FinRL_portfolio_allocation_NeurIPS_2020.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/AI4Finance-LLC/FinRL/blob/master/FinRL_portfolio_allocation_NeurIPS_2020.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yv3IDvrobU37"
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      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Portfolio Allocation\n",
        "\n",
        "Tutorials to use OpenAI DRL to perform portfolio allocation in one Jupyter Notebook | Presented at NeurIPS 2020: Deep RL Workshop\n",
        "\n",
        "* This blog is based on our paper: FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, presented at NeurIPS 2020: Deep RL Workshop.\n",
        "* Check out medium blog for detailed explanations: \n",
        "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
        "* **Pytorch Version** \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kHCfEiTA80V"
      },
      "source": [
        "# Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vUmLTmoQA7_w"
      },
      "source": [
        "* [1. Problem Definition](#0)\n",
        "* [2. Getting Started - Load Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. Check Additional Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download Data](#2)\n",
        "* [4. Preprocess Data](#3)        \n",
        "    * [4.1. Technical Indicators](#3.1)\n",
        "    * [4.2. Perform Feature Engineering](#3.2)\n",
        "* [5.Build Environment](#4)  \n",
        "    * [5.1. Training & Trade Data Split](#4.1)\n",
        "    * [5.2. User-defined Environment](#4.2)   \n",
        "    * [5.3. Initialize Environment](#4.3)    \n",
        "* [6.Implement DRL Algorithms](#5)  \n",
        "* [7.Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "    * [7.3. Baseline Stats](#6.3)   \n",
        "    * [7.3. Compare to Stock Market Index](#6.4)             "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12v1i0jVkg48"
      },
      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L63HKnWvkirx"
      },
      "source": [
        "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\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": "g_emqQCCklVt"
      },
      "source": [
        "<a id='1'></a>\n",
        "# Part 2. Getting Started- Load Python Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cVCcCalAknGn"
      },
      "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": "pT8a0fvhA_TW",
        "outputId": "8c95cdef-f012-474d-e917-45f1ffa89c6c"
      },
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": 2,
      "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-bd7z679f\n",
            "  Running command git clone -q https://github.com/AI4Finance-LLC/FinRL-Library.git /tmp/pip-req-build-bd7z679f\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-szxrp4sk/pyfolio_d7f586c8021647e0bb2f92448edfcce8\n",
            "  Running command git clone -q https://github.com/quantopian/pyfolio.git /tmp/pip-install-szxrp4sk/pyfolio_d7f586c8021647e0bb2f92448edfcce8\n",
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        "id": "h2568cp5bU38"
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      "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": "bNmvYN9YbU4B"
      },
      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntfTb0e2bU4C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d0a00c2-11e4-443a-d33a-4af806032669"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "matplotlib.use('Agg')\n",
        "%matplotlib inline\n",
        "import datetime\n",
        "\n",
        "from finrl.apps import config\n",
        "from finrl.neo_finrl.preprocessor.yahoodownloader import YahooDownloader\n",
        "from finrl.neo_finrl.preprocessor.preprocessors import FeatureEngineer, data_split\n",
        "from finrl.neo_finrl.env_portfolio_allocation.env_portfolio import StockPortfolioEnv\n",
        "from finrl.drl_agents.stablebaselines3.models import DRLAgent\n",
        "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline,convert_daily_return_to_pyfolio_ts\n",
        "\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyfolio/pos.py:27: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
            "  'Module \"zipline.assets\" not found; multipliers will not be applied'\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OlIS2abxkwan"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8bBq7nsBCfF"
      },
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slBria_QbU4F"
      },
      "source": [
        "<a id='2'></a>\n",
        "# Part 3. Download Data\n",
        "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
        "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
        "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CPsuy6d9yRPp",
        "outputId": "4896c195-562d-4f02-ec91-d92f2edc3ea5"
      },
      "source": [
        "print(config.DOW_30_TICKER)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['AAPL', 'MSFT', 'JPM', 'V', 'RTX', 'PG', 'GS', 'NKE', 'DIS', 'AXP', 'HD', 'INTC', 'WMT', 'IBM', 'MRK', 'UNH', 'KO', 'CAT', 'TRV', 'JNJ', 'CVX', 'MCD', 'VZ', 'CSCO', 'XOM', 'BA', 'MMM', 'PFE', 'WBA', 'DD']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEwzMkFHbU4G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3a05dc0-ab1c-434c-9a0e-d2d8d03b7658"
      },
      "source": [
        "df = YahooDownloader(start_date = '2008-01-01',\n",
        "                     end_date = '2021-07-01',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": 6,
      "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:  (101887, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1xC-LpbbU4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "80c1fb6d-feab-402f-9b68-a06f2060cde0"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 7,
      "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>2008-01-02</td>\n",
              "      <td>7.116786</td>\n",
              "      <td>7.152143</td>\n",
              "      <td>6.876786</td>\n",
              "      <td>5.983694</td>\n",
              "      <td>1079178800</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>52.090000</td>\n",
              "      <td>52.320000</td>\n",
              "      <td>50.790001</td>\n",
              "      <td>40.795723</td>\n",
              "      <td>8053700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>87.570000</td>\n",
              "      <td>87.839996</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>63.481632</td>\n",
              "      <td>4303000</td>\n",
              "      <td>BA</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>72.559998</td>\n",
              "      <td>72.669998</td>\n",
              "      <td>70.050003</td>\n",
              "      <td>47.896084</td>\n",
              "      <td>6337800</td>\n",
              "      <td>CAT</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>27.299999</td>\n",
              "      <td>26.209999</td>\n",
              "      <td>19.569740</td>\n",
              "      <td>64338900</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high  ...      volume   tic  day\n",
              "0  2008-01-02   7.116786   7.152143  ...  1079178800  AAPL    2\n",
              "1  2008-01-02  52.090000  52.320000  ...     8053700   AXP    2\n",
              "2  2008-01-02  87.570000  87.839996  ...     4303000    BA    2\n",
              "3  2008-01-02  72.559998  72.669998  ...     6337800   CAT    2\n",
              "4  2008-01-02  27.000000  27.299999  ...    64338900  CSCO    2\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mS1-nxRzbU4i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec8127c7-c68a-495c-9621-c08b9b99bb4b"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(101887, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9UwKwzRbU4l"
      },
      "source": [
        "# Part 4: Preprocess Data\n",
        "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
        "* Add technical indicators. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
        "* Add turbulence index. Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P5h8RbeBHMDQ",
        "outputId": "f7d7cf39-cf54-4cb3-a561-c9c20c986dfa"
      },
      "source": [
        "fe = FeatureEngineer(\n",
        "                    use_technical_indicator=True,\n",
        "                    use_turbulence=False,\n",
        "                    user_defined_feature = False)\n",
        "\n",
        "df = fe.preprocess_data(df)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully added technical indicators\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_zsIW1LAiqCb",
        "outputId": "4d92e81a-826b-4514-fdf0-30079fce2d7a"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(98542, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWB-RoTSbU4s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "79d4e003-eae3-489f-fdf4-ae5b2576d223"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>7.116786</td>\n",
              "      <td>7.152143</td>\n",
              "      <td>6.876786</td>\n",
              "      <td>5.983694</td>\n",
              "      <td>1079178800</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>-66.666667</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3398</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>52.090000</td>\n",
              "      <td>52.320000</td>\n",
              "      <td>50.790001</td>\n",
              "      <td>40.795723</td>\n",
              "      <td>8053700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>-66.666667</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6796</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>87.570000</td>\n",
              "      <td>87.839996</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>63.481632</td>\n",
              "      <td>4303000</td>\n",
              "      <td>BA</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>-66.666667</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10194</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>72.559998</td>\n",
              "      <td>72.669998</td>\n",
              "      <td>70.050003</td>\n",
              "      <td>47.896084</td>\n",
              "      <td>6337800</td>\n",
              "      <td>CAT</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>-66.666667</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13592</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>27.299999</td>\n",
              "      <td>26.209999</td>\n",
              "      <td>19.569740</td>\n",
              "      <td>64338900</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>-66.666667</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             date       open       high  ...  rsi_30     cci_30  dx_30\n",
              "0      2008-01-02   7.116786   7.152143  ...   100.0 -66.666667  100.0\n",
              "3398   2008-01-02  52.090000  52.320000  ...   100.0 -66.666667  100.0\n",
              "6796   2008-01-02  87.570000  87.839996  ...   100.0 -66.666667  100.0\n",
              "10194  2008-01-02  72.559998  72.669998  ...   100.0 -66.666667  100.0\n",
              "13592  2008-01-02  27.000000  27.299999  ...   100.0 -66.666667  100.0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qz9K2vul6RmK"
      },
      "source": [
        "## Add covariance matrix as states"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IhizvNwcrg1n"
      },
      "source": [
        "# add covariance matrix as states\n",
        "df=df.sort_values(['date','tic'],ignore_index=True)\n",
        "df.index = df.date.factorize()[0]\n",
        "\n",
        "cov_list = []\n",
        "return_list = []\n",
        "\n",
        "# look back is one year\n",
        "lookback=252\n",
        "for i in range(lookback,len(df.index.unique())):\n",
        "  data_lookback = df.loc[i-lookback:i,:]\n",
        "  price_lookback=data_lookback.pivot_table(index = 'date',columns = 'tic', values = 'close')\n",
        "  return_lookback = price_lookback.pct_change().dropna()\n",
        "  return_list.append(return_lookback)\n",
        "\n",
        "  covs = return_lookback.cov().values \n",
        "  cov_list.append(covs)\n",
        "\n",
        "  \n",
        "df_cov = pd.DataFrame({'date':df.date.unique()[lookback:],'cov_list':cov_list,'return_list':return_list})\n",
        "df = df.merge(df_cov, on='date')\n",
        "df = df.sort_values(['date','tic']).reset_index(drop=True)\n",
        "        "
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUPwwa13uBQ-",
        "outputId": "85d027e8-583b-4c89-9752-469e35eefaab"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(91234, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "wv3jR1zPrg4g",
        "outputId": "4e03e014-f1fa-4154-be31-e277025dc768"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>cov_list</th>\n",
              "      <th>return_list</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>3.070357</td>\n",
              "      <td>3.133571</td>\n",
              "      <td>3.047857</td>\n",
              "      <td>2.621168</td>\n",
              "      <td>607541200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.083794</td>\n",
              "      <td>42.254781</td>\n",
              "      <td>-80.495083</td>\n",
              "      <td>16.129793</td>\n",
              "      <td>[[0.0013489695390742418, 0.0009909796896398577...</td>\n",
              "      <td>tic             AAPL       AXP        BA  ... ...</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>15.025569</td>\n",
              "      <td>9625600</td>\n",
              "      <td>AXP</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.964124</td>\n",
              "      <td>42.554845</td>\n",
              "      <td>-75.362550</td>\n",
              "      <td>25.776759</td>\n",
              "      <td>[[0.0013489695390742418, 0.0009909796896398577...</td>\n",
              "      <td>tic             AAPL       AXP        BA  ... ...</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",
              "      <td>-0.279798</td>\n",
              "      <td>47.440267</td>\n",
              "      <td>156.995097</td>\n",
              "      <td>5.366299</td>\n",
              "      <td>[[0.0013489695390742418, 0.0009909796896398577...</td>\n",
              "      <td>tic             AAPL       AXP        BA  ... ...</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>31.095800</td>\n",
              "      <td>6277400</td>\n",
              "      <td>CAT</td>\n",
              "      <td>2</td>\n",
              "      <td>0.688541</td>\n",
              "      <td>51.205314</td>\n",
              "      <td>98.432308</td>\n",
              "      <td>26.331746</td>\n",
              "      <td>[[0.0013489695390742418, 0.0009909796896398577...</td>\n",
              "      <td>tic             AAPL       AXP        BA  ... ...</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>12.019092</td>\n",
              "      <td>37513700</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.102729</td>\n",
              "      <td>45.961924</td>\n",
              "      <td>11.978197</td>\n",
              "      <td>13.387087</td>\n",
              "      <td>[[0.0013489695390742418, 0.0009909796896398577...</td>\n",
              "      <td>tic             AAPL       AXP        BA  ... ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  ...                                        return_list\n",
              "0  2008-12-31  ...  tic             AAPL       AXP        BA  ... ...\n",
              "1  2008-12-31  ...  tic             AAPL       AXP        BA  ... ...\n",
              "2  2008-12-31  ...  tic             AAPL       AXP        BA  ... ...\n",
              "3  2008-12-31  ...  tic             AAPL       AXP        BA  ... ...\n",
              "4  2008-12-31  ...  tic             AAPL       AXP        BA  ... ...\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UooHj1OgbU4v"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Design Environment\n",
        "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
        "\n",
        "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
        "\n",
        "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQnmN1qdk88I"
      },
      "source": [
        "## Training data split: 2009-01-01 to 2018-12-31"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NrPxgv4eBQ_R"
      },
      "source": [
        "train = data_split(df, '2009-01-01','2020-07-01')\n",
        "#trade = data_split(df, '2020-01-01', config.END_DATE)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "vU2vXEll0hfk",
        "outputId": "298034f0-fab0-43c9-8428-422606eab3e2"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>cov_list</th>\n",
              "      <th>return_list</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.787006</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "      <td>-0.071164</td>\n",
              "      <td>45.440193</td>\n",
              "      <td>-32.068804</td>\n",
              "      <td>2.140064</td>\n",
              "      <td>[[0.0013661511510990034, 0.001002835238400853,...</td>\n",
              "      <td>tic             AAPL       AXP        BA  ... ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.657365</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "      <td>-0.857433</td>\n",
              "      <td>43.957537</td>\n",
              "      <td>-42.753090</td>\n",
              "      <td>16.335101</td>\n",
              "      <td>[[0.0013661511510990034, 0.001002835238400853,...</td>\n",
              "      <td>tic             AAPL       AXP        BA  ... ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "      <td>-0.002007</td>\n",
              "      <td>50.822042</td>\n",
              "      <td>272.812831</td>\n",
              "      <td>20.494464</td>\n",
              "      <td>[[0.0013661511510990034, 0.001002835238400853,...</td>\n",
