FinRL_portfolio_allocation_NeurIPS_2020.ipynb · yutiansut/FinRL-Library

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FinRL_portfolio_allocation_NeurIPS_2020.ipynb 731.06 KB

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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": "ce6724f7-f6ce-4480-d0de-703f786fd99e"
      },
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (0.4.8)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (3.4.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (3.1.1)\n",
            "Building wheels for collected packages: finrl, yfinance, pyfolio, empyrical\n",
            "  Building wheel for finrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for finrl: filename=finrl-0.3.0-cp37-none-any.whl size=39029 sha256=b5e0e12e95b4121b93cd651c6235b56c1f0036b8cd5fe4282ed341b89b704b71\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-81fatlyd/wheels/9c/19/bf/c644def96612df1ad42c94d5304966797eaa3221dffc5efe0b\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.60-py2.py3-none-any.whl size=23819 sha256=d46d05a6ce39a7748caf23509013c8d4f9dbd94a5df0367083a19f5756645a42\n",
            "  Stored in directory: /root/.cache/pip/wheels/f0/be/a4/846f02c5985562250917b0ab7b33fff737c8e6e8cd5209aa3b\n",
            "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-cp37-none-any.whl size=75776 sha256=11761329471b373c2c50b45eb52816dfdf975ce01142cdb4f4a774fab19b7a13\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-81fatlyd/wheels/43/ce/d9/6752fb6e03205408773235435205a0519d2c608a94f1976e56\n",
            "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for empyrical: filename=empyrical-0.5.5-cp37-none-any.whl size=39780 sha256=2b4515c7d9f959d16244e3b5a89dad1452b2bbc5e8b309d5e43e128f2e87a888\n",
            "  Stored in directory: /root/.cache/pip/wheels/ea/b2/c8/6769d8444d2f2e608fae2641833110668d0ffd1abeb2e9f3fc\n",
            "Successfully built finrl yfinance pyfolio empyrical\n",
            "Installing collected packages: int-date, stockstats, lxml, yfinance, stable-baselines3, empyrical, pyfolio, finrl\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed empyrical-0.5.5 finrl-0.3.0 int-date-0.1.8 lxml-4.6.3 pyfolio-0.9.2+75.g4b901f6 stable-baselines3-1.1.0 stockstats-0.3.2 yfinance-0.1.60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2568cp5bU38"
      },
      "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": "6e525f79-231d-43dd-f00c-a5a881465c58"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "matplotlib.use('Agg')\n",
        "import datetime\n",
        "\n",
        "from finrl.config import config\n",
        "from finrl.marketdata.yahoodownloader import YahooDownloader\n",
        "from finrl.preprocessing.preprocessors import FeatureEngineer\n",
        "from finrl.preprocessing.data import data_split\n",
        "from finrl.env.env_portfolio import StockPortfolioEnv\n",
        "\n",
        "from finrl.model.models import DRLAgent\n",
        "from finrl.trade.backtest import backtest_stats, backtest_plot, get_daily_return, get_baseline,convert_daily_return_to_pyfolio_ts\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")"
      ],
      "execution_count": 2,
      "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": 3,
      "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": "6b21e29d-b834-41e6-8e67-ea4e629b9509"
      },
      "source": [
        "print(config.DOW_30_TICKER)"
      ],
      "execution_count": 4,
      "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": "4e4d770f-658b-481b-d23b-05b7d4878be9"
      },
      "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": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
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            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (101887, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1xC-LpbbU4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "082205e0-c329-4ecb-dc95-2bf6a3de6220"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 6,
      "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",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\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>48.153107</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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mS1-nxRzbU4i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d950a2eb-f544-4808-b270-e65042f91415"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(101887, 8)"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 7
        }
      ]
    },
    {
      "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": "c1cd7ff7-87c7-463d-fda5-c531d160f67b"
      },
      "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": 8,
      "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": "fcebf5d6-d702-4fa8-a05d-0a151f17ac32"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(101887, 12)"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWB-RoTSbU4s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "00fd15d0-9367-4433-c4df-202f29cc4696"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>close</th>\n",
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              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
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              "      <td>50.790001</td>\n",
              "      <td>40.795723</td>\n",
              "      <td>8053700</td>\n",
              "      <td>AXP</td>\n",
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              "      <td>70.050003</td>\n",
              "      <td>48.153107</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": "e3ed980d-535e-401c-f4c0-893af0c62d07"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(94380, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "wv3jR1zPrg4g",
        "outputId": "4c69ec82-9f3e-4055-9237-e3b25c98d417"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 14,
      "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>[[0.0014139106944709861, 0.001180074395265371,...</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>[[0.0014139106944709861, 0.001180074395265371,...</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>[[0.0014139106944709861, 0.001180074395265371,...</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>31.262676</td>\n",
              "      <td>6277400</td>\n",
              "      <td>CAT</td>\n",
              "      <td>2</td>\n",
              "      <td>0.692236</td>\n",
              "      <td>51.205319</td>\n",
              "      <td>98.438755</td>\n",
              "      <td>26.331746</td>\n",
              "      <td>[[0.0014139106944709861, 0.001180074395265371,...</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>[[0.0014139106944709861, 0.001180074395265371,...</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": 309
        },
        "id": "vU2vXEll0hfk",
        "outputId": "b734abd2-aeb6-495b-d379-61012eb04b1c"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>boll_ub</th>\n",
              "      <th>boll_lb</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>close_30_sma</th>\n",
              "      <th>close_60_sma</th>\n",
              "      <th>cov_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>2.791740</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "      <td>0.916411</td>\n",
              "      <td>58.037324</td>\n",
              "      <td>50.483560</td>\n",
              "      <td>58.279413</td>\n",
              "      <td>231.362748</td>\n",
              "      <td>28.830569</td>\n",
              "      <td>53.921125</td>\n",
              "      <td>52.268017</td>\n",
              "      <td>[[0.001427708918378416, 0.0011886537539480882,...</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>15.745411</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "      <td>1.104879</td>\n",
              "      <td>58.632931</td>\n",
              "      <td>50.354922</td>\n",
              "      <td>57.951206</td>\n",
              "      <td>206.221389</td>\n",
              "      <td>31.974391</td>\n",
              "      <td>54.109576</td>\n",
              "      <td>52.400186</td>\n",
              "      <td>[[0.001427708918378416, 0.0011886537539480882,...</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>1.225587</td>\n",
              "      <td>59.109112</td>\n",
              "      <td>50.287868</td>\n",
              "      <td>57.599664</td>\n",
              "      <td>171.846491</td>\n",
              "      <td>31.974391</td>\n",
              "      <td>54.287689</td>\n",
              "      <td>52.521223</td>\n",
              "      <td>[[0.001427708918378416, 0.0011886537539480882,...</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>32.978760</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "      <td>1.288569</td>\n",
              "      <td>59.477777</td>\n",
              "      <td>50.242415</td>\n",
              "      <td>57.159546</td>\n",
              "      <td>138.088204</td>\n",
              "      <td>22.759063</td>\n",
              "      <td>54.486354</td>\n",
              "      <td>52.632345</td>\n",
              "      <td>[[0.001427708918378416, 0.0011886537539480882,...</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>12.683227</td>\n",
              "      <td>40980600</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>4</td>\n",
              "      <td>1.295162</td>\n",
              "      <td>59.737547</td>\n",
              "      <td>50.442506</td>\n",
              "      <td>56.448825</td>\n",
              "      <td>117.945877</td>\n",
              "      <td>22.759063</td>\n",
              "      <td>54.596320</td>\n",
              "      <td>52.735164</td>\n",
              "      <td>[[0.001427708918378416, 0.0011886537539480882,...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  ...                                           cov_list\n",
              "0  2009-01-02  ...  [[0.001427708918378416, 0.0011886537539480882,...\n",
              "0  2009-01-02  ...  [[0.001427708918378416, 0.0011886537539480882,...\n",
              "0  2009-01-02  ...  [[0.001427708918378416, 0.0011886537539480882,...\n",
              "0  2009-01-02  ...  [[0.001427708918378416, 0.0011886537539480882,...\n",
              "0  2009-01-02  ...  [[0.001427708918378416, 0.0011886537539480882,...\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "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": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzD06X0CbU43",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "388788cf-4265-43f5-dce1-c4d415736b26"
      },
      "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": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stock Dimension: 30, State Space: 30\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": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTlOf8SJGdkl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10830d98-6ecf-4876-e680-9cbf590d03f8"
      },
      "source": [
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "print(type(env_train))"
      ],
      "execution_count": 19,
      "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": 20,
      "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": "f4ea92ea-b11d-4992-8ab1-cd088eb4d27d"
      },
      "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": null,
      "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": "66aceecd-9806-4717-c23b-523b3196f041"
      },
      "source": [
        "trained_a2c = agent.train_model(model=model_a2c, \n",
        "                                tb_log_name='a2c',\n",
        "                                total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/a2c/a2c_2\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 352      |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 1        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 12099    |\n",
            "|    policy_loss        | 2.01e+08 |\n",
            "|    std                | 0.959    |\n",
