4.0)\n",
            "Requirement already satisfied: ipython>=3.2.3 in /usr/local/lib/python3.7/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.0) (5.5.0)\n",
            "Requirement already satisfied: seaborn>=0.7.1 in /usr/local/lib/python3.7/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.0) (0.11.1)\n",
            "Collecting empyrical>=0.5.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/74/43/1b997c21411c6ab7c96dc034e160198272c7a785aeea7654c9bcf98bec83/empyrical-0.5.5.tar.gz (52kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 7.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance->finrl==0.3.0) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance->finrl==0.3.0) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance->finrl==0.3.0) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance->finrl==0.3.0) (2020.12.5)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyglet<=1.5.0,>=1.4.0->gym>=0.17->finrl==0.3.0) (0.16.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.4.0->stable-baselines3[extra]->finrl==0.3.0) (3.7.4.3)\n",
            "Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (3.12.4)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (1.0.1)\n",
            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (1.34.1)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (1.8.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (0.6.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (3.3.4)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (1.30.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (0.4.4)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (0.12.0)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.0) (2.6.1)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.0) (4.4.2)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.0) (5.0.5)\n",
            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.0) (4.8.0)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.0) (1.0.18)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.0) (0.7.5)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.0) (0.8.1)\n",
            "Requirement already satisfied: pandas-datareader>=0.2 in /usr/local/lib/python3.7/dist-packages (from empyrical>=0.5.0->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.0) (0.9.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (4.0.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (4.7.2)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (4.2.2)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (1.3.0)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.7/dist-packages (from traitlets>=4.2->ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.0) (0.2.0)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect; sys_platform != \"win32\"->ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.0) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.0) (0.2.5)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (3.4.1)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (3.1.0)\n",
            "Building wheels for collected packages: finrl, yfinance, pyfolio, empyrical\n",
            "  Building wheel for finrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for finrl: filename=finrl-0.3.0-cp37-none-any.whl size=38769 sha256=57cad6d53d142ac9069ef5d3f8766b4423ee7cb6bcbd93cefec163ec0021e900\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-93jhahj2/wheels/9c/19/bf/c644def96612df1ad42c94d5304966797eaa3221dffc5efe0b\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.59-py2.py3-none-any.whl size=23455 sha256=be79f8c3ff69b2e818062d9e7ca122a7c2177fd210d89411a6ea663e654ec24c\n",
            "  Stored in directory: /root/.cache/pip/wheels/f8/2a/0f/4b5a86e1d52e451757eb6bc17fd899629f0925c777741b6d04\n",
            "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-cp37-none-any.whl size=75776 sha256=8b44b4da1bf5809c4ab6c0db6f3acc819190b90ac8ceeb96708b44b221598578\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-93jhahj2/wheels/43/ce/d9/6752fb6e03205408773235435205a0519d2c608a94f1976e56\n",
            "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for empyrical: filename=empyrical-0.5.5-cp37-none-any.whl size=39780 sha256=ead98f0c3e98e34ee4d346c0424bcd9eb8bfd614e8c45093c68c9ccbe2dac5fd\n",
            "  Stored in directory: /root/.cache/pip/wheels/ea/b2/c8/6769d8444d2f2e608fae2641833110668d0ffd1abeb2e9f3fc\n",
            "Successfully built finrl yfinance pyfolio empyrical\n",
            "Installing collected packages: int-date, stockstats, lxml, yfinance, stable-baselines3, empyrical, pyfolio, finrl\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed empyrical-0.5.5 finrl-0.3.0 int-date-0.1.8 lxml-4.6.3 pyfolio-0.9.2+75.g4b901f6 stable-baselines3-1.0 stockstats-0.3.2 yfinance-0.1.59\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.config import config\n",
        "from finrl.marketdata.yahoodownloader import YahooDownloader\n",
        "from finrl.preprocessing.preprocessors import FeatureEngineer\n",
        "from finrl.preprocessing.data import data_split\n",
        "from finrl.env.env_stocktrading import StockTradingEnv\n",
        "from finrl.model.models import DRLAgent,DRLEnsembleAgent\n",
        "from finrl.trade.backtest 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": "fe5cd33c-9434-4372-b836-622a50f0d3d7"
      },
      "source": [
        "# from config.py start_date is a string\n",
        "config.START_DATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2000-01-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzqRRTOX6aFu",
        "outputId": "a42433a2-c7c9-4c06-b53f-28723b4957af"
      },
      "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": "b9293979-3d5b-48f6-9b99-6facf5bc5f1e"
      },
      "source": [
        "df = YahooDownloader(start_date = '2009-01-01',\n",
        "                     end_date = '2021-06-20',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (94020, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "GiRuFOTOtj1Y",
        "outputId": "015897f3-d0d9-49d6-a28d-d2888e3c7a1f"
      },
      "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>2008-12-31</td>\n",
              "      <td>3.070357</td>\n",
              "      <td>3.133571</td>\n",
              "      <td>3.047857</td>\n",
              "      <td>2.621168</td>\n",
              "      <td>607541200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>17.969999</td>\n",
              "      <td>18.750000</td>\n",
              "      <td>17.910000</td>\n",
              "      <td>15.064775</td>\n",
              "      <td>9625600</td>\n",
              "      <td>AXP</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>41.590000</td>\n",
              "      <td>43.049999</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>32.005901</td>\n",
              "      <td>5443100</td>\n",
              "      <td>BA</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>43.700001</td>\n",
              "      <td>45.099998</td>\n",
              "      <td>43.700001</td>\n",
              "      <td>31.262676</td>\n",
              "      <td>6277400</td>\n",
              "      <td>CAT</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>16.180000</td>\n",
              "      <td>16.549999</td>\n",
              "      <td>16.120001</td>\n",
              "      <td>12.102888</td>\n",
              "      <td>37513700</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high        low      close     volume   tic  \\\n",
              "0  2008-12-31   3.070357   3.133571   3.047857   2.621168  607541200  AAPL   \n",
              "1  2008-12-31  17.969999  18.750000  17.910000  15.064775    9625600   AXP   \n",
              "2  2008-12-31  41.590000  43.049999  41.500000  32.005901    5443100    BA   \n",
              "3  2008-12-31  43.700001  45.099998  43.700001  31.262676    6277400   CAT   \n",
              "4  2008-12-31  16.180000  16.549999  16.120001  12.102888   37513700  CSCO   \n",
              "\n",
              "   day  \n",
              "0    2  \n",
              "1    2  \n",
              "2    2  \n",
              "3    2  \n",
              "4    2  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "DSw4ZEzVtj1Z",
        "outputId": "517b31f3-e7b4-419b-9322-517babcd5546"
      },
      "source": [
        "df.tail()"
      ],
      "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>94015</th>\n",
              "      <td>2021-06-14</td>\n",
              "      <td>234.699997</td>\n",
              "      <td>235.220001</td>\n",
              "      <td>232.149994</td>\n",
              "      <td>232.270004</td>\n",
              "      <td>986656</td>\n",
              "      <td>V</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94016</th>\n",
              "      <td>2021-06-14</td>\n",
              "      <td>57.189999</td>\n",
              "      <td>57.270000</td>\n",
              "      <td>56.935001</td>\n",
              "      <td>57.070000</td>\n",
              "      <td>2770637</td>\n",
              "      <td>VZ</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94017</th>\n",
              "      <td>2021-06-14</td>\n",
              "      <td>55.099998</td>\n",
              "      <td>55.075001</td>\n",
              "      <td>54.169998</td>\n",
              "      <td>54.539902</td>\n",
              "      <td>590339</td>\n",
              "      <td>WBA</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94018</th>\n",
              "      <td>2021-06-14</td>\n",
              "      <td>140.929993</td>\n",
              "      <td>140.929993</td>\n",
              "      <td>140.039993</td>\n",
              "      <td>140.220001</td>\n",
              "      <td>1033410</td>\n",
              "      <td>WMT</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94019</th>\n",
              "      <td>2021-06-14</td>\n",
              "      <td>62.299999</td>\n",
              "      <td>62.779999</td>\n",
              "      <td>62.290001</td>\n",
              "      <td>62.639999</td>\n",
              "      <td>3415815</td>\n",
              "      <td>XOM</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             date        open        high         low       close   volume  \\\n",
              "94015  2021-06-14  234.699997  235.220001  232.149994  232.270004   986656   \n",
              "94016  2021-06-14   57.189999   57.270000   56.935001   57.070000  2770637   \n",
              "94017  2021-06-14   55.099998   55.075001   54.169998   54.539902   590339   \n",
              "94018  2021-06-14  140.929993  140.929993  140.039993  140.220001  1033410   \n",
              "94019  2021-06-14   62.299999   62.779999   62.290001   62.639999  3415815   \n",
              "\n",
              "       tic  day  \n",
              "94015    V    0  \n",
              "94016   VZ    0  \n",
              "94017  WBA    0  \n",
              "94018  WMT    0  \n",
              "94019  XOM    0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV3HrZHLh1hy",
        "outputId": "45525740-8ce0-4031-c2c5-bfd6a7a1cd7a"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(94020, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "4hYkeaPiICHS",
        "outputId": "26d3122c-f143-4671-8891-4ac5ce27d2e8"
      },
      "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",
              "        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>2008-12-31</td>\n",
              "      <td>3.070357</td>\n",
              "      <td>3.133571</td>\n",
              "      <td>3.047857</td>\n",
              "      <td>2.621168</td>\n",
              "      <td>607541200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>17.969999</td>\n",
              "      <td>18.750000</td>\n",
              "      <td>17.910000</td>\n",
              "      <td>15.064775</td>\n",
              "      <td>9625600</td>\n",
              "      <td>AXP</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>41.590000</td>\n",
              "      <td>43.049999</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>32.005901</td>\n",
              "      <td>5443100</td>\n",
              "      <td>BA</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>43.700001</td>\n",
              "      <td>45.099998</td>\n",
              "      <td>43.700001</td>\n",
              "      <td>31.262676</td>\n",
              "      <td>6277400</td>\n",
              "      <td>CAT</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>16.180000</td>\n",