              "      <td>tic             AAPL       AXP        BA  ... ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.655109</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "      <td>0.875031</td>\n",
              "      <td>53.661249</td>\n",
              "      <td>129.760396</td>\n",
              "      <td>34.637448</td>\n",
              "      <td>[[0.0013661511510990034, 0.001002835238400853,...</td>\n",
              "      <td>tic             AAPL       AXP        BA  ... ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.505757</td>\n",
              "      <td>40980600</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>4</td>\n",
              "      <td>-0.061562</td>\n",
              "      <td>48.322168</td>\n",
              "      <td>54.454064</td>\n",
              "      <td>2.653808</td>\n",
              "      <td>[[0.0013661511510990034, 0.001002835238400853,...</td>\n",
              "      <td>tic             AAPL       AXP        BA  ... ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  ...                                        return_list\n",
              "0  2009-01-02  ...  tic             AAPL       AXP        BA  ... ...\n",
              "0  2009-01-02  ...  tic             AAPL       AXP        BA  ... ...\n",
              "0  2009-01-02  ...  tic             AAPL       AXP        BA  ... ...\n",
              "0  2009-01-02  ...  tic             AAPL       AXP        BA  ... ...\n",
              "0  2009-01-02  ...  tic             AAPL       AXP        BA  ... ...\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sxQTNjpblAMN"
      },
      "source": [
        "## Environment for Portfolio Allocation\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlfE-VERbU40"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from gym.utils import seeding\n",
        "import gym\n",
        "from gym import spaces\n",
        "import matplotlib\n",
        "matplotlib.use('Agg')\n",
        "import matplotlib.pyplot as plt\n",
        "from stable_baselines3.common.vec_env import DummyVecEnv\n",
        "\n",
        "\n",
        "class StockPortfolioEnv(gym.Env):\n",
        "    \"\"\"A single stock trading environment for OpenAI gym\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        df: DataFrame\n",
        "            input data\n",
        "        stock_dim : int\n",
        "            number of unique stocks\n",
        "        hmax : int\n",
        "            maximum number of shares to trade\n",
        "        initial_amount : int\n",
        "            start money\n",
        "        transaction_cost_pct: float\n",
        "            transaction cost percentage per trade\n",
        "        reward_scaling: float\n",
        "            scaling factor for reward, good for training\n",
        "        state_space: int\n",
        "            the dimension of input features\n",
        "        action_space: int\n",
        "            equals stock dimension\n",
        "        tech_indicator_list: list\n",
        "            a list of technical indicator names\n",
        "        turbulence_threshold: int\n",
        "            a threshold to control risk aversion\n",
        "        day: int\n",
        "            an increment number to control date\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    _sell_stock()\n",
        "        perform sell action based on the sign of the action\n",
        "    _buy_stock()\n",
        "        perform buy action based on the sign of the action\n",
        "    step()\n",
        "        at each step the agent will return actions, then \n",
        "        we will calculate the reward, and return the next observation.\n",
        "    reset()\n",
        "        reset the environment\n",
        "    render()\n",
        "        use render to return other functions\n",
        "    save_asset_memory()\n",
        "        return account value at each time step\n",
        "    save_action_memory()\n",
        "        return actions/positions at each time step\n",
        "        \n",
        "\n",
        "    \"\"\"\n",
        "    metadata = {'render.modes': ['human']}\n",
        "\n",
        "    def __init__(self, \n",
        "                df,\n",
        "                stock_dim,\n",
        "                hmax,\n",
        "                initial_amount,\n",
        "                transaction_cost_pct,\n",
        "                reward_scaling,\n",
        "                state_space,\n",
        "                action_space,\n",
        "                tech_indicator_list,\n",
        "                turbulence_threshold=None,\n",
        "                lookback=252,\n",
        "                day = 0):\n",
        "        #super(StockEnv, self).__init__()\n",
        "        #money = 10 , scope = 1\n",
        "        self.day = day\n",
        "        self.lookback=lookback\n",
        "        self.df = df\n",
        "        self.stock_dim = stock_dim\n",
        "        self.hmax = hmax\n",
        "        self.initial_amount = initial_amount\n",
        "        self.transaction_cost_pct =transaction_cost_pct\n",
        "        self.reward_scaling = reward_scaling\n",
        "        self.state_space = state_space\n",
        "        self.action_space = action_space\n",
        "        self.tech_indicator_list = tech_indicator_list\n",
        "\n",
        "        # action_space normalization and shape is self.stock_dim\n",
        "        self.action_space = spaces.Box(low = 0, high = 1,shape = (self.action_space,)) \n",
        "        # Shape = (34, 30)\n",
        "        # covariance matrix + technical indicators\n",
        "        self.observation_space = spaces.Box(low=-np.inf, high=np.inf, shape = (self.state_space+len(self.tech_indicator_list),self.state_space))\n",
        "\n",
        "        # load data from a pandas dataframe\n",
        "        self.data = self.df.loc[self.day,:]\n",
        "        self.covs = self.data['cov_list'].values[0]\n",
        "        self.state =  np.append(np.array(self.covs), [self.data[tech].values.tolist() for tech in self.tech_indicator_list ], axis=0)\n",
        "        self.terminal = False     \n",
        "        self.turbulence_threshold = turbulence_threshold        \n",
        "        # initalize state: inital portfolio return + individual stock return + individual weights\n",
        "        self.portfolio_value = self.initial_amount\n",
        "\n",
        "        # memorize portfolio value each step\n",
        "        self.asset_memory = [self.initial_amount]\n",
        "        # memorize portfolio return each step\n",
        "        self.portfolio_return_memory = [0]\n",
        "        self.actions_memory=[[1/self.stock_dim]*self.stock_dim]\n",
        "        self.date_memory=[self.data.date.unique()[0]]\n",
        "\n",
        "        \n",
        "    def step(self, actions):\n",
        "        # print(self.day)\n",
        "        self.terminal = self.day >= len(self.df.index.unique())-1\n",
        "        # print(actions)\n",
        "\n",
        "        if self.terminal:\n",
        "            df = pd.DataFrame(self.portfolio_return_memory)\n",
        "            df.columns = ['daily_return']\n",
        "            plt.plot(df.daily_return.cumsum(),'r')\n",
        "            plt.savefig('results/cumulative_reward.png')\n",
        "            plt.close()\n",
        "            \n",
        "            plt.plot(self.portfolio_return_memory,'r')\n",
        "            plt.savefig('results/rewards.png')\n",
        "            plt.close()\n",
        "\n",
        "            print(\"=================================\")\n",
        "            print(\"begin_total_asset:{}\".format(self.asset_memory[0]))           \n",
        "            print(\"end_total_asset:{}\".format(self.portfolio_value))\n",
        "\n",
        "            df_daily_return = pd.DataFrame(self.portfolio_return_memory)\n",
        "            df_daily_return.columns = ['daily_return']\n",
        "            if df_daily_return['daily_return'].std() !=0:\n",
        "              sharpe = (252**0.5)*df_daily_return['daily_return'].mean()/ \\\n",
        "                       df_daily_return['daily_return'].std()\n",
        "              print(\"Sharpe: \",sharpe)\n",
        "            print(\"=================================\")\n",
        "            \n",
        "            return self.state, self.reward, self.terminal,{}\n",
        "\n",
        "        else:\n",
        "            #print(\"Model actions: \",actions)\n",
        "            # actions are the portfolio weight\n",
        "            # normalize to sum of 1\n",
        "            #if (np.array(actions) - np.array(actions).min()).sum() != 0:\n",
        "            #  norm_actions = (np.array(actions) - np.array(actions).min()) / (np.array(actions) - np.array(actions).min()).sum()\n",
        "            #else:\n",
        "            #  norm_actions = actions\n",
        "            weights = self.softmax_normalization(actions) \n",
        "            #print(\"Normalized actions: \", weights)\n",
        "            self.actions_memory.append(weights)\n",
        "            last_day_memory = self.data\n",
        "\n",
        "            #load next state\n",
        "            self.day += 1\n",
        "            self.data = self.df.loc[self.day,:]\n",
        "            self.covs = self.data['cov_list'].values[0]\n",
        "            self.state =  np.append(np.array(self.covs), [self.data[tech].values.tolist() for tech in self.tech_indicator_list ], axis=0)\n",
        "            #print(self.state)\n",
        "            # calcualte portfolio return\n",
        "            # individual stocks' return * weight\n",
        "            portfolio_return = sum(((self.data.close.values / last_day_memory.close.values)-1)*weights)\n",
        "            # update portfolio value\n",
        "            new_portfolio_value = self.portfolio_value*(1+portfolio_return)\n",
        "            self.portfolio_value = new_portfolio_value\n",
        "\n",
        "            # save into memory\n",
        "            self.portfolio_return_memory.append(portfolio_return)\n",
        "            self.date_memory.append(self.data.date.unique()[0])            \n",
        "            self.asset_memory.append(new_portfolio_value)\n",
        "\n",
        "            # the reward is the new portfolio value or end portfolo value\n",
        "            self.reward = new_portfolio_value \n",
        "            #print(\"Step reward: \", self.reward)\n",
        "            #self.reward = self.reward*self.reward_scaling\n",
        "\n",
        "        return self.state, self.reward, self.terminal, {}\n",
        "\n",
        "    def reset(self):\n",
        "        self.asset_memory = [self.initial_amount]\n",
        "        self.day = 0\n",
        "        self.data = self.df.loc[self.day,:]\n",
        "        # load states\n",
        "        self.covs = self.data['cov_list'].values[0]\n",
        "        self.state =  np.append(np.array(self.covs), [self.data[tech].values.tolist() for tech in self.tech_indicator_list ], axis=0)\n",
        "        self.portfolio_value = self.initial_amount\n",
        "        #self.cost = 0\n",
        "        #self.trades = 0\n",
        "        self.terminal = False \n",
        "        self.portfolio_return_memory = [0]\n",
        "        self.actions_memory=[[1/self.stock_dim]*self.stock_dim]\n",
        "        self.date_memory=[self.data.date.unique()[0]] \n",
        "        return self.state\n",
        "    \n",
        "    def render(self, mode='human'):\n",
        "        return self.state\n",
        "        \n",
        "    def softmax_normalization(self, actions):\n",
        "        numerator = np.exp(actions)\n",
        "        denominator = np.sum(np.exp(actions))\n",
        "        softmax_output = numerator/denominator\n",
        "        return softmax_output\n",
        "\n",
        "    \n",
        "    def save_asset_memory(self):\n",
        "        date_list = self.date_memory\n",
        "        portfolio_return = self.portfolio_return_memory\n",
        "        #print(len(date_list))\n",
        "        #print(len(asset_list))\n",
        "        df_account_value = pd.DataFrame({'date':date_list,'daily_return':portfolio_return})\n",
        "        return df_account_value\n",
        "\n",
        "    def save_action_memory(self):\n",
        "        # date and close price length must match actions length\n",
        "        date_list = self.date_memory\n",
        "        df_date = pd.DataFrame(date_list)\n",
        "        df_date.columns = ['date']\n",
        "        \n",
        "        action_list = self.actions_memory\n",
        "        df_actions = pd.DataFrame(action_list)\n",
        "        df_actions.columns = self.data.tic.values\n",
        "        df_actions.index = df_date.date\n",
        "        #df_actions = pd.DataFrame({'date':date_list,'actions':action_list})\n",
        "        return df_actions\n",
        "\n",
        "    def _seed(self, seed=None):\n",
        "        self.np_random, seed = seeding.np_random(seed)\n",
        "        return [seed]\n",
        "\n",
        "    def get_sb_env(self):\n",
        "        e = DummyVecEnv([lambda: self])\n",
        "        obs = e.reset()\n",
        "        return e, obs"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzD06X0CbU43",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39b78cf3-29e9-4736-b658-affb06b96f19"
      },
      "source": [
        "stock_dimension = len(train.tic.unique())\n",
        "state_space = stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stock Dimension: 29, State Space: 29\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyg0_ZuVEVQ5"
      },
      "source": [
        "env_kwargs = {\n",
        "    \"hmax\": 100, \n",
        "    \"initial_amount\": 1000000, \n",
        "    \"transaction_cost_pct\": 0.001, \n",
        "    \"state_space\": state_space, \n",
        "    \"stock_dim\": stock_dimension, \n",
        "    \"tech_indicator_list\": config.TECHNICAL_INDICATORS_LIST, \n",
        "    \"action_space\": stock_dimension, \n",
        "    \"reward_scaling\": 1e-4\n",
        "    \n",
        "}\n",
        "\n",
        "e_train_gym = StockPortfolioEnv(df = train, **env_kwargs)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTlOf8SJGdkl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf5b404e-6d35-4052-c661-1559b8898c4d"
      },
      "source": [
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "print(type(env_train))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2eKIu5UPlPnk"
      },
      "source": [
        "<a id='5'></a>\n",
        "# Part 6: Implement DRL Algorithms\n",
        "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
        "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
        "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
        "design their own DRL algorithms by adapting these DRL algorithms."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDxU0iCEGdnb"
      },
      "source": [
        "# initialize\n",
        "agent = DRLAgent(env = env_train)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hdPe8uzflbXe"
      },
      "source": [
        "### Model 1: **A2C**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1tORf1fIcQ2",
        "outputId": "6793cf91-b7d3-4111-d6e0-bf58a8eff0ca"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "\n",
        "A2C_PARAMS = {\"n_steps\": 5, \"ent_coef\": 0.005, \"learning_rate\": 0.0002}\n",
        "model_a2c = agent.get_model(model_name=\"a2c\",model_kwargs = A2C_PARAMS)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_steps': 5, 'ent_coef': 0.005, 'learning_rate': 0.0002}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DazEdrMpIdyz",
        "outputId": "26b62233-2907-4daf-a80c-9ff1a7644ba1"
      },
      "source": [
        "trained_a2c = agent.train_model(model=model_a2c, \n",
        "                                tb_log_name='a2c',\n",
        "                                total_timesteps=50000)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/a2c/a2c_1\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 150      |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 3        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | 1.93e+08 |\n",
            "|    std                | 0.998    |\n",
            "|    value_loss         | 2.72e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 204      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 4        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | 2.29e+08 |\n",
            "|    std                | 0.998    |\n",
            "|    value_loss         | 4.52e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 230      |\n",
            "|    iterations         | 300      |\n",
            "|    time_elapsed       | 6        |\n",
            "|    total_timesteps    | 1500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.1    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 299      |\n",
            "|    policy_loss        | 4.04e+08 |\n",
            "|    std                | 0.998    |\n",
            "|    value_loss         | 1.05e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 247      |\n",
            "|    iterations         | 400      |\n",
            "|    time_elapsed       | 8        |\n",
            "|    total_timesteps    | 2000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 399      |\n",
            "|    policy_loss        | 4.23e+08 |\n",
            "|    std                | 0.997    |\n",
            "|    value_loss         | 1.31e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 256      |\n",
            "|    iterations         | 500      |\n",