            "|    value_loss         | 2.64e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 350      |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 2        |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 12199    |\n",
            "|    policy_loss        | 2.37e+08 |\n",
            "|    std                | 0.958    |\n",
            "|    value_loss         | 4.39e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 349      |\n",
            "|    iterations         | 300      |\n",
            "|    time_elapsed       | 4        |\n",
            "|    total_timesteps    | 1500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 12299    |\n",
            "|    policy_loss        | 3.76e+08 |\n",
            "|    std                | 0.958    |\n",
            "|    value_loss         | 1.01e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 349      |\n",
            "|    iterations         | 400      |\n",
            "|    time_elapsed       | 5        |\n",
            "|    total_timesteps    | 2000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 12399    |\n",
            "|    policy_loss        | 4.06e+08 |\n",
            "|    std                | 0.958    |\n",
            "|    value_loss         | 1.33e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 349      |\n",
            "|    iterations         | 500      |\n",
            "|    time_elapsed       | 7        |\n",
            "|    total_timesteps    | 2500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 12499    |\n",
            "|    policy_loss        | 5.86e+08 |\n",
            "|    std                | 0.957    |\n",
            "|    value_loss         | 2.7e+14  |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4685300.195654661\n",
            "Sharpe:  1.0453114515340531\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 340       |\n",
            "|    iterations         | 600       |\n",
            "|    time_elapsed       | 8         |\n",
            "|    total_timesteps    | 3000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 12599     |\n",
            "|    policy_loss        | 1.81e+08  |\n",
            "|    std                | 0.956     |\n",
            "|    value_loss         | 2.19e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 341      |\n",
            "|    iterations         | 700      |\n",
            "|    time_elapsed       | 10       |\n",
            "|    total_timesteps    | 3500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 12699    |\n",
            "|    policy_loss        | 2.12e+08 |\n",
            "|    std                | 0.956    |\n",
            "|    value_loss         | 3.4e+13  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 342      |\n",
            "|    iterations         | 800      |\n",
            "|    time_elapsed       | 11       |\n",
            "|    total_timesteps    | 4000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 12799    |\n",
            "|    policy_loss        | 3.43e+08 |\n",
            "|    std                | 0.956    |\n",
            "|    value_loss         | 8.32e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 342       |\n",
            "|    iterations         | 900       |\n",
            "|    time_elapsed       | 13        |\n",
            "|    total_timesteps    | 4500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 12899     |\n",
            "|    policy_loss        | 3.89e+08  |\n",
            "|    std                | 0.955     |\n",
            "|    value_loss         | 1.1e+14   |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 343      |\n",
            "|    iterations         | 1000     |\n",
            "|    time_elapsed       | 14       |\n",
            "|    total_timesteps    | 5000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 12999    |\n",
            "|    policy_loss        | 4.89e+08 |\n",
            "|    std                | 0.955    |\n",
            "|    value_loss         | 2.13e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4211670.620824253\n",
            "Sharpe:  0.9836152322815558\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 339      |\n",
            "|    iterations         | 1100     |\n",
            "|    time_elapsed       | 16       |\n",
            "|    total_timesteps    | 5500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 13099    |\n",
            "|    policy_loss        | 1.73e+08 |\n",
            "|    std                | 0.954    |\n",
            "|    value_loss         | 2.48e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 340      |\n",
            "|    iterations         | 1200     |\n",
            "|    time_elapsed       | 17       |\n",
            "|    total_timesteps    | 6000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 13199    |\n",
            "|    policy_loss        | 2.48e+08 |\n",
            "|    std                | 0.954    |\n",
            "|    value_loss         | 4.39e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 340       |\n",
            "|    iterations         | 1300      |\n",
            "|    time_elapsed       | 19        |\n",
            "|    total_timesteps    | 6500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 13299     |\n",
            "|    policy_loss        | 3.53e+08  |\n",
            "|    std                | 0.953     |\n",
            "|    value_loss         | 9.04e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 340       |\n",
            "|    iterations         | 1400      |\n",
            "|    time_elapsed       | 20        |\n",
            "|    total_timesteps    | 7000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 13399     |\n",
            "|    policy_loss        | 3.96e+08  |\n",
            "|    std                | 0.953     |\n",
            "|    value_loss         | 1.21e+14  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 341      |\n",
            "|    iterations         | 1500     |\n",
            "|    time_elapsed       | 21       |\n",
            "|    total_timesteps    | 7500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 13499    |\n",
            "|    policy_loss        | 5.96e+08 |\n",
            "|    std                | 0.953    |\n",
            "|    value_loss         | 2.56e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4639828.316566328\n",
            "Sharpe:  1.0428808028309948\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 338      |\n",
            "|    iterations         | 1600     |\n",
            "|    time_elapsed       | 23       |\n",
            "|    total_timesteps    | 8000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 13599    |\n",
            "|    policy_loss        | 1.93e+08 |\n",
            "|    std                | 0.952    |\n",
            "|    value_loss         | 2.53e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 339      |\n",
            "|    iterations         | 1700     |\n",
            "|    time_elapsed       | 25       |\n",
            "|    total_timesteps    | 8500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 13699    |\n",
            "|    policy_loss        | 2.44e+08 |\n",
            "|    std                | 0.952    |\n",
            "|    value_loss         | 4.34e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 339      |\n",
            "|    iterations         | 1800     |\n",
            "|    time_elapsed       | 26       |\n",
            "|    total_timesteps    | 9000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.1    |\n",
            "|    explained_variance | 1.79e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 13799    |\n",
            "|    policy_loss        | 3.55e+08 |\n",
            "|    std                | 0.952    |\n",
            "|    value_loss         | 9.29e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 340       |\n",
            "|    iterations         | 1900      |\n",
            "|    time_elapsed       | 27        |\n",
            "|    total_timesteps    | 9500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 13899     |\n",
            "|    policy_loss        | 4.14e+08  |\n",
            "|    std                | 0.952     |\n",
            "|    value_loss         | 1.31e+14  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 340      |\n",
            "|    iterations         | 2000     |\n",
            "|    time_elapsed       | 29       |\n",
            "|    total_timesteps    | 10000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 13999    |\n",
            "|    policy_loss        | 6.22e+08 |\n",
            "|    std                | 0.951    |\n",
            "|    value_loss         | 2.87e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4775229.061094982\n",
            "Sharpe:  1.0650992139820405\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 338      |\n",
            "|    iterations         | 2100     |\n",
            "|    time_elapsed       | 31       |\n",
            "|    total_timesteps    | 10500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 14099    |\n",
            "|    policy_loss        | 1.82e+08 |\n",
            "|    std                | 0.951    |\n",
            "|    value_loss         | 2.24e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 338       |\n",
            "|    iterations         | 2200      |\n",
            "|    time_elapsed       | 32        |\n",
            "|    total_timesteps    | 11000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 14199     |\n",
            "|    policy_loss        | 2.38e+08  |\n",
            "|    std                | 0.95      |\n",
            "|    value_loss         | 4.4e+13   |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 338      |\n",
            "|    iterations         | 2300     |\n",
            "|    time_elapsed       | 33       |\n",
            "|    total_timesteps    | 11500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 2.38e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 14299    |\n",
            "|    policy_loss        | 3.63e+08 |\n",
            "|    std                | 0.949    |\n",
            "|    value_loss         | 9.98e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 339       |\n",
            "|    iterations         | 2400      |\n",
            "|    time_elapsed       | 35        |\n",
            "|    total_timesteps    | 12000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 14399     |\n",
            "|    policy_loss        | 4.3e+08   |\n",
            "|    std                | 0.948     |\n",
            "|    value_loss         | 1.35e+14  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 339      |\n",
            "|    iterations         | 2500     |\n",
            "|    time_elapsed       | 36       |\n",
            "|    total_timesteps    | 12500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 14499    |\n",
            "|    policy_loss        | 6.25e+08 |\n",
            "|    std                | 0.948    |\n",
            "|    value_loss         | 2.75e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4678739.328824231\n",
            "Sharpe:  1.0465241688702438\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 338      |\n",
            "|    iterations         | 2600     |\n",
            "|    time_elapsed       | 38       |\n",
            "|    total_timesteps    | 13000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 14599    |\n",
            "|    policy_loss        | 1.82e+08 |\n",
            "|    std                | 0.948    |\n",
            "|    value_loss         | 1.89e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 338      |\n",
            "|    iterations         | 2700     |\n",
            "|    time_elapsed       | 39       |\n",
            "|    total_timesteps    | 13500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 14699    |\n",
            "|    policy_loss        | 2.21e+08 |\n",
            "|    std                | 0.948    |\n",
            "|    value_loss         | 3.95e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 338      |\n",
            "|    iterations         | 2800     |\n",
