FinRL_stock_trading_fundamental.ipynb

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      "name": "FinRL_stock_trading_fundamental",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mariko-sawada/FinRL/blob/master/FinRL_stock_trading_fundamental.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXaoZs2lh1hi"
      },
      "source": [
        "# Automated stock trading using FinRL with financial data\n",
        "\n",
        "Trained a Deep Reinforcement Learning model using FinRL and companies' financial ratio, and then backtested the model to examine how well-trained the model is\n",
        "\n",
        "* This Google Colabolatory notebook is based on the tutorial of FinRL: https://towardsdatascience.com/finrl-for-quantitative-finance-tutorial-for-multiple-stock-trading-7b00763b7530\n",
        "\n",
        "* This project is a final project of the almuni-mentored research project at Columbia University, Application of Reinforcement Learning to Finance, mentored by Bruce Yang from AI4Finance.\n",
        "* For more detailed explanation, please check out my Medium post: https://medium.com/@mariko.sawada1/automated-stock-trading-with-deep-reinforcement-learning-and-financial-data-a63286ccbe2b\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGunVt8oLCVS"
      },
      "source": [
        "# Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOzAKQ-SLGX6"
      },
      "source": [
        "* [1. Problem Definition](#0)\n",
        "* [2. Getting Started - Load Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. Check Additional Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download Data](#2)\n",
        "* [4. Preprocess fundamental Data](#3)        \n",
        "    * [4-1 Import financial data](#3.1)\n",
        "    * [4-2 Specify items needed to calculate financial ratios](#3.2)\n",
        "    * [4-3 Calculate financial ratios](#3.3)\n",
        "    * [4-4 Deal with NAs and infinite values](#3.4)\n",
        "    * [4-5 Merge stock price data and ratios into one dataframe](#3.5)\n",
        "    * [4-6 Calculate market valuation ratios using daily stock price data](#3.6)\n",
        "* [5.Build Environment](#4)  \n",
        "    * [5.1. Training & Trade Data Split](#4.1)\n",
        "    * [5.2. User-defined Environment](#4.2)   \n",
        "    * [5.3. Initialize Environment](#4.3)    \n",
        "* [6.Implement DRL Algorithms](#5)  \n",
        "* [7.Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "    * [7.3. Baseline Stats](#6.3)   \n",
        "    * [7.3. Compare to Stock Market Index](#6.4)             "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sApkDlD9LIZv"
      },
      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjLD2TZSLKZ-"
      },
      "source": [
        "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
        "\n",
        "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\n",
        "\n",
        "\n",
        "* Action: The action space describes the allowed actions that the agent interacts with the\n",
        "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
        "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
        "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
        "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
        "\n",
        "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
        "values at state s′ and s, respectively\n",
        "\n",
        "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
        "our trading agent observes many different features to better learn in an interactive environment.\n",
        "\n",
        "* Environment: Dow 30 consituents\n",
        "\n",
        "\n",
        "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ffsre789LY08"
      },
      "source": [
        "<a id='1'></a>\n",
        "# Part 2. Load Python Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uy5_PTmOh1hj"
      },
      "source": [
        "<a id='1.1'></a>\n",
        "## 2.1. Install all the packages through FinRL library\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPT0ipYE28wL",
        "outputId": "daf4bea2-41bc-4892-d9af-d0d32ef7356a"
      },
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/AI4Finance-LLC/FinRL-Library.git\n",
            "  Cloning https://github.com/AI4Finance-LLC/FinRL-Library.git to /tmp/pip-req-build-sh40mnb8\n",
            "  Running command git clone -q https://github.com/AI4Finance-LLC/FinRL-Library.git /tmp/pip-req-build-sh40mnb8\n",
            "Collecting pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2\n",
            "  Cloning https://github.com/quantopian/pyfolio.git to /tmp/pip-install-djvoe7ii/pyfolio_43f26b0cfe004ec394108e8d73166b45\n",
            "  Running command git clone -q https://github.com/quantopian/pyfolio.git /tmp/pip-install-djvoe7ii/pyfolio_43f26b0cfe004ec394108e8d73166b45\n",
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            "Requirement already satisfied: int-date>=0.1.7 in /usr/local/lib/python3.7/dist-packages (from stockstats->finrl==0.3.0) (0.1.8)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance->finrl==0.3.0) (0.0.9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osBHhVysOEzi"
      },
      "source": [
        "\n",
        "<a id='1.2'></a>\n",
        "## 2.2. Check if the additional packages needed are present, if not install them. \n",
        "* Yahoo Finance API\n",
        "* pandas\n",
        "* numpy\n",
        "* matplotlib\n",
        "* stockstats\n",
        "* OpenAI gym\n",
        "* stable-baselines\n",
        "* tensorflow\n",
        "* pyfolio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGv01K8Sh1hn"
      },
      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPqeTTwoh1hn"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "# matplotlib.use('Agg')\n",
        "import datetime\n",
        "\n",
        "%matplotlib inline\n",
        "from finrl.apps import config\n",
        "from finrl.neo_finrl.preprocessor.yahoodownloader import YahooDownloader\n",
        "from finrl.neo_finrl.preprocessor.preprocessors import FeatureEngineer, data_split\n",
        "from finrl.neo_finrl.env_stock_trading.env_stocktrading import StockTradingEnv\n",
        "from finrl.drl_agents.stablebaselines3.models import DRLAgent\n",
        "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
        "\n",
        "from pprint import pprint\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")\n",
        "\n",
        "import itertools"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2owTj985RW4"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9A8CN5R5PuZ"
      },
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A289rQWMh1hq"
      },
      "source": [
        "<a id='2'></a>\n",
        "# Part 3. Download Stock Data from Yahoo Finance\n",
        "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
        "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
        "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPeQ7iS-LoMm"
      },
      "source": [
        "\n",
        "\n",
        "-----\n",
        "class YahooDownloader:\n",
        "    Provides methods for retrieving daily stock data from\n",
        "    Yahoo Finance API\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        start_date : str\n",
        "            start date of the data (modified from config.py)\n",
        "        end_date : str\n",
        "            end date of the data (modified from config.py)\n",
        "        ticker_list : list\n",
        "            a list of stock tickers (modified from config.py)\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    fetch_data()\n",
        "        Fetches data from yahoo API\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "h3XJnvrbLp-C",
        "outputId": "c3ea364c-d91d-447b-a675-326c4b768f8d"
      },
      "source": [
        "# from config.py start_date is a string\n",
        "config.START_DATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2009-01-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "FUnY8WEfLq3C",
        "outputId": "93c93063-5bae-478f-a1cb-3487b111f4d9"
      },
      "source": [
        "# from config.py end_date is a string\n",
        "config.END_DATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2021-01-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzqRRTOX6aFu",
        "outputId": "c80a584b-8eff-40fa-a9d7-366d083633c8"
      },
      "source": [
        "print(config.DOW_30_TICKER)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['AAPL', 'MSFT', 'JPM', 'V', 'RTX', 'PG', 'GS', 'NKE', 'DIS', 'AXP', 'HD', 'INTC', 'WMT', 'IBM', 'MRK', 'UNH', 'KO', 'CAT', 'TRV', 'JNJ', 'CVX', 'MCD', 'VZ', 'CSCO', 'XOM', 'BA', 'MMM', 'PFE', 'WBA', 'DD']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCKm4om-s9kE",
        "outputId": "6e58de13-bfa6-4799-dc09-bdc35d98962f"
      },
      "source": [
        "df = YahooDownloader(start_date = '2009-01-01',\n",
        "                     end_date = '2021-01-01',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (90630, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV3HrZHLh1hy",
        "outputId": "0402eafa-636a-47ef-bfca-6fb304b340a5"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90630, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aBKF7sfV-Pi4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "1c354627-665b-4881-d9f6-08d4ae6a843c"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.782837</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.657365</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.655109</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.505757</td>\n",
              "      <td>40980600</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high        low      close     volume   tic  day\n",
              "0  2009-01-02   3.067143   3.251429   3.041429   2.782837  746015200  AAPL    4\n",
              "1  2009-01-02  18.570000  19.520000  18.400000  15.657365   10955700   AXP    4\n",
              "2  2009-01-02  42.799999  45.560001  42.779999  33.941101    7010200    BA    4\n",
              "3  2009-01-02  44.910000  46.980000  44.709999  32.655109    7117200   CAT    4\n",
              "4  2009-01-02  16.410000  17.000000  16.250000  12.505757   40980600  CSCO    4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRWscKiPXXnj"
      },
      "source": [
        "df['date'] = pd.to_datetime(df['date'],format='%Y-%m-%d')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "4hYkeaPiICHS",
        "outputId": "9f07190e-e5a7-49bd-d767-b240c8edc656"
      },
      "source": [
        "df.sort_values(['date','tic'],ignore_index=True).head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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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>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.782837</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.657365</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.655109</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.505757</td>\n",
              "      <td>40980600</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date       open       high        low      close     volume   tic  day\n",
              "0 2009-01-02   3.067143   3.251429   3.041429   2.782837  746015200  AAPL    4\n",
              "1 2009-01-02  18.570000  19.520000  18.400000  15.657365   10955700   AXP    4\n",
              "2 2009-01-02  42.799999  45.560001  42.779999  33.941101    7010200    BA    4\n",
              "3 2009-01-02  44.910000  46.980000  44.709999  32.655109    7117200   CAT    4\n",
              "4 2009-01-02  16.410000  17.000000  16.250000  12.505757   40980600  CSCO    4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqC6c40Zh1iH"
      },
      "source": [
        "# Part 4: Preprocess fundamental data\n",
        "- Import finanical data downloaded from Compustat via WRDS(Wharton Research Data Service)\n",
        "- Preprocess the dataset and calculate financial ratios\n",
        "- Add those ratios to the price data preprocessed in Part 3\n",
        "- Calculate price-related ratios such as P/E and P/B"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VbXEllD2oROq"
      },
      "source": [
        "## 4-1 Import the financial data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PmKP-1ii3RLS",
        "outputId": "50c6794b-2435-4076-e3b5-bb4fd4f07b40"
      },
      "source": [
        "# Import fundamental data from my GitHub repository\n",
        "url = 'https://raw.githubusercontent.com/mariko-sawada/FinRL_with_fundamental_data/main/dow_30_fundamental_wrds.csv'\n",
        "\n",
        "fund = pd.read_csv(url)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (16,25) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Tslhs_O5pOTL",
        "outputId": "3a890e07-14aa-49d8-d94a-9096f564d669"
      },
      "source": [
        "# Check the imported dataset\n",
        "fund.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>gvkey</th>\n",
              "      <th>datadate</th>\n",
              "      <th>fyearq</th>\n",
              "      <th>fqtr</th>\n",
              "      <th>fyr</th>\n",
              "      <th>indfmt</th>\n",
              "      <th>consol</th>\n",
              "      <th>popsrc</th>\n",
              "      <th>datafmt</th>\n",
              "      <th>tic</th>\n",
              "      <th>conm</th>\n",
              "      <th>acctchgq</th>\n",
              "      <th>acctstdq</th>\n",
              "      <th>adrrq</th>\n",
              "      <th>ajexq</th>\n",
              "      <th>ajpq</th>\n",
              "      <th>bsprq</th>\n",
              "      <th>compstq</th>\n",
              "      <th>curcdq</th>\n",
              "      <th>curncdq</th>\n",
              "      <th>currtrq</th>\n",
              "      <th>curuscnq</th>\n",
              "      <th>datacqtr</th>\n",
              "      <th>datafqtr</th>\n",
              "      <th>finalq</th>\n",
              "      <th>ogmq</th>\n",
              "      <th>rp</th>\n",
              "      <th>scfq</th>\n",
              "      <th>srcq</th>\n",
              "      <th>staltq</th>\n",
              "      <th>updq</th>\n",
              "      <th>apdedateq</th>\n",
              "      <th>fdateq</th>\n",
              "      <th>pdateq</th>\n",
              "      <th>rdq</th>\n",
              "      <th>acchgq</th>\n",
              "      <th>acomincq</th>\n",
              "      <th>acoq</th>\n",
              "      <th>actq</th>\n",
              "      <th>altoq</th>\n",
              "      <th>...</th>\n",
              "      <th>uspiy</th>\n",
              "      <th>ustdncy</th>\n",
              "      <th>usubdvpy</th>\n",
              "      <th>utfdocy</th>\n",
              "      <th>utfoscy</th>\n",
              "      <th>utmey</th>\n",
              "      <th>uwkcapcy</th>\n",
              "      <th>wcapchy</th>\n",
              "      <th>wcapcy</th>\n",
              "      <th>wday</th>\n",
              "      <th>wddy</th>\n",
              "      <th>wdepsy</th>\n",
              "      <th>wdpy</th>\n",
              "      <th>xidocy</th>\n",
              "      <th>xidoy</th>\n",
              "      <th>xinty</th>\n",
              "      <th>xiy</th>\n",
              "      <th>xopry</th>\n",
              "      <th>xoptdqpy</th>\n",
              "      <th>xoptdy</th>\n",
              "      <th>xoptepsqpy</th>\n",
              "      <th>xoptepsy</th>\n",
              "      <th>xoptqpy</th>\n",
              "      <th>xopty</th>\n",
              "      <th>xrdy</th>\n",
              "      <th>xsgay</th>\n",
              "      <th>exchg</th>\n",
              "      <th>costat</th>\n",
              "      <th>cshtrq</th>\n",
              "      <th>dvpspq</th>\n",
              "      <th>dvpsxq</th>\n",
              "      <th>mkvaltq</th>\n",
              "      <th>prccq</th>\n",
              "      <th>prchq</th>\n",
              "      <th>prclq</th>\n",
              "      <th>adjex</th>\n",
              "      <th>ggroup</th>\n",
              "      <th>gind</th>\n",
              "      <th>gsector</th>\n",
              "      <th>gsubind</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1447</td>\n",
              "      <td>19990630</td>\n",
              "      <td>1999</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>INDL</td>\n",
              "      <td>C</td>\n",
              "      <td>D</td>\n",
              "      <td>STD</td>\n",
              "      <td>AXP</td>\n",
              "      <td>AMERICAN EXPRESS CO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>GB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USD</td>\n",
              "      <td>USD</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1999Q2</td>\n",
              "      <td>1999Q2</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19990726.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9125.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>A</td>\n",
              "      <td>106125400.0</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>130.1250</td>\n",
              "      <td>142.6250</td>\n",
              "      <td>114.5000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4020</td>\n",
              "      <td>402020</td>\n",
              "      <td>40</td>\n",
              "      <td>40202010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1447</td>\n",
              "      <td>19990930</td>\n",
              "      <td>1999</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>INDL</td>\n",
              "      <td>C</td>\n",
              "      <td>D</td>\n",
              "      <td>STD</td>\n",
              "      <td>AXP</td>\n",
              "      <td>AMERICAN EXPRESS CO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>GB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USD</td>\n",
              "      <td>USD</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1999Q3</td>\n",
              "      <td>1999Q3</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19991025.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13803.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>A</td>\n",
              "      <td>121724600.0</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>135.0000</td>\n",
              "      <td>150.6250</td>\n",
              "      <td>121.8750</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4020</td>\n",
              "      <td>402020</td>\n",
              "      <td>40</td>\n",
              "      <td>40202010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1447</td>\n",
              "      <td>19991231</td>\n",
              "      <td>1999</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>INDL</td>\n",
              "      <td>C</td>\n",
              "      <td>D</td>\n",
              "      <td>STD</td>\n",
              "      <td>AXP</td>\n",
              "      <td>AMERICAN EXPRESS CO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>GB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USD</td>\n",
              "      <td>USD</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1999Q4</td>\n",
              "      <td>1999Q4</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>7.0</td>\n",
              "      <td>53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20000124.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18967.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>A</td>\n",
              "      <td>126218100.0</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>166.2500</td>\n",
              "      <td>168.8750</td>\n",
              "      <td>130.2500</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4020</td>\n",
              "      <td>402020</td>\n",
              "      <td>40</td>\n",
              "      <td>40202010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1447</td>\n",
              "      <td>20000331</td>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>INDL</td>\n",
              "      <td>C</td>\n",
              "      <td>D</td>\n",
              "      <td>STD</td>\n",
              "      <td>AXP</td>\n",
              "      <td>AMERICAN EXPRESS CO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>GB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USD</td>\n",
              "      <td>USD</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2000Q1</td>\n",
              "      <td>2000Q1</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20000424.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5101.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>A</td>\n",
              "      <td>167224700.0</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>148.9375</td>\n",
              "      <td>169.5000</td>\n",
              "      <td>119.5000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4020</td>\n",
              "      <td>402020</td>\n",
              "      <td>40</td>\n",
              "      <td>40202010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1447</td>\n",
              "      <td>20000630</td>\n",
              "      <td>2000</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>INDL</td>\n",
              "      <td>C</td>\n",
              "      <td>D</td>\n",
              "      <td>STD</td>\n",
              "      <td>AXP</td>\n",
              "      <td>AMERICAN EXPRESS CO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>GB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USD</td>\n",
              "      <td>USD</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2000Q2</td>\n",
              "      <td>2000Q2</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20000724.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10425.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>A</td>\n",
              "      <td>325319600.0</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.080</td>\n",
              "      <td>NaN</td>\n",
              "      <td>52.1250</td>\n",
              "      <td>57.1875</td>\n",
              "      <td>43.9375</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4020</td>\n",
              "      <td>402020</td>\n",
              "      <td>40</td>\n",
              "      <td>40202010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 647 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   gvkey  datadate  fyearq  fqtr  fyr  ... adjex ggroup    gind gsector   gsubind\n",
              "0   1447  19990630    1999     2   12  ...   3.0   4020  402020      40  40202010\n",
              "1   1447  19990930    1999     3   12  ...   3.0   4020  402020      40  40202010\n",
              "2   1447  19991231    1999     4   12  ...   3.0   4020  402020      40  40202010\n",
              "3   1447  20000331    2000     1   12  ...   3.0   4020  402020      40  40202010\n",
              "4   1447  20000630    2000     2   12  ...   1.0   4020  402020      40  40202010\n",
              "\n",
              "[5 rows x 647 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9yk1dHTYogEP"
      },
      "source": [
        "## 4-2 Specify items needed to calculate financial ratios\n",
        "- To know more about the data description of the dataset, please check WRDS's website(https://wrds-www.wharton.upenn.edu/). Login will be required."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CD0kFC7Ap02K"
      },