              "      <td>16.549999</td>\n",
              "      <td>16.120001</td>\n",
              "      <td>12.102888</td>\n",
              "      <td>37513700</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high        low      close     volume   tic  \\\n",
              "0  2008-12-31   3.070357   3.133571   3.047857   2.621168  607541200  AAPL   \n",
              "1  2008-12-31  17.969999  18.750000  17.910000  15.064775    9625600   AXP   \n",
              "2  2008-12-31  41.590000  43.049999  41.500000  32.005901    5443100    BA   \n",
              "3  2008-12-31  43.700001  45.099998  43.700001  31.262676    6277400   CAT   \n",
              "4  2008-12-31  16.180000  16.549999  16.120001  12.102888   37513700  CSCO   \n",
              "\n",
              "   day  \n",
              "0    2  \n",
              "1    2  \n",
              "2    2  \n",
              "3    2  \n",
              "4    2  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "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": "3_bMl6zaHCqS"
      },
      "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": "1a887a88-8cb0-4d02-c518-374278251850"
      },
      "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": [
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n",
            "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Successfully added technical indicators\n",
            "Successfully added turbulence index\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_3v-ycktj1b"
      },
      "source": [
        "list_ticker = processed[\"tic\"].unique().tolist()\n",
        "list_date = list(pd.date_range(processed['date'].min(),processed['date'].max()).astype(str))\n",
        "combination = list(itertools.product(list_date,list_ticker))\n",
        "\n",
        "processed_full = pd.DataFrame(combination,columns=[\"date\",\"tic\"]).merge(processed,on=[\"date\",\"tic\"],how=\"left\")\n",
        "processed_full = processed_full[processed_full['date'].isin(processed['date'])]\n",
        "processed_full = processed_full.sort_values(['date','tic'])\n",
        "\n",
        "processed_full = processed_full.fillna(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "grvhGJJII3Xn",
        "outputId": "eded8f63-ac44-4a51-f2bb-4a54a07cbd11"
      },
      "source": [
        "processed_full.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>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</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>44590</th>\n",
              "      <td>2013-01-25</td>\n",
              "      <td>IBM</td>\n",
              "      <td>204.449997</td>\n",
              "      <td>205.179993</td>\n",
              "      <td>204.130005</td>\n",
              "      <td>149.229141</td>\n",
              "      <td>3358900.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.753800</td>\n",
              "      <td>62.188802</td>\n",
              "      <td>285.212182</td>\n",
              "      <td>48.193964</td>\n",
              "      <td>53.089731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34242</th>\n",
              "      <td>2012-02-15</td>\n",
              "      <td>JNJ</td>\n",
              "      <td>64.510002</td>\n",
              "      <td>64.949997</td>\n",
              "      <td>64.470001</td>\n",
              "      <td>49.345165</td>\n",
              "      <td>9023400.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.064815</td>\n",
              "      <td>49.406220</td>\n",
              "      <td>-123.926289</td>\n",
              "      <td>24.610651</td>\n",
              "      <td>18.832488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89299</th>\n",
              "      <td>2017-02-23</td>\n",
              "      <td>NKE</td>\n",
              "      <td>58.110001</td>\n",
              "      <td>58.400002</td>\n",
              "      <td>57.270000</td>\n",
              "      <td>54.669735</td>\n",
              "      <td>12316300.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.204400</td>\n",
              "      <td>63.109817</td>\n",
              "      <td>148.750970</td>\n",
              "      <td>49.919662</td>\n",
              "      <td>28.035344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14562</th>\n",
              "      <td>2010-04-30</td>\n",
              "      <td>JNJ</td>\n",
              "      <td>65.129997</td>\n",
              "      <td>65.339996</td>\n",
              "      <td>64.300003</td>\n",
              "      <td>46.090996</td>\n",
              "      <td>14255900.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.019782</td>\n",
              "      <td>49.114488</td>\n",
              "      <td>-94.015885</td>\n",
              "      <td>16.185865</td>\n",
              "      <td>44.229784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63417</th>\n",
              "      <td>2014-10-14</td>\n",
              "      <td>WBA</td>\n",
              "      <td>60.740002</td>\n",
              "      <td>61.400002</td>\n",
              "      <td>59.910000</td>\n",
              "      <td>50.916355</td>\n",
              "      <td>8488500.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.315171</td>\n",
              "      <td>44.788229</td>\n",
              "      <td>-51.379821</td>\n",
              "      <td>4.149420</td>\n",
              "      <td>49.813588</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             date  tic        open        high         low       close  \\\n",
              "44590  2013-01-25  IBM  204.449997  205.179993  204.130005  149.229141   \n",
              "34242  2012-02-15  JNJ   64.510002   64.949997   64.470001   49.345165   \n",
              "89299  2017-02-23  NKE   58.110001   58.400002   57.270000   54.669735   \n",
              "14562  2010-04-30  JNJ   65.129997   65.339996   64.300003   46.090996   \n",
              "63417  2014-10-14  WBA   60.740002   61.400002   59.910000   50.916355   \n",
              "\n",
              "           volume  day      macd     rsi_30      cci_30      dx_30  turbulence  \n",
              "44590   3358900.0  4.0  1.753800  62.188802  285.212182  48.193964   53.089731  \n",
              "34242   9023400.0  2.0 -0.064815  49.406220 -123.926289  24.610651   18.832488  \n",
              "89299  12316300.0  3.0  1.204400  63.109817  148.750970  49.919662   28.035344  \n",
              "14562  14255900.0  4.0 -0.019782  49.114488  -94.015885  16.185865   44.229784  \n",
              "63417   8488500.0  1.0 -0.315171  44.788229  -51.379821   4.149420   49.813588  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "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": "85af7d0d-3cf3-4473-a62d-e9712c92fe44"
      },
      "source": [
        "stock_dimension = len(processed_full.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 = '2015-10-01'\n",
        "val_test_start = '2015-10-01'\n",
        "val_test_end = '2020-07-20'\n",
        "\n",
        "ensemble_agent = DRLEnsembleAgent(df=processed_full,\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\": 128\n",
        "                    }\n",
        "\n",
        "DDPG_model_kwargs = {\n",
        "                      \"action_noise\":\"ornstein_uhlenbeck\",\n",
        "                      \"buffer_size\": 50_000,\n",
        "                      \"learning_rate\": 0.000005,\n",
        "                      \"batch_size\": 128\n",
        "                    }\n",
        "\n",
        "timesteps_dict = {'a2c' : 30_000, \n",
        "                 'ppo' : 100_000, \n",
        "                 'ddpg' : 10_000\n",
        "                 }\n",
        "\n",
        "\n",
        "timesteps_dict = {'a2c' : 1_000, \n",
        "                 'ppo' : 1_000, \n",
        "                 'ddpg' : 1_000\n",
        "                 }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_1lyCECstj1e",
        "scrolled": true,
        "outputId": "940e556d-8fbd-4112-c463-b155f6a0f581"
      },
      "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",
            "65.06420184954358\n",
            "turbulence_threshold:  58.53457789561306\n",
            "======Model training from:  2009-01-01 to  2015-10-02\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 1.01e+06 |\n",
            "|    total_cost         | 7.7e+03  |\n",
            "|    total_reward       | 1.46e+04 |\n",
            "|    total_reward_pct   | 1.46     |\n",
            "|    total_trades       | 1517     |\n",
            "| time/                 |          |\n",
            "|    fps                | 161      |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 3        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.297   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | 15.3     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.375    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 159      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 6        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -29.2    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -93.4    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 5.13     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2015-10-02 to  2016-01-04\n",
            "A2C Sharpe Ratio:  -0.3961959338036803\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 2.08e+06 |\n",
            "|    total_cost         | 1.45e+05 |\n",
            "|    total_reward       | 1.08e+06 |\n",
            "|    total_reward_pct   | 108      |\n",
            "|    total_trades       | 49251    |\n",
            "| time/                 |          |\n",
            "|    fps                | 186      |\n",
            "|    iterations         | 1        |\n",
            "|    time_elapsed       | 10       |\n",
            "|    total_timesteps    | 2048     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -154     |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 200      |\n",
            "|    policy_loss        | 27.3     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 2.52     |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2015-10-02 to  2016-01-04\n",
            "PPO Sharpe Ratio:  -0.3155920921841227\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2015-10-02 to  2016-01-04\n",
            "======Best Model Retraining from:  2009-01-01 to  2016-01-04\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.2e+06     |\n",
            "|    total_cost           | 1.55e+05    |\n",
            "|    total_reward         | 1.2e+06     |\n",
            "|    total_reward_pct     | 120         |\n",
            "|    total_trades         | 51075       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 180         |\n",
            "|    iterations           | 1           |\n",
            "|    time_elapsed         | 11          |\n",
            "|    total_timesteps      | 2048        |\n",
            "| train/                  |             |\n",
            "|    actor_loss           | 475         |\n",
            "|    approx_kl            | 0.013258189 |\n",
            "|    clip_fraction        | 0.208       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    critic_loss          | 897         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -118        |\n",
            "|    learning_rate        | 5e-06       |\n",
            "|    loss                 | 4.5         |\n",
            "|    n_updates            | 1699        |\n",
            "|    policy_gradient_loss | -0.0269     |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 10.3        |\n",
            "-----------------------------------------\n",
            "======Trading from:  2016-01-04 to  2016-04-05\n",
            "============================================\n",
            "28.88334230533451\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2016-01-04\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 1.02e+06    |\n",
            "|    total_cost           | 4.55e+03    |\n",
            "|    total_reward         | 1.61e+04    |\n",
            "|    total_reward_pct     | 1.61        |\n",
            "|    total_trades         | 1168        |\n",
            "| time/                   |             |\n",
            "|    fps                  | 131         |\n",
            "|    iterations           | 100         |\n",
            "|    time_elapsed         | 3           |\n",