            "|    time_elapsed       | 41       |\n",
            "|    total_timesteps    | 14000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 14799    |\n",
            "|    policy_loss        | 3.3e+08  |\n",
            "|    std                | 0.948    |\n",
            "|    value_loss         | 8.54e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 338      |\n",
            "|    iterations         | 2900     |\n",
            "|    time_elapsed       | 42       |\n",
            "|    total_timesteps    | 14500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 14899    |\n",
            "|    policy_loss        | 4.24e+08 |\n",
            "|    std                | 0.947    |\n",
            "|    value_loss         | 1.26e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 339       |\n",
            "|    iterations         | 3000      |\n",
            "|    time_elapsed       | 44        |\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          | 14999     |\n",
            "|    policy_loss        | 5.96e+08  |\n",
            "|    std                | 0.947     |\n",
            "|    value_loss         | 2.6e+14   |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4677079.483218055\n",
            "Sharpe:  1.043334299291766\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 336       |\n",
            "|    iterations         | 3100      |\n",
            "|    time_elapsed       | 46        |\n",
            "|    total_timesteps    | 15500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.9     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 15099     |\n",
            "|    policy_loss        | 1.66e+08  |\n",
            "|    std                | 0.947     |\n",
            "|    value_loss         | 2e+13     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 3200     |\n",
            "|    time_elapsed       | 47       |\n",
            "|    total_timesteps    | 16000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.9    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 15199    |\n",
            "|    policy_loss        | 2.31e+08 |\n",
            "|    std                | 0.947    |\n",
            "|    value_loss         | 3.68e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 337      |\n",
            "|    iterations         | 3300     |\n",
            "|    time_elapsed       | 48       |\n",
            "|    total_timesteps    | 16500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 15299    |\n",
            "|    policy_loss        | 3.32e+08 |\n",
            "|    std                | 0.946    |\n",
            "|    value_loss         | 8.59e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 337       |\n",
            "|    iterations         | 3400      |\n",
            "|    time_elapsed       | 50        |\n",
            "|    total_timesteps    | 17000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.9     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 15399     |\n",
            "|    policy_loss        | 3.93e+08  |\n",
            "|    std                | 0.945     |\n",
            "|    value_loss         | 1.15e+14  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 337       |\n",
            "|    iterations         | 3500      |\n",
            "|    time_elapsed       | 51        |\n",
            "|    total_timesteps    | 17500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.8     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 15499     |\n",
            "|    policy_loss        | 5.3e+08   |\n",
            "|    std                | 0.944     |\n",
            "|    value_loss         | 2.09e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4359923.802114374\n",
            "Sharpe:  1.0008163852772658\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 3600     |\n",
            "|    time_elapsed       | 53       |\n",
            "|    total_timesteps    | 18000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 1.79e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 15599    |\n",
            "|    policy_loss        | 1.7e+08  |\n",
            "|    std                | 0.944    |\n",
            "|    value_loss         | 1.92e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 3700     |\n",
            "|    time_elapsed       | 54       |\n",
            "|    total_timesteps    | 18500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 15699    |\n",
            "|    policy_loss        | 2.33e+08 |\n",
            "|    std                | 0.943    |\n",
            "|    value_loss         | 3.59e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 3800     |\n",
            "|    time_elapsed       | 56       |\n",
            "|    total_timesteps    | 19000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 15799    |\n",
            "|    policy_loss        | 3.35e+08 |\n",
            "|    std                | 0.944    |\n",
            "|    value_loss         | 8.35e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 337      |\n",
            "|    iterations         | 3900     |\n",
            "|    time_elapsed       | 57       |\n",
            "|    total_timesteps    | 19500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 15899    |\n",
            "|    policy_loss        | 3.89e+08 |\n",
            "|    std                | 0.944    |\n",
            "|    value_loss         | 1.06e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 337       |\n",
            "|    iterations         | 4000      |\n",
            "|    time_elapsed       | 59        |\n",
            "|    total_timesteps    | 20000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.8     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 15999     |\n",
            "|    policy_loss        | 5.99e+08  |\n",
            "|    std                | 0.944     |\n",
            "|    value_loss         | 2.18e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4518146.7620793665\n",
            "Sharpe:  1.017512586785335\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 4100     |\n",
            "|    time_elapsed       | 60       |\n",
            "|    total_timesteps    | 20500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 16099    |\n",
            "|    policy_loss        | 1.81e+08 |\n",
            "|    std                | 0.943    |\n",
            "|    value_loss         | 2.24e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 336       |\n",
            "|    iterations         | 4200      |\n",
            "|    time_elapsed       | 62        |\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          | 16199     |\n",
            "|    policy_loss        | 2.17e+08  |\n",
            "|    std                | 0.943     |\n",
            "|    value_loss         | 3.98e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 4300     |\n",
            "|    time_elapsed       | 63       |\n",
            "|    total_timesteps    | 21500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 16299    |\n",
            "|    policy_loss        | 3.55e+08 |\n",
            "|    std                | 0.942    |\n",
            "|    value_loss         | 9.99e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 4400     |\n",
            "|    time_elapsed       | 65       |\n",
            "|    total_timesteps    | 22000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 16399    |\n",
            "|    policy_loss        | 4.37e+08 |\n",
            "|    std                | 0.942    |\n",
            "|    value_loss         | 1.35e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 337      |\n",
            "|    iterations         | 4500     |\n",
            "|    time_elapsed       | 66       |\n",
            "|    total_timesteps    | 22500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 16499    |\n",
            "|    policy_loss        | 5.77e+08 |\n",
            "|    std                | 0.941    |\n",
            "|    value_loss         | 2.56e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4947928.885546611\n",
            "Sharpe:  1.0770541591532077\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 4600     |\n",
            "|    time_elapsed       | 68       |\n",
            "|    total_timesteps    | 23000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 16599    |\n",
            "|    policy_loss        | 1.56e+08 |\n",
            "|    std                | 0.94     |\n",
            "|    value_loss         | 1.75e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 4700     |\n",
            "|    time_elapsed       | 69       |\n",
            "|    total_timesteps    | 23500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.7    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 16699    |\n",
            "|    policy_loss        | 2.11e+08 |\n",
            "|    std                | 0.94     |\n",
            "|    value_loss         | 3.38e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 4800     |\n",
            "|    time_elapsed       | 71       |\n",
            "|    total_timesteps    | 24000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 16799    |\n",
            "|    policy_loss        | 3.25e+08 |\n",
            "|    std                | 0.94     |\n",
            "|    value_loss         | 8.02e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 4900     |\n",
            "|    time_elapsed       | 72       |\n",
            "|    total_timesteps    | 24500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 16899    |\n",
            "|    policy_loss        | 4.07e+08 |\n",
            "|    std                | 0.94     |\n",
            "|    value_loss         | 1.14e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 5000     |\n",
            "|    time_elapsed       | 74       |\n",
            "|    total_timesteps    | 25000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 16999    |\n",
            "|    policy_loss        | 5.45e+08 |\n",
            "|    std                | 0.939    |\n",
            "|    value_loss         | 2.2e+14  |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4708435.905981962\n",
            "Sharpe:  1.0421275396424545\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 5100     |\n",
            "|    time_elapsed       | 75       |\n",
            "|    total_timesteps    | 25500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 17099    |\n",
            "|    policy_loss        | 1.74e+08 |\n",
            "|    std                | 0.939    |\n",
            "|    value_loss         | 2.32e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 5200     |\n",
            "|    time_elapsed       | 77       |\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          | 17199    |\n",
            "|    policy_loss        | 2.26e+08 |\n",
            "|    std                | 0.938    |\n",
            "|    value_loss         | 3.94e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 5300     |\n",
            "|    time_elapsed       | 78       |\n",
            "|    total_timesteps    | 26500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 17299    |\n",
            "|    policy_loss        | 3.16e+08 |\n",
            "|    std                | 0.938    |\n",
            "|    value_loss         | 7.8e+13  |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 336       |\n",
            "|    iterations         | 5400      |\n",
            "|    time_elapsed       | 80        |\n",
            "|    total_timesteps    | 27000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.6     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 17399     |\n",
            "|    policy_loss        | 3.95e+08  |\n",
            "|    std                | 0.938     |\n",
            "|    value_loss         | 1.14e+14  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 5500     |\n",
            "|    time_elapsed       | 81       |\n",
            "|    total_timesteps    | 27500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 17499    |\n",