            "|    time_elapsed       | 9        |\n",
            "|    total_timesteps    | 2500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.1    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 499      |\n",
            "|    policy_loss        | 5.81e+08 |\n",
            "|    std                | 0.997    |\n",
            "|    value_loss         | 2.44e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4947619.780454191\n",
            "Sharpe:  0.8521509187283361\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 258       |\n",
            "|    iterations         | 600       |\n",
            "|    time_elapsed       | 11        |\n",
            "|    total_timesteps    | 3000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 599       |\n",
            "|    policy_loss        | 1.36e+08  |\n",
            "|    std                | 0.997     |\n",
            "|    value_loss         | 1.21e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 265      |\n",
            "|    iterations         | 700      |\n",
            "|    time_elapsed       | 13       |\n",
            "|    total_timesteps    | 3500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 699      |\n",
            "|    policy_loss        | 2.08e+08 |\n",
            "|    std                | 0.996    |\n",
            "|    value_loss         | 3.33e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 269       |\n",
            "|    iterations         | 800       |\n",
            "|    time_elapsed       | 14        |\n",
            "|    total_timesteps    | 4000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 799       |\n",
            "|    policy_loss        | 3.02e+08  |\n",
            "|    std                | 0.996     |\n",
            "|    value_loss         | 6.32e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 272      |\n",
            "|    iterations         | 900      |\n",
            "|    time_elapsed       | 16       |\n",
            "|    total_timesteps    | 4500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 2.38e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 899      |\n",
            "|    policy_loss        | 4.09e+08 |\n",
            "|    std                | 0.996    |\n",
            "|    value_loss         | 1.14e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 275      |\n",
            "|    iterations         | 1000     |\n",
            "|    time_elapsed       | 18       |\n",
            "|    total_timesteps    | 5000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 999      |\n",
            "|    policy_loss        | 5.29e+08 |\n",
            "|    std                | 0.996    |\n",
            "|    value_loss         | 1.62e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 278      |\n",
            "|    iterations         | 1100     |\n",
            "|    time_elapsed       | 19       |\n",
            "|    total_timesteps    | 5500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1099     |\n",
            "|    policy_loss        | 6.81e+08 |\n",
            "|    std                | 0.995    |\n",
            "|    value_loss         | 2.91e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5256363.347985752\n",
            "Sharpe:  0.8827324589483913\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 278      |\n",
            "|    iterations         | 1200     |\n",
            "|    time_elapsed       | 21       |\n",
            "|    total_timesteps    | 6000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1199     |\n",
            "|    policy_loss        | 1.53e+08 |\n",
            "|    std                | 0.995    |\n",
            "|    value_loss         | 1.8e+13  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 279      |\n",
            "|    iterations         | 1300     |\n",
            "|    time_elapsed       | 23       |\n",
            "|    total_timesteps    | 6500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 1.79e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1299     |\n",
            "|    policy_loss        | 1.96e+08 |\n",
            "|    std                | 0.995    |\n",
            "|    value_loss         | 2.98e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 280      |\n",
            "|    iterations         | 1400     |\n",
            "|    time_elapsed       | 24       |\n",
            "|    total_timesteps    | 7000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1399     |\n",
            "|    policy_loss        | 3.12e+08 |\n",
            "|    std                | 0.995    |\n",
            "|    value_loss         | 6.57e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 282       |\n",
            "|    iterations         | 1500      |\n",
            "|    time_elapsed       | 26        |\n",
            "|    total_timesteps    | 7500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1499      |\n",
            "|    policy_loss        | 3.61e+08  |\n",
            "|    std                | 0.996     |\n",
            "|    value_loss         | 8.94e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 284      |\n",
            "|    iterations         | 1600     |\n",
            "|    time_elapsed       | 28       |\n",
            "|    total_timesteps    | 8000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1599     |\n",
            "|    policy_loss        | 4.43e+08 |\n",
            "|    std                | 0.996    |\n",
            "|    value_loss         | 1.67e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 285       |\n",
            "|    iterations         | 1700      |\n",
            "|    time_elapsed       | 29        |\n",
            "|    total_timesteps    | 8500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1699      |\n",
            "|    policy_loss        | 5.89e+08  |\n",
            "|    std                | 0.995     |\n",
            "|    value_loss         | 2.44e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4924025.62195718\n",
            "Sharpe:  0.8441786909726531\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 284       |\n",
            "|    iterations         | 1800      |\n",
            "|    time_elapsed       | 31        |\n",
            "|    total_timesteps    | 9000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1799      |\n",
            "|    policy_loss        | 1.68e+08  |\n",
            "|    std                | 0.994     |\n",
            "|    value_loss         | 2.13e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 286       |\n",
            "|    iterations         | 1900      |\n",
            "|    time_elapsed       | 33        |\n",
            "|    total_timesteps    | 9500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1899      |\n",
            "|    policy_loss        | 2.29e+08  |\n",
            "|    std                | 0.994     |\n",
            "|    value_loss         | 3.9e+13   |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 287      |\n",
            "|    iterations         | 2000     |\n",
            "|    time_elapsed       | 34       |\n",
            "|    total_timesteps    | 10000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1999     |\n",
            "|    policy_loss        | 3.15e+08 |\n",
            "|    std                | 0.993    |\n",
            "|    value_loss         | 7.68e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 288      |\n",
            "|    iterations         | 2100     |\n",
            "|    time_elapsed       | 36       |\n",
            "|    total_timesteps    | 10500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2099     |\n",
            "|    policy_loss        | 4.01e+08 |\n",
            "|    std                | 0.993    |\n",
            "|    value_loss         | 1.09e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 289      |\n",
            "|    iterations         | 2200     |\n",
            "|    time_elapsed       | 38       |\n",
            "|    total_timesteps    | 11000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.9    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2199     |\n",
            "|    policy_loss        | 5.5e+08  |\n",
            "|    std                | 0.993    |\n",
            "|    value_loss         | 2.24e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 290       |\n",
            "|    iterations         | 2300      |\n",
            "|    time_elapsed       | 39        |\n",
            "|    total_timesteps    | 11500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.9     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2299      |\n",
            "|    policy_loss        | 5.33e+08  |\n",
            "|    std                | 0.993     |\n",
            "|    value_loss         | 2.14e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4982489.678803913\n",
            "Sharpe:  0.8552046817106821\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 289      |\n",
            "|    iterations         | 2400     |\n",
            "|    time_elapsed       | 41       |\n",
            "|    total_timesteps    | 12000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2399     |\n",
            "|    policy_loss        | 1.64e+08 |\n",
            "|    std                | 0.993    |\n",
            "|    value_loss         | 2.11e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 290      |\n",
            "|    iterations         | 2500     |\n",
            "|    time_elapsed       | 42       |\n",
            "|    total_timesteps    | 12500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2499     |\n",
            "|    policy_loss        | 2.15e+08 |\n",
            "|    std                | 0.993    |\n",
            "|    value_loss         | 3.99e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 291       |\n",
            "|    iterations         | 2600      |\n",
            "|    time_elapsed       | 44        |\n",
            "|    total_timesteps    | 13000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.9     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2599      |\n",
            "|    policy_loss        | 3.37e+08  |\n",
            "|    std                | 0.992     |\n",
            "|    value_loss         | 8.69e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 292      |\n",
            "|    iterations         | 2700     |\n",
            "|    time_elapsed       | 46       |\n",
            "|    total_timesteps    | 13500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2699     |\n",
            "|    policy_loss        | 3.97e+08 |\n",
            "|    std                | 0.992    |\n",
            "|    value_loss         | 1.16e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 293       |\n",
            "|    iterations         | 2800      |\n",
            "|    time_elapsed       | 47        |\n",
            "|    total_timesteps    | 14000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.9     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2799      |\n",
            "|    policy_loss        | 5.01e+08  |\n",
            "|    std                | 0.992     |\n",
            "|    value_loss         | 2.23e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4983386.35610803\n",
            "Sharpe:  0.8547109683374965\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 292       |\n",
            "|    iterations         | 2900      |\n",
            "|    time_elapsed       | 49        |\n",
            "|    total_timesteps    | 14500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.9     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2899      |\n",
            "|    policy_loss        | 1.26e+08  |\n",
            "|    std                | 0.992     |\n",
            "|    value_loss         | 8.44e+12  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 293      |\n",
            "|    iterations         | 3000     |\n",
            "|    time_elapsed       | 51       |\n",
            "|    total_timesteps    | 15000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.9    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2999     |\n",
            "|    policy_loss        | 2.15e+08 |\n",
            "|    std                | 0.991    |\n",
            "|    value_loss         | 2.91e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 293      |\n",
            "|    iterations         | 3100     |\n",
            "|    time_elapsed       | 52       |\n",
            "|    total_timesteps    | 15500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3099     |\n",
            "|    policy_loss        | 2.35e+08 |\n",
            "|    std                | 0.991    |\n",
            "|    value_loss         | 4.57e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 294      |\n",
            "|    iterations         | 3200     |\n",
            "|    time_elapsed       | 54       |\n",
            "|    total_timesteps    | 16000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3199     |\n",
            "|    policy_loss        | 3.68e+08 |\n",
            "|    std                | 0.99     |\n",
            "|    value_loss         | 9.19e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 295      |\n",
            "|    iterations         | 3300     |\n",
            "|    time_elapsed       | 55       |\n",
            "|    total_timesteps    | 16500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3299     |\n",
            "|    policy_loss        | 4.27e+08 |\n",
            "|    std                | 0.99     |\n",
            "|    value_loss         | 1.28e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 295       |\n",
            "|    iterations         | 3400      |\n",
            "|    time_elapsed       | 57        |\n",
            "|    total_timesteps    | 17000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.8     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3399      |\n",
            "|    policy_loss        | 5.07e+08  |\n",
            "|    std                | 0.989     |\n",
            "|    value_loss         | 2.1e+14   |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4850893.279908747\n",
            "Sharpe:  0.8379486094766025\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 295      |\n",
            "|    iterations         | 3500     |\n",
            "|    time_elapsed       | 59       |\n",
            "|    total_timesteps    | 17500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3499     |\n",
            "|    policy_loss        | 1.27e+08 |\n",
            "|    std                | 0.989    |\n",
            "|    value_loss         | 1.26e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 295      |\n",
            "|    iterations         | 3600     |\n",
            "|    time_elapsed       | 60       |\n",
            "|    total_timesteps    | 18000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3599     |\n",
            "|    policy_loss        | 1.98e+08 |\n",
            "|    std                | 0.988    |\n",
            "|    value_loss         | 2.94e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 295       |\n",
            "|    iterations         | 3700      |\n",
            "|    time_elapsed       | 62        |\n",
            "|    total_timesteps    | 18500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.8     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3699      |\n",
            "|    policy_loss        | 2.48e+08  |\n",
            "|    std                | 0.988     |\n",
            "|    value_loss         | 5.2e+13   |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 296      |\n",
            "|    iterations         | 3800     |\n",
            "|    time_elapsed       | 64       |\n",
            "|    total_timesteps    | 19000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3799     |\n",
            "|    policy_loss        | 3.42e+08 |\n",
            "|    std                | 0.988    |\n",
            "|    value_loss         | 9.02e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 296      |\n",
            "|    iterations         | 3900     |\n",
            "|    time_elapsed       | 65       |\n",
            "|    total_timesteps    | 19500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3899     |\n",
            "|    policy_loss        | 4.34e+08 |\n",
            "|    std                | 0.988    |\n",
            "|    value_loss         | 1.39e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 296      |\n",
            "|    iterations         | 4000     |\n",
            "|    time_elapsed       | 67       |\n",
            "|    total_timesteps    | 20000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3999     |\n",
            "|    policy_loss        | 5.6e+08  |\n",
            "|    std                | 0.988    |\n",
            "|    value_loss         | 2.47e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4647489.129320578\n",
            "Sharpe:  0.8222750760796416\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 296      |\n",
            "|    iterations         | 4100     |\n",
            "|    time_elapsed       | 69       |\n",
            "|    total_timesteps    | 20500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4099     |\n",
            "|    policy_loss        | 1.73e+08 |\n",
            "|    std                | 0.989    |\n",
            "|    value_loss         | 2e+13    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 296      |\n",
            "|    iterations         | 4200     |\n",
            "|    time_elapsed       | 70       |\n",
            "|    total_timesteps    | 21000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4199     |\n",
            "|    policy_loss        | 2.11e+08 |\n",