      "source": [
        "# List items that are used to calculate financial ratios\n",
        "\n",
        "items = [\n",
        "    'datadate', # Date\n",
        "    'tic', # Ticker\n",
        "    'oiadpq', # Quarterly operating income\n",
        "    'revtq', # Quartely revenue\n",
        "    'niq', # Quartely net income\n",
        "    'atq', # Total asset\n",
        "    'teqq', # Shareholder's equity\n",
        "    'epspiy', # EPS(Basic) incl. Extraordinary items\n",
        "    'ceqq', # Common Equity\n",
        "    'cshoq', # Common Shares Outstanding\n",
        "    'dvpspq', # Dividends per share\n",
        "    'actq', # Current assets\n",
        "    'lctq', # Current liabilities\n",
        "    'cheq', # Cash & Equivalent\n",
        "    'rectq', # Recievalbles\n",
        "    'cogsq', # Cost of  Goods Sold\n",
        "    'invtq', # Inventories\n",
        "    'apq',# Account payable\n",
        "    'dlttq', # Long term debt\n",
        "    'dlcq', # Debt in current liabilites\n",
        "    'ltq' # Liabilities   \n",
        "]\n",
        "\n",
        "# Omit items that will not be used\n",
        "fund_data = fund[items]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jE7UNYtIqFkv"
      },
      "source": [
        "# Rename column names for the sake of readability\n",
        "fund_data = fund_data.rename(columns={\n",
        "    'datadate':'date', # Date\n",
        "    'oiadpq':'op_inc_q', # Quarterly operating income\n",
        "    'revtq':'rev_q', # Quartely revenue\n",
        "    'niq':'net_inc_q', # Quartely net income\n",
        "    'atq':'tot_assets', # Assets\n",
        "    'teqq':'sh_equity', # Shareholder's equity\n",
        "    'epspiy':'eps_incl_ex', # EPS(Basic) incl. Extraordinary items\n",
        "    'ceqq':'com_eq', # Common Equity\n",
        "    'cshoq':'sh_outstanding', # Common Shares Outstanding\n",
        "    'dvpspq':'div_per_sh', # Dividends per share\n",
        "    'actq':'cur_assets', # Current assets\n",
        "    'lctq':'cur_liabilities', # Current liabilities\n",
        "    'cheq':'cash_eq', # Cash & Equivalent\n",
        "    'rectq':'receivables', # Receivalbles\n",
        "    'cogsq':'cogs_q', # Cost of  Goods Sold\n",
        "    'invtq':'inventories', # Inventories\n",
        "    'apq': 'payables',# Account payable\n",
        "    'dlttq':'long_debt', # Long term debt\n",
        "    'dlcq':'short_debt', # Debt in current liabilites\n",
        "    'ltq':'tot_liabilities' # Liabilities   \n",
        "})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "A0sszApfqO6D",
        "outputId": "64e373e9-dcb0-4f93-db30-6f2401dff548"
      },
      "source": [
        "# Check the data\n",
        "fund_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>op_inc_q</th>\n",
              "      <th>rev_q</th>\n",
              "      <th>net_inc_q</th>\n",
              "      <th>tot_assets</th>\n",
              "      <th>sh_equity</th>\n",
              "      <th>eps_incl_ex</th>\n",
              "      <th>com_eq</th>\n",
              "      <th>sh_outstanding</th>\n",
              "      <th>div_per_sh</th>\n",
              "      <th>cur_assets</th>\n",
              "      <th>cur_liabilities</th>\n",
              "      <th>cash_eq</th>\n",
              "      <th>receivables</th>\n",
              "      <th>cogs_q</th>\n",
              "      <th>inventories</th>\n",
              "      <th>payables</th>\n",
              "      <th>long_debt</th>\n",
              "      <th>short_debt</th>\n",
              "      <th>tot_liabilities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19990630</td>\n",
              "      <td>AXP</td>\n",
              "      <td>896.0</td>\n",
              "      <td>5564.0</td>\n",
              "      <td>646.0</td>\n",
              "      <td>132452.0</td>\n",
              "      <td>9762.0</td>\n",
              "      <td>2.73</td>\n",
              "      <td>9762.0</td>\n",
              "      <td>449.0</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6096.0</td>\n",
              "      <td>46774.0</td>\n",
              "      <td>4668.0</td>\n",
              "      <td>448.0</td>\n",
              "      <td>22282.0</td>\n",
              "      <td>7005.0</td>\n",
              "      <td>24785.0</td>\n",
              "      <td>122690.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>19990930</td>\n",
              "      <td>AXP</td>\n",
              "      <td>906.0</td>\n",
              "      <td>5584.0</td>\n",
              "      <td>648.0</td>\n",
              "      <td>132616.0</td>\n",
              "      <td>9744.0</td>\n",
              "      <td>4.18</td>\n",
              "      <td>9744.0</td>\n",
              "      <td>447.6</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5102.0</td>\n",
              "      <td>48827.0</td>\n",
              "      <td>4678.0</td>\n",
              "      <td>284.0</td>\n",
              "      <td>23587.0</td>\n",
              "      <td>6720.0</td>\n",
              "      <td>24683.0</td>\n",
              "      <td>122872.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19991231</td>\n",
              "      <td>AXP</td>\n",
              "      <td>845.0</td>\n",
              "      <td>6009.0</td>\n",
              "      <td>606.0</td>\n",
              "      <td>148517.0</td>\n",
              "      <td>10095.0</td>\n",
              "      <td>5.54</td>\n",
              "      <td>10095.0</td>\n",
              "      <td>446.9</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10391.0</td>\n",
              "      <td>54033.0</td>\n",
              "      <td>5164.0</td>\n",
              "      <td>277.0</td>\n",
              "      <td>25719.0</td>\n",
              "      <td>4685.0</td>\n",
              "      <td>32437.0</td>\n",
              "      <td>138422.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20000331</td>\n",
              "      <td>AXP</td>\n",
              "      <td>920.0</td>\n",
              "      <td>6021.0</td>\n",
              "      <td>656.0</td>\n",
              "      <td>150662.0</td>\n",
              "      <td>10253.0</td>\n",
              "      <td>1.48</td>\n",
              "      <td>10253.0</td>\n",
              "      <td>444.7</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7425.0</td>\n",
              "      <td>53663.0</td>\n",
              "      <td>5101.0</td>\n",
              "      <td>315.0</td>\n",
              "      <td>26379.0</td>\n",
              "      <td>5670.0</td>\n",
              "      <td>29342.0</td>\n",
              "      <td>140409.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20000630</td>\n",
              "      <td>AXP</td>\n",
              "      <td>1046.0</td>\n",
              "      <td>6370.0</td>\n",
              "      <td>740.0</td>\n",
              "      <td>148553.0</td>\n",
              "      <td>10509.0</td>\n",
              "      <td>1.05</td>\n",
              "      <td>10509.0</td>\n",
              "      <td>1333.0</td>\n",
              "      <td>0.080</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6841.0</td>\n",
              "      <td>54286.0</td>\n",
              "      <td>5324.0</td>\n",
              "      <td>261.0</td>\n",
              "      <td>29536.0</td>\n",
              "      <td>5336.0</td>\n",
              "      <td>26170.0</td>\n",
              "      <td>138044.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       date  tic  op_inc_q  ...  long_debt  short_debt  tot_liabilities\n",
              "0  19990630  AXP     896.0  ...     7005.0     24785.0         122690.0\n",
              "1  19990930  AXP     906.0  ...     6720.0     24683.0         122872.0\n",
              "2  19991231  AXP     845.0  ...     4685.0     32437.0         138422.0\n",
              "3  20000331  AXP     920.0  ...     5670.0     29342.0         140409.0\n",
              "4  20000630  AXP    1046.0  ...     5336.0     26170.0         138044.0\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xPvwtMQUqZdP"
      },
      "source": [
        "## 4-3 Calculate financial ratios\n",
        "- For items from Profit/Loss statements, we calculate LTM (Last Twelve Months) and use them to derive profitability related ratios such as Operating Maring and ROE. For items from balance sheets, we use the numbers on the day.\n",
        "- To check the definitions of the financial ratios calculated here, please refer to CFI's website: https://corporatefinanceinstitute.com/resources/knowledge/finance/financial-ratios/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cfWtEophqS33",
        "outputId": "8a33a6be-5dee-4dbd-9141-3c1b2e86090b"
      },
      "source": [
        "# Calculate financial ratios\n",
        "date = pd.to_datetime(fund_data['date'],format='%Y%m%d')\n",
        "\n",
        "tic = fund_data['tic'].to_frame('tic')\n",
        "\n",
        "# Profitability ratios\n",
        "# Operating Margin\n",
        "OPM = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='OPM')\n",
        "for i in range(0, fund_data.shape[0]):\n",
        "    if i-3 < 0:\n",
        "        OPM[i] = np.nan\n",
        "    elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
        "        OPM.iloc[i] = np.nan\n",
        "    else:\n",
        "        OPM.iloc[i] = np.sum(fund_data['op_inc_q'].iloc[i-3:i])/np.sum(fund_data['rev_q'].iloc[i-3:i])\n",
        "\n",
        "# Net Profit Margin        \n",
        "NPM = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='NPM')\n",
        "for i in range(0, fund_data.shape[0]):\n",
        "    if i-3 < 0:\n",
        "        NPM[i] = np.nan\n",
        "    elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
        "        NPM.iloc[i] = np.nan\n",
        "    else:\n",
        "        NPM.iloc[i] = np.sum(fund_data['net_inc_q'].iloc[i-3:i])/np.sum(fund_data['rev_q'].iloc[i-3:i])\n",
        "\n",
        "# Return On Assets\n",
        "ROA = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='ROA')\n",
        "for i in range(0, fund_data.shape[0]):\n",
        "    if i-3 < 0:\n",
        "        ROA[i] = np.nan\n",
        "    elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
        "        ROA.iloc[i] = np.nan\n",
        "    else:\n",
        "        ROA.iloc[i] = np.sum(fund_data['net_inc_q'].iloc[i-3:i])/fund_data['tot_assets'].iloc[i]\n",
        "\n",
        "# Return on Equity\n",
        "ROE = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='ROE')\n",
        "for i in range(0, fund_data.shape[0]):\n",
        "    if i-3 < 0:\n",
        "        ROE[i] = np.nan\n",
        "    elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
        "        ROE.iloc[i] = np.nan\n",
        "    else:\n",
        "        ROE.iloc[i] = np.sum(fund_data['net_inc_q'].iloc[i-3:i])/fund_data['sh_equity'].iloc[i]        \n",
        "\n",
        "# For calculating valuation ratios in the next subpart, calculate per share items in advance\n",
        "# Earnings Per Share       \n",
        "EPS = fund_data['eps_incl_ex'].to_frame('EPS')\n",
        "\n",
        "# Book Per Share\n",
        "BPS = (fund_data['com_eq']/fund_data['sh_outstanding']).to_frame('BPS') # Need to check units\n",
        "\n",
        "#Dividend Per Share\n",
        "DPS = fund_data['div_per_sh'].to_frame('DPS')\n",
        "\n",
        "# Liquidity ratios\n",
        "# Current ratio\n",
        "cur_ratio = (fund_data['cur_assets']/fund_data['cur_liabilities']).to_frame('cur_ratio')\n",
        "\n",
        "# Quick ratio\n",
        "quick_ratio = ((fund_data['cash_eq'] + fund_data['receivables'] )/fund_data['cur_liabilities']).to_frame('quick_ratio')\n",
        "\n",
        "# Cash ratio\n",
        "cash_ratio = (fund_data['cash_eq']/fund_data['cur_liabilities']).to_frame('cash_ratio')\n",
        "\n",
        "\n",
        "# Efficiency ratios\n",
        "# Inventory turnover ratio\n",
        "inv_turnover = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='inv_turnover')\n",
        "for i in range(0, fund_data.shape[0]):\n",
        "    if i-3 < 0:\n",
        "        inv_turnover[i] = np.nan\n",
        "    elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
        "        inv_turnover.iloc[i] = np.nan\n",
        "    else:\n",
        "        inv_turnover.iloc[i] = np.sum(fund_data['cogs_q'].iloc[i-3:i])/fund_data['inventories'].iloc[i]\n",
        "\n",
        "# Receivables turnover ratio       \n",
        "acc_rec_turnover = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='acc_rec_turnover')\n",
        "for i in range(0, fund_data.shape[0]):\n",
        "    if i-3 < 0:\n",
        "        acc_rec_turnover[i] = np.nan\n",
        "    elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
        "        acc_rec_turnover.iloc[i] = np.nan\n",
        "    else:\n",
        "        acc_rec_turnover.iloc[i] = np.sum(fund_data['rev_q'].iloc[i-3:i])/fund_data['receivables'].iloc[i]\n",
        "\n",
        "# Payable turnover ratio\n",
        "acc_pay_turnover = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='acc_pay_turnover')\n",
        "for i in range(0, fund_data.shape[0]):\n",
        "    if i-3 < 0:\n",
        "        acc_pay_turnover[i] = np.nan\n",
        "    elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
        "        acc_pay_turnover.iloc[i] = np.nan\n",
        "    else:\n",
        "        acc_pay_turnover.iloc[i] = np.sum(fund_data['cogs_q'].iloc[i-3:i])/fund_data['payables'].iloc[i]\n",
        "        \n",
        "## Leverage financial ratios\n",
        "# Debt ratio\n",
        "debt_ratio = (fund_data['tot_liabilities']/fund_data['tot_assets']).to_frame('debt_ratio')\n",
        "\n",
        "# Debt to Equity ratio\n",
        "debt_to_equity = (fund_data['tot_liabilities']/fund_data['sh_equity']).to_frame('debt_to_equity')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: divide by zero encountered in double_scalars\n",
            "  from ipykernel import kernelapp as app\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  from ipykernel import kernelapp as app\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:25: RuntimeWarning: divide by zero encountered in double_scalars\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:25: RuntimeWarning: invalid value encountered in double_scalars\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:77: RuntimeWarning: divide by zero encountered in double_scalars\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:77: RuntimeWarning: invalid value encountered in double_scalars\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwFVopRDqcby"
      },
      "source": [
        "# Create a dataframe that merges all the ratios\n",
        "ratios = pd.concat([date,tic,OPM,NPM,ROA,ROE,EPS,BPS,DPS,\n",
        "                    cur_ratio,quick_ratio,cash_ratio,inv_turnover,acc_rec_turnover,acc_pay_turnover,\n",
        "                   debt_ratio,debt_to_equity], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Mvnw7izFsJcT",
        "outputId": "c98a42dc-31da-490e-9cff-b5aec7dc0359"
      },
      "source": [
        "# Check the ratio data\n",
        "ratios.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>OPM</th>\n",
              "      <th>NPM</th>\n",
              "      <th>ROA</th>\n",
              "      <th>ROE</th>\n",
              "      <th>EPS</th>\n",
              "      <th>BPS</th>\n",
              "      <th>DPS</th>\n",
              "      <th>cur_ratio</th>\n",
              "      <th>quick_ratio</th>\n",
              "      <th>cash_ratio</th>\n",
              "      <th>inv_turnover</th>\n",
              "      <th>acc_rec_turnover</th>\n",
              "      <th>acc_pay_turnover</th>\n",
              "      <th>debt_ratio</th>\n",
              "      <th>debt_to_equity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1999-06-30</td>\n",
              "      <td>AXP</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.73</td>\n",
              "      <td>21.741648</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.926298</td>\n",
              "      <td>12.568121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1999-09-30</td>\n",
              "      <td>AXP</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.18</td>\n",
              "      <td>21.769437</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.926525</td>\n",
              "      <td>12.610016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1999-12-31</td>\n",
              "      <td>AXP</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.54</td>\n",
              "      <td>22.588946</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.932028</td>\n",
              "      <td>13.711937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2000-03-31</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.154281</td>\n",
              "      <td>0.110742</td>\n",
              "      <td>0.012611</td>\n",
              "      <td>0.185312</td>\n",
              "      <td>1.48</td>\n",
              "      <td>23.055993</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>46.0635</td>\n",
              "      <td>0.319717</td>\n",
              "      <td>0.550059</td>\n",
              "      <td>0.931947</td>\n",
              "      <td>13.694431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2000-06-30</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.151641</td>\n",
              "      <td>0.108436</td>\n",
              "      <td>0.0128574</td>\n",
              "      <td>0.181749</td>\n",
              "      <td>1.05</td>\n",
              "      <td>7.883721</td>\n",
              "      <td>0.080</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>57.2529</td>\n",
              "      <td>0.324467</td>\n",
              "      <td>0.505925</td>\n",
              "      <td>0.929258</td>\n",
              "      <td>13.135788</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  tic       OPM  ... acc_pay_turnover debt_ratio debt_to_equity\n",
              "0 1999-06-30  AXP       NaN  ...              NaN   0.926298      12.568121\n",
              "1 1999-09-30  AXP       NaN  ...              NaN   0.926525      12.610016\n",
              "2 1999-12-31  AXP       NaN  ...              NaN   0.932028      13.711937\n",
              "3 2000-03-31  AXP  0.154281  ...         0.550059   0.931947      13.694431\n",
              "4 2000-06-30  AXP  0.151641  ...         0.505925   0.929258      13.135788\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AvG67ouguUKF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "b21001b0-a5ec-4635-c184-69f361914c62"
      },
      "source": [
        "ratios.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>OPM</th>\n",
              "      <th>NPM</th>\n",
              "      <th>ROA</th>\n",
              "      <th>ROE</th>\n",
              "      <th>EPS</th>\n",
              "      <th>BPS</th>\n",
              "      <th>DPS</th>\n",
              "      <th>cur_ratio</th>\n",
              "      <th>quick_ratio</th>\n",
              "      <th>cash_ratio</th>\n",
              "      <th>inv_turnover</th>\n",
              "      <th>acc_rec_turnover</th>\n",
              "      <th>acc_pay_turnover</th>\n",
              "      <th>debt_ratio</th>\n",
              "      <th>debt_to_equity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2451</th>\n",
              "      <td>2020-03-31</td>\n",
              "      <td>V</td>\n",
              "      <td>0.667517</td>\n",
              "      <td>0.521213</td>\n",
              "      <td>0.129058</td>\n",
              "      <td>0.271736</td>\n",
              "      <td>2.85</td>\n",
              "      <td>13.647142</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.248714</td>\n",
              "      <td>1.140070</td>\n",
              "      <td>0.955150</td>\n",
              "      <td>inf</td>\n",
              "      <td>6.11635</td>\n",
              "      <td>2.69754</td>\n",
              "      <td>0.525062</td>\n",
              "      <td>1.105537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2452</th>\n",
              "      <td>2020-06-30</td>\n",
              "      <td>V</td>\n",
              "      <td>0.668385</td>\n",
              "      <td>0.519867</td>\n",
              "      <td>0.120448</td>\n",
              "      <td>0.264075</td>\n",
              "      <td>3.92</td>\n",
              "      <td>14.203947</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.553478</td>\n",
              "      <td>1.443292</td>\n",
              "      <td>1.221925</td>\n",
              "      <td>inf</td>\n",
              "      <td>5.06313</td>\n",
              "      <td>1.88951</td>\n",
              "      <td>0.543886</td>\n",
              "      <td>1.192433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2453</th>\n",
              "      <td>2020-09-30</td>\n",
              "      <td>V</td>\n",
              "      <td>0.654464</td>\n",
              "      <td>0.52129</td>\n",
              "      <td>0.107873</td>\n",
              "      <td>0.241066</td>\n",
              "      <td>4.90</td>\n",
              "      <td>14.653484</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.905238</td>\n",
              "      <td>1.784838</td>\n",
              "      <td>1.579807</td>\n",
              "      <td>inf</td>\n",
              "      <td>5.62857</td>\n",
              "      <td>2.73037</td>\n",
              "      <td>0.552515</td>\n",
              "      <td>1.234714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2454</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>V</td>\n",
              "      <td>0.638994</td>\n",
              "      <td>0.480876</td>\n",
              "      <td>0.0944222</td>\n",
              "      <td>0.201545</td>\n",
              "      <td>1.42</td>\n",
              "      <td>15.908283</td>\n",
              "      <td>0.32</td>\n",
              "      <td>2.121065</td>\n",
              "      <td>1.969814</td>\n",
              "      <td>1.700081</td>\n",
              "      <td>inf</td>\n",
              "      <td>4.72531</td>\n",
              "      <td>2.34787</td>\n",
              "      <td>0.531507</td>\n",
              "      <td>1.134505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2455</th>\n",
              "      <td>2021-03-31</td>\n",
              "      <td>V</td>\n",
              "      <td>0.640128</td>\n",
              "      <td>0.488704</td>\n",
              "      <td>0.0952179</td>\n",
              "      <td>0.202568</td>\n",
              "      <td>2.80</td>\n",
              "      <td>16.088525</td>\n",
              "      <td>0.32</td>\n",
              "      <td>2.116356</td>\n",
              "      <td>1.954292</td>\n",
              "      <td>1.700574</td>\n",
              "      <td>inf</td>\n",
              "      <td>4.84496</td>\n",
              "      <td>2.36736</td>\n",
              "      <td>0.529946</td>\n",
              "      <td>1.127414</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           date tic       OPM  ... acc_pay_turnover debt_ratio debt_to_equity\n",
              "2451 2020-03-31   V  0.667517  ...          2.69754   0.525062       1.105537\n",
              "2452 2020-06-30   V  0.668385  ...          1.88951   0.543886       1.192433\n",