            "|    policy_loss        | 6.04e+08 |\n",
            "|    std                | 0.937    |\n",
            "|    value_loss         | 2.22e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4591802.064526513\n",
            "Sharpe:  1.0188228298492967\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 336       |\n",
            "|    iterations         | 5600      |\n",
            "|    time_elapsed       | 83        |\n",
            "|    total_timesteps    | 28000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.6     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 17599     |\n",
            "|    policy_loss        | 1.73e+08  |\n",
            "|    std                | 0.937     |\n",
            "|    value_loss         | 2.22e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 5700     |\n",
            "|    time_elapsed       | 84       |\n",
            "|    total_timesteps    | 28500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 17699    |\n",
            "|    policy_loss        | 2.13e+08 |\n",
            "|    std                | 0.937    |\n",
            "|    value_loss         | 3.68e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 5800     |\n",
            "|    time_elapsed       | 86       |\n",
            "|    total_timesteps    | 29000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 17799    |\n",
            "|    policy_loss        | 3.15e+08 |\n",
            "|    std                | 0.937    |\n",
            "|    value_loss         | 7.34e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 5900     |\n",
            "|    time_elapsed       | 87       |\n",
            "|    total_timesteps    | 29500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 17899    |\n",
            "|    policy_loss        | 3.56e+08 |\n",
            "|    std                | 0.936    |\n",
            "|    value_loss         | 1.01e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 6000     |\n",
            "|    time_elapsed       | 89       |\n",
            "|    total_timesteps    | 30000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 17999    |\n",
            "|    policy_loss        | 5.88e+08 |\n",
            "|    std                | 0.935    |\n",
            "|    value_loss         | 2.08e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4389104.288134387\n",
            "Sharpe:  0.9933788463870157\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 335      |\n",
            "|    iterations         | 6100     |\n",
            "|    time_elapsed       | 90       |\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          | 18099    |\n",
            "|    policy_loss        | 1.72e+08 |\n",
            "|    std                | 0.935    |\n",
            "|    value_loss         | 2.2e+13  |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 336       |\n",
            "|    iterations         | 6200      |\n",
            "|    time_elapsed       | 92        |\n",
            "|    total_timesteps    | 31000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.5     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 18199     |\n",
            "|    policy_loss        | 2.32e+08  |\n",
            "|    std                | 0.934     |\n",
            "|    value_loss         | 3.84e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 336       |\n",
            "|    iterations         | 6300      |\n",
            "|    time_elapsed       | 93        |\n",
            "|    total_timesteps    | 31500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.5     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 18299     |\n",
            "|    policy_loss        | 3.14e+08  |\n",
            "|    std                | 0.935     |\n",
            "|    value_loss         | 7.79e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 6400     |\n",
            "|    time_elapsed       | 95       |\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          | 18399    |\n",
            "|    policy_loss        | 3.81e+08 |\n",
            "|    std                | 0.934    |\n",
            "|    value_loss         | 9.57e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 336      |\n",
            "|    iterations         | 6500     |\n",
            "|    time_elapsed       | 96       |\n",
            "|    total_timesteps    | 32500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 18499    |\n",
            "|    policy_loss        | 5.48e+08 |\n",
            "|    std                | 0.933    |\n",
            "|    value_loss         | 2.3e+14  |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4580263.352082179\n",
            "Sharpe:  1.0226861102653615\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 335      |\n",
            "|    iterations         | 6600     |\n",
            "|    time_elapsed       | 98       |\n",
            "|    total_timesteps    | 33000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 18599    |\n",
            "|    policy_loss        | 1.57e+08 |\n",
            "|    std                | 0.933    |\n",
            "|    value_loss         | 1.92e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 335      |\n",
            "|    iterations         | 6700     |\n",
            "|    time_elapsed       | 99       |\n",
            "|    total_timesteps    | 33500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.5    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 18699    |\n",
            "|    policy_loss        | 2.32e+08 |\n",
            "|    std                | 0.933    |\n",
            "|    value_loss         | 3.7e+13  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 335      |\n",
            "|    iterations         | 6800     |\n",
            "|    time_elapsed       | 101      |\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          | 18799    |\n",
            "|    policy_loss        | 3.06e+08 |\n",
            "|    std                | 0.933    |\n",
            "|    value_loss         | 7.37e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 334      |\n",
            "|    iterations         | 6900     |\n",
            "|    time_elapsed       | 103      |\n",
            "|    total_timesteps    | 34500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 18899    |\n",
            "|    policy_loss        | 3.57e+08 |\n",
            "|    std                | 0.932    |\n",
            "|    value_loss         | 9.15e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 334      |\n",
            "|    iterations         | 7000     |\n",
            "|    time_elapsed       | 104      |\n",
            "|    total_timesteps    | 35000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 18999    |\n",
            "|    policy_loss        | 5.49e+08 |\n",
            "|    std                | 0.931    |\n",
            "|    value_loss         | 2.57e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4583070.081894048\n",
            "Sharpe:  1.0296700608185065\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 333      |\n",
            "|    iterations         | 7100     |\n",
            "|    time_elapsed       | 106      |\n",
            "|    total_timesteps    | 35500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 19099    |\n",
            "|    policy_loss        | 1.68e+08 |\n",
            "|    std                | 0.931    |\n",
            "|    value_loss         | 1.88e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 333       |\n",
            "|    iterations         | 7200      |\n",
            "|    time_elapsed       | 107       |\n",
            "|    total_timesteps    | 36000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 19199     |\n",
            "|    policy_loss        | 2.09e+08  |\n",
            "|    std                | 0.931     |\n",
            "|    value_loss         | 3.39e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 333       |\n",
            "|    iterations         | 7300      |\n",
            "|    time_elapsed       | 109       |\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          | 19299     |\n",
            "|    policy_loss        | 3.17e+08  |\n",
            "|    std                | 0.931     |\n",
            "|    value_loss         | 7.95e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 333      |\n",
            "|    iterations         | 7400     |\n",
            "|    time_elapsed       | 111      |\n",
            "|    total_timesteps    | 37000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 19399    |\n",
            "|    policy_loss        | 3.68e+08 |\n",
            "|    std                | 0.931    |\n",
            "|    value_loss         | 9.27e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 332      |\n",
            "|    iterations         | 7500     |\n",
            "|    time_elapsed       | 112      |\n",
            "|    total_timesteps    | 37500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 19499    |\n",
            "|    policy_loss        | 6.09e+08 |\n",
            "|    std                | 0.931    |\n",
            "|    value_loss         | 2.31e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4576426.405502999\n",
            "Sharpe:  1.0235768164756291\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 332      |\n",
            "|    iterations         | 7600     |\n",
            "|    time_elapsed       | 114      |\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          | 19599    |\n",
            "|    policy_loss        | 1.59e+08 |\n",
            "|    std                | 0.931    |\n",
            "|    value_loss         | 2.02e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 331      |\n",
            "|    iterations         | 7700     |\n",
            "|    time_elapsed       | 115      |\n",
            "|    total_timesteps    | 38500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 19699    |\n",
            "|    policy_loss        | 2.21e+08 |\n",
            "|    std                | 0.93     |\n",
            "|    value_loss         | 3.36e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 331      |\n",
            "|    iterations         | 7800     |\n",
            "|    time_elapsed       | 117      |\n",
            "|    total_timesteps    | 39000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 19799    |\n",
            "|    policy_loss        | 3.26e+08 |\n",
            "|    std                | 0.93     |\n",
            "|    value_loss         | 8.54e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 331      |\n",
            "|    iterations         | 7900     |\n",
            "|    time_elapsed       | 119      |\n",
            "|    total_timesteps    | 39500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 19899    |\n",
            "|    policy_loss        | 3.73e+08 |\n",
            "|    std                | 0.93     |\n",
            "|    value_loss         | 1.15e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 331       |\n",
            "|    iterations         | 8000      |\n",
            "|    time_elapsed       | 120       |\n",
            "|    total_timesteps    | 40000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.3     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 19999     |\n",
            "|    policy_loss        | 5.89e+08  |\n",
            "|    std                | 0.929     |\n",
            "|    value_loss         | 2.49e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4940621.780834714\n",
            "Sharpe:  1.0767272532158483\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 330      |\n",
            "|    iterations         | 8100     |\n",
            "|    time_elapsed       | 122      |\n",
            "|    total_timesteps    | 40500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 20099    |\n",
            "|    policy_loss        | 1.5e+08  |\n",
            "|    std                | 0.928    |\n",
            "|    value_loss         | 1.82e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 330       |\n",