            "|    std                | 0.988    |\n",
            "|    value_loss         | 3.06e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 296      |\n",
            "|    iterations         | 4300     |\n",
            "|    time_elapsed       | 72       |\n",
            "|    total_timesteps    | 21500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4299     |\n",
            "|    policy_loss        | 2.94e+08 |\n",
            "|    std                | 0.988    |\n",
            "|    value_loss         | 6.91e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 297       |\n",
            "|    iterations         | 4400      |\n",
            "|    time_elapsed       | 73        |\n",
            "|    total_timesteps    | 22000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.8     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 4399      |\n",
            "|    policy_loss        | 3.7e+08   |\n",
            "|    std                | 0.988     |\n",
            "|    value_loss         | 1.04e+14  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 4500     |\n",
            "|    time_elapsed       | 75       |\n",
            "|    total_timesteps    | 22500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4499     |\n",
            "|    policy_loss        | 5.48e+08 |\n",
            "|    std                | 0.988    |\n",
            "|    value_loss         | 2.05e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 4600     |\n",
            "|    time_elapsed       | 77       |\n",
            "|    total_timesteps    | 23000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4599     |\n",
            "|    policy_loss        | 6.6e+08  |\n",
            "|    std                | 0.987    |\n",
            "|    value_loss         | 3.23e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5218141.028240858\n",
            "Sharpe:  0.8745331898162475\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 4700     |\n",
            "|    time_elapsed       | 79       |\n",
            "|    total_timesteps    | 23500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4699     |\n",
            "|    policy_loss        | 1.66e+08 |\n",
            "|    std                | 0.986    |\n",
            "|    value_loss         | 1.93e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 4800     |\n",
            "|    time_elapsed       | 80       |\n",
            "|    total_timesteps    | 24000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4799     |\n",
            "|    policy_loss        | 2.26e+08 |\n",
            "|    std                | 0.986    |\n",
            "|    value_loss         | 3.74e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 4900     |\n",
            "|    time_elapsed       | 82       |\n",
            "|    total_timesteps    | 24500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4899     |\n",
            "|    policy_loss        | 3.48e+08 |\n",
            "|    std                | 0.986    |\n",
            "|    value_loss         | 8.01e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 298      |\n",
            "|    iterations         | 5000     |\n",
            "|    time_elapsed       | 83       |\n",
            "|    total_timesteps    | 25000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.7    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4999     |\n",
            "|    policy_loss        | 3.53e+08 |\n",
            "|    std                | 0.984    |\n",
            "|    value_loss         | 1.07e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 298      |\n",
            "|    iterations         | 5100     |\n",
            "|    time_elapsed       | 85       |\n",
            "|    total_timesteps    | 25500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5099     |\n",
            "|    policy_loss        | 5.16e+08 |\n",
            "|    std                | 0.984    |\n",
            "|    value_loss         | 2.12e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 298      |\n",
            "|    iterations         | 5200     |\n",
            "|    time_elapsed       | 87       |\n",
            "|    total_timesteps    | 26000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.6    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5199     |\n",
            "|    policy_loss        | 6.13e+08 |\n",
            "|    std                | 0.983    |\n",
            "|    value_loss         | 2.74e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5383028.662466644\n",
            "Sharpe:  0.8936439940751995\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 5300     |\n",
            "|    time_elapsed       | 89       |\n",
            "|    total_timesteps    | 26500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5299     |\n",
            "|    policy_loss        | 1.85e+08 |\n",
            "|    std                | 0.983    |\n",
            "|    value_loss         | 2.22e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 5400     |\n",
            "|    time_elapsed       | 90       |\n",
            "|    total_timesteps    | 27000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5399     |\n",
            "|    policy_loss        | 2.44e+08 |\n",
            "|    std                | 0.982    |\n",
            "|    value_loss         | 3.91e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 297       |\n",
            "|    iterations         | 5500      |\n",
            "|    time_elapsed       | 92        |\n",
            "|    total_timesteps    | 27500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.6     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 5499      |\n",
            "|    policy_loss        | 3.2e+08   |\n",
            "|    std                | 0.982     |\n",
            "|    value_loss         | 7.6e+13   |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 5600     |\n",
            "|    time_elapsed       | 94       |\n",
            "|    total_timesteps    | 28000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5599     |\n",
            "|    policy_loss        | 3.9e+08  |\n",
            "|    std                | 0.981    |\n",
            "|    value_loss         | 1.07e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 5700     |\n",
            "|    time_elapsed       | 95       |\n",
            "|    total_timesteps    | 28500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5699     |\n",
            "|    policy_loss        | 5.31e+08 |\n",
            "|    std                | 0.98     |\n",
            "|    value_loss         | 2.36e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4938418.793976344\n",
            "Sharpe:  0.8524635335321091\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 5800     |\n",
            "|    time_elapsed       | 97       |\n",
            "|    total_timesteps    | 29000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.6    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5799     |\n",
            "|    policy_loss        | 1.15e+08 |\n",
            "|    std                | 0.98     |\n",
            "|    value_loss         | 9.32e+12 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 5900     |\n",
            "|    time_elapsed       | 99       |\n",
            "|    total_timesteps    | 29500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5899     |\n",
            "|    policy_loss        | 1.98e+08 |\n",
            "|    std                | 0.979    |\n",
            "|    value_loss         | 3.11e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 6000     |\n",
            "|    time_elapsed       | 100      |\n",
            "|    total_timesteps    | 30000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5999     |\n",
            "|    policy_loss        | 2.73e+08 |\n",
            "|    std                | 0.979    |\n",
            "|    value_loss         | 5.46e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 6100     |\n",
            "|    time_elapsed       | 102      |\n",
            "|    total_timesteps    | 30500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.5    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6099     |\n",
            "|    policy_loss        | 3.64e+08 |\n",
            "|    std                | 0.979    |\n",
            "|    value_loss         | 1.05e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 6200     |\n",
            "|    time_elapsed       | 104      |\n",
            "|    total_timesteps    | 31000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6199     |\n",
            "|    policy_loss        | 4.67e+08 |\n",
            "|    std                | 0.979    |\n",
            "|    value_loss         | 1.59e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 6300     |\n",
            "|    time_elapsed       | 105      |\n",
            "|    total_timesteps    | 31500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.5    |\n",
            "|    explained_variance | 3.58e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6299     |\n",
            "|    policy_loss        | 6.46e+08 |\n",
            "|    std                | 0.979    |\n",
            "|    value_loss         | 2.72e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5107682.330574452\n",
            "Sharpe:  0.862566827885531\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 6400     |\n",
            "|    time_elapsed       | 107      |\n",
            "|    total_timesteps    | 32000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.5    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6399     |\n",
            "|    policy_loss        | 1.45e+08 |\n",
            "|    std                | 0.979    |\n",
            "|    value_loss         | 1.56e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 297       |\n",
            "|    iterations         | 6500      |\n",
            "|    time_elapsed       | 109       |\n",
            "|    total_timesteps    | 32500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.5     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 6499      |\n",
            "|    policy_loss        | 1.78e+08  |\n",
            "|    std                | 0.979     |\n",
            "|    value_loss         | 2.6e+13   |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 6600     |\n",
            "|    time_elapsed       | 110      |\n",
            "|    total_timesteps    | 33000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6599     |\n",
            "|    policy_loss        | 2.68e+08 |\n",
            "|    std                | 0.978    |\n",
            "|    value_loss         | 5.4e+13  |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 297       |\n",
            "|    iterations         | 6700      |\n",
            "|    time_elapsed       | 112       |\n",
            "|    total_timesteps    | 33500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.5     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 6699      |\n",
            "|    policy_loss        | 3.09e+08  |\n",
            "|    std                | 0.977     |\n",
            "|    value_loss         | 7.91e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 6800     |\n",
            "|    time_elapsed       | 114      |\n",
            "|    total_timesteps    | 34000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.5    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6799     |\n",
            "|    policy_loss        | 4.48e+08 |\n",
            "|    std                | 0.977    |\n",
            "|    value_loss         | 1.61e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 297       |\n",
            "|    iterations         | 6900      |\n",
            "|    time_elapsed       | 115       |\n",
            "|    total_timesteps    | 34500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.4     |\n",
            "|    explained_variance | -3.58e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 6899      |\n",
            "|    policy_loss        | 5.77e+08  |\n",
            "|    std                | 0.976     |\n",
            "|    value_loss         | 2.48e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4922680.366924848\n",
            "Sharpe:  0.8487477275124381\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 7000     |\n",
            "|    time_elapsed       | 117      |\n",
            "|    total_timesteps    | 35000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6999     |\n",
            "|    policy_loss        | 1.61e+08 |\n",
            "|    std                | 0.976    |\n",
            "|    value_loss         | 1.92e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 7100     |\n",
            "|    time_elapsed       | 119      |\n",
            "|    total_timesteps    | 35500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7099     |\n",
            "|    policy_loss        | 2.47e+08 |\n",
            "|    std                | 0.975    |\n",
            "|    value_loss         | 4.03e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 7200     |\n",
            "|    time_elapsed       | 120      |\n",
            "|    total_timesteps    | 36000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7199     |\n",
            "|    policy_loss        | 3.3e+08  |\n",
            "|    std                | 0.975    |\n",
            "|    value_loss         | 8.02e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 7300     |\n",
            "|    time_elapsed       | 122      |\n",
            "|    total_timesteps    | 36500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7299     |\n",
            "|    policy_loss        | 3.68e+08 |\n",
            "|    std                | 0.975    |\n",
            "|    value_loss         | 1.05e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 298      |\n",
            "|    iterations         | 7400     |\n",
            "|    time_elapsed       | 124      |\n",
            "|    total_timesteps    | 37000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 1.79e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7399     |\n",
            "|    policy_loss        | 6.75e+08 |\n",
            "|    std                | 0.975    |\n",
            "|    value_loss         | 2.81e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 298       |\n",
            "|    iterations         | 7500      |\n",
            "|    time_elapsed       | 125       |\n",
            "|    total_timesteps    | 37500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 7499      |\n",
            "|    policy_loss        | 6.98e+08  |\n",
            "|    std                | 0.975     |\n",
            "|    value_loss         | 4.02e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5463707.333840418\n",
            "Sharpe:  0.8998439105824559\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 297       |\n",
            "|    iterations         | 7600      |\n",
            "|    time_elapsed       | 127       |\n",
            "|    total_timesteps    | 38000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 7599      |\n",
            "|    policy_loss        | 1.51e+08  |\n",
            "|    std                | 0.974     |\n",
            "|    value_loss         | 1.94e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 7700     |\n",
            "|    time_elapsed       | 129      |\n",
            "|    total_timesteps    | 38500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 1.79e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7699     |\n",
            "|    policy_loss        | 2.25e+08 |\n",
            "|    std                | 0.974    |\n",
            "|    value_loss         | 3.79e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 7800     |\n",
            "|    time_elapsed       | 130      |\n",
            "|    total_timesteps    | 39000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7799     |\n",
            "|    policy_loss        | 3.35e+08 |\n",
            "|    std                | 0.974    |\n",
            "|    value_loss         | 8.94e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 297       |\n",
            "|    iterations         | 7900      |\n",
            "|    time_elapsed       | 132       |\n",
            "|    total_timesteps    | 39500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 7899      |\n",
            "|    policy_loss        | 4.16e+08  |\n",
            "|    std                | 0.974     |\n",
            "|    value_loss         | 1.05e+14  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 8000     |\n",
            "|    time_elapsed       | 134      |\n",
            "|    total_timesteps    | 40000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7999     |\n",
            "|    policy_loss        | 5.55e+08 |\n",
            "|    std                | 0.974    |\n",
            "|    value_loss         | 2.16e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 8100     |\n",
            "|    time_elapsed       | 136      |\n",
            "|    total_timesteps    | 40500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8099     |\n",
            "|    policy_loss        | 6.54e+08 |\n",
            "|    std                | 0.973    |\n",
            "|    value_loss         | 2.94e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5167968.235818689\n",
            "Sharpe:  0.867187881540966\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 8200     |\n",
            "|    time_elapsed       | 137      |\n",
            "|    total_timesteps    | 41000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8199     |\n",
            "|    policy_loss        | 2.03e+08 |\n",
            "|    std                | 0.973    |\n",
            "|    value_loss         | 2.78e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 297       |\n",