            "|    total_timesteps      | 500         |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.008723546 |\n",
            "|    clip_fraction        | 0.203       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -66.7       |\n",
            "|    learning_rate        | 0.0005      |\n",
            "|    loss                 | 5.21        |\n",
            "|    n_updates            | 99          |\n",
            "|    policy_gradient_loss | -0.0337     |\n",
            "|    policy_loss          | -54         |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 3.6         |\n",
            "-----------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 136      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 7        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -37      |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -21.1    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.47     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2016-01-04 to  2016-04-05\n",
            "A2C Sharpe Ratio:  0.3131495877240711\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 2e+06    |\n",
            "|    total_cost         | 1.56e+05 |\n",
            "|    total_reward       | 1e+06    |\n",
            "|    total_reward_pct   | 100      |\n",
            "|    total_trades       | 51198    |\n",
            "| time/                 |          |\n",
            "|    fps                | 142      |\n",
            "|    iterations         | 1        |\n",
            "|    time_elapsed       | 14       |\n",
            "|    total_timesteps    | 2048     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -15.9    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 200      |\n",
            "|    policy_loss        | 60       |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 5.63     |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2016-01-04 to  2016-04-05\n",
            "PPO Sharpe Ratio:  0.3107054578545648\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2016-01-04 to  2016-04-05\n",
            "======Best Model Retraining from:  2009-01-01 to  2016-04-05\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 1.09e+06    |\n",
            "|    total_cost           | 1.44e+03    |\n",
            "|    total_reward         | 9.04e+04    |\n",
            "|    total_reward_pct     | 9.04        |\n",
            "|    total_trades         | 973         |\n",
            "| time/                   |             |\n",
            "|    fps                  | 139         |\n",
            "|    iterations           | 100         |\n",
            "|    time_elapsed         | 3           |\n",
            "|    total_timesteps      | 500         |\n",
            "| train/                  |             |\n",
            "|    actor_loss           | -182        |\n",
            "|    approx_kl            | 0.017709333 |\n",
            "|    clip_fraction        | 0.184       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    critic_loss          | 238         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | -1.49       |\n",
            "|    learning_rate        | 0.0005      |\n",
            "|    loss                 | 4.53        |\n",
            "|    n_updates            | 99          |\n",
            "|    policy_gradient_loss | -0.0207     |\n",
            "|    policy_loss          | 21.8        |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 0.353       |\n",
            "-----------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 144      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 6        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -22.6    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -102     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 6.38     |\n",
            "------------------------------------\n",
            "======Trading from:  2016-04-05 to  2016-07-05\n",
            "============================================\n",
            "17.027755767988026\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2016-04-05\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 1.03e+06 |\n",
            "|    total_cost         | 6.89e+03 |\n",
            "|    total_reward       | 3.24e+04 |\n",
            "|    total_reward_pct   | 3.24     |\n",
            "|    total_trades       | 1691     |\n",
            "| time/                 |          |\n",
            "|    fps                | 115      |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 4        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -5.23    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | -41.1    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.874    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 126      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 7        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | -20.8    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -71.8    |\n",
            "|    std                | 0.998    |\n",
            "|    value_loss         | 3.13     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2016-04-05 to  2016-07-05\n",
            "A2C Sharpe Ratio:  0.016688790381713642\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 2.19e+06 |\n",
            "|    total_cost         | 1.61e+05 |\n",
            "|    total_reward       | 1.19e+06 |\n",
            "|    total_reward_pct   | 119      |\n",
            "|    total_trades       | 52682    |\n",
            "| time/                 |          |\n",
            "|    fps                | 159      |\n",
            "|    iterations         | 1        |\n",
            "|    time_elapsed       | 12       |\n",
            "|    total_timesteps    | 2048     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | -90.2    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 200      |\n",
            "|    policy_loss        | -4.8     |\n",
            "|    std                | 0.998    |\n",
            "|    value_loss         | 2.59     |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2016-04-05 to  2016-07-05\n",
            "PPO Sharpe Ratio:  6.034481708606799e-05\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2016-04-05 to  2016-07-05\n",
            "======Best Model Retraining from:  2009-01-01 to  2016-07-05\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======Trading from:  2016-07-05 to  2016-10-03\n",
            "============================================\n",
            "17.73517147931603\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2016-07-05\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "------------------------------------------\n",
            "| environment/            |              |\n",
            "|    portfolio_value      | 1.05e+06     |\n",
            "|    total_cost           | 1.17e+03     |\n",
            "|    total_reward         | 5.43e+04     |\n",
            "|    total_reward_pct     | 5.43         |\n",
            "|    total_trades         | 1256         |\n",
            "| time/                   |              |\n",
            "|    fps                  | 117          |\n",
            "|    iterations           | 100          |\n",
            "|    time_elapsed         | 4            |\n",
            "|    total_timesteps      | 500          |\n",
            "| train/                  |              |\n",
            "|    actor_loss           | -383         |\n",
            "|    approx_kl            | 0.0067959446 |\n",
            "|    clip_fraction        | 0.22         |\n",
            "|    clip_range           | 0.2          |\n",
            "|    critic_loss          | 730          |\n",
            "|    entropy_loss         | -42.6        |\n",
            "|    explained_variance   | -5.28        |\n",
            "|    learning_rate        | 0.0005       |\n",
            "|    loss                 | 7.22         |\n",
            "|    n_updates            | 99           |\n",
            "|    policy_gradient_loss | -0.0269      |\n",
            "|    policy_loss          | 25.5         |\n",
            "|    std                  | 1            |\n",
            "|    value_loss           | 0.642        |\n",
            "------------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 128      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 7        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -960     |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -33.3    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.24     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2016-07-05 to  2016-10-03\n",
            "A2C Sharpe Ratio:  -0.00228507087416849\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 2.06e+06 |\n",
            "|    total_cost         | 1.71e+05 |\n",
            "|    total_reward       | 1.06e+06 |\n",
            "|    total_reward_pct   | 106      |\n",
            "|    total_trades       | 54833    |\n",
            "| time/                 |          |\n",
            "|    fps                | 157      |\n",
            "|    iterations         | 1        |\n",
            "|    time_elapsed       | 13       |\n",
            "|    total_timesteps    | 2048     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -83.5    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 200      |\n",
            "|    policy_loss        | 74       |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 5.81     |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2016-07-05 to  2016-10-03\n",
            "PPO Sharpe Ratio:  -0.026252480223975937\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2016-07-05 to  2016-10-03\n",
            "======Best Model Retraining from:  2009-01-01 to  2016-10-03\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======Trading from:  2016-10-03 to  2017-01-03\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "============================================\n",
            "148.64723872108536\n",
            "turbulence_threshold:  58.53457789561306\n",
            "======Model training from:  2009-01-01 to  2016-10-03\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 1.1e+06     |\n",
            "|    total_cost           | 1.08e+03    |\n",
            "|    total_reward         | 9.67e+04    |\n",
            "|    total_reward_pct     | 9.67        |\n",
            "|    total_trades         | 1096        |\n",
            "| time/                   |             |\n",
            "|    fps                  | 128         |\n",
            "|    iterations           | 100         |\n",
            "|    time_elapsed         | 3           |\n",
            "|    total_timesteps      | 500         |\n",
            "| train/                  |             |\n",
            "|    actor_loss           | 15.8        |\n",
            "|    approx_kl            | 0.015514828 |\n",
            "|    clip_fraction        | 0.192       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    critic_loss          | 12.5        |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | -292        |\n",
            "|    learning_rate        | 0.0005      |\n",
            "|    loss                 | 3.3         |\n",
            "|    n_updates            | 99          |\n",
            "|    policy_gradient_loss | -0.0241     |\n",
            "|    policy_loss          | 0.157       |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 0.0807      |\n",
            "-----------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 127      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 7        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | 1.87     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.397    |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2016-10-03 to  2017-01-03\n",
            "A2C Sharpe Ratio:  0.08420877456902223\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 2.02e+06 |\n",
            "|    total_cost         | 1.79e+05 |\n",
            "|    total_reward       | 1.02e+06 |\n",
            "|    total_reward_pct   | 102      |\n",
            "|    total_trades       | 56475    |\n",
            "| time/                 |          |\n",
            "|    fps                | 145      |\n",
            "|    iterations         | 1        |\n",
            "|    time_elapsed       | 14       |\n",
            "|    total_timesteps    | 2048     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 200      |\n",