            "|    iterations         | 8200      |\n",
            "|    time_elapsed       | 123       |\n",
            "|    total_timesteps    | 41000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.3     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 20199     |\n",
            "|    policy_loss        | 1.78e+08  |\n",
            "|    std                | 0.928     |\n",
            "|    value_loss         | 2.61e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 330      |\n",
            "|    iterations         | 8300     |\n",
            "|    time_elapsed       | 125      |\n",
            "|    total_timesteps    | 41500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.3    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 20299    |\n",
            "|    policy_loss        | 3.09e+08 |\n",
            "|    std                | 0.927    |\n",
            "|    value_loss         | 6.16e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 330       |\n",
            "|    iterations         | 8400      |\n",
            "|    time_elapsed       | 127       |\n",
            "|    total_timesteps    | 42000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.3     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 20399     |\n",
            "|    policy_loss        | 3.35e+08  |\n",
            "|    std                | 0.927     |\n",
            "|    value_loss         | 9.63e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 330      |\n",
            "|    iterations         | 8500     |\n",
            "|    time_elapsed       | 128      |\n",
            "|    total_timesteps    | 42500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 20499    |\n",
            "|    policy_loss        | 5.1e+08  |\n",
            "|    std                | 0.927    |\n",
            "|    value_loss         | 1.7e+14  |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4118580.1744499537\n",
            "Sharpe:  0.9620511561976229\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 329       |\n",
            "|    iterations         | 8600      |\n",
            "|    time_elapsed       | 130       |\n",
            "|    total_timesteps    | 43000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.3     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 20599     |\n",
            "|    policy_loss        | 1.52e+08  |\n",
            "|    std                | 0.927     |\n",
            "|    value_loss         | 1.83e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 329      |\n",
            "|    iterations         | 8700     |\n",
            "|    time_elapsed       | 131      |\n",
            "|    total_timesteps    | 43500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.3    |\n",
            "|    explained_variance | 2.38e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 20699    |\n",
            "|    policy_loss        | 2.01e+08 |\n",
            "|    std                | 0.927    |\n",
            "|    value_loss         | 2.66e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 329      |\n",
            "|    iterations         | 8800     |\n",
            "|    time_elapsed       | 133      |\n",
            "|    total_timesteps    | 44000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 20799    |\n",
            "|    policy_loss        | 2.8e+08  |\n",
            "|    std                | 0.927    |\n",
            "|    value_loss         | 6.24e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 329       |\n",
            "|    iterations         | 8900      |\n",
            "|    time_elapsed       | 135       |\n",
            "|    total_timesteps    | 44500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.3     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 20899     |\n",
            "|    policy_loss        | 3.31e+08  |\n",
            "|    std                | 0.926     |\n",
            "|    value_loss         | 9.61e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 329      |\n",
            "|    iterations         | 9000     |\n",
            "|    time_elapsed       | 136      |\n",
            "|    total_timesteps    | 45000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 20999    |\n",
            "|    policy_loss        | 4.87e+08 |\n",
            "|    std                | 0.926    |\n",
            "|    value_loss         | 1.68e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4246562.525827188\n",
            "Sharpe:  0.9779057432228896\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 329      |\n",
            "|    iterations         | 9100     |\n",
            "|    time_elapsed       | 138      |\n",
            "|    total_timesteps    | 45500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 21099    |\n",
            "|    policy_loss        | 1.54e+08 |\n",
            "|    std                | 0.926    |\n",
            "|    value_loss         | 1.7e+13  |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 329       |\n",
            "|    iterations         | 9200      |\n",
            "|    time_elapsed       | 139       |\n",
            "|    total_timesteps    | 46000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 21199     |\n",
            "|    policy_loss        | 1.94e+08  |\n",
            "|    std                | 0.925     |\n",
            "|    value_loss         | 2.63e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 329       |\n",
            "|    iterations         | 9300      |\n",
            "|    time_elapsed       | 141       |\n",
            "|    total_timesteps    | 46500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 21299     |\n",
            "|    policy_loss        | 2.97e+08  |\n",
            "|    std                | 0.925     |\n",
            "|    value_loss         | 6.54e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 329      |\n",
            "|    iterations         | 9400     |\n",
            "|    time_elapsed       | 142      |\n",
            "|    total_timesteps    | 47000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.2    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 21399    |\n",
            "|    policy_loss        | 3.59e+08 |\n",
            "|    std                | 0.925    |\n",
            "|    value_loss         | 9.92e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 329       |\n",
            "|    iterations         | 9500      |\n",
            "|    time_elapsed       | 144       |\n",
            "|    total_timesteps    | 47500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 21499     |\n",
            "|    policy_loss        | 4.6e+08   |\n",
            "|    std                | 0.924     |\n",
            "|    value_loss         | 1.94e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4580219.125422531\n",
            "Sharpe:  1.0290957953577193\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 328      |\n",
            "|    iterations         | 9600     |\n",
            "|    time_elapsed       | 146      |\n",
            "|    total_timesteps    | 48000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 21599    |\n",
            "|    policy_loss        | 1.49e+08 |\n",
            "|    std                | 0.923    |\n",
            "|    value_loss         | 1.61e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 328      |\n",
            "|    iterations         | 9700     |\n",
            "|    time_elapsed       | 147      |\n",
            "|    total_timesteps    | 48500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.1    |\n",
            "|    explained_variance | 2.38e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 21699    |\n",
            "|    policy_loss        | 1.83e+08 |\n",
            "|    std                | 0.923    |\n",
            "|    value_loss         | 2.48e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 328      |\n",
            "|    iterations         | 9800     |\n",
            "|    time_elapsed       | 149      |\n",
            "|    total_timesteps    | 49000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.1    |\n",
            "|    explained_variance | 1.79e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 21799    |\n",
            "|    policy_loss        | 3.18e+08 |\n",
            "|    std                | 0.922    |\n",
            "|    value_loss         | 6.2e+13  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 328      |\n",
            "|    iterations         | 9900     |\n",
            "|    time_elapsed       | 150      |\n",
            "|    total_timesteps    | 49500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 21899    |\n",
            "|    policy_loss        | 3.49e+08 |\n",
            "|    std                | 0.922    |\n",
            "|    value_loss         | 8.39e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 328      |\n",
            "|    iterations         | 10000    |\n",
            "|    time_elapsed       | 152      |\n",
            "|    total_timesteps    | 50000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.1    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 21999    |\n",
            "|    policy_loss        | 4.71e+08 |\n",
            "|    std                | 0.921    |\n",
            "|    value_loss         | 1.69e+14 |\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": "4cb82ab6-914d-4d2b-a4ee-e47c8557dad3"
      },
      "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": "3989711a-cbed-4649-e71c-566d52917478"
      },
      "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_3\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 458  |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 4    |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4917364.6278486075\n",
            "Sharpe:  1.074414829116363\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 391            |\n",
            "|    iterations           | 2              |\n",
            "|    time_elapsed         | 10             |\n",
            "|    total_timesteps      | 4096           |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -7.8231096e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -3.71e+14      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 7.78e+14       |\n",
            "|    n_updates            | 10             |\n",
            "|    policy_gradient_loss | -6.16e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 1.57e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4996331.100586685\n",
            "Sharpe:  1.0890927964884638\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 373            |\n",
            "|    iterations           | 3              |\n",
            "|    time_elapsed         | 16             |\n",
            "|    total_timesteps      | 6144           |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -3.5390258e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -8.76e+14      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.1e+15        |\n",
            "|    n_updates            | 20             |\n",
            "|    policy_gradient_loss | -4.29e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.33e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4751039.2878817525\n",
            "Sharpe:  1.0560179406423764\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 365            |\n",
            "|    iterations           | 4              |\n",
            "|    time_elapsed         | 22             |\n",
            "|    total_timesteps      | 8192           |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.6763806e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -8.01e+15      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.25e+15       |\n",
            "|    n_updates            | 30             |\n",
            "|    policy_gradient_loss | -5.58e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.59e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4769059.347696523\n",