            "|    iterations         | 8300      |\n",
            "|    time_elapsed       | 139       |\n",
            "|    total_timesteps    | 41500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8299      |\n",
            "|    policy_loss        | 2.3e+08   |\n",
            "|    std                | 0.973     |\n",
            "|    value_loss         | 4.59e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 8400     |\n",
            "|    time_elapsed       | 141      |\n",
            "|    total_timesteps    | 42000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8399     |\n",
            "|    policy_loss        | 3.64e+08 |\n",
            "|    std                | 0.972    |\n",
            "|    value_loss         | 1.08e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 297       |\n",
            "|    iterations         | 8500      |\n",
            "|    time_elapsed       | 142       |\n",
            "|    total_timesteps    | 42500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.3     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8499      |\n",
            "|    policy_loss        | 4.48e+08  |\n",
            "|    std                | 0.971     |\n",
            "|    value_loss         | 1.39e+14  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 8600     |\n",
            "|    time_elapsed       | 144      |\n",
            "|    total_timesteps    | 43000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8599     |\n",
            "|    policy_loss        | 5.65e+08 |\n",
            "|    std                | 0.972    |\n",
            "|    value_loss         | 2.49e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5295644.3420574665\n",
            "Sharpe:  0.8781589563976329\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 296      |\n",
            "|    iterations         | 8700     |\n",
            "|    time_elapsed       | 146      |\n",
            "|    total_timesteps    | 43500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8699     |\n",
            "|    policy_loss        | 1.32e+08 |\n",
            "|    std                | 0.972    |\n",
            "|    value_loss         | 1.15e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 8800     |\n",
            "|    time_elapsed       | 148      |\n",
            "|    total_timesteps    | 44000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8799     |\n",
            "|    policy_loss        | 1.97e+08 |\n",
            "|    std                | 0.971    |\n",
            "|    value_loss         | 3.06e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 8900     |\n",
            "|    time_elapsed       | 149      |\n",
            "|    total_timesteps    | 44500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8899     |\n",
            "|    policy_loss        | 2.8e+08  |\n",
            "|    std                | 0.97     |\n",
            "|    value_loss         | 5.78e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 297       |\n",
            "|    iterations         | 9000      |\n",
            "|    time_elapsed       | 151       |\n",
            "|    total_timesteps    | 45000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.3     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8999      |\n",
            "|    policy_loss        | 3.58e+08  |\n",
            "|    std                | 0.97      |\n",
            "|    value_loss         | 1.03e+14  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 297       |\n",
            "|    iterations         | 9100      |\n",
            "|    time_elapsed       | 153       |\n",
            "|    total_timesteps    | 45500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 9099      |\n",
            "|    policy_loss        | 4.32e+08  |\n",
            "|    std                | 0.969     |\n",
            "|    value_loss         | 1.47e+14  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 9200     |\n",
            "|    time_elapsed       | 154      |\n",
            "|    total_timesteps    | 46000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9199     |\n",
            "|    policy_loss        | 5.89e+08 |\n",
            "|    std                | 0.969    |\n",
            "|    value_loss         | 2.59e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4733644.272087766\n",
            "Sharpe:  0.8277849696877948\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 9300     |\n",
            "|    time_elapsed       | 156      |\n",
            "|    total_timesteps    | 46500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9299     |\n",
            "|    policy_loss        | 1.47e+08 |\n",
            "|    std                | 0.969    |\n",
            "|    value_loss         | 1.74e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 297       |\n",
            "|    iterations         | 9400      |\n",
            "|    time_elapsed       | 158       |\n",
            "|    total_timesteps    | 47000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.2     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 9399      |\n",
            "|    policy_loss        | 1.9e+08   |\n",
            "|    std                | 0.969     |\n",
            "|    value_loss         | 2.79e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 9500     |\n",
            "|    time_elapsed       | 159      |\n",
            "|    total_timesteps    | 47500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.2    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9499     |\n",
            "|    policy_loss        | 3e+08    |\n",
            "|    std                | 0.969    |\n",
            "|    value_loss         | 6.81e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 9600     |\n",
            "|    time_elapsed       | 161      |\n",
            "|    total_timesteps    | 48000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9599     |\n",
            "|    policy_loss        | 3.53e+08 |\n",
            "|    std                | 0.968    |\n",
            "|    value_loss         | 1.02e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 9700     |\n",
            "|    time_elapsed       | 163      |\n",
            "|    total_timesteps    | 48500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9699     |\n",
            "|    policy_loss        | 4.88e+08 |\n",
            "|    std                | 0.968    |\n",
            "|    value_loss         | 1.81e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 297      |\n",
            "|    iterations         | 9800     |\n",
            "|    time_elapsed       | 164      |\n",
            "|    total_timesteps    | 49000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.2    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9799     |\n",
            "|    policy_loss        | 5.74e+08 |\n",
            "|    std                | 0.967    |\n",
            "|    value_loss         | 2.77e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4989645.374122748\n",
            "Sharpe:  0.8500872008767442\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 297       |\n",
            "|    iterations         | 9900      |\n",
            "|    time_elapsed       | 166       |\n",
            "|    total_timesteps    | 49500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 9899      |\n",
            "|    policy_loss        | 1.91e+08  |\n",
            "|    std                | 0.967     |\n",
            "|    value_loss         | 2.26e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 296      |\n",
            "|    iterations         | 10000    |\n",
            "|    time_elapsed       | 168      |\n",
            "|    total_timesteps    | 50000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.2    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9999     |\n",
            "|    policy_loss        | 2.36e+08 |\n",
            "|    std                | 0.967    |\n",
            "|    value_loss         | 4.14e+13 |\n",
            "------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lvrqTro3lhAh"
      },
      "source": [
        "### Model 2: **PPO**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kVXta7jVKMhV",
        "outputId": "15272479-a9fa-4468-c7b5-13ee6b39f479"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "PPO_PARAMS = {\n",
        "    \"n_steps\": 2048,\n",
        "    \"ent_coef\": 0.005,\n",
        "    \"learning_rate\": 0.0001,\n",
        "    \"batch_size\": 128,\n",
        "}\n",
        "model_ppo = agent.get_model(\"ppo\",model_kwargs = PPO_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_steps': 2048, 'ent_coef': 0.005, 'learning_rate': 0.0001, 'batch_size': 128}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z5XlUIszKUGx",
        "outputId": "80f1c902-43f5-44ad-eb1e-8ae498cd8cd6"
      },
      "source": [
        "trained_ppo = agent.train_model(model=model_ppo, \n",
        "                             tb_log_name='ppo',\n",
        "                             total_timesteps=80000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ppo/ppo_1\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 348  |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 5    |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5407408.522645024\n",
            "Sharpe:  0.8945150154722756\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 314       |\n",
            "|    iterations           | 2         |\n",
            "|    time_elapsed         | 13        |\n",
            "|    total_timesteps      | 4096      |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | -1.19e-07 |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 8.62e+14  |\n",
            "|    n_updates            | 10        |\n",
            "|    policy_gradient_loss | -4.67e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 1.7e+15   |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5506664.127558984\n",
            "Sharpe:  0.8978272251870139\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 307       |\n",
            "|    iterations           | 3         |\n",
            "|    time_elapsed         | 19        |\n",
            "|    total_timesteps      | 6144      |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 5.96e-08  |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.56e+15  |\n",
            "|    n_updates            | 20        |\n",
            "|    policy_gradient_loss | -5.11e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.29e+15  |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 308       |\n",
            "|    iterations           | 4         |\n",
            "|    time_elapsed         | 26        |\n",
            "|    total_timesteps      | 8192      |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 1.19e-07  |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 2.09e+15  |\n",
            "|    n_updates            | 30        |\n",
            "|    policy_gradient_loss | -3.64e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 4.02e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4973206.12529279\n",
            "Sharpe:  0.852165346616796\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 307       |\n",
            "|    iterations           | 5         |\n",
            "|    time_elapsed         | 33        |\n",
            "|    total_timesteps      | 10240     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 0         |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.04e+15  |\n",
            "|    n_updates            | 40        |\n",
            "|    policy_gradient_loss | -4.76e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 2.29e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4983200.930282411\n",
            "Sharpe:  0.8548590279297212\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 305       |\n",
            "|    iterations           | 6         |\n",
            "|    time_elapsed         | 40        |\n",
            "|    total_timesteps      | 12288     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | -1.19e-07 |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 9.92e+14  |\n",
            "|    n_updates            | 50        |\n",
            "|    policy_gradient_loss | -5.26e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 2.31e+15  |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 306       |\n",
            "|    iterations           | 7         |\n",
            "|    time_elapsed         | 46        |\n",
            "|    total_timesteps      | 14336     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 1.19e-07  |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.42e+15  |\n",
            "|    n_updates            | 60        |\n",
            "|    policy_gradient_loss | -4.58e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.17e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5004305.2400275655\n",
            "Sharpe:  0.8580894170419451\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 304       |\n",
            "|    iterations           | 8         |\n",
            "|    time_elapsed         | 53        |\n",
            "|    total_timesteps      | 16384     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 1.19e-07  |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.75e+15  |\n",
            "|    n_updates            | 70        |\n",
            "|    policy_gradient_loss | -3.64e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.36e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4767293.424762546\n",
            "Sharpe:  0.8327081862255113\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 302       |\n",
            "|    iterations           | 9         |\n",
            "|    time_elapsed         | 60        |\n",
            "|    total_timesteps      | 18432     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 1.79e-07  |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 7.95e+14  |\n",
            "|    n_updates            | 80        |\n",
            "|    policy_gradient_loss | -6.24e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 1.53e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5718116.186481766\n",
            "Sharpe:  0.9168397946401206\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 302       |\n",
            "|    iterations           | 10        |\n",
            "|    time_elapsed         | 67        |\n",
            "|    total_timesteps      | 20480     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 0         |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.46e+15  |\n",
            "|    n_updates            | 90        |\n",
            "|    policy_gradient_loss | -4.56e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 2.72e+15  |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| time/                   |          |\n",
            "|    fps                  | 302      |\n",
            "|    iterations           | 11       |\n",
            "|    time_elapsed         | 74       |\n",
            "|    total_timesteps      | 22528    |\n",
            "| train/                  |          |\n",
            "|    approx_kl            | 0.0      |\n",
            "|    clip_fraction        | 0        |\n",
            "|    clip_range           | 0.2      |\n",
            "|    entropy_loss         | -42.6    |\n",
            "|    explained_variance   | 0        |\n",
            "|    learning_rate        | 0.0001   |\n",
            "|    loss                 | 2.14e+15 |\n",
            "|    n_updates            | 100      |\n",
            "|    policy_gradient_loss | -2.9e-07 |\n",
            "|    std                  | 1        |\n",
            "|    value_loss           | 4.48e+15 |\n",
            "--------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5160448.888370996\n",
            "Sharpe:  0.8687380292111129\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 300       |\n",
            "|    iterations           | 12        |\n",
            "|    time_elapsed         | 81        |\n",
            "|    total_timesteps      | 24576     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | -1.19e-07 |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.05e+15  |\n",
            "|    n_updates            | 110       |\n",
            "|    policy_gradient_loss | -4.37e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 2.13e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5037083.305754823\n",
            "Sharpe:  0.8608949299257355\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 299       |\n",
            "|    iterations           | 13        |\n",
            "|    time_elapsed         | 89        |\n",
            "|    total_timesteps      | 26624     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | -1.19e-07 |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.57e+15  |\n",
            "|    n_updates            | 120       |\n",
            "|    policy_gradient_loss | -5.12e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 2.69e+15  |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 299       |\n",
            "|    iterations           | 14        |\n",
            "|    time_elapsed         | 95        |\n",
            "|    total_timesteps      | 28672     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | -1.19e-07 |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.75e+15  |\n",
            "|    n_updates            | 130       |\n",
            "|    policy_gradient_loss | -4.11e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.16e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5298096.091470836\n",
            "Sharpe:  0.8835774791782369\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 298       |\n",
            "|    iterations           | 15        |\n",