            "|    policy_loss        | -40.4    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 2.99     |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2016-10-03 to  2017-01-03\n",
            "PPO Sharpe Ratio:  0.1460928091525725\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2016-10-03 to  2017-01-03\n",
            "======Best Model Retraining from:  2009-01-01 to  2017-01-03\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======Trading from:  2017-01-03 to  2017-04-04\n",
            "============================================\n",
            "37.21549666568847\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2017-01-03\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 1.09e+06    |\n",
            "|    total_cost           | 5.51e+03    |\n",
            "|    total_reward         | 9.02e+04    |\n",
            "|    total_reward_pct     | 9.02        |\n",
            "|    total_trades         | 1185        |\n",
            "| time/                   |             |\n",
            "|    fps                  | 122         |\n",
            "|    iterations           | 100         |\n",
            "|    time_elapsed         | 4           |\n",
            "|    total_timesteps      | 500         |\n",
            "| train/                  |             |\n",
            "|    actor_loss           | 497         |\n",
            "|    approx_kl            | 0.008380447 |\n",
            "|    clip_fraction        | 0.205       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    critic_loss          | 1.85e+03    |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -13.5       |\n",
            "|    learning_rate        | 0.0005      |\n",
            "|    loss                 | 3.06        |\n",
            "|    n_updates            | 99          |\n",
            "|    policy_gradient_loss | -0.0282     |\n",
            "|    policy_loss          | -50.7       |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 1.5         |\n",
            "-----------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 121      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 8        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -75.3    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 3.81     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2017-01-03 to  2017-04-04\n",
            "A2C Sharpe Ratio:  0.09893129526360668\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 2.23e+06 |\n",
            "|    total_cost         | 1.84e+05 |\n",
            "|    total_reward       | 1.23e+06 |\n",
            "|    total_reward_pct   | 123      |\n",
            "|    total_trades       | 58249    |\n",
            "| time/                 |          |\n",
            "|    fps                | 144      |\n",
            "|    iterations         | 1        |\n",
            "|    time_elapsed       | 14       |\n",
            "|    total_timesteps    | 2048     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -218     |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 200      |\n",
            "|    policy_loss        | -84.3    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 5.44     |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2017-01-03 to  2017-04-04\n",
            "PPO Sharpe Ratio:  -0.006554631353107863\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2017-01-03 to  2017-04-04\n",
            "======Best Model Retraining from:  2009-01-01 to  2017-04-04\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======Trading from:  2017-04-04 to  2017-07-05\n",
            "============================================\n",
            "29.16055314622776\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2017-04-04\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 1.12e+06    |\n",
            "|    total_cost           | 1.07e+03    |\n",
            "|    total_reward         | 1.16e+05    |\n",
            "|    total_reward_pct     | 11.6        |\n",
            "|    total_trades         | 921         |\n",
            "| time/                   |             |\n",
            "|    fps                  | 143         |\n",
            "|    iterations           | 100         |\n",
            "|    time_elapsed         | 3           |\n",
            "|    total_timesteps      | 500         |\n",
            "| train/                  |             |\n",
            "|    actor_loss           | -83.3       |\n",
            "|    approx_kl            | 0.018647425 |\n",
            "|    clip_fraction        | 0.235       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    critic_loss          | 134         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -35.7       |\n",
            "|    learning_rate        | 0.0005      |\n",
            "|    loss                 | 6.17        |\n",
            "|    n_updates            | 99          |\n",
            "|    policy_gradient_loss | -0.0303     |\n",
            "|    policy_loss          | -84.5       |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 4           |\n",
            "-----------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 145      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 6        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -9.33    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -62.9    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 2.42     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2017-04-04 to  2017-07-05\n",
            "A2C Sharpe Ratio:  0.4569108819535561\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 1.05e+06 |\n",
            "|    total_cost         | 7.3e+03  |\n",
            "|    total_reward       | 4.95e+04 |\n",
            "|    total_reward_pct   | 4.95     |\n",
            "|    total_trades       | 1631     |\n",
            "| time/                 |          |\n",
            "|    fps                | 169      |\n",
            "|    iterations         | 1        |\n",
            "|    time_elapsed       | 12       |\n",
            "|    total_timesteps    | 2048     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -458     |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 200      |\n",
            "|    policy_loss        | 83.5     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 4.85     |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2017-04-04 to  2017-07-05\n",
            "PPO Sharpe Ratio:  0.14616401257950548\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2017-04-04 to  2017-07-05\n",
            "======Best Model Retraining from:  2009-01-01 to  2017-07-05\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "---------------------------------------\n",
            "| environment/            |           |\n",
            "|    portfolio_value      | 1.05e+06  |\n",
            "|    total_cost           | 2.26e+03  |\n",
            "|    total_reward         | 5.1e+04   |\n",
            "|    total_reward_pct     | 5.1       |\n",
            "|    total_trades         | 1042      |\n",
            "| time/                   |           |\n",
            "|    fps                  | 143       |\n",
            "|    iterations           | 100       |\n",
            "|    time_elapsed         | 3         |\n",
            "|    total_timesteps      | 500       |\n",
            "| train/                  |           |\n",
            "|    actor_loss           | 45.8      |\n",
            "|    approx_kl            | 0.0171892 |\n",
            "|    clip_fraction        | 0.237     |\n",
            "|    clip_range           | 0.2       |\n",
            "|    critic_loss          | 35.2      |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | nan       |\n",
            "|    learning_rate        | 0.0005    |\n",
            "|    loss                 | 5.49      |\n",
            "|    n_updates            | 99        |\n",
            "|    policy_gradient_loss | -0.0287   |\n",
            "|    policy_loss          | 3         |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 0.0804    |\n",
            "---------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 140      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 7        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -129     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 9.5      |\n",
            "------------------------------------\n",
            "======Trading from:  2017-07-05 to  2017-10-03\n",
            "============================================\n",
            "16.601643111745915\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2017-07-05\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "-------------------------------------\n",
            "| environment/          |           |\n",
            "|    portfolio_value    | 1.14e+06  |\n",
            "|    total_cost         | 8.53e+03  |\n",
            "|    total_reward       | 1.43e+05  |\n",
            "|    total_reward_pct   | 14.3      |\n",
            "|    total_trades       | 1659      |\n",
            "| time/                 |           |\n",
            "|    fps                | 143       |\n",
            "|    iterations         | 100       |\n",
            "|    time_elapsed       | 3         |\n",
            "|    total_timesteps    | 500       |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.6     |\n",
            "|    explained_variance | -2.11e+03 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 99        |\n",
            "|    policy_loss        | -28       |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 1.27      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 145      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 6        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -39.6    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -117     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 7.99     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2017-07-05 to  2017-10-03\n",
            "A2C Sharpe Ratio:  0.08517765039421371\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 1.01e+06 |\n",
            "|    total_cost         | 7.77e+03 |\n",
            "|    total_reward       | 9.28e+03 |\n",
            "|    total_reward_pct   | 0.928    |\n",
            "|    total_trades       | 1585     |\n",
            "| time/                 |          |\n",
            "|    fps                | 161      |\n",
            "|    iterations         | 1        |\n",
            "|    time_elapsed       | 12       |\n",
            "|    total_timesteps    | 2048     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 200      |\n",
            "|    policy_loss        | 57       |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 4.26     |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2017-07-05 to  2017-10-03\n",
            "PPO Sharpe Ratio:  0.45455960328054734\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "======DDPG Validation from:  2017-07-05 to  2017-10-03\n",
            "======Best Model Retraining from:  2009-01-01 to  2017-10-03\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======Trading from:  2017-10-03 to  2018-01-03\n",
            "============================================\n",
            "nan\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2017-10-03\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 1.28e+06    |\n",
            "|    total_cost           | 3.74e+03    |\n",
            "|    total_reward         | 2.77e+05    |\n",
            "|    total_reward_pct     | 27.7        |\n",
            "|    total_trades         | 943         |\n",
            "| time/                   |             |\n",
            "|    fps                  | 135         |\n",
            "|    iterations           | 100         |\n",
            "|    time_elapsed         | 3           |\n",
            "|    total_timesteps      | 500         |\n",
            "| train/                  |             |\n",
            "|    actor_loss           | 207         |\n",
            "|    approx_kl            | 0.015050514 |\n",
            "|    clip_fraction        | 0.197       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    critic_loss          | 137         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | -248        |\n",
            "|    learning_rate        | 0.0005      |\n",
            "|    loss                 | 6.75        |\n",
            "|    n_updates            | 99          |\n",