            "Sharpe:  1.056814654380227\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 360            |\n",
            "|    iterations           | 5              |\n",
            "|    time_elapsed         | 28             |\n",
            "|    total_timesteps      | 10240          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -5.5879354e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -2.55e+16      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.24e+15       |\n",
            "|    n_updates            | 40             |\n",
            "|    policy_gradient_loss | -4.9e-07       |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.7e+15        |\n",
            "--------------------------------------------\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 358            |\n",
            "|    iterations           | 6              |\n",
            "|    time_elapsed         | 34             |\n",
            "|    total_timesteps      | 12288          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | 1.13621354e-07 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -9.17e+16      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.35e+15       |\n",
            "|    n_updates            | 50             |\n",
            "|    policy_gradient_loss | -4.28e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.77e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4816491.86007194\n",
            "Sharpe:  1.0636199939613733\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 356           |\n",
            "|    iterations           | 7             |\n",
            "|    time_elapsed         | 40            |\n",
            "|    total_timesteps      | 14336         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.5390258e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.42e+17     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.03e+15      |\n",
            "|    n_updates            | 60            |\n",
            "|    policy_gradient_loss | -6.52e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.94e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4631919.83090099\n",
            "Sharpe:  1.0396504731290799\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 354           |\n",
            "|    iterations           | 8             |\n",
            "|    time_elapsed         | 46            |\n",
            "|    total_timesteps      | 16384         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.7508864e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -6.93e+17     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 9.83e+14      |\n",
            "|    n_updates            | 70            |\n",
            "|    policy_gradient_loss | -5.78e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.06e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4728763.286321457\n",
            "Sharpe:  1.052390302374202\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 353           |\n",
            "|    iterations           | 9             |\n",
            "|    time_elapsed         | 52            |\n",
            "|    total_timesteps      | 18432         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.4703484e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.72e+18     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.25e+15      |\n",
            "|    n_updates            | 80            |\n",
            "|    policy_gradient_loss | -4.84e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.33e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4439983.024798136\n",
            "Sharpe:  1.013829383303325\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 352            |\n",
            "|    iterations           | 10             |\n",
            "|    time_elapsed         | 58             |\n",
            "|    total_timesteps      | 20480          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.3038516e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.7e+18       |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.17e+15       |\n",
            "|    n_updates            | 90             |\n",
            "|    policy_gradient_loss | -4.82e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.58e+15       |\n",
            "--------------------------------------------\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 352           |\n",
            "|    iterations           | 11            |\n",
            "|    time_elapsed         | 63            |\n",
            "|    total_timesteps      | 22528         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | -9.313226e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.85e+18     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.2e+15       |\n",
            "|    n_updates            | 100           |\n",
            "|    policy_gradient_loss | -5.2e-07      |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.51e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5048884.524536961\n",
            "Sharpe:  1.0963911876706685\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 351           |\n",
            "|    iterations           | 12            |\n",
            "|    time_elapsed         | 69            |\n",
            "|    total_timesteps      | 24576         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.7252903e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -2.67e+18     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.44e+15      |\n",
            "|    n_updates            | 110           |\n",
            "|    policy_gradient_loss | -4.53e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.8e+15       |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4824229.456193555\n",
            "Sharpe:  1.0648549464252506\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 351           |\n",
            "|    iterations           | 13            |\n",
            "|    time_elapsed         | 75            |\n",
            "|    total_timesteps      | 26624         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.3527613e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -3.38e+18     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 7.89e+14      |\n",
            "|    n_updates            | 120           |\n",
            "|    policy_gradient_loss | -6.06e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.76e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4602974.615591427\n",
            "Sharpe:  1.034753433280377\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 350           |\n",
            "|    iterations           | 14            |\n",
            "|    time_elapsed         | 81            |\n",
            "|    total_timesteps      | 28672         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 8.8475645e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.75e+19     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.23e+15      |\n",
            "|    n_updates            | 130           |\n",
            "|    policy_gradient_loss | -5.8e-07      |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.27e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4608422.583401322\n",
            "Sharpe:  1.035300880612428\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 349           |\n",
            "|    iterations           | 15            |\n",
            "|    time_elapsed         | 87            |\n",
            "|    total_timesteps      | 30720         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.3038516e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -7.71e+18     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.22e+15      |\n",
            "|    n_updates            | 140           |\n",
            "|    policy_gradient_loss | -5.63e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.39e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4826869.636472441\n",
            "Sharpe:  1.0676330284861433\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 348            |\n",
            "|    iterations           | 16             |\n",
            "|    time_elapsed         | 94             |\n",
            "|    total_timesteps      | 32768          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.4901161e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.51e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.22e+15       |\n",
            "|    n_updates            | 150            |\n",
            "|    policy_gradient_loss | -5.78e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.7e+15        |\n",
            "--------------------------------------------\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 346           |\n",
            "|    iterations           | 17            |\n",
            "|    time_elapsed         | 100           |\n",
            "|    total_timesteps      | 34816         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | -5.401671e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.48e+19     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.48e+15      |\n",
            "|    n_updates            | 160           |\n",
            "|    policy_gradient_loss | -3.96e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.81e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4364006.929301854\n",
            "Sharpe:  1.002176631256902\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 345            |\n",
            "|    iterations           | 18             |\n",
            "|    time_elapsed         | 106            |\n",
            "|    total_timesteps      | 36864          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.0803342e-07 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.15e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 8.41e+14       |\n",
            "|    n_updates            | 170            |\n",
            "|    policy_gradient_loss | -4.91e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 1.58e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4796634.5596691\n",
            "Sharpe:  1.0678319491053092\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 344            |\n",
            "|    iterations           | 19             |\n",
            "|    time_elapsed         | 112            |\n",
            "|    total_timesteps      | 38912          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.3038516e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -4.21e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.03e+15       |\n",
            "|    n_updates            | 180            |\n",
            "|    policy_gradient_loss | -5.6e-07       |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.02e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4969786.413399254\n",
            "Sharpe:  1.0823021486710163\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 344            |\n",
            "|    iterations           | 20             |\n",
            "|    time_elapsed         | 118            |\n",
            "|    total_timesteps      | 40960          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -6.7055225e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -6.41e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.22e+15       |\n",
            "|    n_updates            | 190            |\n",
            "|    policy_gradient_loss | -2.87e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.4e+15        |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4885480.801922398\n",
            "Sharpe:  1.0729451877791811\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 343            |\n",
            "|    iterations           | 21             |\n",
            "|    time_elapsed         | 125            |\n",
            "|    total_timesteps      | 43008          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -5.5879354e-09 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -6.85e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.62e+15       |\n",
            "|    n_updates            | 200            |\n",