            "|    time_elapsed         | 103       |\n",
            "|    total_timesteps      | 30720     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 1.19e-07  |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.43e+15  |\n",
            "|    n_updates            | 140       |\n",
            "|    policy_gradient_loss | -4.82e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.08e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4961477.565456702\n",
            "Sharpe:  0.8562661606561773\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 298       |\n",
            "|    iterations           | 16        |\n",
            "|    time_elapsed         | 109       |\n",
            "|    total_timesteps      | 32768     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 5.96e-08  |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1e+15     |\n",
            "|    n_updates            | 150       |\n",
            "|    policy_gradient_loss | -5.88e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 2.03e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5527273.862226817\n",
            "Sharpe:  0.9011641451672711\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 297       |\n",
            "|    iterations           | 17        |\n",
            "|    time_elapsed         | 116       |\n",
            "|    total_timesteps      | 34816     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
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            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 0         |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.52e+15  |\n",
            "|    n_updates            | 160       |\n",
            "|    policy_gradient_loss | -3.48e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.22e+15  |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 296       |\n",
            "|    iterations           | 18        |\n",
            "|    time_elapsed         | 124       |\n",
            "|    total_timesteps      | 36864     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
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            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 0         |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 2.13e+15  |\n",
            "|    n_updates            | 170       |\n",
            "|    policy_gradient_loss | -3.79e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 4.35e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5181675.476854653\n",
            "Sharpe:  0.8666935031584689\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 295       |\n",
            "|    iterations           | 19        |\n",
            "|    time_elapsed         | 131       |\n",
            "|    total_timesteps      | 38912     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
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            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | -1.19e-07 |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 8.44e+14  |\n",
            "|    n_updates            | 180       |\n",
            "|    policy_gradient_loss | -4.85e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 1.79e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4801715.467888956\n",
            "Sharpe:  0.8392510886342651\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 295       |\n",
            "|    iterations           | 20        |\n",
            "|    time_elapsed         | 138       |\n",
            "|    total_timesteps      | 40960     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
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            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | -1.19e-07 |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.38e+15  |\n",
            "|    n_updates            | 190       |\n",
            "|    policy_gradient_loss | -5.06e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 2.86e+15  |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 295       |\n",
            "|    iterations           | 21        |\n",
            "|    time_elapsed         | 145       |\n",
            "|    total_timesteps      | 43008     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
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            "|    entropy_loss         | -42.6     |\n",
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            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.62e+15  |\n",
            "|    n_updates            | 200       |\n",
            "|    policy_gradient_loss | -4.68e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.1e+15   |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4968176.3422929365\n",
            "Sharpe:  0.853754454109652\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 293       |\n",
            "|    iterations           | 22        |\n",
            "|    time_elapsed         | 153       |\n",
            "|    total_timesteps      | 45056     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
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            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 5.96e-08  |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.11e+15  |\n",
            "|    n_updates            | 210       |\n",
            "|    policy_gradient_loss | -5.42e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 2.32e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5333210.09190493\n",
            "Sharpe:  0.8892998023531967\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 293       |\n",
            "|    iterations           | 23        |\n",
            "|    time_elapsed         | 160       |\n",
            "|    total_timesteps      | 47104     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
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            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 1.19e-07  |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.03e+15  |\n",
            "|    n_updates            | 220       |\n",
            "|    policy_gradient_loss | -5.01e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 2.26e+15  |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 293       |\n",
            "|    iterations           | 24        |\n",
            "|    time_elapsed         | 167       |\n",
            "|    total_timesteps      | 49152     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
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            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 0         |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.94e+15  |\n",
            "|    n_updates            | 230       |\n",
            "|    policy_gradient_loss | -3.64e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.71e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5028653.28685496\n",
            "Sharpe:  0.8567058309087507\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 293       |\n",
            "|    iterations           | 25        |\n",
            "|    time_elapsed         | 174       |\n",
            "|    total_timesteps      | 51200     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
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            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 0         |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.75e+15  |\n",
            "|    n_updates            | 240       |\n",
            "|    policy_gradient_loss | -3.74e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.8e+15   |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5396539.202192561\n",
            "Sharpe:  0.8909163947432485\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 293       |\n",
            "|    iterations           | 26        |\n",
            "|    time_elapsed         | 181       |\n",
            "|    total_timesteps      | 53248     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
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            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 0         |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 8.89e+14  |\n",
            "|    n_updates            | 250       |\n",
            "|    policy_gradient_loss | -6.69e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 1.66e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4983470.113884904\n",
            "Sharpe:  0.855726983366501\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 293       |\n",
            "|    iterations           | 27        |\n",
            "|    time_elapsed         | 188       |\n",
            "|    total_timesteps      | 55296     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
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            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 0         |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.57e+15  |\n",
            "|    n_updates            | 260       |\n",
            "|    policy_gradient_loss | -4.51e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.26e+15  |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 294       |\n",
            "|    iterations           | 28        |\n",
            "|    time_elapsed         | 194       |\n",
            "|    total_timesteps      | 57344     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | -1.19e-07 |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.62e+15  |\n",
            "|    n_updates            | 270       |\n",
            "|    policy_gradient_loss | -2.94e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.53e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5077533.069990031\n",
            "Sharpe:  0.8698859623755926\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 294       |\n",
            "|    iterations           | 29        |\n",
            "|    time_elapsed         | 201       |\n",
            "|    total_timesteps      | 59392     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 0         |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.04e+15  |\n",
            "|    n_updates            | 280       |\n",
            "|    policy_gradient_loss | -3.94e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 2.26e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4718933.011698665\n",
            "Sharpe:  0.8300365631029007\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 294       |\n",
            "|    iterations           | 30        |\n",
            "|    time_elapsed         | 208       |\n",
            "|    total_timesteps      | 61440     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 1.19e-07  |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.46e+15  |\n",
            "|    n_updates            | 290       |\n",
            "|    policy_gradient_loss | -4.45e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 2.48e+15  |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 294       |\n",
            "|    iterations           | 31        |\n",
            "|    time_elapsed         | 215       |\n",
            "|    total_timesteps      | 63488     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 5.96e-08  |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.67e+15  |\n",
            "|    n_updates            | 300       |\n",
            "|    policy_gradient_loss | -4.25e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.25e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5400644.928048184\n",
            "Sharpe:  0.8915105700881634\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 294       |\n",
            "|    iterations           | 32        |\n",
            "|    time_elapsed         | 222       |\n",
            "|    total_timesteps      | 65536     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
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            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 0         |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.81e+15  |\n",
            "|    n_updates            | 310       |\n",
            "|    policy_gradient_loss | -3.69e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.63e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5244251.123995519\n",
            "Sharpe:  0.8820405593077815\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 294       |\n",
            "|    iterations           | 33        |\n",
            "|    time_elapsed         | 229       |\n",
            "|    total_timesteps      | 67584     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 0         |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.05e+15  |\n",
            "|    n_updates            | 320       |\n",
            "|    policy_gradient_loss | -5.81e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 1.94e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5323679.181851208\n",
            "Sharpe:  0.8782748494629397\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 293       |\n",
            "|    iterations           | 34        |\n",
            "|    time_elapsed         | 236       |\n",
            "|    total_timesteps      | 69632     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 0         |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.91e+15  |\n",
            "|    n_updates            | 330       |\n",
            "|    policy_gradient_loss | -4.24e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.54e+15  |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 294       |\n",
            "|    iterations           | 35        |\n",
            "|    time_elapsed         | 243       |\n",
            "|    total_timesteps      | 71680     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 0         |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.97e+15  |\n",
            "|    n_updates            | 340       |\n",
            "|    policy_gradient_loss | -3.79e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 4.1e+15   |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5231003.911583127\n",
            "Sharpe:  0.8742792085154348\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 294       |\n",
            "|    iterations           | 36        |\n",
            "|    time_elapsed         | 250       |\n",
            "|    total_timesteps      | 73728     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 1.79e-07  |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.06e+15  |\n",
            "|    n_updates            | 350       |\n",
            "|    policy_gradient_loss | -5.38e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 2.07e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5431876.339119544\n",
            "Sharpe:  0.8973709821852228\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 293       |\n",
            "|    iterations           | 37        |\n",
            "|    time_elapsed         | 258       |\n",
            "|    total_timesteps      | 75776     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | -1.19e-07 |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.6e+15   |\n",
            "|    n_updates            | 360       |\n",
            "|    policy_gradient_loss | -4.84e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.03e+15  |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 293       |\n",
            "|    iterations           | 38        |\n",
            "|    time_elapsed         | 265       |\n",
            "|    total_timesteps      | 77824     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 0         |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.85e+15  |\n",
            "|    n_updates            | 370       |\n",
            "|    policy_gradient_loss | -3.88e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 3.85e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5174445.4087701775\n",
            "Sharpe:  0.8746583729733338\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 292       |\n",
            "|    iterations           | 39        |\n",
            "|    time_elapsed         | 272       |\n",
            "|    total_timesteps      | 79872     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 1.19e-07  |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 1.42e+15  |\n",
            "|    n_updates            | 380       |\n",
            "|    policy_gradient_loss | -4.64e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 2.86e+15  |\n",
            "---------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4789004.776252521\n",
            "Sharpe:  0.8357083479334512\n",
            "=================================\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 292       |\n",
            "|    iterations           | 40        |\n",
            "|    time_elapsed         | 280       |\n",
            "|    total_timesteps      | 81920     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0       |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -42.6     |\n",
            "|    explained_variance   | 0         |\n",
            "|    learning_rate        | 0.0001    |\n",
            "|    loss                 | 8.65e+14  |\n",
            "|    n_updates            | 390       |\n",
            "|    policy_gradient_loss | -6.17e-07 |\n",
            "|    std                  | 1         |\n",
            "|    value_loss           | 2e+15     |\n",
            "---------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a3Iuv554xYFH"
      },
      "source": [
        "### Model 3: **DDPG**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ojmppgo4LPLz",
        "outputId": "bb2459a2-3231-481e-9628-4ace4256f9e1"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "DDPG_PARAMS = {\"batch_size\": 128, \"buffer_size\": 50000, \"learning_rate\": 0.001}\n",
        "\n",
        "\n",