              "2453 2020-09-30   V  0.654464  ...          2.73037   0.552515       1.234714\n",
              "2454 2020-12-31   V  0.638994  ...          2.34787   0.531507       1.134505\n",
              "2455 2021-03-31   V  0.640128  ...          2.36736   0.529946       1.127414\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JULhnNv8uaOB"
      },
      "source": [
        "## 4-4 Deal with NAs and infinite values\n",
        "- We replace N/A and infinite values with zero so that they can be recognized as a state"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nuKPlGe4sNzQ"
      },
      "source": [
        "# Replace NAs infinite values with zero\n",
        "final_ratios = ratios.copy()\n",
        "final_ratios = final_ratios.fillna(0)\n",
        "final_ratios = final_ratios.replace(np.inf,0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "wc_rvvm1sRDd",
        "outputId": "5ca396ec-fca0-420e-b4bc-3d3cd857e6b8"
      },
      "source": [
        "final_ratios.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>OPM</th>\n",
              "      <th>NPM</th>\n",
              "      <th>ROA</th>\n",
              "      <th>ROE</th>\n",
              "      <th>EPS</th>\n",
              "      <th>BPS</th>\n",
              "      <th>DPS</th>\n",
              "      <th>cur_ratio</th>\n",
              "      <th>quick_ratio</th>\n",
              "      <th>cash_ratio</th>\n",
              "      <th>inv_turnover</th>\n",
              "      <th>acc_rec_turnover</th>\n",
              "      <th>acc_pay_turnover</th>\n",
              "      <th>debt_ratio</th>\n",
              "      <th>debt_to_equity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1999-06-30</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.73</td>\n",
              "      <td>21.741648</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.926298</td>\n",
              "      <td>12.568121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1999-09-30</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.18</td>\n",
              "      <td>21.769437</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.926525</td>\n",
              "      <td>12.610016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1999-12-31</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.54</td>\n",
              "      <td>22.588946</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.932028</td>\n",
              "      <td>13.711937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2000-03-31</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.154281</td>\n",
              "      <td>0.110742</td>\n",
              "      <td>0.012611</td>\n",
              "      <td>0.185312</td>\n",
              "      <td>1.48</td>\n",
              "      <td>23.055993</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>46.063492</td>\n",
              "      <td>0.319717</td>\n",
              "      <td>0.550059</td>\n",
              "      <td>0.931947</td>\n",
              "      <td>13.694431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2000-06-30</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.151641</td>\n",
              "      <td>0.108436</td>\n",
              "      <td>0.012857</td>\n",
              "      <td>0.181749</td>\n",
              "      <td>1.05</td>\n",
              "      <td>7.883721</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57.252874</td>\n",
              "      <td>0.324467</td>\n",
              "      <td>0.505925</td>\n",
              "      <td>0.929258</td>\n",
              "      <td>13.135788</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  tic       OPM  ...  acc_pay_turnover  debt_ratio  debt_to_equity\n",
              "0 1999-06-30  AXP  0.000000  ...          0.000000    0.926298       12.568121\n",
              "1 1999-09-30  AXP  0.000000  ...          0.000000    0.926525       12.610016\n",
              "2 1999-12-31  AXP  0.000000  ...          0.000000    0.932028       13.711937\n",
              "3 2000-03-31  AXP  0.154281  ...          0.550059    0.931947       13.694431\n",
              "4 2000-06-30  AXP  0.151641  ...          0.505925    0.929258       13.135788\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "RKwmRfs5sfra",
        "outputId": "fe49554b-8bad-4f42-bd8e-44a23e5f228b"
      },
      "source": [
        "final_ratios.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>tic</th>\n",
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              "      <th>ROE</th>\n",
              "      <th>EPS</th>\n",
              "      <th>BPS</th>\n",
              "      <th>DPS</th>\n",
              "      <th>cur_ratio</th>\n",
              "      <th>quick_ratio</th>\n",
              "      <th>cash_ratio</th>\n",
              "      <th>inv_turnover</th>\n",
              "      <th>acc_rec_turnover</th>\n",
              "      <th>acc_pay_turnover</th>\n",
              "      <th>debt_ratio</th>\n",
              "      <th>debt_to_equity</th>\n",
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              "      <th>2451</th>\n",
              "      <td>2020-03-31</td>\n",
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              "      <td>0.521213</td>\n",
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              "      <td>2.85</td>\n",
              "      <td>13.647142</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.248714</td>\n",
              "      <td>1.140070</td>\n",
              "      <td>0.955150</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.116350</td>\n",
              "      <td>2.697537</td>\n",
              "      <td>0.525062</td>\n",
              "      <td>1.105537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2452</th>\n",
              "      <td>2020-06-30</td>\n",
              "      <td>V</td>\n",
              "      <td>0.668385</td>\n",
              "      <td>0.519867</td>\n",
              "      <td>0.120448</td>\n",
              "      <td>0.264075</td>\n",
              "      <td>3.92</td>\n",
              "      <td>14.203947</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.553478</td>\n",
              "      <td>1.443292</td>\n",
              "      <td>1.221925</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.063131</td>\n",
              "      <td>1.889507</td>\n",
              "      <td>0.543886</td>\n",
              "      <td>1.192433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2453</th>\n",
              "      <td>2020-09-30</td>\n",
              "      <td>V</td>\n",
              "      <td>0.654464</td>\n",
              "      <td>0.521290</td>\n",
              "      <td>0.107873</td>\n",
              "      <td>0.241066</td>\n",
              "      <td>4.90</td>\n",
              "      <td>14.653484</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.905238</td>\n",
              "      <td>1.784838</td>\n",
              "      <td>1.579807</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.628571</td>\n",
              "      <td>2.730366</td>\n",
              "      <td>0.552515</td>\n",
              "      <td>1.234714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2454</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>V</td>\n",
              "      <td>0.638994</td>\n",
              "      <td>0.480876</td>\n",
              "      <td>0.094422</td>\n",
              "      <td>0.201545</td>\n",
              "      <td>1.42</td>\n",
              "      <td>15.908283</td>\n",
              "      <td>0.32</td>\n",
              "      <td>2.121065</td>\n",
              "      <td>1.969814</td>\n",
              "      <td>1.700081</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.725314</td>\n",
              "      <td>2.347866</td>\n",
              "      <td>0.531507</td>\n",
              "      <td>1.134505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2455</th>\n",
              "      <td>2021-03-31</td>\n",
              "      <td>V</td>\n",
              "      <td>0.640128</td>\n",
              "      <td>0.488704</td>\n",
              "      <td>0.095218</td>\n",
              "      <td>0.202568</td>\n",
              "      <td>2.80</td>\n",
              "      <td>16.088525</td>\n",
              "      <td>0.32</td>\n",
              "      <td>2.116356</td>\n",
              "      <td>1.954292</td>\n",
              "      <td>1.700574</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.844961</td>\n",
              "      <td>2.367357</td>\n",
              "      <td>0.529946</td>\n",
              "      <td>1.127414</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           date tic       OPM  ...  acc_pay_turnover  debt_ratio  debt_to_equity\n",
              "2451 2020-03-31   V  0.667517  ...          2.697537    0.525062        1.105537\n",
              "2452 2020-06-30   V  0.668385  ...          1.889507    0.543886        1.192433\n",
              "2453 2020-09-30   V  0.654464  ...          2.730366    0.552515        1.234714\n",
              "2454 2020-12-31   V  0.638994  ...          2.347866    0.531507        1.134505\n",
              "2455 2021-03-31   V  0.640128  ...          2.367357    0.529946        1.127414\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "66kjM0lhu91F"
      },
      "source": [
        "## 4-5 Merge stock price data and ratios into one dataframe\n",
        "- Merge the price dataframe preprocessed in Part 3 and the ratio dataframe created in this part\n",
        "- Since the prices are daily and ratios are quartely, we have NAs in the ratio columns after merging the two dataframes. We deal with this by backfilling the ratios."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kixon2tR3RLT"
      },
      "source": [
        "list_ticker = df[\"tic\"].unique().tolist()\n",
        "list_date = list(pd.date_range(df['date'].min(),df['date'].max()))\n",
        "combination = list(itertools.product(list_date,list_ticker))\n",
        "\n",
        "# Merge stock price data and ratios into one dataframe\n",
        "processed_full = pd.DataFrame(combination,columns=[\"date\",\"tic\"]).merge(df,on=[\"date\",\"tic\"],how=\"left\")\n",
        "processed_full = processed_full.merge(final_ratios,how='left',on=['date','tic'])\n",
        "processed_full = processed_full.sort_values(['tic','date'])\n",
        "\n",
        "# Backfill the ratio data to make them daily\n",
        "processed_full = processed_full.bfill(axis='rows')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CGU69Ccfw_bR"
      },
      "source": [
        "## 4-6 Calculate market valuation ratios using daily stock price data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EhiYLZPBVZNW"
      },
      "source": [
        "# Calculate P/E, P/B and dividend yield using daily closing price\n",
        "processed_full['PE'] = processed_full['close']/processed_full['EPS']\n",
        "processed_full['PB'] = processed_full['close']/processed_full['BPS']\n",
        "processed_full['Div_yield'] = processed_full['DPS']/processed_full['close']\n",
        "\n",
        "# Drop per share items used for the above calculation\n",
        "processed_full = processed_full.drop(columns=['day','EPS','BPS','DPS'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "grvhGJJII3Xn",
        "outputId": "4c607ac7-e705-473f-9485-3ce11def7e8b"
      },
      "source": [
        "# Check the final data\n",
        "processed_full.sort_values(['date','tic'],ignore_index=True).head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>OPM</th>\n",
              "      <th>NPM</th>\n",
              "      <th>ROA</th>\n",
              "      <th>ROE</th>\n",
              "      <th>cur_ratio</th>\n",
              "      <th>quick_ratio</th>\n",
              "      <th>cash_ratio</th>\n",
              "      <th>inv_turnover</th>\n",
              "      <th>acc_rec_turnover</th>\n",
              "      <th>acc_pay_turnover</th>\n",
              "      <th>debt_ratio</th>\n",
              "      <th>debt_to_equity</th>\n",
              "      <th>PE</th>\n",
              "      <th>PB</th>\n",
              "      <th>Div_yield</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.782837</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>0.217886</td>\n",
              "      <td>0.163846</td>\n",
              "      <td>0.103222</td>\n",
              "      <td>0.183579</td>\n",
              "      <td>2.461857</td>\n",
              "      <td>2.039779</td>\n",
              "      <td>1.818995</td>\n",
              "      <td>54.403846</td>\n",
              "      <td>8.972003</td>\n",
              "      <td>4.269115</td>\n",
              "      <td>0.437727</td>\n",
              "      <td>0.778495</td>\n",
              "      <td>0.639733</td>\n",
              "      <td>0.102096</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AXP</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.657365</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>0.093973</td>\n",
              "      <td>0.072040</td>\n",
              "      <td>0.014094</td>\n",
              "      <td>0.108238</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.351354</td>\n",
              "      <td>0.653355</td>\n",
              "      <td>0.869784</td>\n",
              "      <td>6.679531</td>\n",
              "      <td>50.507629</td>\n",
              "      <td>1.450032</td>\n",
              "      <td>0.011496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>BA</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>0.047307</td>\n",
              "      <td>0.032525</td>\n",
              "      <td>0.026400</td>\n",
              "      <td>-2.870334</td>\n",
              "      <td>0.927883</td>\n",
              "      <td>0.368463</td>\n",
              "      <td>0.148507</td>\n",
              "      <td>2.329670</td>\n",
              "      <td>6.815203</td>\n",
              "      <td>2.076967</td>\n",
              "      <td>1.009198</td>\n",
              "      <td>-109.722986</td>\n",
              "      <td>39.012760</td>\n",
              "      <td>-35.751054</td>\n",
              "      <td>0.012374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CAT</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.655109</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>0.124545</td>\n",
              "      <td>0.066662</td>\n",
              "      <td>0.040891</td>\n",
              "      <td>0.415878</td>\n",
              "      <td>1.343293</td>\n",
              "      <td>0.890488</td>\n",
              "      <td>0.163158</td>\n",
              "      <td>3.540791</td>\n",
              "      <td>2.460351</td>\n",
              "      <td>8.472455</td>\n",
              "      <td>0.893715</td>\n",
              "      <td>9.089489</td>\n",
              "      <td>-171.868997</td>\n",
              "      <td>3.151894</td>\n",
              "      <td>0.012862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.505757</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>0.234698</td>\n",
              "      <td>0.196418</td>\n",
              "      <td>0.097593</td>\n",
              "      <td>0.162793</td>\n",
              "      <td>2.792929</td>\n",
              "      <td>2.498162</td>\n",
              "      <td>2.170759</td>\n",
              "      <td>9.054201</td>\n",
              "      <td>6.844634</td>\n",
              "      <td>16.036800</td>\n",
              "      <td>0.400215</td>\n",
              "      <td>0.667591</td>\n",
              "      <td>19.850408</td>\n",
              "      <td>1.986886</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CVX</td>\n",
              "      <td>74.230003</td>\n",
              "      <td>77.300003</td>\n",
              "      <td>73.580002</td>\n",
              "      <td>46.800758</td>\n",
              "      <td>13695900.0</td>\n",
              "      <td>0.141417</td>\n",
              "      <td>0.097223</td>\n",
              "      <td>0.117691</td>\n",
              "      <td>0.213663</td>\n",
              "      <td>1.368819</td>\n",
              "      <td>0.952878</td>\n",
              "      <td>0.373760</td>\n",
              "      <td>23.920348</td>\n",
              "      <td>13.387209</td>\n",
              "      <td>11.276861</td>\n",
              "      <td>0.449174</td>\n",
              "      <td>0.815455</td>\n",
              "      <td>50.870390</td>\n",
              "      <td>1.074527</td>\n",
              "      <td>0.013889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>DD</td>\n",
              "      <td>21.605234</td>\n",
              "      <td>22.060680</td>\n",
              "      <td>20.993229</td>\n",
              "      <td>15.219840</td>\n",
              "      <td>13251037.0</td>\n",
              "      <td>0.167221</td>\n",
              "      <td>0.102157</td>\n",
              "      <td>0.045834</td>\n",
              "      <td>0.084230</td>\n",
              "      <td>1.175830</td>\n",
              "      <td>0.815629</td>\n",
              "      <td>0.330748</td>\n",
              "      <td>11.310223</td>\n",
              "      <td>5.725855</td>\n",
              "      <td>4.287167</td>\n",
              "      <td>0.455848</td>\n",
              "      <td>0.837721</td>\n",
              "      <td>19.265620</td>\n",
              "      <td>0.832398</td>\n",
              "      <td>0.022996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>DIS</td>\n",
              "      <td>22.760000</td>\n",
              "      <td>24.030001</td>\n",
              "      <td>22.500000</td>\n",
              "      <td>20.597496</td>\n",
              "      <td>9796600.0</td>\n",
              "      <td>0.167221</td>\n",
              "      <td>0.102157</td>\n",
              "      <td>0.045834</td>\n",
              "      <td>0.084230</td>\n",
              "      <td>1.175830</td>\n",
              "      <td>0.815629</td>\n",
              "      <td>0.330748</td>\n",
              "      <td>11.310223</td>\n",
              "      <td>5.725855</td>\n",
              "      <td>4.287167</td>\n",
              "      <td>0.455848</td>\n",
              "      <td>0.837721</td>\n",
              "      <td>26.072780</td>\n",
              "      <td>1.126511</td>\n",
              "      <td>0.016992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>GS</td>\n",
              "      <td>84.019997</td>\n",
              "      <td>87.620003</td>\n",
              "      <td>82.190002</td>\n",
              "      <td>72.315208</td>\n",
              "      <td>14088500.0</td>\n",
              "      <td>0.608183</td>\n",
              "      <td>0.023205</td>\n",
              "      <td>0.000876</td>\n",
              "      <td>0.012761</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.036288</td>\n",
              "      <td>0.116995</td>\n",
              "      <td>0.044756</td>\n",
              "      <td>0.930155</td>\n",
              "      <td>13.542445</td>\n",
              "      <td>20.780232</td>\n",
              "      <td>0.731821</td>\n",
              "      <td>0.006454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>HD</td>\n",
              "      <td>23.070000</td>\n",
              "      <td>24.190001</td>\n",
              "      <td>22.959999</td>\n",
              "      <td>17.705078</td>\n",
              "      <td>14902500.0</td>\n",
              "      <td>0.082162</td>\n",
              "      <td>0.040825</td>\n",
              "      <td>0.056214</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.198063</td>\n",
              "      <td>0.134224</td>\n",
              "      <td>0.047073</td>\n",
              "      <td>3.526750</td>\n",
              "      <td>58.313786</td>\n",
              "      <td>7.806097</td>\n",
              "      <td>0.568142</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>13.212745</td>\n",
              "      <td>1.689138</td>\n",
              "      <td>0.012708</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date   tic       open  ...          PE         PB  Div_yield\n",
              "0 2009-01-02  AAPL   3.067143  ...    0.639733   0.102096   0.000000\n",
              "1 2009-01-02   AXP  18.570000  ...   50.507629   1.450032   0.011496\n",
              "2 2009-01-02    BA  42.799999  ...   39.012760 -35.751054   0.012374\n",
              "3 2009-01-02   CAT  44.910000  ... -171.868997   3.151894   0.012862\n",
              "4 2009-01-02  CSCO  16.410000  ...   19.850408   1.986886   0.000000\n",
              "5 2009-01-02   CVX  74.230003  ...   50.870390   1.074527   0.013889\n",
              "6 2009-01-02    DD  21.605234  ...   19.265620   0.832398   0.022996\n",
              "7 2009-01-02   DIS  22.760000  ...   26.072780   1.126511   0.016992\n",
              "8 2009-01-02    GS  84.019997  ...   20.780232   0.731821   0.006454\n",
              "9 2009-01-02    HD  23.070000  ...   13.212745   1.689138   0.012708\n",
              "\n",
              "[10 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QsYaY0Dh1iw"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Design Environment\n",
        "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
        "\n",
        "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
        "\n",
        "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5TOhcryx44bb"
      },
      "source": [
        "## 5-1 Split data into training and trade dataset\n",
        "- Training data split: 2009-01-01 to 2018-12-31\n",
        "- Trade data split: 2019-01-01 to 2020-09-30"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0qaVGjLtgbI",
        "outputId": "a23b2cfe-43a1-47de-9ea4-8ac15db256d2"
      },
      "source": [
        "train = data_split(processed_full, '2009-01-01','2019-01-01')\n",
        "trade = data_split(processed_full, '2019-01-01','2021-01-01')\n",
        "# Check the length of the two datasets\n",
        "print(len(train))\n",
        "print(len(trade))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "109530\n",
            "21930\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "p52zNCOhTtLR",
        "outputId": "acdc5e42-4bc1-4b54-a542-cd3a7da41a79"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>OPM</th>\n",
              "      <th>NPM</th>\n",
              "      <th>ROA</th>\n",
              "      <th>ROE</th>\n",
              "      <th>cur_ratio</th>\n",
              "      <th>quick_ratio</th>\n",
              "      <th>cash_ratio</th>\n",
              "      <th>inv_turnover</th>\n",
              "      <th>acc_rec_turnover</th>\n",
              "      <th>acc_pay_turnover</th>\n",
              "      <th>debt_ratio</th>\n",
              "      <th>debt_to_equity</th>\n",
              "      <th>PE</th>\n",
              "      <th>PB</th>\n",
              "      <th>Div_yield</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>3.067143</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AXP</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.657365</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>BA</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200.0</td>\n",
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              "      <td>0.032525</td>\n",
              "      <td>0.026400</td>\n",