            "|    policy_gradient_loss | -5.24e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.95e+15       |\n",
            "--------------------------------------------\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 343           |\n",
            "|    iterations           | 22            |\n",
            "|    time_elapsed         | 131           |\n",
            "|    total_timesteps      | 45056         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.8067658e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -7.01e+19     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.34e+15      |\n",
            "|    n_updates            | 210           |\n",
            "|    policy_gradient_loss | -4.62e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.93e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5613709.009268909\n",
            "Sharpe:  1.1673870008513114\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 342            |\n",
            "|    iterations           | 23             |\n",
            "|    time_elapsed         | 137            |\n",
            "|    total_timesteps      | 47104          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -2.0489097e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -6.72e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.41e+15       |\n",
            "|    n_updates            | 220            |\n",
            "|    policy_gradient_loss | -4.78e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.71e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5043800.590470289\n",
            "Sharpe:  1.0953673306850924\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 342           |\n",
            "|    iterations           | 24            |\n",
            "|    time_elapsed         | 143           |\n",
            "|    total_timesteps      | 49152         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.4214387e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.37e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.01e+15      |\n",
            "|    n_updates            | 230           |\n",
            "|    policy_gradient_loss | -5.28e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.26e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4776576.852863929\n",
            "Sharpe:  1.0593811754233755\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 342           |\n",
            "|    iterations           | 25            |\n",
            "|    time_elapsed         | 149           |\n",
            "|    total_timesteps      | 51200         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.4703484e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -3.27e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.21e+15      |\n",
            "|    n_updates            | 240           |\n",
            "|    policy_gradient_loss | -4.82e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.46e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4468393.200157898\n",
            "Sharpe:  1.0192746589767419\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 341           |\n",
            "|    iterations           | 26            |\n",
            "|    time_elapsed         | 156           |\n",
            "|    total_timesteps      | 53248         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.6077032e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.96e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.31e+15      |\n",
            "|    n_updates            | 250           |\n",
            "|    policy_gradient_loss | -5.36e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.59e+15      |\n",
            "-------------------------------------------\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 341            |\n",
            "|    iterations           | 27             |\n",
            "|    time_elapsed         | 162            |\n",
            "|    total_timesteps      | 55296          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.3038516e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.68e+20      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.33e+15       |\n",
            "|    n_updates            | 260            |\n",
            "|    policy_gradient_loss | -3.77e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.51e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4875234.39450474\n",
            "Sharpe:  1.0721137742534572\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 340            |\n",
            "|    iterations           | 28             |\n",
            "|    time_elapsed         | 168            |\n",
            "|    total_timesteps      | 57344          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.2479722e-07 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.66e+20      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.59e+15       |\n",
            "|    n_updates            | 270            |\n",
            "|    policy_gradient_loss | -4.61e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.8e+15        |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4600459.210918712\n",
            "Sharpe:  1.034756153745345\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 340           |\n",
            "|    iterations           | 29            |\n",
            "|    time_elapsed         | 174           |\n",
            "|    total_timesteps      | 59392         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | -4.284084e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.26e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 8.07e+14      |\n",
            "|    n_updates            | 280           |\n",
            "|    policy_gradient_loss | -5.44e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.62e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4526188.381438201\n",
            "Sharpe:  1.0293846869900876\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 339            |\n",
            "|    iterations           | 30             |\n",
            "|    time_elapsed         | 180            |\n",
            "|    total_timesteps      | 61440          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -2.4214387e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -6.44e+20      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.12e+15       |\n",
            "|    n_updates            | 290            |\n",
            "|    policy_gradient_loss | -5.65e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.1e+15        |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4487836.803716703\n",
            "Sharpe:  1.010974660894394\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 339            |\n",
            "|    iterations           | 31             |\n",
            "|    time_elapsed         | 187            |\n",
            "|    total_timesteps      | 63488          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -2.6077032e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -4.47e+20      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.14e+15       |\n",
            "|    n_updates            | 300            |\n",
            "|    policy_gradient_loss | -4.8e-07       |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.25e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4480729.650671386\n",
            "Sharpe:  1.0219085518652522\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 339            |\n",
            "|    iterations           | 32             |\n",
            "|    time_elapsed         | 193            |\n",
            "|    total_timesteps      | 65536          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -2.0302832e-07 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -3.87e+20      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.28e+15       |\n",
            "|    n_updates            | 310            |\n",
            "|    policy_gradient_loss | -4.4e-07       |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.51e+15       |\n",
            "--------------------------------------------\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 339          |\n",
            "|    iterations           | 33           |\n",
            "|    time_elapsed         | 199          |\n",
            "|    total_timesteps      | 67584        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 1.359731e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -42.6        |\n",
            "|    explained_variance   | -3.68e+20    |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 1.24e+15     |\n",
            "|    n_updates            | 320          |\n",
            "|    policy_gradient_loss | -4.51e-07    |\n",
            "|    std                  | 1            |\n",
            "|    value_loss           | 2.66e+15     |\n",
            "------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4399373.734699048\n",
            "Sharpe:  1.005407087483561\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 338           |\n",
            "|    iterations           | 34            |\n",
            "|    time_elapsed         | 205           |\n",
            "|    total_timesteps      | 69632         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.2351742e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -2.29e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 8.5e+14       |\n",
            "|    n_updates            | 330           |\n",
            "|    policy_gradient_loss | -5.56e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.64e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4305742.921261859\n",
            "Sharpe:  0.9945061913961891\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 338           |\n",
            "|    iterations           | 35            |\n",
            "|    time_elapsed         | 211           |\n",
            "|    total_timesteps      | 71680         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.3411045e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -7.11e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 7.97e+14      |\n",
            "|    n_updates            | 340           |\n",
            "|    policy_gradient_loss | -6.48e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.8e+15       |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4794175.629957249\n",
            "Sharpe:  1.0611635246548963\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 338            |\n",
            "|    iterations           | 36             |\n",
            "|    time_elapsed         | 217            |\n",
            "|    total_timesteps      | 73728          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -3.3527613e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.16e+21      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.07e+15       |\n",
            "|    n_updates            | 350            |\n",
            "|    policy_gradient_loss | -4.82e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.06e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4467487.416264421\n",
            "Sharpe:  1.021012208464475\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 338          |\n",
            "|    iterations           | 37           |\n",
            "|    time_elapsed         | 224          |\n",
            "|    total_timesteps      | 75776        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 5.401671e-08 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -42.6        |\n",
            "|    explained_variance   | -9.89e+20    |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 1.46e+15     |\n",
            "|    n_updates            | 360          |\n",
            "|    policy_gradient_loss | -4.78e-07    |\n",
            "|    std                  | 1            |\n",
            "|    value_loss           | 2.75e+15     |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 338           |\n",
            "|    iterations           | 38            |\n",
            "|    time_elapsed         | 229           |\n",