        "model_ddpg = agent.get_model(\"ddpg\",model_kwargs = DDPG_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 50000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bWt6BIR0LT25",
        "outputId": "d523d157-f2c7-4667-9971-214ddc51fcb1"
      },
      "source": [
        "trained_ddpg = agent.train_model(model=model_ddpg, \n",
        "                             tb_log_name='ddpg',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ddpg/ddpg_2\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4625995.900359718\n",
            "Sharpe:  1.040202670783119\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 4         |\n",
            "|    fps             | 22        |\n",
            "|    time_elapsed    | 439       |\n",
            "|    total timesteps | 10064     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -6.99e+07 |\n",
            "|    critic_loss     | 7.27e+12  |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 7548      |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 8         |\n",
            "|    fps             | 20        |\n",
            "|    time_elapsed    | 980       |\n",
            "|    total timesteps | 20128     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.44e+08 |\n",
            "|    critic_loss     | 1.81e+13  |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 17612     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 12        |\n",
            "|    fps             | 19        |\n",
            "|    time_elapsed    | 1542      |\n",
            "|    total timesteps | 30192     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.88e+08 |\n",
            "|    critic_loss     | 2.72e+13  |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 27676     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 16        |\n",
            "|    fps             | 18        |\n",
            "|    time_elapsed    | 2133      |\n",
            "|    total timesteps | 40256     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -2.15e+08 |\n",
            "|    critic_loss     | 3.45e+13  |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 37740     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 20       |\n",
            "|    fps             | 17       |\n",
            "|    time_elapsed    | 2874     |\n",
            "|    total timesteps | 50320    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -2.3e+08 |\n",
            "|    critic_loss     | 4.05e+13 |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 47804    |\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SPEXBcm-uBJo"
      },
      "source": [
        "### Model 4: **SAC**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaWf2QeiLqyO",
        "outputId": "f74b3a46-6195-43f3-d321-512a3318f767"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "SAC_PARAMS = {\n",
        "    \"batch_size\": 128,\n",
        "    \"buffer_size\": 100000,\n",
        "    \"learning_rate\": 0.0003,\n",
        "    \"learning_starts\": 100,\n",
        "    \"ent_coef\": \"auto_0.1\",\n",
        "}\n",
        "\n",
        "model_sac = agent.get_model(\"sac\",model_kwargs = SAC_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 100000, 'learning_rate': 0.0003, 'learning_starts': 100, 'ent_coef': 'auto_0.1'}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZgYVPqtKLvi3",
        "outputId": "d633cfb8-2379-485c-9c2b-5737f0fa2b23"
      },
      "source": [
        "trained_sac = agent.train_model(model=model_sac, \n",
        "                             tb_log_name='sac',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/sac/sac_1\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4449463.498168942\n",
            "Sharpe:  1.01245667390232\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418643.239765096\n",
            "Sharpe:  1.0135796594260282\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418644.1960784905\n",
            "Sharpe:  1.0135797537524718\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418659.429680678\n",
            "Sharpe:  1.013581852537709\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 4         |\n",
            "|    fps             | 12        |\n",
            "|    time_elapsed    | 783       |\n",
            "|    total timesteps | 10064     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -8.83e+07 |\n",
            "|    critic_loss     | 6.57e+12  |\n",
            "|    ent_coef        | 2.24      |\n",
            "|    ent_coef_loss   | -205      |\n",
            "|    learning_rate   | 0.0003    |\n",
            "|    n_updates       | 9963      |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418651.576406099\n",
            "Sharpe:  1.013581224026754\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418670.948269031\n",
            "Sharpe:  1.0135838030234754\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418682.278829884\n",
            "Sharpe:  1.013585596968056\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418791.911955293\n",
            "Sharpe:  1.0136007328171013\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 8         |\n",
            "|    fps             | 12        |\n",
            "|    time_elapsed    | 1585      |\n",
            "|    total timesteps | 20128     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.51e+08 |\n",
            "|    critic_loss     | 1.12e+13  |\n",
            "|    ent_coef        | 41.7      |\n",
            "|    ent_coef_loss   | -670      |\n",
            "|    learning_rate   | 0.0003    |\n",
            "|    n_updates       | 20027     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418737.365107464\n",
            "Sharpe:  1.0135970410224868\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418754.895735274\n",
            "Sharpe:  1.0135965589029627\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4419325.814567342\n",
            "Sharpe:  1.0136807224228588\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418142.473513333\n",
            "Sharpe:  1.0135234795926031\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 12        |\n",
            "|    fps             | 12        |\n",
            "|    time_elapsed    | 2400      |\n",
            "|    total timesteps | 30192     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.85e+08 |\n",
            "|    critic_loss     | 1.87e+13  |\n",
            "|    ent_coef        | 725       |\n",
            "|    ent_coef_loss   | -673      |\n",
            "|    learning_rate   | 0.0003    |\n",
            "|    n_updates       | 30091     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4422046.188863339\n",
            "Sharpe:  1.0140936726052256\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4424919.463828854\n",
            "Sharpe:  1.014521127041106\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4427483.152494239\n",
            "Sharpe:  1.0148626804754584\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4460697.650185859\n",
            "Sharpe:  1.019852362102548\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 16        |\n",
            "|    fps             | 12        |\n",
            "|    time_elapsed    | 3210      |\n",
            "|    total timesteps | 40256     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.93e+08 |\n",
            "|    critic_loss     | 1.62e+13  |\n",
            "|    ent_coef        | 1.01e+04  |\n",
            "|    ent_coef_loss   | -238      |\n",
            "|    learning_rate   | 0.0003    |\n",
            "|    n_updates       | 40155     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4434035.982803257\n",
            "Sharpe:  1.0161512551319891\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4454728.906041551\n",
            "Sharpe:  1.018484863448905\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4475667.120269234\n",
            "Sharpe:  1.0215545521682856\n",
            "=================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iidB5E27dfzh"
      },
      "source": [
        "### Model 5: **TD3**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XtRp1mWydkvs",
        "outputId": "dda00a46-58c8-44af-ec89-a0561f8e1c5c"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "TD3_PARAMS = {\"batch_size\": 100, \n",
        "              \"buffer_size\": 1000000, \n",
        "              \"learning_rate\": 0.001}\n",
        "\n",
        "model_td3 = agent.get_model(\"td3\",model_kwargs = TD3_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 100, 'buffer_size': 1000000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "argM0DtodmNL",
        "outputId": "d9e00099-54c5-4c4b-d39d-2486b69027cc"
      },
      "source": [
        "trained_td3 = agent.train_model(model=model_td3, \n",
        "                             tb_log_name='td3',\n",
        "                             total_timesteps=30000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/td3/td3_1\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5232441.848437611\n",
            "Sharpe:  0.8749907118878204\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5140658.98428856\n",
            "Sharpe:  0.8628057073557059\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5140658.98428856\n",
            "Sharpe:  0.8628057073557059\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5140658.98428856\n",
            "Sharpe:  0.8628057073557059\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 4         |\n",
            "|    fps             | 25        |\n",
            "|    time_elapsed    | 445       |\n",
            "|    total timesteps | 11572     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -4.69e+07 |\n",
            "|    critic_loss     | 1.08e+13  |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 8679      |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5140658.98428856\n",
            "Sharpe:  0.8628057073557059\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5140658.98428856\n",
            "Sharpe:  0.8628057073557059\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5140658.98428856\n",
            "Sharpe:  0.8628057073557059\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5140658.98428856\n",
            "Sharpe:  0.8628057073557059\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 8         |\n",
            "|    fps             | 23        |\n",
            "|    time_elapsed    | 985       |\n",
            "|    total timesteps | 23144     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.05e+08 |\n",
            "|    critic_loss     | 2.77e+13  |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 20251     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5140658.98428856\n",
            "Sharpe:  0.8628057073557059\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5140658.98428856\n",
            "Sharpe:  0.8628057073557059\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5140658.98428856\n",
            "Sharpe:  0.8628057073557059\n",
            "=================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E2Ma6YpTlnuZ"
      },
      "source": [
        "## Trading\n",
        "Assume that we have $1,000,000 initial capital at 2019-01-01. We use the DDPG model to trade Dow jones 30 stocks."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uas8U6k455sI"
      },
      "source": [
        "trade = data_split(df,'2020-07-01', '2021-07-01')\n",
        "e_trade_gym = StockPortfolioEnv(df = trade, **env_kwargs)\n"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcGYlhyal205",
        "outputId": "51fadecf-2131-4d51-94c3-2495b2868e25"
      },
      "source": [
        "trade.shape"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7308, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qq4W9FbSstT7",
        "outputId": "3fef48c1-e6af-4154-a25b-3b0425467217"
      },
      "source": [
        "df_daily_return, df_actions = DRLAgent.DRL_prediction(model=trained_a2c,\n",
        "                        environment = e_trade_gym)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:1440814.3031686214\n",
            "Sharpe:  2.4096154370898826\n",
            "=================================\n",
            "hit end!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "uJvj3pXt_Ukg",
        "outputId": "ebee51ff-ba41-4b1b-c7a0-65e44b5168f8"
      },
      "source": [
        "df_daily_return.head()"
      ],
      "execution_count": 29,
      "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>daily_return</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-07-02</td>\n",
              "      <td>0.005434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>0.016787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-07-07</td>\n",
              "      <td>-0.017630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-07-08</td>\n",
              "      <td>0.002322</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  daily_return\n",
              "0  2020-07-01      0.000000\n",
              "1  2020-07-02      0.005434\n",
              "2  2020-07-06      0.016787\n",
              "3  2020-07-07     -0.017630\n",
              "4  2020-07-08      0.002322"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXMMG_9SdKTu"
      },
      "source": [
        "df_daily_return.to_csv('df_daily_return.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "tByVcZ2L9TAJ",
        "outputId": "dfb0f99b-751c-47b2-c618-c698dda42a6a"
      },
      "source": [
        "df_actions.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>AXP</th>\n",
              "      <th>BA</th>\n",
              "      <th>CAT</th>\n",
              "      <th>CSCO</th>\n",
              "      <th>CVX</th>\n",
              "      <th>DD</th>\n",
              "      <th>DIS</th>\n",
              "      <th>GS</th>\n",
              "      <th>HD</th>\n",
              "      <th>IBM</th>\n",
              "      <th>INTC</th>\n",
              "      <th>JNJ</th>\n",
              "      <th>JPM</th>\n",
              "      <th>KO</th>\n",
              "      <th>MCD</th>\n",
              "      <th>MMM</th>\n",
              "      <th>MRK</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NKE</th>\n",
              "      <th>PFE</th>\n",
              "      <th>PG</th>\n",
              "      <th>RTX</th>\n",
              "      <th>TRV</th>\n",
              "      <th>UNH</th>\n",
              "      <th>V</th>\n",
              "      <th>VZ</th>\n",
              "      <th>WBA</th>\n",
              "      <th>WMT</th>\n",
              "      <th>XOM</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-07-01</th>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-02</th>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-06</th>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-07</th>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-08</th>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "      <td>0.045909</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                AAPL       AXP        BA  ...       WBA       WMT       XOM\n",
              "date                                      ...                              \n",
              "2020-07-01  0.033333  0.033333  0.033333  ...  0.033333  0.033333  0.033333\n",
              "2020-07-02  0.045909  0.045909  0.016889  ...  0.045909  0.045909  0.045909\n",
              "2020-07-06  0.045909  0.045909  0.016889  ...  0.045909  0.045909  0.045909\n",
              "2020-07-07  0.045909  0.045909  0.016889  ...  0.045909  0.045909  0.045909\n",
              "2020-07-08  0.045909  0.045909  0.016889  ...  0.045909  0.045909  0.045909\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBX3Y68o1vRG"
      },
      "source": [
        "df_actions.to_csv('df_actions.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qFO42LcomPUT"
      },
      "source": [
        "<a id='6'></a>\n",
        "# Part 7: Backtest Our Strategy\n",
        "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fAvxipWFmUe8"
      },
      "source": [
        "<a id='6.1'></a>\n",
        "## 7.1 BackTestStats\n",
        "pass in df_account_value, this information is stored in env class\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oGu3PCa8l6L"
      },
      "source": [
        "from pyfolio import timeseries\n",
        "DRL_strat = convert_daily_return_to_pyfolio_ts(df_daily_return)\n",
        "perf_func = timeseries.perf_stats \n",
        "perf_stats_all = perf_func( returns=DRL_strat, \n",
        "                              factor_returns=DRL_strat, \n",
        "                                positions=None, transactions=None, turnover_denom=\"AGB\")"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hqvwr6SY8l9A",
        "outputId": "72786eb4-f44d-412e-e33c-3a6f362a2d29"
      },
      "source": [
        "print(\"==============DRL Strategy Stats===========\")\n",
        "perf_stats_all"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============DRL Strategy Stats===========\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Annual return          0.440814\n",
              "Cumulative returns     0.440814\n",
              "Annual volatility      0.156741\n",
              "Sharpe ratio           2.409615\n",
              "Calmar ratio           5.479908\n",
              "Stability              0.944362\n",
              "Max drawdown          -0.080442\n",
              "Omega ratio            1.496055\n",
              "Sortino ratio          3.871739\n",
              "Skew                   0.096449\n",
              "Kurtosis               1.454288\n",
              "Tail ratio             1.171062\n",
              "Daily value at risk   -0.018249\n",
              "Alpha                  0.000000\n",
              "Beta                   1.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XcWzfa6UloDM",
        "outputId": "9d93ce9d-e5a3-4cf7-fab3-1731c5fefbf9"
      },
      "source": [