            "|    policy_gradient_loss | -0.0245     |\n",
            "|    policy_loss          | -33.7       |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 0.668       |\n",
            "-----------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 139      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 7        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -18.5    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | 7.14     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.775    |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2017-10-03 to  2018-01-03\n",
            "A2C Sharpe Ratio:  0.6253173059589403\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 1.08e+06 |\n",
            "|    total_cost         | 8.57e+03 |\n",
            "|    total_reward       | 8.4e+04  |\n",
            "|    total_reward_pct   | 8.4      |\n",
            "|    total_trades       | 1529     |\n",
            "| time/                 |          |\n",
            "|    fps                | 162      |\n",
            "|    iterations         | 1        |\n",
            "|    time_elapsed       | 12       |\n",
            "|    total_timesteps    | 2048     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -185     |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 200      |\n",
            "|    policy_loss        | 26.7     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 6.01     |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2017-10-03 to  2018-01-03\n",
            "PPO Sharpe Ratio:  0.4989263666864768\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2017-10-03 to  2018-01-03\n",
            "======Best Model Retraining from:  2009-01-01 to  2018-01-03\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======Trading from:  2018-01-03 to  2018-04-05\n",
            "============================================\n",
            "nan\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2018-01-03\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 1.31e+06    |\n",
            "|    total_cost           | 919         |\n",
            "|    total_reward         | 3.15e+05    |\n",
            "|    total_reward_pct     | 31.5        |\n",
            "|    total_trades         | 912         |\n",
            "| time/                   |             |\n",
            "|    fps                  | 137         |\n",
            "|    iterations           | 100         |\n",
            "|    time_elapsed         | 3           |\n",
            "|    total_timesteps      | 500         |\n",
            "| train/                  |             |\n",
            "|    actor_loss           | -396        |\n",
            "|    approx_kl            | 0.009693978 |\n",
            "|    clip_fraction        | 0.211       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    critic_loss          | 1.23e+03    |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -1.58e+04   |\n",
            "|    learning_rate        | 0.0005      |\n",
            "|    loss                 | 4.45        |\n",
            "|    n_updates            | 99          |\n",
            "|    policy_gradient_loss | -0.0223     |\n",
            "|    policy_loss          | -0.551      |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 0.435       |\n",
            "-----------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 139      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 7        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -8.38    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -33.9    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.13     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2018-01-03 to  2018-04-05\n",
            "A2C Sharpe Ratio:  -0.1647826484871316\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "-------------------------------------\n",
            "| environment/          |           |\n",
            "|    portfolio_value    | 9.38e+05  |\n",
            "|    total_cost         | 1.07e+04  |\n",
            "|    total_reward       | -6.22e+04 |\n",
            "|    total_reward_pct   | -6.22     |\n",
            "|    total_trades       | 1685      |\n",
            "| time/                 |           |\n",
            "|    fps                | 164       |\n",
            "|    iterations         | 1         |\n",
            "|    time_elapsed       | 12        |\n",
            "|    total_timesteps    | 2048      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.7     |\n",
            "|    explained_variance | -11.2     |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 200       |\n",
            "|    policy_loss        | -130      |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 9.12      |\n",
            "-------------------------------------\n",
            "======PPO Validation from:  2018-01-03 to  2018-04-05\n",
            "PPO Sharpe Ratio:  -0.16697855652938168\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2018-01-03 to  2018-04-05\n",
            "======Best Model Retraining from:  2009-01-01 to  2018-04-05\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "======Trading from:  2018-04-05 to  2018-07-05\n",
            "============================================\n",
            "nan\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2018-04-05\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 1.37e+06    |\n",
            "|    total_cost           | 1.27e+03    |\n",
            "|    total_reward         | 3.66e+05    |\n",
            "|    total_reward_pct     | 36.6        |\n",
            "|    total_trades         | 1272        |\n",
            "| time/                   |             |\n",
            "|    fps                  | 136         |\n",
            "|    iterations           | 100         |\n",
            "|    time_elapsed         | 3           |\n",
            "|    total_timesteps      | 500         |\n",
            "| train/                  |             |\n",
            "|    actor_loss           | 50.2        |\n",
            "|    approx_kl            | 0.012256902 |\n",
            "|    clip_fraction        | 0.227       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    critic_loss          | 278         |\n",
            "|    entropy_loss         | -42.8       |\n",
            "|    explained_variance   | -0.531      |\n",
            "|    learning_rate        | 0.0005      |\n",
            "|    loss                 | 3.55        |\n",
            "|    n_updates            | 99          |\n",
            "|    policy_gradient_loss | -0.0305     |\n",
            "|    policy_loss          | -27.4       |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 0.69        |\n",
            "-----------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 137      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 7        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -50.4    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.44     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2018-04-05 to  2018-07-05\n",
            "A2C Sharpe Ratio:  0.061383239896851856\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 1.01e+06 |\n",
            "|    total_cost         | 9.32e+03 |\n",
            "|    total_reward       | 1.1e+04  |\n",
            "|    total_reward_pct   | 1.1      |\n",
            "|    total_trades       | 1639     |\n",
            "| time/                 |          |\n",
            "|    fps                | 155      |\n",
            "|    iterations         | 1        |\n",
            "|    time_elapsed       | 13       |\n",
            "|    total_timesteps    | 2048     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -36.8    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 200      |\n",
            "|    policy_loss        | 21.4     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.2      |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2018-04-05 to  2018-07-05\n",
            "PPO Sharpe Ratio:  -0.04052628396144\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2018-04-05 to  2018-07-05\n",
            "======Best Model Retraining from:  2009-01-01 to  2018-07-05\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 9.79e+05    |\n",
            "|    total_cost           | 2.85e+03    |\n",
            "|    total_reward         | -2.09e+04   |\n",
            "|    total_reward_pct     | -2.09       |\n",
            "|    total_trades         | 1009        |\n",
            "| time/                   |             |\n",
            "|    fps                  | 110         |\n",
            "|    iterations           | 100         |\n",
            "|    time_elapsed         | 4           |\n",
            "|    total_timesteps      | 500         |\n",
            "| train/                  |             |\n",
            "|    actor_loss           | -255        |\n",
            "|    approx_kl            | 0.019786617 |\n",
            "|    clip_fraction        | 0.167       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    critic_loss          | 317         |\n",
            "|    entropy_loss         | -42.8       |\n",
            "|    explained_variance   | -1.46       |\n",
            "|    learning_rate        | 0.0005      |\n",
            "|    loss                 | 5.36        |\n",
            "|    n_updates            | 99          |\n",
            "|    policy_gradient_loss | -0.0273     |\n",
            "|    policy_loss          | 1.47        |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 0.196       |\n",
            "-----------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 117      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 8        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -1.58    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.209    |\n",
            "------------------------------------\n",
            "======Trading from:  2018-07-05 to  2018-10-03\n",
            "============================================\n",
            "nan\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2018-07-05\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 1.52e+06 |\n",
            "|    total_cost         | 9.76e+03 |\n",
            "|    total_reward       | 5.15e+05 |\n",
            "|    total_reward_pct   | 51.5     |\n",
            "|    total_trades       | 1657     |\n",
            "| time/                 |          |\n",
            "|    fps                | 119      |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 4        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -23.2    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | -35.5    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 2.79     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 128      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 7        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -4.17    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -28.3    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.32     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2018-07-05 to  2018-10-03\n",
            "A2C Sharpe Ratio:  0.2583452539806243\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 1.05e+06 |\n",
            "|    total_cost         | 1.03e+04 |\n",
            "|    total_reward       | 4.53e+04 |\n",
            "|    total_reward_pct   | 4.53     |\n",
            "|    total_trades       | 1616     |\n",
            "| time/                 |          |\n",
            "|    fps                | 152      |\n",
            "|    iterations         | 1        |\n",
            "|    time_elapsed       | 13       |\n",
            "|    total_timesteps    | 2048     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -38      |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 200      |\n",
            "|    policy_loss        | 41.6     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 4.46     |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2018-07-05 to  2018-10-03\n",
            "PPO Sharpe Ratio:  0.4917986785984965\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "======DDPG Validation from:  2018-07-05 to  2018-10-03\n",
            "======Best Model Retraining from:  2009-01-01 to  2018-10-03\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "----------------------------------------\n",
            "| environment/            |            |\n",