            "|    total_timesteps      | 77824         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.6763806e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -7.64e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.25e+15      |\n",
            "|    n_updates            | 370           |\n",
            "|    policy_gradient_loss | -4.54e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.57e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4806649.219027834\n",
            "Sharpe:  1.0604486398186765\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 338          |\n",
            "|    iterations           | 39           |\n",
            "|    time_elapsed         | 236          |\n",
            "|    total_timesteps      | 79872        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 4.284084e-08 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -42.6        |\n",
            "|    explained_variance   | -6.96e+20    |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 1.28e+15     |\n",
            "|    n_updates            | 380          |\n",
            "|    policy_gradient_loss | -5.9e-07     |\n",
            "|    std                  | 1            |\n",
            "|    value_loss           | 2.44e+15     |\n",
            "------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4653147.508966551\n",
            "Sharpe:  1.043189911078732\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 338           |\n",
            "|    iterations           | 40            |\n",
            "|    time_elapsed         | 242           |\n",
            "|    total_timesteps      | 81920         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 6.3329935e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.04e+21     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.01e+15      |\n",
            "|    n_updates            | 390           |\n",
            "|    policy_gradient_loss | -5.33e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.82e+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": 21,
      "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": 22,
      "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": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcGYlhyal205",
        "outputId": "2c3687e9-a64f-477a-9b09-e07384eca7e5"
      },
      "source": [
        "trade.shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7560, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qq4W9FbSstT7",
        "outputId": "630dd78f-1b43-44a4-88c0-b39f05f76879"
      },
      "source": [
        "df_daily_return, df_actions = DRLAgent.DRL_prediction(model=trained_td3,\n",
        "                        environment = e_trade_gym)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:1413910.4880224664\n",
            "Sharpe:  2.296762324220152\n",
            "=================================\n",
            "hit end!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "uJvj3pXt_Ukg",
        "outputId": "8ce5ac7a-9e06-4b5f-a45f-a17af59dbc17"
      },
      "source": [
        "df_daily_return.head()"
      ],
      "execution_count": 26,
      "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>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>0.007160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>0.015798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-07-07</td>\n",
              "      <td>-0.014930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-07-08</td>\n",
              "      <td>0.003304</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      0.007160\n",
              "2  2020-07-06      0.015798\n",
              "3  2020-07-07     -0.014930\n",
              "4  2020-07-08      0.003304"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXMMG_9SdKTu"
      },
      "source": [
        "df_daily_return.to_csv('df_daily_return.csv')"
      ],
      "execution_count": 27,
      "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": 28,
      "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",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "  <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": 29,
      "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": "ad5bec89-c77e-4fbe-d5b9-9e0c9040e82a"
      },
      "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          0.413910\n",
              "Cumulative returns     0.413910\n",
              "Annual volatility      0.156187\n",
              "Sharpe ratio           2.296762\n",
              "Calmar ratio           4.578747\n",
              "Stability              0.943776\n",
              "Max drawdown          -0.090398\n",
              "Omega ratio            1.469140\n",
              "Sortino ratio          3.633253\n",
              "Skew                   0.099136\n",
              "Kurtosis               2.287689\n",
              "Tail ratio             1.113668\n",
              "Daily value at risk   -0.018254\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": "d408efbf-a07d-4c46-84e2-11ab669b8fdc"
      },
      "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": "ec34e43d-1f12-404a-94e4-66bef757e059"
      },
      "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": 38,
      "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>41.391%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>41.391%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>15.619%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>2.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>4.58</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>-9.04%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>3.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>2.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>1.11</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>0.05</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>9.04</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>4.08</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>3.94</td>\n",
              "      <td>2021-01-12</td>\n",
              "      <td>2021-01-29</td>\n",
              "      <td>2021-02-05</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.03</td>\n",
              "      <td>2021-05-07</td>\n",
              "      <td>2021-05-12</td>\n",
              "      <td>2021-06-03</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.91</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": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-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": "73ea9c81-7c29-4760-9624-50ee93b7f36d"
      },
      "source": [
        "!pip install PyPortfolioOpt"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting PyPortfolioOpt\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/12/dd/bbe51f36f323c4eee59ec94638b24de1daeda9537403c363cbfab8cd1d29/PyPortfolioOpt-1.4.2-py3-none-any.whl (60kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 3.3MB/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",
            "Requirement already satisfied: scipy<2.0,>=1.3 in /usr/local/lib/python3.7/dist-packages (from PyPortfolioOpt) (1.4.1)\n",
            "Collecting cvxpy<2.0.0,>=1.1.10\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f0/95/e7eb169a7802fe0c5c50dd7f29c2e9d357b5f29c70adc3f5ca2ab684a04b/cvxpy-1.1.13.tar.gz (1.3MB)\n",
            "\u001b[K     |████████████████████████████████| 1.3MB 10.1MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\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: 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: 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: 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: 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: 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",
            "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",
            "Building wheels for collected packages: cvxpy\n",
            "  Building wheel for cvxpy (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for cvxpy: filename=cvxpy-1.1.13-cp37-cp37m-linux_x86_64.whl size=2736145 sha256=eb869177a8d9bdfdcb6326f53330f8bb63271d0e6ab84092fb7fb3ca8add56ba\n",
            "  Stored in directory: /root/.cache/pip/wheels/f9/78/00/f29636789ee83434953b5442f16ec3f9834a68e7fd0393c220\n",
            "Successfully built cvxpy\n",
            "Installing collected packages: cvxpy, PyPortfolioOpt\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-1.1.13\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0NiefM7hHn0"
      },
      "source": [
        "from pypfopt.efficient_frontier import EfficientFrontier\n",
        "from pypfopt import risk_models"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDYDIBH9hcUP"
      },
      "source": [
        "unique_tic = trade.tic.unique()\n",
        "unique_trade_date = trade.date.unique()"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "8qvk_pJ4iV32",
        "outputId": "b73ba5d3-0858-4281-d353-a8aa8bf83d51"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 42,
      "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",
              "    .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>[[0.0014139106944709861, 0.001180074395265371,...</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>[[0.0014139106944709861, 0.001180074395265371,...</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>[[0.0014139106944709861, 0.001180074395265371,...</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>31.262676</td>\n",
              "      <td>6277400</td>\n",
              "      <td>CAT</td>\n",
              "      <td>2</td>\n",
              "      <td>0.692236</td>\n",
              "      <td>51.205319</td>\n",
              "      <td>98.438755</td>\n",
              "      <td>26.331746</td>\n",
              "      <td>[[0.0014139106944709861, 0.001180074395265371,...</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>[[0.0014139106944709861, 0.001180074395265371,...</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": 42
        }
      ]
    },
    {
      "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": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "S2zmv1ISkpXu",
        "outputId": "4f43080d-d4f5-47c6-fdb1-cc65b0c92840"
      },
      "source": [
        "portfolio.head()"
      ],
      "execution_count": 45,
      "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": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fj9-2gDXhPCG"
      },
      "source": [
        "time_ind = pd.Series(df_daily_return.date)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5RmNa7m_f590"
      },
      "source": [
        "td3_cumpod =(df_daily_return.daily_return+1).cumprod()-1"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y821cLKkhCn6"
      },
      "source": [
        "min_var_cumpod =(portfolio.account_value.pct_change()+1).cumprod()-1"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5E1X9FFGgqeZ"
      },
      "source": [
        "dji_cumpod =(baseline_returns+1).cumprod()-1"
      ],
      "execution_count": 49,
      "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": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CM-NJKa8g7Jp"
      },
      "source": [
        "trace0_portfolio = go.Scatter(x = time_ind, y = td3_cumpod, mode = 'lines', name = 'TD3 (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": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "35nVVmEuhGa1",
        "outputId": "29b731d4-a492-4a45-f850-c5f88acefa42"
      },
      "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": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"3149c73d-a034-43dc-8c6d-37b1d3fb64fc\" 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(\"3149c73d-a034-43dc-8c6d-37b1d3fb64fc\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        '3149c73d-a034-43dc-8c6d-37b1d3fb64fc',\n",
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          "metadata": {
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXd-N5Rmh6H7"
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      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}

一键复制编辑原始数据按行查看历史

提交于 2021-07-04 02:16 . Created using Colaboratory

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