              "      <td>-2.870334</td>\n",
              "      <td>0.927883</td>\n",
              "      <td>0.368463</td>\n",
              "      <td>0.148507</td>\n",
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              "      <td>39.012760</td>\n",
              "      <td>-35.751054</td>\n",
              "      <td>0.012374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CAT</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.655109</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>0.124545</td>\n",
              "      <td>0.066662</td>\n",
              "      <td>0.040891</td>\n",
              "      <td>0.415878</td>\n",
              "      <td>1.343293</td>\n",
              "      <td>0.890488</td>\n",
              "      <td>0.163158</td>\n",
              "      <td>3.540791</td>\n",
              "      <td>2.460351</td>\n",
              "      <td>8.472455</td>\n",
              "      <td>0.893715</td>\n",
              "      <td>9.089489</td>\n",
              "      <td>-171.868997</td>\n",
              "      <td>3.151894</td>\n",
              "      <td>0.012862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.505757</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>0.234698</td>\n",
              "      <td>0.196418</td>\n",
              "      <td>0.097593</td>\n",
              "      <td>0.162793</td>\n",
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              "      <td>6.844634</td>\n",
              "      <td>16.036800</td>\n",
              "      <td>0.400215</td>\n",
              "      <td>0.667591</td>\n",
              "      <td>19.850408</td>\n",
              "      <td>1.986886</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date   tic       open  ...          PE         PB  Div_yield\n",
              "0 2009-01-02  AAPL   3.067143  ...    0.639733   0.102096   0.000000\n",
              "0 2009-01-02   AXP  18.570000  ...   50.507629   1.450032   0.011496\n",
              "0 2009-01-02    BA  42.799999  ...   39.012760 -35.751054   0.012374\n",
              "0 2009-01-02   CAT  44.910000  ... -171.868997   3.151894   0.012862\n",
              "0 2009-01-02  CSCO  16.410000  ...   19.850408   1.986886   0.000000\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "k9zU9YaTTvFq",
        "outputId": "6fa26570-2a9b-4ede-f752-24ea4b7f890e"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
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              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
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              "      <th>NPM</th>\n",
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              "      <th>cash_ratio</th>\n",
              "      <th>inv_turnover</th>\n",
              "      <th>acc_rec_turnover</th>\n",
              "      <th>acc_pay_turnover</th>\n",
              "      <th>debt_ratio</th>\n",
              "      <th>debt_to_equity</th>\n",
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              "      <th>Div_yield</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
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              "      <td>39.712502</td>\n",
              "      <td>38.557499</td>\n",
              "      <td>38.382229</td>\n",
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              "    <tr>\n",
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              "      <td>93.910004</td>\n",
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              "      <td>93.769997</td>\n",
              "      <td>91.803406</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>BA</td>\n",
              "      <td>316.190002</td>\n",
              "      <td>323.950012</td>\n",
              "      <td>313.709991</td>\n",
              "      <td>314.645142</td>\n",
              "      <td>3292200.0</td>\n",
              "      <td>0.116496</td>\n",
              "      <td>0.102682</td>\n",
              "      <td>0.066409</td>\n",
              "      <td>34.409483</td>\n",
              "      <td>1.070490</td>\n",
              "      <td>0.262465</td>\n",
              "      <td>0.092436</td>\n",
              "      <td>0.933164</td>\n",
              "      <td>5.468453</td>\n",
              "      <td>4.151637</td>\n",
              "      <td>0.998070</td>\n",
              "      <td>517.142241</td>\n",
              "      <td>83.019826</td>\n",
              "      <td>1418.196271</td>\n",
              "      <td>0.006531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>CAT</td>\n",
              "      <td>124.029999</td>\n",
              "      <td>127.879997</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>117.506577</td>\n",
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              "      <td>1.428582</td>\n",
              "      <td>0.919490</td>\n",
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              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>42.279999</td>\n",
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              "      <td>39.496738</td>\n",
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              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date   tic        open  ...         PE           PB  Div_yield\n",
              "0 2019-01-01  AAPL   38.722500  ...   5.728691     1.670488   0.019019\n",
              "0 2019-01-01   AXP   93.910004  ...  50.720114     3.458432   0.004248\n",
              "0 2019-01-01    BA  316.190002  ...  83.019826  1418.196271   0.006531\n",
              "0 2019-01-01   CAT  124.029999  ...  35.716285     4.351801   0.007319\n",
              "0 2019-01-01  CSCO   42.279999  ...  28.011871     4.283841   0.008355\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qqGG78pLyCX7"
      },
      "source": [
        "## 5-2 Set up the training environment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2zqII8rMIqn",
        "outputId": "8aab1853-50b6-4fdb-bb5b-040a889b4c74"
      },
      "source": [
        "ratio_list = ['OPM', 'NPM','ROA', 'ROE', 'cur_ratio', 'quick_ratio', 'cash_ratio', 'inv_turnover','acc_rec_turnover', 'acc_pay_turnover', 'debt_ratio', 'debt_to_equity',\n",
        "       'PE', 'PB', 'Div_yield']\n",
        "\n",
        "stock_dimension = len(train.tic.unique())\n",
        "state_space = 1 + 2*stock_dimension + len(ratio_list)*stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stock Dimension: 30, State Space: 511\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWyp84Ltto19"
      },
      "source": [
        "# Parameters for the environment\n",
        "env_kwargs = {\n",
        "    \"hmax\": 100, \n",
        "    \"initial_amount\": 1000000, \n",
        "    \"buy_cost_pct\": 0.001,\n",
        "    \"sell_cost_pct\": 0.001,\n",
        "    \"state_space\": state_space, \n",
        "    \"stock_dim\": stock_dimension, \n",
        "    \"tech_indicator_list\": ratio_list, \n",
        "    \"action_space\": stock_dimension, \n",
        "    \"reward_scaling\": 1e-4\n",
        "    \n",
        "}\n",
        "\n",
        "#Establish the training environment using StockTradingEnv() class\n",
        "e_train_gym = StockTradingEnv(df = train, **env_kwargs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64EoqOrQjiVf"
      },
      "source": [
        "## Environment for Training\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwSvvPjutpqS",
        "outputId": "038afe48-4b1d-4ca9-fc2a-0e94312a607d"
      },
      "source": [
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "print(type(env_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMNR5nHjh1iz"
      },
      "source": [
        "<a id='5'></a>\n",
        "# Part 6: Implement DRL Algorithms\n",
        "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
        "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
        "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
        "design their own DRL algorithms by adapting these DRL algorithms."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "364PsqckttcQ"
      },
      "source": [
        "# Set up the agent using DRLAgent() class using the environment created in the previous part\n",
        "agent = DRLAgent(env = env_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDmqOyF9h1iz"
      },
      "source": [
        "### Model Training: 5 models, A2C DDPG, PPO, TD3, SAC\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uijiWgkuh1jB"
      },
      "source": [
        "### Model 1: A2C\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUCnkn-HIbmj",
        "outputId": "74cb8fcc-f643-442a-d592-0704db70dc5f"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "model_a2c = agent.get_model(\"a2c\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0007}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0GVpkWGqH4-D",
        "outputId": "0cd8f84e-5665-4be9-fb1a-344d4a4f1ce3"
      },
      "source": [
        "trained_a2c = agent.train_model(model=model_a2c, \n",
        "                             tb_log_name='a2c',\n",
        "                             total_timesteps=100000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/a2c/a2c_1\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 60       |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 8        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0.157    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | 165      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 18.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 68       |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 14       |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0.0513   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | 51.6     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 17.2     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 71        |\n",
            "|    iterations         | 300       |\n",
            "|    time_elapsed       | 20        |\n",
            "|    total_timesteps    | 1500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.9     |\n",
            "|    explained_variance | -1.19e-06 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 299       |\n",
            "|    policy_loss        | -1.03     |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 0.541     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 73       |\n",
            "|    iterations         | 400      |\n",
            "|    time_elapsed       | 27       |\n",
            "|    total_timesteps    | 2000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 399      |\n",
            "|    policy_loss        | 103      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 5.78     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 74       |\n",
            "|    iterations         | 500      |\n",
            "|    time_elapsed       | 33       |\n",
            "|    total_timesteps    | 2500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 499      |\n",
            "|    policy_loss        | 158      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 23.9     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 74       |\n",
            "|    iterations         | 600      |\n",
            "|    time_elapsed       | 40       |\n",
            "|    total_timesteps    | 3000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 599      |\n",
            "|    policy_loss        | 62.7     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 2.76     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 75       |\n",
            "|    iterations         | 700      |\n",
            "|    time_elapsed       | 46       |\n",
            "|    total_timesteps    | 3500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 699      |\n",
            "|    policy_loss        | -136     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 17.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 1.91e+06 |\n",
            "|    total_cost         | 7.56e+04 |\n",
            "|    total_reward       | 9.14e+05 |\n",
            "|    total_reward_pct   | 91.4     |\n",
            "|    total_trades       | 73316    |\n",
            "| time/                 |          |\n",
            "|    fps                | 75       |\n",
            "|    iterations         | 800      |\n",
            "|    time_elapsed       | 52       |\n",
            "|    total_timesteps    | 4000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 799      |\n",
            "|    policy_loss        | -50.6    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 1.55     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 75       |\n",
            "|    iterations         | 900      |\n",
            "|    time_elapsed       | 59       |\n",
            "|    total_timesteps    | 4500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 1.79e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 899      |\n",
            "|    policy_loss        | 76.5     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 2.69     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 1000     |\n",
            "|    time_elapsed       | 65       |\n",
            "|    total_timesteps    | 5000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 999      |\n",
            "|    policy_loss        | 175      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 17.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 1100     |\n",
            "|    time_elapsed       | 72       |\n",
            "|    total_timesteps    | 5500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1099     |\n",
            "|    policy_loss        | -14.7    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 0.158    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 1200     |\n",
            "|    time_elapsed       | 78       |\n",
            "|    total_timesteps    | 6000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1199     |\n",
            "|    policy_loss        | -84.2    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 3.71     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 1300      |\n",
            "|    time_elapsed       | 84        |\n",
            "|    total_timesteps    | 6500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.3     |\n",
            "|    explained_variance | -4.77e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1299      |\n",
            "|    policy_loss        | -63.5     |\n",
            "|    std                | 1.02      |\n",
            "|    value_loss         | 3.31      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 1400     |\n",
            "|    time_elapsed       | 91       |\n",
            "|    total_timesteps    | 7000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1399     |\n",
            "|    policy_loss        | -309     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 60.1     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 2.29e+06 |\n",
            "|    total_cost         | 2.63e+04 |\n",
            "|    total_reward       | 1.29e+06 |\n",
            "|    total_reward_pct   | 129      |\n",
            "|    total_trades       | 60684    |\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 1500     |\n",
            "|    time_elapsed       | 97       |\n",
            "|    total_timesteps    | 7500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.4    |\n",
            "|    explained_variance | -0.33    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1499     |\n",
            "|    policy_loss        | 61.1     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 3.06     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 1600     |\n",
            "|    time_elapsed       | 103      |\n",
            "|    total_timesteps    | 8000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1599     |\n",
            "|    policy_loss        | -19.4    |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 0.955    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 1700      |\n",
            "|    time_elapsed       | 110       |\n",
            "|    total_timesteps    | 8500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.5     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1699      |\n",
            "|    policy_loss        | -50.8     |\n",
            "|    std                | 1.03      |\n",
            "|    value_loss         | 2.76      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 1800     |\n",
            "|    time_elapsed       | 116      |\n",
            "|    total_timesteps    | 9000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1799     |\n",
            "|    policy_loss        | 19.4     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 0.647    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 1900     |\n",
            "|    time_elapsed       | 123      |\n",
            "|    total_timesteps    | 9500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1899     |\n",
            "|    policy_loss        | -187     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 22.8     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 2000      |\n",
            "|    time_elapsed       | 129       |\n",
            "|    total_timesteps    | 10000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.5     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1999      |\n",
            "|    policy_loss        | -71.4     |\n",
            "|    std                | 1.03      |\n",
            "|    value_loss         | 4.75      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2100     |\n",
            "|    time_elapsed       | 135      |\n",
            "|    total_timesteps    | 10500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2099     |\n",
            "|    policy_loss        | 73.2     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 4.46     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.38e+06 |\n",
            "|    total_cost         | 3.17e+04 |\n",
            "|    total_reward       | 3.38e+06 |\n",
            "|    total_reward_pct   | 338      |\n",
            "|    total_trades       | 65214    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2200     |\n",
            "|    time_elapsed       | 142      |\n",
            "|    total_timesteps    | 11000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2199     |\n",
            "|    policy_loss        | -37.4    |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 1.05     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2300     |\n",
            "|    time_elapsed       | 148      |\n",
            "|    total_timesteps    | 11500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2299     |\n",
            "|    policy_loss        | -52.1    |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 2.56     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2400     |\n",
            "|    time_elapsed       | 155      |\n",
            "|    total_timesteps    | 12000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.7    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2399     |\n",
            "|    policy_loss        | -91.9    |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 6.52     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2500     |\n",
            "|    time_elapsed       | 161      |\n",
            "|    total_timesteps    | 12500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.7    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2499     |\n",
            "|    policy_loss        | 19.6     |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 0.766    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2600     |\n",
            "|    time_elapsed       | 167      |\n",
            "|    total_timesteps    | 13000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2599     |\n",
            "|    policy_loss        | -58.4    |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 2.59     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2700     |\n",
            "|    time_elapsed       | 174      |\n",
            "|    total_timesteps    | 13500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2699     |\n",
            "|    policy_loss        | -177     |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 24.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2800     |\n",
            "|    time_elapsed       | 180      |\n",
            "|    total_timesteps    | 14000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2799     |\n",
            "|    policy_loss        | 54.1     |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 1.96     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2900     |\n",
            "|    time_elapsed       | 187      |\n",
            "|    total_timesteps    | 14500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2899     |\n",
            "|    policy_loss        | 127      |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 15.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 2.92e+06 |\n",
            "|    total_cost         | 1.85e+04 |\n",
            "|    total_reward       | 1.92e+06 |\n",
            "|    total_reward_pct   | 192      |\n",
            "|    total_trades       | 67453    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 3000     |\n",
            "|    time_elapsed       | 193      |\n",
            "|    total_timesteps    | 15000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2999     |\n",
            "|    policy_loss        | -97.9    |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 5.52     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 3100     |\n",
            "|    time_elapsed       | 199      |\n",
            "|    total_timesteps    | 15500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3099     |\n",
            "|    policy_loss        | -87.5    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 4.94     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 3200     |\n",
            "|    time_elapsed       | 206      |\n",
            "|    total_timesteps    | 16000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3199     |\n",
            "|    policy_loss        | -199     |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 21.3     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 3300      |\n",
            "|    time_elapsed       | 212       |\n",
            "|    total_timesteps    | 16500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44       |\n",
            "|    explained_variance | -3.58e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3299      |\n",
            "|    policy_loss        | 59.4      |\n",
            "|    std                | 1.05      |\n",
            "|    value_loss         | 4.14      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 3400     |\n",
            "|    time_elapsed       | 219      |\n",
            "|    total_timesteps    | 17000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3399     |\n",
            "|    policy_loss        | -32.7    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 1.65     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 3500     |\n",
            "|    time_elapsed       | 225      |\n",
            "|    total_timesteps    | 17500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3499     |\n",