        "#baseline stats\n",
        "print(\"==============Get Baseline Stats===========\")\n",
        "baseline_df = get_baseline(\n",
        "        ticker=\"^DJI\", \n",
        "        start = df_daily_return.loc[0,'date'],\n",
        "        end = df_daily_return.loc[len(df_daily_return)-1,'date'])\n",
        "\n",
        "stats = backtest_stats(baseline_df, value_col_name = 'close')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (251, 8)\n",
            "Annual return          0.334042\n",
            "Cumulative returns     0.332517\n",
            "Annual volatility      0.146033\n",
            "Sharpe ratio           2.055458\n",
            "Calmar ratio           3.740347\n",
            "Stability              0.945402\n",
            "Max drawdown          -0.089308\n",
            "Omega ratio            1.408111\n",
            "Sortino ratio          3.075978\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.078766\n",
            "Daily value at risk   -0.017207\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lVVCMVSAmcrI"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2fqSEF5PfjjT",
        "outputId": "57a01f56-08ec-4a32-a6ea-a43ba77b08d0"
      },
      "source": [
        "import pyfolio\n",
        "%matplotlib inline\n",
        "\n",
        "baseline_df = get_baseline(\n",
        "        ticker='^DJI', start=df_daily_return.loc[0,'date'], end='2021-07-01'\n",
        "    )\n",
        "\n",
        "baseline_returns = get_daily_return(baseline_df, value_col_name=\"close\")\n",
        "\n",
        "with pyfolio.plotting.plotting_context(font_scale=1.1):\n",
        "        pyfolio.create_full_tear_sheet(returns = DRL_strat,\n",
        "                                       benchmark_rets=baseline_returns, set_context=False)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (252, 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>2020-07-01</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2021-06-30</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>12</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>44.081%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>44.081%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>15.674%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>2.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>5.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-8.044%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>3.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>1.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>1.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-1.825%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>1.02</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>8.04</td>\n",
              "      <td>2020-09-02</td>\n",
              "      <td>2020-10-28</td>\n",
              "      <td>2020-11-09</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.06</td>\n",
              "      <td>2021-06-04</td>\n",
              "      <td>2021-06-18</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.78</td>\n",
              "      <td>2021-01-20</td>\n",
              "      <td>2021-01-29</td>\n",
              "      <td>2021-02-08</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.25</td>\n",
              "      <td>2021-02-24</td>\n",
              "      <td>2021-02-26</td>\n",
              "      <td>2021-03-08</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.21</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>2020-07-14</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyfolio/tears.py:907: UserWarning: Passed returns do not overlap with anyinteresting times.\n",
            "  'interesting times.', UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2DgsIW-fh0s"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "te3Ibcj5hUbz"
      },
      "source": [
        "## Min-Variance Portfolio Allocation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1VE2eUEuhMKs",
        "outputId": "1aa5c6bd-efab-41e3-bb3d-135d8d77a310"
      },
      "source": [
        "!pip install PyPortfolioOpt"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting PyPortfolioOpt\n",
            "  Downloading PyPortfolioOpt-1.4.2-py3-none-any.whl (60 kB)\n",
            "\u001b[?25l\r\u001b[K     |█████▍                          | 10 kB 21.7 MB/s eta 0:00:01\r\u001b[K     |██████████▉                     | 20 kB 27.9 MB/s eta 0:00:01\r\u001b[K     |████████████████▎               | 30 kB 28.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▋          | 40 kB 20.1 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 51 kB 9.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 60 kB 4.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.12 in /usr/local/lib/python3.7/dist-packages (from PyPortfolioOpt) (1.19.5)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from PyPortfolioOpt) (1.1.5)\n",
            "Collecting cvxpy<2.0.0,>=1.1.10\n",
            "  Downloading cvxpy-1.1.14-cp37-cp37m-manylinux_2_24_x86_64.whl (2.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.7 MB 18.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy<2.0,>=1.3 in /usr/local/lib/python3.7/dist-packages (from PyPortfolioOpt) (1.4.1)\n",
            "Requirement already satisfied: scs>=1.1.6 in /usr/local/lib/python3.7/dist-packages (from cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (2.1.4)\n",
            "Requirement already satisfied: ecos>=2 in /usr/local/lib/python3.7/dist-packages (from cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (2.0.7.post1)\n",
            "Requirement already satisfied: osqp>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (0.6.2.post0)\n",
            "Requirement already satisfied: qdldl in /usr/local/lib/python3.7/dist-packages (from osqp>=0.4.1->cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (0.1.5.post0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->PyPortfolioOpt) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->PyPortfolioOpt) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19->PyPortfolioOpt) (1.15.0)\n",
            "Installing collected packages: cvxpy, PyPortfolioOpt\n",
            "  Attempting uninstall: cvxpy\n",
            "    Found existing installation: cvxpy 1.0.31\n",
            "    Uninstalling cvxpy-1.0.31:\n",
            "      Successfully uninstalled cvxpy-1.0.31\n",
            "Successfully installed PyPortfolioOpt-1.4.2 cvxpy-1.1.14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0NiefM7hHn0"
      },
      "source": [
        "from pypfopt.efficient_frontier import EfficientFrontier\n",
        "from pypfopt import risk_models"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDYDIBH9hcUP"
      },
      "source": [
        "unique_tic = trade.tic.unique()\n",
        "unique_trade_date = trade.date.unique()"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "8qvk_pJ4iV32",
        "outputId": "bd8e85ae-502f-4af3-a555-1a32b4ab6692"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>cov_list</th>\n",
              "      <th>return_list</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>3.070357</td>\n",
              "      <td>3.133571</td>\n",
              "      <td>3.047857</td>\n",
              "      <td>2.621168</td>\n",
              "      <td>607541200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.083794</td>\n",
              "      <td>42.254781</td>\n",
              "      <td>-80.495083</td>\n",
              "      <td>16.129793</td>\n",
              "      <td>[[0.0013489695390742418, 0.0009909796896398577...</td>\n",
              "      <td>tic             AAPL       AXP        BA  ... ...</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>15.025569</td>\n",
              "      <td>9625600</td>\n",
              "      <td>AXP</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.964124</td>\n",
              "      <td>42.554845</td>\n",
              "      <td>-75.362550</td>\n",
              "      <td>25.776759</td>\n",
              "      <td>[[0.0013489695390742418, 0.0009909796896398577...</td>\n",
              "      <td>tic             AAPL       AXP        BA  ... ...</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",
              "      <td>-0.279798</td>\n",
              "      <td>47.440267</td>\n",
              "      <td>156.995097</td>\n",
              "      <td>5.366299</td>\n",
              "      <td>[[0.0013489695390742418, 0.0009909796896398577...</td>\n",
              "      <td>tic             AAPL       AXP        BA  ... ...</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>31.095800</td>\n",
              "      <td>6277400</td>\n",
              "      <td>CAT</td>\n",
              "      <td>2</td>\n",
              "      <td>0.688541</td>\n",
              "      <td>51.205314</td>\n",
              "      <td>98.432308</td>\n",
              "      <td>26.331746</td>\n",
              "      <td>[[0.0013489695390742418, 0.0009909796896398577...</td>\n",
              "      <td>tic             AAPL       AXP        BA  ... ...</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>12.019092</td>\n",
              "      <td>37513700</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.102729</td>\n",
              "      <td>45.961924</td>\n",
              "      <td>11.978197</td>\n",
              "      <td>13.387087</td>\n",
              "      <td>[[0.0013489695390742418, 0.0009909796896398577...</td>\n",
              "      <td>tic             AAPL       AXP        BA  ... ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  ...                                        return_list\n",
              "0  2008-12-31  ...  tic             AAPL       AXP        BA  ... ...\n",
              "1  2008-12-31  ...  tic             AAPL       AXP        BA  ... ...\n",
              "2  2008-12-31  ...  tic             AAPL       AXP        BA  ... ...\n",
              "3  2008-12-31  ...  tic             AAPL       AXP        BA  ... ...\n",
              "4  2008-12-31  ...  tic             AAPL       AXP        BA  ... ...\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EICNukJZgnWl"
      },
      "source": [
        "#calculate_portfolio_minimum_variance\n",
        "portfolio = pd.DataFrame(index = range(1), columns = unique_trade_date)\n",
        "initial_capital = 1000000\n",
        "portfolio.loc[0,unique_trade_date[0]] = initial_capital\n",
        "\n",
        "for i in range(len( unique_trade_date)-1):\n",
        "    df_temp = df[df.date==unique_trade_date[i]].reset_index(drop=True)\n",
        "    df_temp_next = df[df.date==unique_trade_date[i+1]].reset_index(drop=True)\n",
        "    #Sigma = risk_models.sample_cov(df_temp.return_list[0])\n",
        "    #calculate covariance matrix\n",
        "    Sigma = df_temp.return_list[0].cov()\n",
        "    #portfolio allocation\n",
        "    ef_min_var = EfficientFrontier(None, Sigma,weight_bounds=(0, 0.1))\n",
        "    #minimum variance\n",
        "    raw_weights_min_var = ef_min_var.min_volatility()\n",
        "    #get weights\n",
        "    cleaned_weights_min_var = ef_min_var.clean_weights()\n",
        "    \n",
        "    #current capital\n",
        "    cap = portfolio.iloc[0, i]\n",
        "    #current cash invested for each stock\n",
        "    current_cash = [element * cap for element in list(cleaned_weights_min_var.values())]\n",
        "    # current held shares\n",
        "    current_shares = list(np.array(current_cash)\n",
        "                                      / np.array(df_temp.close))\n",
        "    # next time period price\n",
        "    next_price = np.array(df_temp_next.close)\n",
        "    ##next_price * current share to calculate next total account value \n",
        "    portfolio.iloc[0, i+1] = np.dot(current_shares, next_price)\n",
        "    \n",
        "portfolio=portfolio.T\n",
        "portfolio.columns = ['account_value']"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "S2zmv1ISkpXu",
        "outputId": "5e5aa185-6400-4b98-ac8d-f8d6f5614b55"
      },
      "source": [
        "portfolio.head()"
      ],
      "execution_count": 39,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-07-01</th>\n",
              "      <td>1000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-02</th>\n",
              "      <td>1.00666e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-06</th>\n",
              "      <td>1.01678e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-07</th>\n",
              "      <td>1.01512e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-08</th>\n",
              "      <td>1.01319e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           account_value\n",
              "2020-07-01       1000000\n",
              "2020-07-02   1.00666e+06\n",
              "2020-07-06   1.01678e+06\n",
              "2020-07-07   1.01512e+06\n",
              "2020-07-08   1.01319e+06"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5RmNa7m_f590"
      },
      "source": [
        "a2c_cumpod =(df_daily_return.daily_return+1).cumprod()-1"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y821cLKkhCn6"
      },
      "source": [
        "min_var_cumpod =(portfolio.account_value.pct_change()+1).cumprod()-1"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5E1X9FFGgqeZ"
      },
      "source": [
        "dji_cumpod =(baseline_returns+1).cumprod()-1"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9g4VpoqlMqR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ih6Jim-blNKY"
      },
      "source": [
        "## Plotly: DRL, Min-Variance, DJIA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJRH-FX3hTRZ"
      },
      "source": [
        "from datetime import datetime as dt\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly\n",
        "import plotly.graph_objs as go"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mpfgJcG5PInj"
      },
      "source": [
        "time_ind = pd.Series(df_daily_return.date)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CM-NJKa8g7Jp"
      },
      "source": [
        "trace0_portfolio = go.Scatter(x = time_ind, y = a2c_cumpod, mode = 'lines', name = 'A2C (Portfolio Allocation)')\n",
        "\n",
        "trace1_portfolio = go.Scatter(x = time_ind, y = dji_cumpod, mode = 'lines', name = 'DJIA')\n",
        "trace2_portfolio = go.Scatter(x = time_ind, y = min_var_cumpod, mode = 'lines', name = 'Min-Variance')\n",
        "#trace3_portfolio = go.Scatter(x = time_ind, y = ddpg_cumpod, mode = 'lines', name = 'DDPG')\n",
        "#trace4_portfolio = go.Scatter(x = time_ind, y = addpg_cumpod, mode = 'lines', name = 'Adaptive-DDPG')\n",
        "#trace5_portfolio = go.Scatter(x = time_ind, y = min_cumpod, mode = 'lines', name = 'Min-Variance')\n",
        "\n",
        "#trace4 = go.Scatter(x = time_ind, y = addpg_cumpod, mode = 'lines', name = 'Adaptive-DDPG')\n",
        "\n",
        "#trace2 = go.Scatter(x = time_ind, y = portfolio_cost_minv, mode = 'lines', name = 'Min-Variance')\n",
        "#trace3 = go.Scatter(x = time_ind, y = spx_value, mode = 'lines', name = 'SPX')"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "35nVVmEuhGa1",
        "outputId": "b94e1ded-3d28-493f-b5d0-4ea4bf7efe08"
      },
      "source": [
        "fig = go.Figure()\n",
        "fig.add_trace(trace0_portfolio)\n",
        "\n",
        "fig.add_trace(trace1_portfolio)\n",
        "\n",
        "fig.add_trace(trace2_portfolio)\n",
        "\n",
        "\n",
        "\n",
        "fig.update_layout(\n",
        "    legend=dict(\n",
        "        x=0,\n",
        "        y=1,\n",
        "        traceorder=\"normal\",\n",
        "        font=dict(\n",
        "            family=\"sans-serif\",\n",
        "            size=15,\n",
        "            color=\"black\"\n",
        "        ),\n",
        "        bgcolor=\"White\",\n",
        "        bordercolor=\"white\",\n",
        "        borderwidth=2\n",
        "        \n",
        "    ),\n",
        ")\n",
        "#fig.update_layout(legend_orientation=\"h\")\n",
        "fig.update_layout(title={\n",
        "        #'text': \"Cumulative Return using FinRL\",\n",
        "        'y':0.85,\n",
        "        'x':0.5,\n",
        "        'xanchor': 'center',\n",
        "        'yanchor': 'top'})\n",
        "#with Transaction cost\n",
        "#fig.update_layout(title =  'Quarterly Trade Date')\n",
        "fig.update_layout(\n",
        "#    margin=dict(l=20, r=20, t=20, b=20),\n",
        "\n",
        "    paper_bgcolor='rgba(1,1,0,0)',\n",
        "    plot_bgcolor='rgba(1, 1, 0, 0)',\n",
        "    #xaxis_title=\"Date\",\n",
        "    yaxis_title=\"Cumulative Return\",\n",
        "xaxis={'type': 'date', \n",
        "       'tick0': time_ind[0], \n",
        "        'tickmode': 'linear', \n",
        "       'dtick': 86400000.0 *80}\n",
        "\n",
        ")\n",
        "fig.update_xaxes(showline=True,linecolor='black',showgrid=True, gridwidth=1, gridcolor='LightSteelBlue',mirror=True)\n",
        "fig.update_yaxes(showline=True,linecolor='black',showgrid=True, gridwidth=1, gridcolor='LightSteelBlue',mirror=True)\n",
        "fig.update_yaxes(zeroline=True, zerolinewidth=1, zerolinecolor='LightSteelBlue')\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
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              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"8b4b7c14-f063-40dc-9503-3c31f9216bfc\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"8b4b7c14-f063-40dc-9503-3c31f9216bfc\")) {\n",
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              "        if (!display || display === 'none') {{\n",
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              "            Plotly.purge(gd);\n",
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              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXd-N5Rmh6H7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}