            "|    portfolio_value      | 1.07e+06   |\n",
            "|    total_cost           | 2.59e+03   |\n",
            "|    total_reward         | 6.95e+04   |\n",
            "|    total_reward_pct     | 6.95       |\n",
            "|    total_trades         | 929        |\n",
            "| time/                   |            |\n",
            "|    fps                  | 153        |\n",
            "|    iterations           | 1          |\n",
            "|    time_elapsed         | 13         |\n",
            "|    total_timesteps      | 2048       |\n",
            "| train/                  |            |\n",
            "|    actor_loss           | -423       |\n",
            "|    approx_kl            | 0.01623707 |\n",
            "|    clip_fraction        | 0.227      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    critic_loss          | 802        |\n",
            "|    entropy_loss         | -42.6      |\n",
            "|    explained_variance   | -184       |\n",
            "|    learning_rate        | 5e-06      |\n",
            "|    loss                 | 7.74       |\n",
            "|    n_updates            | 2392       |\n",
            "|    policy_gradient_loss | -0.0272    |\n",
            "|    std                  | 1          |\n",
            "|    value_loss           | 15.8       |\n",
            "----------------------------------------\n",
            "======Trading from:  2018-10-03 to  2019-01-04\n",
            "============================================\n",
            "nan\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2018-10-03\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 1.32e+06    |\n",
            "|    total_cost           | 1.02e+04    |\n",
            "|    total_reward         | 3.18e+05    |\n",
            "|    total_reward_pct     | 31.8        |\n",
            "|    total_trades         | 1742        |\n",
            "| time/                   |             |\n",
            "|    fps                  | 132         |\n",
            "|    iterations           | 100         |\n",
            "|    time_elapsed         | 3           |\n",
            "|    total_timesteps      | 500         |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.021027597 |\n",
            "|    clip_fraction        | 0.228       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -127        |\n",
            "|    learning_rate        | 0.0005      |\n",
            "|    loss                 | 7.26        |\n",
            "|    n_updates            | 99          |\n",
            "|    policy_gradient_loss | -0.025      |\n",
            "|    policy_loss          | -12.4       |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 0.165       |\n",
            "-----------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 134      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 7        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -23.9    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -21.9    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.977    |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2018-10-03 to  2019-01-04\n",
            "A2C Sharpe Ratio:  -0.2930598272739722\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "-------------------------------------\n",
            "| environment/          |           |\n",
            "|    portfolio_value    | 8.67e+05  |\n",
            "|    total_cost         | 8.9e+03   |\n",
            "|    total_reward       | -1.33e+05 |\n",
            "|    total_reward_pct   | -13.3     |\n",
            "|    total_trades       | 1541      |\n",
            "| time/                 |           |\n",
            "|    fps                | 155       |\n",
            "|    iterations         | 1         |\n",
            "|    time_elapsed       | 13        |\n",
            "|    total_timesteps    | 2048      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.6     |\n",
            "|    explained_variance | nan       |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 200       |\n",
            "|    policy_loss        | -19       |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 1.04      |\n",
            "-------------------------------------\n",
            "======PPO Validation from:  2018-10-03 to  2019-01-04\n",
            "PPO Sharpe Ratio:  -0.31117347358633396\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2018-10-03 to  2019-01-04\n",
            "======Best Model Retraining from:  2009-01-01 to  2019-01-04\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======Trading from:  2019-01-04 to  2019-04-05\n",
            "============================================\n",
            "nan\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2019-01-04\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 1.53e+06    |\n",
            "|    total_cost           | 2.33e+03    |\n",
            "|    total_reward         | 5.3e+05     |\n",
            "|    total_reward_pct     | 53          |\n",
            "|    total_trades         | 811         |\n",
            "| time/                   |             |\n",
            "|    fps                  | 130         |\n",
            "|    iterations           | 100         |\n",
            "|    time_elapsed         | 3           |\n",
            "|    total_timesteps      | 500         |\n",
            "| train/                  |             |\n",
            "|    actor_loss           | 211         |\n",
            "|    approx_kl            | 0.012763677 |\n",
            "|    clip_fraction        | 0.178       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    critic_loss          | 387         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -7.15e+03   |\n",
            "|    learning_rate        | 0.0005      |\n",
            "|    loss                 | 7.66        |\n",
            "|    n_updates            | 99          |\n",
            "|    policy_gradient_loss | -0.0272     |\n",
            "|    policy_loss          | -62.3       |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 3.42        |\n",
            "-----------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 131      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 7        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -34.1    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -64      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 2.71     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2019-01-04 to  2019-04-05\n",
            "A2C Sharpe Ratio:  0.46957388408607487\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 1.1e+06  |\n",
            "|    total_cost         | 8.33e+03 |\n",
            "|    total_reward       | 9.51e+04 |\n",
            "|    total_reward_pct   | 9.51     |\n",
            "|    total_trades       | 1573     |\n",
            "| time/                 |          |\n",
            "|    fps                | 150      |\n",
            "|    iterations         | 1        |\n",
            "|    time_elapsed       | 13       |\n",
            "|    total_timesteps    | 2048     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 200      |\n",
            "|    policy_loss        | -25.9    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.47     |\n",
            "------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "======PPO Validation from:  2019-01-04 to  2019-04-05\n",
            "PPO Sharpe Ratio:  0.442674028996573\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2019-01-04 to  2019-04-05\n",
            "======Best Model Retraining from:  2009-01-01 to  2019-04-05\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======Trading from:  2019-04-05 to  2019-07-08\n",
            "============================================\n",
            "nan\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2019-04-05\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 1.59e+06    |\n",
            "|    total_cost           | 4.47e+03    |\n",
            "|    total_reward         | 5.88e+05    |\n",
            "|    total_reward_pct     | 58.8        |\n",
            "|    total_trades         | 1030        |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 100         |\n",
            "|    time_elapsed         | 4           |\n",
            "|    total_timesteps      | 500         |\n",
            "| train/                  |             |\n",
            "|    actor_loss           | -47.5       |\n",
            "|    approx_kl            | 0.008430626 |\n",
            "|    clip_fraction        | 0.187       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    critic_loss          | 232         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | -1.88       |\n",
            "|    learning_rate        | 0.0005      |\n",
            "|    loss                 | 4.88        |\n",
            "|    n_updates            | 99          |\n",
            "|    policy_gradient_loss | -0.0253     |\n",
            "|    policy_loss          | -16.5       |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 0.323       |\n",
            "-----------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 101      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 9        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.247   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -66      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 2.67     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2019-04-05 to  2019-07-08\n",
            "A2C Sharpe Ratio:  0.30290074237834286\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 1.04e+06 |\n",
            "|    total_cost         | 1.08e+04 |\n",
            "|    total_reward       | 4.42e+04 |\n",
            "|    total_reward_pct   | 4.42     |\n",
            "|    total_trades       | 1610     |\n",
            "| time/                 |          |\n",
            "|    fps                | 140      |\n",
            "|    iterations         | 1        |\n",
            "|    time_elapsed       | 14       |\n",
            "|    total_timesteps    | 2048     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -12.8    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 200      |\n",
            "|    policy_loss        | -12.1    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 2.17     |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2019-04-05 to  2019-07-08\n",
            "PPO Sharpe Ratio:  0.11037047668856044\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2019-04-05 to  2019-07-08\n",
            "======Best Model Retraining from:  2009-01-01 to  2019-07-08\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 1.03e+06    |\n",
            "|    total_cost           | 4.08e+03    |\n",
            "|    total_reward         | 2.72e+04    |\n",
            "|    total_reward_pct     | 2.72        |\n",
            "|    total_trades         | 1069        |\n",
            "| time/                   |             |\n",
            "|    fps                  | 125         |\n",
            "|    iterations           | 100         |\n",
            "|    time_elapsed         | 3           |\n",
            "|    total_timesteps      | 500         |\n",
            "| train/                  |             |\n",
            "|    actor_loss           | 89.7        |\n",
            "|    approx_kl            | 0.011282537 |\n",
            "|    clip_fraction        | 0.198       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    critic_loss          | 58.1        |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -0.0831     |\n",
            "|    learning_rate        | 0.0005      |\n",
            "|    loss                 | 6.85        |\n",
            "|    n_updates            | 99          |\n",
            "|    policy_gradient_loss | -0.0234     |\n",
            "|    policy_loss          | 24.3        |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 0.444       |\n",
            "-----------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 129      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 7        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -319     |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | 34.5     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.05     |\n",
            "------------------------------------\n",
            "======Trading from:  2019-07-08 to  2019-10-04\n",