            "|    policy_loss        | -85.8    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 4.92     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 3600      |\n",
            "|    time_elapsed       | 231       |\n",
            "|    total_timesteps    | 18000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44       |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3599      |\n",
            "|    policy_loss        | 118       |\n",
            "|    std                | 1.05      |\n",
            "|    value_loss         | 14.3      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.75e+06 |\n",
            "|    total_cost         | 2.33e+04 |\n",
            "|    total_reward       | 2.75e+06 |\n",
            "|    total_reward_pct   | 275      |\n",
            "|    total_trades       | 65849    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 3700     |\n",
            "|    time_elapsed       | 238      |\n",
            "|    total_timesteps    | 18500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3699     |\n",
            "|    policy_loss        | -44.2    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 1.43     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 3800      |\n",
            "|    time_elapsed       | 244       |\n",
            "|    total_timesteps    | 19000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.9     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3799      |\n",
            "|    policy_loss        | 59.3      |\n",
            "|    std                | 1.05      |\n",
            "|    value_loss         | 1.79      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 3900     |\n",
            "|    time_elapsed       | 251      |\n",
            "|    total_timesteps    | 19500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3899     |\n",
            "|    policy_loss        | 82.2     |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 3.91     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4000     |\n",
            "|    time_elapsed       | 257      |\n",
            "|    total_timesteps    | 20000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3999     |\n",
            "|    policy_loss        | -153     |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 13.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4100     |\n",
            "|    time_elapsed       | 263      |\n",
            "|    total_timesteps    | 20500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4099     |\n",
            "|    policy_loss        | 88       |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 4.7      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4200     |\n",
            "|    time_elapsed       | 270      |\n",
            "|    total_timesteps    | 21000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | -0.0553  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4199     |\n",
            "|    policy_loss        | -65      |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 5.44     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4300     |\n",
            "|    time_elapsed       | 276      |\n",
            "|    total_timesteps    | 21500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4299     |\n",
            "|    policy_loss        | -55.7    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 2.66     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.83e+06 |\n",
            "|    total_cost         | 8.83e+04 |\n",
            "|    total_reward       | 2.83e+06 |\n",
            "|    total_reward_pct   | 283      |\n",
            "|    total_trades       | 71634    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4400     |\n",
            "|    time_elapsed       | 283      |\n",
            "|    total_timesteps    | 22000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | -0.0125  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4399     |\n",
            "|    policy_loss        | 94.9     |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 5.92     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4500     |\n",
            "|    time_elapsed       | 289      |\n",
            "|    total_timesteps    | 22500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4499     |\n",
            "|    policy_loss        | -95.7    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 6.98     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4600     |\n",
            "|    time_elapsed       | 296      |\n",
            "|    total_timesteps    | 23000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4599     |\n",
            "|    policy_loss        | 0.903    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 0.28     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4700     |\n",
            "|    time_elapsed       | 302      |\n",
            "|    total_timesteps    | 23500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4699     |\n",
            "|    policy_loss        | -79.2    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 2.97     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4800     |\n",
            "|    time_elapsed       | 308      |\n",
            "|    total_timesteps    | 24000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4799     |\n",
            "|    policy_loss        | -23.5    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 0.842    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4900     |\n",
            "|    time_elapsed       | 315      |\n",
            "|    total_timesteps    | 24500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4899     |\n",
            "|    policy_loss        | 54.6     |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 7.28     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5000     |\n",
            "|    time_elapsed       | 321      |\n",
            "|    total_timesteps    | 25000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4999     |\n",
            "|    policy_loss        | 34.9     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 1.99     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5100     |\n",
            "|    time_elapsed       | 327      |\n",
            "|    total_timesteps    | 25500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5099     |\n",
            "|    policy_loss        | 352      |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 72.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.24e+06 |\n",
            "|    total_cost         | 1.66e+04 |\n",
            "|    total_reward       | 2.24e+06 |\n",
            "|    total_reward_pct   | 224      |\n",
            "|    total_trades       | 61666    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5200     |\n",
            "|    time_elapsed       | 334      |\n",
            "|    total_timesteps    | 26000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5199     |\n",
            "|    policy_loss        | 2.88     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 0.137    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5300     |\n",
            "|    time_elapsed       | 340      |\n",
            "|    total_timesteps    | 26500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5299     |\n",
            "|    policy_loss        | -222     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 30.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5400     |\n",
            "|    time_elapsed       | 347      |\n",
            "|    total_timesteps    | 27000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5399     |\n",
            "|    policy_loss        | -11.7    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 0.156    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5500     |\n",
            "|    time_elapsed       | 353      |\n",
            "|    total_timesteps    | 27500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5499     |\n",
            "|    policy_loss        | 165      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 13.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5600     |\n",
            "|    time_elapsed       | 359      |\n",
            "|    total_timesteps    | 28000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5599     |\n",
            "|    policy_loss        | 127      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 15.9     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 5700      |\n",
            "|    time_elapsed       | 366       |\n",
            "|    total_timesteps    | 28500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 5699      |\n",
            "|    policy_loss        | 26.7      |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 0.48      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5800     |\n",
            "|    time_elapsed       | 372      |\n",
            "|    total_timesteps    | 29000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5799     |\n",
            "|    policy_loss        | 242      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 37       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.16e+06 |\n",
            "|    total_cost         | 1.22e+04 |\n",
            "|    total_reward       | 2.16e+06 |\n",
            "|    total_reward_pct   | 216      |\n",
            "|    total_trades       | 60801    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5900     |\n",
            "|    time_elapsed       | 379      |\n",
            "|    total_timesteps    | 29500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5899     |\n",
            "|    policy_loss        | 72.7     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 4.37     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6000     |\n",
            "|    time_elapsed       | 385      |\n",
            "|    total_timesteps    | 30000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5999     |\n",
            "|    policy_loss        | 58       |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 4.2      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6100     |\n",
            "|    time_elapsed       | 392      |\n",
            "|    total_timesteps    | 30500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6099     |\n",
            "|    policy_loss        | -132     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 9.64     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6200     |\n",
            "|    time_elapsed       | 398      |\n",
            "|    total_timesteps    | 31000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6199     |\n",
            "|    policy_loss        | 25.9     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 1.75     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 6300      |\n",
            "|    time_elapsed       | 404       |\n",
            "|    total_timesteps    | 31500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 6299      |\n",
            "|    policy_loss        | -84.4     |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 5.68      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6400     |\n",
            "|    time_elapsed       | 411      |\n",
            "|    total_timesteps    | 32000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6399     |\n",
            "|    policy_loss        | -74.6    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 3.73     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6500     |\n",
            "|    time_elapsed       | 417      |\n",
            "|    total_timesteps    | 32500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6499     |\n",
            "|    policy_loss        | 120      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 21.8     |\n",
            "------------------------------------\n",
            "day: 3650, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4297821.94\n",
            "total_reward: 3297821.94\n",
            "total_cost: 12437.82\n",
            "total_trades: 58594\n",
            "Sharpe: 0.695\n",
            "=================================\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.3e+06  |\n",
            "|    total_cost         | 1.24e+04 |\n",
            "|    total_reward       | 3.3e+06  |\n",
            "|    total_reward_pct   | 330      |\n",
            "|    total_trades       | 58594    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6600     |\n",
            "|    time_elapsed       | 424      |\n",
            "|    total_timesteps    | 33000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | -0.112   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6599     |\n",
            "|    policy_loss        | 63.4     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 4.21     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6700     |\n",
            "|    time_elapsed       | 430      |\n",
            "|    total_timesteps    | 33500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6699     |\n",
            "|    policy_loss        | 31.2     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 3.92     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 6800      |\n",
            "|    time_elapsed       | 437       |\n",
            "|    total_timesteps    | 34000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.3     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 6799      |\n",
            "|    policy_loss        | 141       |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 16.5      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6900     |\n",
            "|    time_elapsed       | 443      |\n",
            "|    total_timesteps    | 34500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6899     |\n",
            "|    policy_loss        | -198     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 23.1     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7000     |\n",
            "|    time_elapsed       | 450      |\n",
            "|    total_timesteps    | 35000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6999     |\n",
            "|    policy_loss        | 132      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 12.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7100     |\n",
            "|    time_elapsed       | 456      |\n",
            "|    total_timesteps    | 35500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7099     |\n",
            "|    policy_loss        | -99.9    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 5.45     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 7200      |\n",
            "|    time_elapsed       | 462       |\n",
            "|    total_timesteps    | 36000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 7199      |\n",
            "|    policy_loss        | 210       |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 32.8      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 7300      |\n",
            "|    time_elapsed       | 469       |\n",
            "|    total_timesteps    | 36500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 7299      |\n",
            "|    policy_loss        | -476      |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 271       |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.94e+06 |\n",
            "|    total_cost         | 8.73e+03 |\n",
            "|    total_reward       | 3.94e+06 |\n",
            "|    total_reward_pct   | 394      |\n",
            "|    total_trades       | 58374    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7400     |\n",
            "|    time_elapsed       | 475      |\n",
            "|    total_timesteps    | 37000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7399     |\n",
            "|    policy_loss        | 42.2     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 2.51     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7500     |\n",
            "|    time_elapsed       | 482      |\n",
            "|    total_timesteps    | 37500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7499     |\n",
            "|    policy_loss        | 140      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 10.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7600     |\n",
            "|    time_elapsed       | 488      |\n",
            "|    total_timesteps    | 38000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7599     |\n",
            "|    policy_loss        | -201     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 45.9     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7700     |\n",
            "|    time_elapsed       | 495      |\n",
            "|    total_timesteps    | 38500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7699     |\n",
            "|    policy_loss        | 28.5     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 1.14     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7800     |\n",
            "|    time_elapsed       | 501      |\n",
            "|    total_timesteps    | 39000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7799     |\n",
            "|    policy_loss        | 520      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 162      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7900     |\n",
            "|    time_elapsed       | 508      |\n",
            "|    total_timesteps    | 39500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7899     |\n",
            "|    policy_loss        | -54.3    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 2.66     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8000     |\n",
            "|    time_elapsed       | 514      |\n",
            "|    total_timesteps    | 40000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7999     |\n",
            "|    policy_loss        | 58.2     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 2.26     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| environment/          |           |\n",
            "|    portfolio_value    | 4.63e+06  |\n",
            "|    total_cost         | 8.6e+03   |\n",
            "|    total_reward       | 3.63e+06  |\n",
            "|    total_reward_pct   | 363       |\n",
            "|    total_trades       | 58469     |\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 8100      |\n",
            "|    time_elapsed       | 520       |\n",
            "|    total_timesteps    | 40500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.3     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 8099      |\n",
            "|    policy_loss        | -20.9     |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 0.355     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8200     |\n",
            "|    time_elapsed       | 527      |\n",
            "|    total_timesteps    | 41000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8199     |\n",
            "|    policy_loss        | -38.6    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 0.935    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8300     |\n",
            "|    time_elapsed       | 533      |\n",
            "|    total_timesteps    | 41500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8299     |\n",
            "|    policy_loss        | 11.9     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 0.774    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8400     |\n",
            "|    time_elapsed       | 540      |\n",
            "|    total_timesteps    | 42000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8399     |\n",
            "|    policy_loss        | -39.8    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 1.31     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8500     |\n",
            "|    time_elapsed       | 546      |\n",
            "|    total_timesteps    | 42500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8499     |\n",
            "|    policy_loss        | -128     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 16.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8600     |\n",
            "|    time_elapsed       | 553      |\n",
            "|    total_timesteps    | 43000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8599     |\n",
            "|    policy_loss        | 70.4     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 2.69     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8700     |\n",
            "|    time_elapsed       | 559      |\n",
            "|    total_timesteps    | 43500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8699     |\n",
            "|    policy_loss        | 170      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 57.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.48e+06 |\n",
            "|    total_cost         | 5.81e+03 |\n",
            "|    total_reward       | 3.48e+06 |\n",
            "|    total_reward_pct   | 348      |\n",
            "|    total_trades       | 57804    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8800     |\n",
            "|    time_elapsed       | 565      |\n",
            "|    total_timesteps    | 44000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0.00216  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8799     |\n",
            "|    policy_loss        | -63      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 3.26     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8900     |\n",
            "|    time_elapsed       | 572      |\n",
            "|    total_timesteps    | 44500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8899     |\n",
            "|    policy_loss        | -111     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 7.1      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 9000     |\n",
            "|    time_elapsed       | 579      |\n",
            "|    total_timesteps    | 45000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8999     |\n",
            "|    policy_loss        | -72.9    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 3.48     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 9100      |\n",
            "|    time_elapsed       | 585       |\n",
            "|    total_timesteps    | 45500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 9099      |\n",
            "|    policy_loss        | -18.6     |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 0.765     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 9200     |\n",
            "|    time_elapsed       | 592      |\n",
            "|    total_timesteps    | 46000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9199     |\n",
            "|    policy_loss        | 71.5     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 2.94     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 9300     |\n",
            "|    time_elapsed       | 598      |\n",
            "|    total_timesteps    | 46500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9299     |\n",
            "|    policy_loss        | -8.18    |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.324    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 9400     |\n",
            "|    time_elapsed       | 605      |\n",
            "|    total_timesteps    | 47000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9399     |\n",
            "|    policy_loss        | -262     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 34.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.96e+06 |\n",
            "|    total_cost         | 4.92e+03 |\n",
            "|    total_reward       | 2.96e+06 |\n",
            "|    total_reward_pct   | 296      |\n",
            "|    total_trades       | 58228    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 9500     |\n",
            "|    time_elapsed       | 611      |\n",
            "|    total_timesteps    | 47500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 0.111    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9499     |\n",
            "|    policy_loss        | 58.7     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 2.02     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 9600     |\n",
            "|    time_elapsed       | 618      |\n",
            "|    total_timesteps    | 48000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9599     |\n",
            "|    policy_loss        | 103      |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 7.2      |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 9700      |\n",
            "|    time_elapsed       | 624       |\n",
            "|    total_timesteps    | 48500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.6     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 9699      |\n",
            "|    policy_loss        | -62       |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 4.3       |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 9800     |\n",
            "|    time_elapsed       | 631      |\n",
            "|    total_timesteps    | 49000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9799     |\n",
            "|    policy_loss        | 25.5     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 1.05     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 9900      |\n",
            "|    time_elapsed       | 637       |\n",
            "|    total_timesteps    | 49500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.6     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 9899      |\n",
            "|    policy_loss        | 34.8      |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 1.79      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10000    |\n",
            "|    time_elapsed       | 644      |\n",
            "|    total_timesteps    | 50000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9999     |\n",
            "|    policy_loss        | -288     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 45.4     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 10100     |\n",
            "|    time_elapsed       | 650       |\n",
            "|    total_timesteps    | 50500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.7     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 10099     |\n",
            "|    policy_loss        | 176       |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 17.8      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10200    |\n",
            "|    time_elapsed       | 657      |\n",
            "|    total_timesteps    | 51000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.7    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10199    |\n",
            "|    policy_loss        | -22.3    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 1.66     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.38e+06 |\n",
            "|    total_cost         | 5.66e+03 |\n",
            "|    total_reward       | 3.38e+06 |\n",
            "|    total_reward_pct   | 338      |\n",
            "|    total_trades       | 61155    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10300    |\n",
            "|    time_elapsed       | 664      |\n",
            "|    total_timesteps    | 51500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.8    |\n",
            "|    explained_variance | 3.46e-06 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10299    |\n",
            "|    policy_loss        | -18.2    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.59     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10400    |\n",
            "|    time_elapsed       | 670      |\n",
            "|    total_timesteps    | 52000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10399    |\n",
            "|    policy_loss        | -166     |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 15.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10500    |\n",
            "|    time_elapsed       | 677      |\n",
            "|    total_timesteps    | 52500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10499    |\n",
            "|    policy_loss        | -1.79    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.733    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10600    |\n",
            "|    time_elapsed       | 683      |\n",
            "|    total_timesteps    | 53000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10599    |\n",
            "|    policy_loss        | -46.4    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 1.3      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10700    |\n",
            "|    time_elapsed       | 690      |\n",
            "|    total_timesteps    | 53500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10699    |\n",
            "|    policy_loss        | 358      |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 72.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10800    |\n",
            "|    time_elapsed       | 696      |\n",
            "|    total_timesteps    | 54000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10799    |\n",
            "|    policy_loss        | 26.8     |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.888    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10900    |\n",
            "|    time_elapsed       | 703      |\n",
            "|    total_timesteps    | 54500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10899    |\n",
            "|    policy_loss        | -124     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 34.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.93e+06 |\n",
            "|    total_cost         | 7.95e+03 |\n",
            "|    total_reward       | 2.93e+06 |\n",
            "|    total_reward_pct   | 293      |\n",
            "|    total_trades       | 62120    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11000    |\n",
            "|    time_elapsed       | 710      |\n",
            "|    total_timesteps    | 55000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10999    |\n",
            "|    policy_loss        | 88.9     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 4.3      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11100    |\n",
            "|    time_elapsed       | 716      |\n",
            "|    total_timesteps    | 55500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11099    |\n",
            "|    policy_loss        | 25.5     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 0.656    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11200    |\n",
            "|    time_elapsed       | 723      |\n",
            "|    total_timesteps    | 56000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11199    |\n",
            "|    policy_loss        | -91.8    |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 5        |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11300    |\n",
            "|    time_elapsed       | 729      |\n",
            "|    total_timesteps    | 56500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11299    |\n",
            "|    policy_loss        | -168     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 18       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11400    |\n",
            "|    time_elapsed       | 736      |\n",
            "|    total_timesteps    | 57000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.1    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11399    |\n",
            "|    policy_loss        | 163      |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 11.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11500    |\n",
            "|    time_elapsed       | 742      |\n",
            "|    total_timesteps    | 57500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.1    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11499    |\n",
            "|    policy_loss        | -269     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 35.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11600    |\n",
            "|    time_elapsed       | 749      |\n",
            "|    total_timesteps    | 58000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11599    |\n",
            "|    policy_loss        | 117      |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 19.2     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.86e+06 |\n",
            "|    total_cost         | 9.73e+03 |\n",
            "|    total_reward       | 2.86e+06 |\n",
            "|    total_reward_pct   | 286      |\n",
            "|    total_trades       | 59593    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11700    |\n",
            "|    time_elapsed       | 756      |\n",
            "|    total_timesteps    | 58500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11699    |\n",
            "|    policy_loss        | 146      |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 15       |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 11800     |\n",
            "|    time_elapsed       | 762       |\n",
            "|    total_timesteps    | 59000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -45.1     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 11799     |\n",
            "|    policy_loss        | -6.42     |\n",
            "|    std                | 1.09      |\n",
            "|    value_loss         | 0.452     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11900    |\n",
            "|    time_elapsed       | 769      |\n",
            "|    total_timesteps    | 59500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11899    |\n",
            "|    policy_loss        | -116     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 8.42     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 12000    |\n",
            "|    time_elapsed       | 775      |\n",
            "|    total_timesteps    | 60000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11999    |\n",
            "|    policy_loss        | 115      |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 7.46     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 12100    |\n",
            "|    time_elapsed       | 782      |\n",
            "|    total_timesteps    | 60500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 12099    |\n",
            "|    policy_loss        | 27.9     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 2.52     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 12200    |\n",
            "|    time_elapsed       | 788      |\n",
            "|    total_timesteps    | 61000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 12199    |\n",
            "|    policy_loss        | -49.7    |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 4.56     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 12300     |\n",
            "|    time_elapsed       | 795       |\n",
            "|    total_timesteps    | 61500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -45.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 12299     |\n",
            "|    policy_loss        | -61.2     |\n",
            "|    std                | 1.09      |\n",
            "|    value_loss         | 2.91      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 12400    |\n",
            "|    time_elapsed       | 801      |\n",
            "|    total_timesteps    | 62000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 12399    |\n",
            "|    policy_loss        | 74.6     |\n",
            "|    std                | 1.1      |\n",
            "|    value_loss         | 15.6     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| environment/          |           |\n",
            "|    portfolio_value    | 4.12e+06  |\n",
            "|    total_cost         | 5.9e+03   |\n",
            "|    total_reward       | 3.12e+06  |\n",
            "|    total_reward_pct   | 312       |\n",
            "|    total_trades       | 61004     |\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 12500     |\n",
            "|    time_elapsed       | 808       |\n",
            "|    total_timesteps    | 62500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -45.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 12499     |\n",
            "|    policy_loss        | 61        |\n",
            "|    std                | 1.1       |\n",
            "|    value_loss         | 3.23      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 12600    |\n",
            "|    time_elapsed       | 815      |\n",
            "|    total_timesteps    | 63000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 12599    |\n",
            "|    policy_loss        | 74.9     |\n",
            "|    std                | 1.1      |\n",
            "|    value_loss         | 2.61     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 12700    |\n",
            "|    time_elapsed       | 821      |\n",
            "|    total_timesteps    | 63500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 12699    |\n",
            "|    policy_loss        | 77.4     |\n",
            "|    std                | 1.1      |\n",
            "|    value_loss         | 3.81     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 12800    |\n",
            "|    time_elapsed       | 828      |\n",
            "|    total_timesteps    | 64000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 12799    |\n",
            "|    policy_loss        | -14.7    |\n",
            "|    std                | 1.1      |\n",
            "|    value_loss         | 7.94     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 12900     |\n",
            "|    time_elapsed       | 834       |\n",
            "|    total_timesteps    | 64500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -45.5     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 12899     |\n",
            "|    policy_loss        | 1.03e+03  |\n",
            "|    std                | 1.1       |\n",
            "|    value_loss         | 505       |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13000    |\n",
            "|    time_elapsed       | 841      |\n",
            "|    total_timesteps    | 65000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 12999    |\n",
            "|    policy_loss        | 124      |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 11.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13100    |\n",
            "|    time_elapsed       | 848      |\n",
            "|    total_timesteps    | 65500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13099    |\n",
            "|    policy_loss        | -15      |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 1.22     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.65e+06 |\n",
            "|    total_cost         | 8.07e+03 |\n",
            "|    total_reward       | 3.65e+06 |\n",
            "|    total_reward_pct   | 365      |\n",
            "|    total_trades       | 62460    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13200    |\n",
            "|    time_elapsed       | 854      |\n",
            "|    total_timesteps    | 66000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13199    |\n",
            "|    policy_loss        | 46.3     |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 0.915    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13300    |\n",
            "|    time_elapsed       | 861      |\n",
            "|    total_timesteps    | 66500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13299    |\n",
            "|    policy_loss        | -14.1    |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 0.13     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13400    |\n",
            "|    time_elapsed       | 867      |\n",
            "|    total_timesteps    | 67000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13399    |\n",
            "|    policy_loss        | 65       |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 4.92     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13500    |\n",
            "|    time_elapsed       | 874      |\n",
            "|    total_timesteps    | 67500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13499    |\n",
            "|    policy_loss        | 121      |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 7.18     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 13600     |\n",
            "|    time_elapsed       | 880       |\n",
            "|    total_timesteps    | 68000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -45.7     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 13599     |\n",
            "|    policy_loss        | 81.3      |\n",
            "|    std                | 1.11      |\n",
            "|    value_loss         | 14.1      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13700    |\n",
            "|    time_elapsed       | 887      |\n",
            "|    total_timesteps    | 68500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.7    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13699    |\n",
            "|    policy_loss        | 104      |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 5.04     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13800    |\n",
            "|    time_elapsed       | 893      |\n",
            "|    total_timesteps    | 69000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13799    |\n",
            "|    policy_loss        | -263     |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 31.9     |\n",
            "------------------------------------\n",
            "day: 3650, episode: 20\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3959377.31\n",
            "total_reward: 2959377.31\n",
            "total_cost: 5535.60\n",
            "total_trades: 64004\n",
            "Sharpe: 0.755\n",
            "=================================\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.96e+06 |\n",
            "|    total_cost         | 5.54e+03 |\n",
            "|    total_reward       | 2.96e+06 |\n",
            "|    total_reward_pct   | 296      |\n",
            "|    total_trades       | 64004    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13900    |\n",
            "|    time_elapsed       | 900      |\n",
            "|    total_timesteps    | 69500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13899    |\n",
            "|    policy_loss        | 25.9     |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 0.428    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14000    |\n",
            "|    time_elapsed       | 907      |\n",
            "|    total_timesteps    | 70000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13999    |\n",
            "|    policy_loss        | 40.9     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 1.52     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14100    |\n",
            "|    time_elapsed       | 913      |\n",
            "|    total_timesteps    | 70500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14099    |\n",
            "|    policy_loss        | -0.995   |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 0.024    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14200    |\n",
            "|    time_elapsed       | 920      |\n",
            "|    total_timesteps    | 71000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14199    |\n",
            "|    policy_loss        | 21.5     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 0.753    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14300    |\n",
            "|    time_elapsed       | 926      |\n",
            "|    total_timesteps    | 71500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14299    |\n",
            "|    policy_loss        | 96.7     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 5.54     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14400    |\n",
            "|    time_elapsed       | 933      |\n",
            "|    total_timesteps    | 72000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14399    |\n",
            "|    policy_loss        | 99.8     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 5.77     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14500    |\n",
            "|    time_elapsed       | 940      |\n",
            "|    total_timesteps    | 72500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14499    |\n",
            "|    policy_loss        | 43.6     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 1.32     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14600    |\n",
            "|    time_elapsed       | 946      |\n",
            "|    total_timesteps    | 73000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14599    |\n",
            "|    policy_loss        | -874     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 365      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.87e+06 |\n",
            "|    total_cost         | 5.34e+03 |\n",
            "|    total_reward       | 2.87e+06 |\n",
            "|    total_reward_pct   | 287      |\n",
            "|    total_trades       | 67475    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14700    |\n",
            "|    time_elapsed       | 953      |\n",
            "|    total_timesteps    | 73500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14699    |\n",
            "|    policy_loss        | 36.2     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 0.616    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14800    |\n",
            "|    time_elapsed       | 960      |\n",
            "|    total_timesteps    | 74000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14799    |\n",
            "|    policy_loss        | -129     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 12.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14900    |\n",
            "|    time_elapsed       | 966      |\n",
            "|    total_timesteps    | 74500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46      |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14899    |\n",
            "|    policy_loss        | 27.7     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 1.76     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 15000    |\n",
            "|    time_elapsed       | 973      |\n",
            "|    total_timesteps    | 75000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14999    |\n",
            "|    policy_loss        | 84       |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 5.1      |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 15100     |\n",
            "|    time_elapsed       | 979       |\n",
            "|    total_timesteps    | 75500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -45.9     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 15099     |\n",