            "============================================\n",
            "nan\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2019-07-08\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 1.54e+06 |\n",
            "|    total_cost         | 1.07e+04 |\n",
            "|    total_reward       | 5.41e+05 |\n",
            "|    total_reward_pct   | 54.1     |\n",
            "|    total_trades       | 1673     |\n",
            "| time/                 |          |\n",
            "|    fps                | 125      |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 3        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -1.15    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | 33.2     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.924    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 130       |\n",
            "|    iterations         | 200       |\n",
            "|    time_elapsed       | 7         |\n",
            "|    total_timesteps    | 1000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.7     |\n",
            "|    explained_variance | -1.59e+08 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 199       |\n",
            "|    policy_loss        | -39.5     |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 1.84      |\n",
            "-------------------------------------\n",
            "======A2C Validation from:  2019-07-08 to  2019-10-04\n",
            "A2C Sharpe Ratio:  -0.17485777648526207\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------------------------------\n",
            "| environment/          |           |\n",
            "|    portfolio_value    | 9.39e+05  |\n",
            "|    total_cost         | 7.91e+03  |\n",
            "|    total_reward       | -6.14e+04 |\n",
            "|    total_reward_pct   | -6.14     |\n",
            "|    total_trades       | 1500      |\n",
            "| time/                 |           |\n",
            "|    fps                | 144       |\n",
            "|    iterations         | 1         |\n",
            "|    time_elapsed       | 14        |\n",
            "|    total_timesteps    | 2048      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.7     |\n",
            "|    explained_variance | -44.6     |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 200       |\n",
            "|    policy_loss        | -26.6     |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 1.56      |\n",
            "-------------------------------------\n",
            "======PPO Validation from:  2019-07-08 to  2019-10-04\n",
            "PPO Sharpe Ratio:  -0.09927674223889364\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2019-07-08 to  2019-10-04\n",
            "======Best Model Retraining from:  2009-01-01 to  2019-10-04\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======Trading from:  2019-10-04 to  2020-01-06\n",
            "============================================\n",
            "nan\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2019-10-04\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 1.61e+06    |\n",
            "|    total_cost           | 1.32e+03    |\n",
            "|    total_reward         | 6.08e+05    |\n",
            "|    total_reward_pct     | 60.8        |\n",
            "|    total_trades         | 1181        |\n",
            "| time/                   |             |\n",
            "|    fps                  | 121         |\n",
            "|    iterations           | 100         |\n",
            "|    time_elapsed         | 4           |\n",
            "|    total_timesteps      | 500         |\n",
            "| train/                  |             |\n",
            "|    actor_loss           | -266        |\n",
            "|    approx_kl            | 0.021247772 |\n",
            "|    clip_fraction        | 0.218       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    critic_loss          | 551         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | 0.23        |\n",
            "|    learning_rate        | 0.0005      |\n",
            "|    loss                 | 5.12        |\n",
            "|    n_updates            | 99          |\n",
            "|    policy_gradient_loss | -0.0199     |\n",
            "|    policy_loss          | -12.6       |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 0.198       |\n",
            "-----------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 125      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 7        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -1.05    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | 26.7     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.09     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2019-10-04 to  2020-01-06\n",
            "A2C Sharpe Ratio:  0.05764099868169824\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 1.01e+06 |\n",
            "|    total_cost         | 9.92e+03 |\n",
            "|    total_reward       | 7.8e+03  |\n",
            "|    total_reward_pct   | 0.78     |\n",
            "|    total_trades       | 1548     |\n",
            "| time/                 |          |\n",
            "|    fps                | 134      |\n",
            "|    iterations         | 1        |\n",
            "|    time_elapsed       | 15       |\n",
            "|    total_timesteps    | 2048     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -26.9    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 200      |\n",
            "|    policy_loss        | 14.9     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.713    |\n",
            "------------------------------------\n",
            "======PPO Validation from:  2019-10-04 to  2020-01-06\n",
            "PPO Sharpe Ratio:  0.4151507984365526\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2019-10-04 to  2020-01-06\n",
            "======Best Model Retraining from:  2009-01-01 to  2020-01-06\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 1.02e+06    |\n",
            "|    total_cost           | 1.81e+03    |\n",
            "|    total_reward         | 2.09e+04    |\n",
            "|    total_reward_pct     | 2.09        |\n",
            "|    total_trades         | 1237        |\n",
            "| time/                   |             |\n",
            "|    fps                  | 136         |\n",
            "|    iterations           | 1           |\n",
            "|    time_elapsed         | 15          |\n",
            "|    total_timesteps      | 2048        |\n",
            "| train/                  |             |\n",
            "|    actor_loss           | -133        |\n",
            "|    approx_kl            | 0.012345975 |\n",
            "|    clip_fraction        | 0.234       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    critic_loss          | 112         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -120        |\n",
            "|    learning_rate        | 5e-06       |\n",
            "|    loss                 | 4.03        |\n",
            "|    n_updates            | 2707        |\n",
            "|    policy_gradient_loss | -0.0193     |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 10.6        |\n",
            "-----------------------------------------\n",
            "======Trading from:  2020-01-06 to  2020-04-06\n",
            "============================================\n",
            "nan\n",
            "turbulence_threshold:  286.9562782260909\n",
            "======Model training from:  2009-01-01 to  2020-01-06\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 1.27e+06    |\n",
            "|    total_cost           | 8.68e+03    |\n",
            "|    total_reward         | 2.74e+05    |\n",
            "|    total_reward_pct     | 27.4        |\n",
            "|    total_trades         | 1403        |\n",
            "| time/                   |             |\n",
            "|    fps                  | 121         |\n",
            "|    iterations           | 100         |\n",
            "|    time_elapsed         | 4           |\n",
            "|    total_timesteps      | 500         |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.008977065 |\n",
            "|    clip_fraction        | 0.201       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | -289        |\n",
            "|    learning_rate        | 0.0005      |\n",
            "|    loss                 | 6.25        |\n",
            "|    n_updates            | 99          |\n",
            "|    policy_gradient_loss | -0.0251     |\n",
            "|    policy_loss          | -27.3       |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 0.536       |\n",
            "-----------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 123      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 8        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -68.2    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -29.7    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.09     |\n",
            "------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "======A2C Validation from:  2020-01-06 to  2020-04-06\n",
            "A2C Sharpe Ratio:  -0.4120363107692286\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "-------------------------------------\n",
            "| environment/          |           |\n",
            "|    portfolio_value    | 7.83e+05  |\n",
            "|    total_cost         | 7.44e+03  |\n",
            "|    total_reward       | -2.17e+05 |\n",
            "|    total_reward_pct   | -21.7     |\n",
            "|    total_trades       | 1284      |\n",
            "| time/                 |           |\n",
            "|    fps                | 140       |\n",
            "|    iterations         | 1         |\n",
            "|    time_elapsed       | 14        |\n",
            "|    total_timesteps    | 2048      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.7     |\n",
            "|    explained_variance | -8.97     |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 200       |\n",
            "|    policy_loss        | 50.4      |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 4.98      |\n",
            "-------------------------------------\n",
            "======PPO Validation from:  2020-01-06 to  2020-04-06\n",
            "PPO Sharpe Ratio:  -0.415416644640626\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======DDPG Validation from:  2020-01-06 to  2020-04-06\n",
            "======Best Model Retraining from:  2009-01-01 to  2020-04-06\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "======Trading from:  2020-04-06 to  2020-07-07\n",
            "Ensemble Strategy took:  33.55119262933731  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "-0qd8acMtj1f",
        "outputId": "ad842ea3-170a-4249-ed96-164eec113644"
      },
      "source": [
        "df_summary"
      ],
      "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>Iter</th>\n",
              "      <th>Val Start</th>\n",
              "      <th>Val End</th>\n",
              "      <th>Model Used</th>\n",
              "      <th>A2C Sharpe</th>\n",
              "      <th>PPO Sharpe</th>\n",
              "      <th>DDPG Sharpe</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>126</td>\n",
              "      <td>2015-10-02</td>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>PPO</td>\n",
              "      <td>-0.396196</td>\n",
              "      <td>-0.315592</td>\n",
              "      <td>-0.3697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>189</td>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>2016-04-05</td>\n",
              "      <td>A2C</td>\n",
              "      <td>0.31315</td>\n",
              "      <td>0.310705</td>\n",
              "      <td>0.285361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>252</td>\n",
              "      <td>2016-04-05</td>\n",
              "      <td>2016-07-05</td>\n",
              "      <td>DDPG</td>\n",
              "      <td>0.0166888</td>\n",
              "      <td>6.03448e-05</td>\n",
              "      <td>0.0524238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>315</td>\n",
              "      <td>2016-07-05</td>\n",
              "      <td>2016-10-03</td>\n",
              "      <td>DDPG</td>\n",
              "      <td>-0.00228507</td>\n",
              "      <td>-0.0262525</td>\n",
              "      <td>0.0377812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>378</td>\n",
              "      <td>2016-10-03</td>\n",
              "      <td>2017-01-03</td>\n",
              "      <td>DDPG</td>\n",
              "      <td>0.0842088</td>\n",
              "      <td>0.146093</td>\n",
              "      <td>0.324498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>441</td>\n",
              "      <td>2017-01-03</td>\n",
              "      <td>2017-04-04</td>\n",
              "      <td>DDPG</td>\n",
              "      <td>0.0989313</td>\n",
              "      <td>-0.00655463</td>\n",
              "      <td>0.204108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>504</td>\n",
              "      <td>2017-04-04</td>\n",
              "      <td>2017-07-05</td>\n",
              "      <td>A2C</td>\n",
              "      <td>0.456911</td>\n",
              "      <td>0.146164</td>\n",
              "      <td>0.319113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>567</td>\n",
              "      <td>2017-07-05</td>\n",
              "      <td>2017-10-03</td>\n",
              "      <td>DDPG</td>\n",
              "      <td>0.0851777</td>\n",
              "      <td>0.45456</td>\n",
              "      <td>0.511453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>630</td>\n",
              "      <td>2017-10-03</td>\n",
              "      <td>2018-01-03</td>\n",
              "      <td>DDPG</td>\n",
              "      <td>0.625317</td>\n",
              "      <td>0.498926</td>\n",
              "      <td>0.816116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>693</td>\n",
              "      <td>2018-01-03</td>\n",
              "      <td>2018-04-05</td>\n",
              "      <td>DDPG</td>\n",
              "      <td>-0.164783</td>\n",
              "      <td>-0.166979</td>\n",
              "      <td>-0.0971064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>756</td>\n",
              "      <td>2018-04-05</td>\n",
              "      <td>2018-07-05</td>\n",
              "      <td>A2C</td>\n",
              "      <td>0.0613832</td>\n",
              "      <td>-0.0405263</td>\n",
              "      <td>-0.0854798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>819</td>\n",
              "      <td>2018-07-05</td>\n",
              "      <td>2018-10-03</td>\n",
              "      <td>PPO</td>\n",
              "      <td>0.258345</td>\n",
              "      <td>0.491799</td>\n",
              "      <td>0.429445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>882</td>\n",
              "      <td>2018-10-03</td>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>DDPG</td>\n",
              "      <td>-0.29306</td>\n",
              "      <td>-0.311173</td>\n",
              "      <td>-0.253584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>945</td>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>2019-04-05</td>\n",
              "      <td>DDPG</td>\n",
              "      <td>0.469574</td>\n",
              "      <td>0.442674</td>\n",
              "      <td>0.648216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1008</td>\n",
              "      <td>2019-04-05</td>\n",
              "      <td>2019-07-08</td>\n",
              "      <td>A2C</td>\n",
              "      <td>0.302901</td>\n",
              "      <td>0.11037</td>\n",
              "      <td>0.124695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1071</td>\n",
              "      <td>2019-07-08</td>\n",
              "      <td>2019-10-04</td>\n",
              "      <td>DDPG</td>\n",
              "      <td>-0.174858</td>\n",
              "      <td>-0.0992767</td>\n",
              "      <td>-0.0790365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1134</td>\n",
              "      <td>2019-10-04</td>\n",
              "      <td>2020-01-06</td>\n",
              "      <td>PPO</td>\n",
              "      <td>0.057641</td>\n",
              "      <td>0.415151</td>\n",
              "      <td>0.107693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1197</td>\n",
              "      <td>2020-01-06</td>\n",
              "      <td>2020-04-06</td>\n",
              "      <td>DDPG</td>\n",
              "      <td>-0.412036</td>\n",
              "      <td>-0.415417</td>\n",
              "      <td>-0.288057</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Iter   Val Start     Val End Model Used  A2C Sharpe   PPO Sharpe  \\\n",
              "0    126  2015-10-02  2016-01-04        PPO   -0.396196    -0.315592   \n",
              "1    189  2016-01-04  2016-04-05        A2C     0.31315     0.310705   \n",
              "2    252  2016-04-05  2016-07-05       DDPG   0.0166888  6.03448e-05   \n",
              "3    315  2016-07-05  2016-10-03       DDPG -0.00228507   -0.0262525   \n",
              "4    378  2016-10-03  2017-01-03       DDPG   0.0842088     0.146093   \n",
              "5    441  2017-01-03  2017-04-04       DDPG   0.0989313  -0.00655463   \n",
              "6    504  2017-04-04  2017-07-05        A2C    0.456911     0.146164   \n",
              "7    567  2017-07-05  2017-10-03       DDPG   0.0851777      0.45456   \n",
              "8    630  2017-10-03  2018-01-03       DDPG    0.625317     0.498926   \n",
              "9    693  2018-01-03  2018-04-05       DDPG   -0.164783    -0.166979   \n",
              "10   756  2018-04-05  2018-07-05        A2C   0.0613832   -0.0405263   \n",
              "11   819  2018-07-05  2018-10-03        PPO    0.258345     0.491799   \n",
              "12   882  2018-10-03  2019-01-04       DDPG    -0.29306    -0.311173   \n",
              "13   945  2019-01-04  2019-04-05       DDPG    0.469574     0.442674   \n",
              "14  1008  2019-04-05  2019-07-08        A2C    0.302901      0.11037   \n",
              "15  1071  2019-07-08  2019-10-04       DDPG   -0.174858   -0.0992767   \n",
              "16  1134  2019-10-04  2020-01-06        PPO    0.057641     0.415151   \n",
              "17  1197  2020-01-06  2020-04-06       DDPG   -0.412036    -0.415417   \n",
              "\n",
              "   DDPG Sharpe  \n",
              "0      -0.3697  \n",
              "1     0.285361  \n",
              "2    0.0524238  \n",
              "3    0.0377812  \n",
              "4     0.324498  \n",
              "5     0.204108  \n",
              "6     0.319113  \n",
              "7     0.511453  \n",
              "8     0.816116  \n",
              "9   -0.0971064  \n",
              "10  -0.0854798  \n",
              "11    0.429445  \n",
              "12   -0.253584  \n",
              "13    0.648216  \n",
              "14    0.124695  \n",
              "15  -0.0790365  \n",
              "16    0.107693  \n",
              "17   -0.288057  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "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_full[(processed_full.date > val_test_start)&(processed_full.date <= val_test_end)].date.unique()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9mKF7GGtj1g",
        "scrolled": true,
        "outputId": "5de4e5e6-d74b-4d12-b786-7e299dabd6a5"
      },
      "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:  0.5906602480862464\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyosyW7_tj1g",
        "outputId": "29307dfb-1d27-4fb7-dc7a-7a34a4902d59"
      },
      "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>1.000098e+06</td>\n",
              "      <td>2016-01-05</td>\n",
              "      <td>0.000098</td>\n",
              "      <td>2016-01-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.982350e+05</td>\n",
              "      <td>2016-01-06</td>\n",
              "      <td>-0.001862</td>\n",
              "      <td>2016-01-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.950237e+05</td>\n",
              "      <td>2016-01-07</td>\n",
              "      <td>-0.003217</td>\n",
              "      <td>2016-01-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.948886e+05</td>\n",
              "      <td>2016-01-08</td>\n",
              "      <td>-0.000136</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   1.000098e+06  2016-01-05      0.000098  2016-01-05\n",
              "2   9.982350e+05  2016-01-06     -0.001862  2016-01-06\n",
              "3   9.950237e+05  2016-01-07     -0.003217  2016-01-07\n",
              "4   9.948886e+05  2016-01-08     -0.000136  2016-01-08"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wLsRdw2Ctj1h",
        "outputId": "01dd70f8-639f-49f7-8e75-60ebc88b0e4e"
      },
      "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 0x1a38b6e860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "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": "1053083a-d74c-48b0-a623-de33282e2fff"
      },
      "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          0.087584\n",
            "Cumulative returns     0.459095\n",
            "Annual volatility      0.165637\n",
            "Sharpe ratio           0.590660\n",
            "Calmar ratio           0.350857\n",
            "Stability              0.798822\n",
            "Max drawdown          -0.249630\n",
            "Omega ratio            1.127499\n",
            "Sortino ratio          0.810664\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.938521\n",
            "Daily value at risk   -0.020480\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6825tCrlHCqY",
        "outputId": "66248e13-f4ae-439e-c8e4-8101792ba3d9"
      },
      "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:  (1133, 8)\n",
            "Annual return          0.095357\n",
            "Cumulative returns     0.506062\n",
            "Annual volatility      0.205013\n",
            "Sharpe ratio           0.547962\n",
            "Calmar ratio           0.257121\n",
            "Stability              0.772163\n",
            "Max drawdown          -0.370862\n",
            "Omega ratio            1.131863\n",
            "Sortino ratio          0.749922\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.843359\n",
            "Daily value at risk   -0.025383\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": "dtYSrsOaHCqY",
        "outputId": "d0f7c03f-ebfb-429a-d029-0c99b320059a"
      },
      "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",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (1133, 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>2020-07-02</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>53</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>8.286%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>43.032%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>16.545%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>0.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>0.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-24.963%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>0.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-2.047%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.56</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>24.96</td>\n",
              "      <td>2020-02-14</td>\n",
              "      <td>2020-05-13</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>16.76</td>\n",
              "      <td>2018-10-03</td>\n",
              "      <td>2018-12-24</td>\n",
              "      <td>2019-02-05</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10.91</td>\n",
              "      <td>2019-02-28</td>\n",
              "      <td>2019-03-22</td>\n",
              "      <td>2020-01-16</td>\n",
              "      <td>231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10.47</td>\n",
              "      <td>2018-02-01</td>\n",
              "      <td>2018-03-23</td>\n",
              "      <td>2018-07-26</td>\n",
              "      <td>126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.07</td>\n",
              "      <td>2016-08-15</td>\n",
              "      <td>2016-11-04</td>\n",
              "      <td>2016-12-07</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tf0xH-2gHCqY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}

一键复制编辑原始数据按行查看历史

提交于 2021-06-14 23:21 . Created using Colaboratory

马建仓 AI 助手

尝试更多

代码解读

代码找茬

代码优化

https://gitee.com/yutiansut/FinRL-Library.git

[email protected]:yutiansut/FinRL-Library.git

yutiansut

FinRL-Library

master

yutiansut/FinRL-Library

简介

发行版

贡献者

近期动态

yutiansut/FinRL-Library .gitee-modal { width: 500px !important; }

简介

发行版

贡献者

近期动态

搜索帮助

yutiansut/FinRL-Library