            "|    policy_loss        | -7.35     |\n",
            "|    std                | 1.12      |\n",
            "|    value_loss         | 1.71      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 15200    |\n",
            "|    time_elapsed       | 986      |\n",
            "|    total_timesteps    | 76000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 15199    |\n",
            "|    policy_loss        | 126      |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 8.75     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 15300    |\n",
            "|    time_elapsed       | 993      |\n",
            "|    total_timesteps    | 76500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 15299    |\n",
            "|    policy_loss        | 190      |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 31.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.14e+06 |\n",
            "|    total_cost         | 4.28e+03 |\n",
            "|    total_reward       | 3.14e+06 |\n",
            "|    total_reward_pct   | 314      |\n",
            "|    total_trades       | 66224    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 15400    |\n",
            "|    time_elapsed       | 999      |\n",
            "|    total_timesteps    | 77000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 15399    |\n",
            "|    policy_loss        | 15.4     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 0.418    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 15500     |\n",
            "|    time_elapsed       | 1006      |\n",
            "|    total_timesteps    | 77500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.1     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 15499     |\n",
            "|    policy_loss        | -1.22     |\n",
            "|    std                | 1.13      |\n",
            "|    value_loss         | 0.0144    |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 15600    |\n",
            "|    time_elapsed       | 1012     |\n",
            "|    total_timesteps    | 78000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 15599    |\n",
            "|    policy_loss        | 126      |\n",
            "|    std                | 1.13     |\n",
            "|    value_loss         | 6.33     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 15700     |\n",
            "|    time_elapsed       | 1019      |\n",
            "|    total_timesteps    | 78500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 15699     |\n",
            "|    policy_loss        | -1.57     |\n",
            "|    std                | 1.13      |\n",
            "|    value_loss         | 0.0992    |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 15800    |\n",
            "|    time_elapsed       | 1026     |\n",
            "|    total_timesteps    | 79000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.2    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 15799    |\n",
            "|    policy_loss        | 177      |\n",
            "|    std                | 1.13     |\n",
            "|    value_loss         | 15.9     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 15900    |\n",
            "|    time_elapsed       | 1032     |\n",
            "|    total_timesteps    | 79500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 15899    |\n",
            "|    policy_loss        | -99.7    |\n",
            "|    std                | 1.13     |\n",
            "|    value_loss         | 7.94     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 16000     |\n",
            "|    time_elapsed       | 1039      |\n",
            "|    total_timesteps    | 80000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 15999     |\n",
            "|    policy_loss        | -482      |\n",
            "|    std                | 1.13      |\n",
            "|    value_loss         | 114       |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| environment/          |           |\n",
            "|    portfolio_value    | 3.94e+06  |\n",
            "|    total_cost         | 3.49e+03  |\n",
            "|    total_reward       | 2.94e+06  |\n",
            "|    total_reward_pct   | 294       |\n",
            "|    total_trades       | 64301     |\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 16100     |\n",
            "|    time_elapsed       | 1045      |\n",
            "|    total_timesteps    | 80500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 16099     |\n",
            "|    policy_loss        | -23.5     |\n",
            "|    std                | 1.13      |\n",
            "|    value_loss         | 1.91      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 16200    |\n",
            "|    time_elapsed       | 1052     |\n",
            "|    total_timesteps    | 81000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 16199    |\n",
            "|    policy_loss        | 46.5     |\n",
            "|    std                | 1.13     |\n",
            "|    value_loss         | 3.81     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 16300    |\n",
            "|    time_elapsed       | 1058     |\n",
            "|    total_timesteps    | 81500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 16299    |\n",
            "|    policy_loss        | 18.1     |\n",
            "|    std                | 1.13     |\n",
            "|    value_loss         | 0.592    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 16400    |\n",
            "|    time_elapsed       | 1065     |\n",
            "|    total_timesteps    | 82000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 16399    |\n",
            "|    policy_loss        | 151      |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 11.9     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 16500     |\n",
            "|    time_elapsed       | 1071      |\n",
            "|    total_timesteps    | 82500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.3     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 16499     |\n",
            "|    policy_loss        | -151      |\n",
            "|    std                | 1.13      |\n",
            "|    value_loss         | 18.1      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 16600    |\n",
            "|    time_elapsed       | 1078     |\n",
            "|    total_timesteps    | 83000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 16599    |\n",
            "|    policy_loss        | -409     |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 79       |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 16700     |\n",
            "|    time_elapsed       | 1084      |\n",
            "|    total_timesteps    | 83500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.3     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 16699     |\n",
            "|    policy_loss        | 44.8      |\n",
            "|    std                | 1.14      |\n",
            "|    value_loss         | 7.51      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.12e+06 |\n",
            "|    total_cost         | 3.41e+03 |\n",
            "|    total_reward       | 3.12e+06 |\n",
            "|    total_reward_pct   | 312      |\n",
            "|    total_trades       | 61475    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 16800    |\n",
            "|    time_elapsed       | 1090     |\n",
            "|    total_timesteps    | 84000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 16799    |\n",
            "|    policy_loss        | 11       |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 0.286    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 16900    |\n",
            "|    time_elapsed       | 1097     |\n",
            "|    total_timesteps    | 84500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.4    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 16899    |\n",
            "|    policy_loss        | -24.2    |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 5.16     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 17000    |\n",
            "|    time_elapsed       | 1103     |\n",
            "|    total_timesteps    | 85000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.4    |\n",
            "|    explained_variance | 1.79e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 16999    |\n",
            "|    policy_loss        | 38.8     |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 2.14     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 17100    |\n",
            "|    time_elapsed       | 1110     |\n",
            "|    total_timesteps    | 85500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 17099    |\n",
            "|    policy_loss        | 1.28     |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 0.161    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 17200    |\n",
            "|    time_elapsed       | 1116     |\n",
            "|    total_timesteps    | 86000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 17199    |\n",
            "|    policy_loss        | -175     |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 14.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 17300    |\n",
            "|    time_elapsed       | 1123     |\n",
            "|    total_timesteps    | 86500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 17299    |\n",
            "|    policy_loss        | -126     |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 11.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 17400    |\n",
            "|    time_elapsed       | 1129     |\n",
            "|    total_timesteps    | 87000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 17399    |\n",
            "|    policy_loss        | -48.4    |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 1.55     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 17500     |\n",
            "|    time_elapsed       | 1136      |\n",
            "|    total_timesteps    | 87500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 17499     |\n",
            "|    policy_loss        | 139       |\n",
            "|    std                | 1.14      |\n",
            "|    value_loss         | 10.4      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| environment/          |           |\n",
            "|    portfolio_value    | 4.17e+06  |\n",
            "|    total_cost         | 6.22e+03  |\n",
            "|    total_reward       | 3.17e+06  |\n",
            "|    total_reward_pct   | 317       |\n",
            "|    total_trades       | 58146     |\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 17600     |\n",
            "|    time_elapsed       | 1142      |\n",
            "|    total_timesteps    | 88000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 17599     |\n",
            "|    policy_loss        | 10.5      |\n",
            "|    std                | 1.14      |\n",
            "|    value_loss         | 0.0937    |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 17700    |\n",
            "|    time_elapsed       | 1149     |\n",
            "|    total_timesteps    | 88500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 17699    |\n",
            "|    policy_loss        | -62.6    |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 2.89     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 17800    |\n",
            "|    time_elapsed       | 1156     |\n",
            "|    total_timesteps    | 89000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 17799    |\n",
            "|    policy_loss        | 64.3     |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 1.99     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 17900     |\n",
            "|    time_elapsed       | 1162      |\n",
            "|    total_timesteps    | 89500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 17899     |\n",
            "|    policy_loss        | 36.1      |\n",
            "|    std                | 1.14      |\n",
            "|    value_loss         | 1.18      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 18000     |\n",
            "|    time_elapsed       | 1169      |\n",
            "|    total_timesteps    | 90000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 17999     |\n",
            "|    policy_loss        | 117       |\n",
            "|    std                | 1.14      |\n",
            "|    value_loss         | 13.6      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 18100    |\n",
            "|    time_elapsed       | 1176     |\n",
            "|    total_timesteps    | 90500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18099    |\n",
            "|    policy_loss        | 138      |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 25.8     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 18200    |\n",
            "|    time_elapsed       | 1183     |\n",
            "|    total_timesteps    | 91000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18199    |\n",
            "|    policy_loss        | -109     |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 19.1     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.4e+06  |\n",
            "|    total_cost         | 8.75e+03 |\n",
            "|    total_reward       | 3.4e+06  |\n",
            "|    total_reward_pct   | 340      |\n",
            "|    total_trades       | 64975    |\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 18300    |\n",
            "|    time_elapsed       | 1189     |\n",
            "|    total_timesteps    | 91500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.5    |\n",
            "|    explained_variance | -0.0014  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18299    |\n",
            "|    policy_loss        | 22       |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 0.829    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 18400    |\n",
            "|    time_elapsed       | 1196     |\n",
            "|    total_timesteps    | 92000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18399    |\n",
            "|    policy_loss        | 12.7     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 0.192    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 18500    |\n",
            "|    time_elapsed       | 1202     |\n",
            "|    total_timesteps    | 92500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18499    |\n",
            "|    policy_loss        | -80.5    |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 5.62     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 18600    |\n",
            "|    time_elapsed       | 1209     |\n",
            "|    total_timesteps    | 93000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18599    |\n",
            "|    policy_loss        | 127      |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 8.09     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 18700    |\n",
            "|    time_elapsed       | 1215     |\n",
            "|    total_timesteps    | 93500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18699    |\n",
            "|    policy_loss        | -108     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 17.2     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 18800     |\n",
            "|    time_elapsed       | 1222      |\n",
            "|    total_timesteps    | 94000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.7     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 18799     |\n",
            "|    policy_loss        | -145      |\n",
            "|    std                | 1.15      |\n",
            "|    value_loss         | 11.8      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 18900     |\n",
            "|    time_elapsed       | 1229      |\n",
            "|    total_timesteps    | 94500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.6     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 18899     |\n",
            "|    policy_loss        | 150       |\n",
            "|    std                | 1.15      |\n",
            "|    value_loss         | 14.4      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.16e+06 |\n",
            "|    total_cost         | 7.62e+03 |\n",
            "|    total_reward       | 3.16e+06 |\n",
            "|    total_reward_pct   | 316      |\n",
            "|    total_trades       | 66603    |\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19000    |\n",
            "|    time_elapsed       | 1235     |\n",
            "|    total_timesteps    | 95000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18999    |\n",
            "|    policy_loss        | 260      |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 30.2     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19100    |\n",
            "|    time_elapsed       | 1242     |\n",
            "|    total_timesteps    | 95500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19099    |\n",
            "|    policy_loss        | 97.7     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 7.94     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19200    |\n",
            "|    time_elapsed       | 1248     |\n",
            "|    total_timesteps    | 96000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19199    |\n",
            "|    policy_loss        | 53.5     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 1.59     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19300    |\n",
            "|    time_elapsed       | 1255     |\n",
            "|    total_timesteps    | 96500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19299    |\n",
            "|    policy_loss        | 165      |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 15.8     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19400    |\n",
            "|    time_elapsed       | 1262     |\n",
            "|    total_timesteps    | 97000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19399    |\n",
            "|    policy_loss        | 1.48     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 0.168    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19500    |\n",
            "|    time_elapsed       | 1268     |\n",
            "|    total_timesteps    | 97500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.7    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19499    |\n",
            "|    policy_loss        | -463     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 104      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19600    |\n",
            "|    time_elapsed       | 1275     |\n",
            "|    total_timesteps    | 98000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19599    |\n",
            "|    policy_loss        | -39.8    |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 1.29     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 19700     |\n",
            "|    time_elapsed       | 1281      |\n",
            "|    total_timesteps    | 98500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.8     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 19699     |\n",
            "|    policy_loss        | -1.11e+03 |\n",
            "|    std                | 1.15      |\n",
            "|    value_loss         | 583       |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| environment/          |           |\n",
            "|    portfolio_value    | 4.42e+06  |\n",
            "|    total_cost         | 5.67e+03  |\n",
            "|    total_reward       | 3.42e+06  |\n",
            "|    total_reward_pct   | 342       |\n",
            "|    total_trades       | 57577     |\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 19800     |\n",
            "|    time_elapsed       | 1288      |\n",
            "|    total_timesteps    | 99000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.7     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 19799     |\n",
            "|    policy_loss        | -3.03     |\n",
            "|    std                | 1.15      |\n",
            "|    value_loss         | 0.427     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19900    |\n",
            "|    time_elapsed       | 1295     |\n",
            "|    total_timesteps    | 99500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19899    |\n",
            "|    policy_loss        | -127     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 6.54     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 20000    |\n",
            "|    time_elapsed       | 1301     |\n",
            "|    total_timesteps    | 100000   |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.8    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19999    |\n",
            "|    policy_loss        | 79.1     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 4.91     |\n",
            "------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRiOtrywfAo1"
      },
      "source": [
        "### Model 2: DDPG"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2YadjfnLwgt",
        "outputId": "6344f714-db1f-40f7-c62e-bed8c0e76bc2"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "model_ddpg = agent.get_model(\"ddpg\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 50000, 'learning_rate': 0.001}\n",
            "Using cuda device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "tCDa78rqfO_a",
        "outputId": "06d093c2-4c33-4167-bd97-5edb82dd4e71"
      },
      "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_3\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 66       |\n",
            "|    time_elapsed    | 218      |\n",
            "|    total timesteps | 14604    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -40.1    |\n",
            "|    critic_loss     | 213      |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 10953    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 62       |\n",
            "|    time_elapsed    | 463      |\n",
            "|    total timesteps | 29208    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -14.9    |\n",
            "|    critic_loss     | 2.92     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 25557    |\n",
            "---------------------------------\n",
            "day: 3650, episode: 40\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3024712.17\n",
            "total_reward: 2024712.17\n",
            "total_cost: 999.00\n",
            "total_trades: 72983\n",
            "Sharpe: 0.626\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 12       |\n",
            "|    fps             | 61       |\n",
            "|    time_elapsed    | 710      |\n",
            "|    total timesteps | 43812    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -10.4    |\n",
            "|    critic_loss     | 2.28     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 40161    |\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gDkU-j-fCmZ"
      },
      "source": [
        "### Model 3: PPO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5D5PFUhMzSV",
        "outputId": "d5ba80d5-b33e-4f54-8c70-51f6b003119c"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "PPO_PARAMS = {\n",
        "    \"n_steps\": 2048,\n",
        "    \"ent_coef\": 0.01,\n",
        "    \"learning_rate\": 0.00025,\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': 0.01, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Gt8eIQKYM4G3",
        "outputId": "81bbadeb-1229-490c-a8a5-78d523595a14"
      },
      "source": [
        "trained_ppo = agent.train_model(model=model_ppo, \n",
        "                             tb_log_name='ppo',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ppo/ppo_1\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 76   |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 26   |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 75          |\n",
            "|    iterations           | 2           |\n",
            "|    time_elapsed         | 54          |\n",
            "|    total_timesteps      | 4096        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.018077655 |\n",
            "|    clip_fraction        | 0.231       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -0.0123     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3.1         |\n",
            "|    n_updates            | 10          |\n",
            "|    policy_gradient_loss | -0.0292     |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 7.21        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 75          |\n",
            "|    iterations           | 3           |\n",
            "|    time_elapsed         | 81          |\n",
            "|    total_timesteps      | 6144        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.013950874 |\n",
            "|    clip_fraction        | 0.17        |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -0.00143    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.92        |\n",
            "|    n_updates            | 20          |\n",
            "|    policy_gradient_loss | -0.0229     |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 13.6        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 74         |\n",
            "|    iterations           | 4          |\n",
            "|    time_elapsed         | 109        |\n",
            "|    total_timesteps      | 8192       |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.01641549 |\n",
            "|    clip_fraction        | 0.183      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -42.6      |\n",
            "|    explained_variance   | -0.0118    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 6.06       |\n",
            "|    n_updates            | 30         |\n",
            "|    policy_gradient_loss | -0.0282    |\n",
            "|    std                  | 1          |\n",
            "|    value_loss           | 11.9       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 136         |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.025979618 |\n",
            "|    clip_fraction        | 0.241       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | -0.00485    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 13          |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0157     |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 19.3        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 6           |\n",
            "|    time_elapsed         | 163         |\n",
            "|    total_timesteps      | 12288       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.016590398 |\n",
            "|    clip_fraction        | 0.21        |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | -0.0153     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3.53        |\n",
            "|    n_updates            | 50          |\n",
            "|    policy_gradient_loss | -0.0251     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 11          |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 7           |\n",
            "|    time_elapsed         | 191         |\n",
            "|    total_timesteps      | 14336       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020591479 |\n",
            "|    clip_fraction        | 0.229       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.8       |\n",
            "|    explained_variance   | 0.0132      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.55        |\n",
            "|    n_updates            | 60          |\n",
            "|    policy_gradient_loss | -0.0197     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 14.2        |\n",
            "-----------------------------------------\n",
            "day: 3650, episode: 20\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2689089.43\n",
            "total_reward: 1689089.43\n",
            "total_cost: 380034.51\n",
            "total_trades: 104033\n",
            "Sharpe: 0.638\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 8           |\n",
            "|    time_elapsed         | 218         |\n",
            "|    total_timesteps      | 16384       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020969098 |\n",
            "|    clip_fraction        | 0.268       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.9       |\n",
            "|    explained_variance   | -0.000622   |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 7           |\n",
            "|    n_updates            | 70          |\n",
            "|    policy_gradient_loss | -0.0173     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 12.7        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 74         |\n",
            "|    iterations           | 9          |\n",
            "|    time_elapsed         | 246        |\n",
            "|    total_timesteps      | 18432      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02511882 |\n",
            "|    clip_fraction        | 0.28       |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43        |\n",
            "|    explained_variance   | -0.0182    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 5.87       |\n",
            "|    n_updates            | 80         |\n",
            "|    policy_gradient_loss | -0.0152    |\n",
            "|    std                  | 1.02       |\n",
            "|    value_loss           | 11.4       |\n",
            "----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 74         |\n",
            "|    iterations           | 10         |\n",
            "|    time_elapsed         | 273        |\n",
            "|    total_timesteps      | 20480      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02978786 |\n",
            "|    clip_fraction        | 0.276      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.1      |\n",
            "|    explained_variance   | 0.00553    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 7.83       |\n",
            "|    n_updates            | 90         |\n",
            "|    policy_gradient_loss | -0.0169    |\n",
            "|    std                  | 1.02       |\n",
            "|    value_loss           | 18         |\n",
            "----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 74         |\n",
            "|    iterations           | 11         |\n",
            "|    time_elapsed         | 301        |\n",
            "|    total_timesteps      | 22528      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02986148 |\n",
            "|    clip_fraction        | 0.263      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.1      |\n",
            "|    explained_variance   | -0.0465    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 4.94       |\n",
            "|    n_updates            | 100        |\n",
            "|    policy_gradient_loss | -0.0156    |\n",
            "|    std                  | 1.02       |\n",
            "|    value_loss           | 9.13       |\n",
            "----------------------------------------\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 74        |\n",
            "|    iterations           | 12        |\n",
            "|    time_elapsed         | 328       |\n",
            "|    total_timesteps      | 24576     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0287299 |\n",
            "|    clip_fraction        | 0.267     |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -43.2     |\n",
            "|    explained_variance   | -0.00762  |\n",
            "|    learning_rate        | 0.00025   |\n",
            "|    loss                 | 9.6       |\n",
            "|    n_updates            | 110       |\n",
            "|    policy_gradient_loss | -0.0165   |\n",
            "|    std                  | 1.02      |\n",
            "|    value_loss           | 20.9      |\n",
            "---------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 13          |\n",
            "|    time_elapsed         | 355         |\n",
            "|    total_timesteps      | 26624       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.023247771 |\n",
            "|    clip_fraction        | 0.264       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.2       |\n",
            "|    explained_variance   | -0.0257     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 2.04        |\n",
            "|    n_updates            | 120         |\n",
            "|    policy_gradient_loss | -0.0105     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 6.91        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 14          |\n",
            "|    time_elapsed         | 382         |\n",
            "|    total_timesteps      | 28672       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020957708 |\n",
            "|    clip_fraction        | 0.243       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.3       |\n",
            "|    explained_variance   | -0.00506    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3.57        |\n",
            "|    n_updates            | 130         |\n",
            "|    policy_gradient_loss | -0.0166     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 11.4        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 15          |\n",
            "|    time_elapsed         | 410         |\n",
            "|    total_timesteps      | 30720       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.032833345 |\n",
            "|    clip_fraction        | 0.296       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.4       |\n",
            "|    explained_variance   | -0.0181     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 2.71        |\n",
            "|    n_updates            | 140         |\n",
            "|    policy_gradient_loss | -0.0192     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 7.09        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 74         |\n",
            "|    iterations           | 16         |\n",
            "|    time_elapsed         | 437        |\n",
            "|    total_timesteps      | 32768      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02443498 |\n",
            "|    clip_fraction        | 0.241      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.5      |\n",
            "|    explained_variance   | -0.0293    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 4.9        |\n",
            "|    n_updates            | 150        |\n",
            "|    policy_gradient_loss | -0.0277    |\n",
            "|    std                  | 1.03       |\n",
            "|    value_loss           | 8.48       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 17          |\n",
            "|    time_elapsed         | 464         |\n",
            "|    total_timesteps      | 34816       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.016761096 |\n",
            "|    clip_fraction        | 0.233       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.5       |\n",
            "|    explained_variance   | -0.0188     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3           |\n",
            "|    n_updates            | 160         |\n",
            "|    policy_gradient_loss | -0.0162     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 9.8         |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 74         |\n",
            "|    iterations           | 18         |\n",
            "|    time_elapsed         | 491        |\n",
            "|    total_timesteps      | 36864      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.03664797 |\n",
            "|    clip_fraction        | 0.34       |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.6      |\n",
            "|    explained_variance   | 0.00438    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 4.71       |\n",
            "|    n_updates            | 170        |\n",
            "|    policy_gradient_loss | -0.0206    |\n",
            "|    std                  | 1.04       |\n",
            "|    value_loss           | 6.72       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 19          |\n",
            "|    time_elapsed         | 518         |\n",
            "|    total_timesteps      | 38912       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.030327313 |\n",
            "|    clip_fraction        | 0.308       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.6       |\n",
            "|    explained_variance   | 0.0309      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6           |\n",
            "|    n_updates            | 180         |\n",
            "|    policy_gradient_loss | -0.00991    |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 11.2        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 75          |\n",
            "|    iterations           | 20          |\n",
            "|    time_elapsed         | 545         |\n",
            "|    total_timesteps      | 40960       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.028725432 |\n",
            "|    clip_fraction        | 0.244       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.7       |\n",
            "|    explained_variance   | 0.0224      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 1.66        |\n",
            "|    n_updates            | 190         |\n",
            "|    policy_gradient_loss | -0.00988    |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 6.45        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 75          |\n",
            "|    iterations           | 21          |\n",
            "|    time_elapsed         | 573         |\n",
            "|    total_timesteps      | 43008       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.035785355 |\n",
            "|    clip_fraction        | 0.252       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.8       |\n",
            "|    explained_variance   | -0.00199    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 7.69        |\n",
            "|    n_updates            | 200         |\n",
            "|    policy_gradient_loss | -0.00915    |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 14.2        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 75         |\n",
            "|    iterations           | 22         |\n",
            "|    time_elapsed         | 600        |\n",
            "|    total_timesteps      | 45056      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02488321 |\n",
            "|    clip_fraction        | 0.259      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.8      |\n",
            "|    explained_variance   | 0.0224     |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 3.55       |\n",
            "|    n_updates            | 210        |\n",
            "|    policy_gradient_loss | -0.00496   |\n",
            "|    std                  | 1.04       |\n",
            "|    value_loss           | 8.94       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 75          |\n",
            "|    iterations           | 23          |\n",
            "|    time_elapsed         | 627         |\n",
            "|    total_timesteps      | 47104       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.024153344 |\n",
            "|    clip_fraction        | 0.272       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.9       |\n",
            "|    explained_variance   | -0.0405     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.38        |\n",
            "|    n_updates            | 220         |\n",
            "|    policy_gradient_loss | -0.0153     |\n",
            "|    std                  | 1.05        |\n",
            "|    value_loss           | 14.4        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 75          |\n",
            "|    iterations           | 24          |\n",
            "|    time_elapsed         | 654         |\n",
            "|    total_timesteps      | 49152       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.037768982 |\n",
            "|    clip_fraction        | 0.309       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44         |\n",
            "|    explained_variance   | -0.00958    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 4.37        |\n",
            "|    n_updates            | 230         |\n",
            "|    policy_gradient_loss | 0.00366     |\n",
            "|    std                  | 1.05        |\n",
            "|    value_loss           | 12.4        |\n",
            "-----------------------------------------\n",
            "day: 3650, episode: 30\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3250700.91\n",
            "total_reward: 2250700.91\n",
            "total_cost: 329606.76\n",
            "total_trades: 98701\n",
            "Sharpe: 0.750\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 75          |\n",
            "|    iterations           | 25          |\n",
            "|    time_elapsed         | 681         |\n",
            "|    total_timesteps      | 51200       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.034196474 |\n",
            "|    clip_fraction        | 0.301       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.1       |\n",
            "|    explained_variance   | -0.00227    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3.84        |\n",
            "|    n_updates            | 240         |\n",
            "|    policy_gradient_loss | -0.0192     |\n",
            "|    std                  | 1.05        |\n",
            "|    value_loss           | 11.4        |\n",
            "-----------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Zpv4S0-fDBv"
      },
      "source": [
        "### Model 4: TD3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSAHhV4Xc-bh",
        "outputId": "000d49bb-5566-41a2-b2fa-1012d8f5db43"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "TD3_PARAMS = {\"batch_size\": 100, \n",
        "              \"buffer_size\": 1000000, \n",
        "              \"learning_rate\": 0.001}\n",
        "\n",
        "model_td3 = agent.get_model(\"td3\",model_kwargs = TD3_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 100, 'buffer_size': 1000000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSRxNYAxdKpU",
        "outputId": "617083d0-d21d-40e1-b7ac-1b9b403ca355"
      },
      "source": [
        "trained_td3 = agent.train_model(model=model_td3, \n",
        "                             tb_log_name='td3',\n",
        "                             total_timesteps=30000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/td3/td3_1\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 26       |\n",
            "|    time_elapsed    | 559      |\n",
            "|    total timesteps | 14604    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 4.64     |\n",
            "|    critic_loss     | 720      |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 10953    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 22       |\n",
            "|    time_elapsed    | 1289     |\n",
            "|    total timesteps | 29208    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 10.6     |\n",
            "|    critic_loss     | 14.2     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 25557    |\n",
            "---------------------------------\n",
            "day: 3650, episode: 40\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3319811.55\n",
            "total_reward: 2319811.55\n",
            "total_cost: 998.99\n",
            "total_trades: 51100\n",
            "Sharpe: 0.649\n",
            "=================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dr49PotrfG01"
      },
      "source": [
        "### Model 5: SAC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwOhVjqRkCdM",
        "outputId": "98852a78-1426-40cf-a7c4-f74490940a70"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "SAC_PARAMS = {\n",
        "    \"batch_size\": 128,\n",
        "    \"buffer_size\": 1000000,\n",
        "    \"learning_rate\": 0.0001,\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': 1000000, 'learning_rate': 0.0001, 'learning_starts': 100, 'ent_coef': 'auto_0.1'}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8RSdKCckJyH",
        "outputId": "857cb26c-81af-41a0-df39-c57942be7909"
      },
      "source": [
        "trained_sac = agent.train_model(model=model_sac, \n",
        "                             tb_log_name='sac',\n",
        "                             total_timesteps=80000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/sac/sac_2\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 21       |\n",
            "|    time_elapsed    | 685      |\n",
            "|    total timesteps | 14604    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 172      |\n",
            "|    critic_loss     | 28.6     |\n",
            "|    ent_coef        | 0.0742   |\n",
            "|    ent_coef_loss   | -126     |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 14503    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 20       |\n",
            "|    time_elapsed    | 1401     |\n",
            "|    total timesteps | 29208    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 9.68     |\n",
            "|    critic_loss     | 9.81     |\n",
            "|    ent_coef        | 0.0174   |\n",
            "|    ent_coef_loss   | -173     |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 29107    |\n",
            "---------------------------------\n",
            "day: 3650, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4889674.97\n",
            "total_reward: 3889674.97\n",
            "total_cost: 7706.97\n",
            "total_trades: 70158\n",
            "Sharpe: 0.752\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 12       |\n",
            "|    fps             | 20       |\n",
            "|    time_elapsed    | 2114     |\n",
            "|    total timesteps | 43812    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -13.3    |\n",
            "|    critic_loss     | 14       |\n",
            "|    ent_coef        | 0.00427  |\n",
            "|    ent_coef_loss   | -128     |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 43711    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 16       |\n",
            "|    fps             | 20       |\n",
            "|    time_elapsed    | 2842     |\n",
            "|    total timesteps | 58416    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -7       |\n",
            "|    critic_loss     | 8.71     |\n",
            "|    ent_coef        | 0.00148  |\n",
            "|    ent_coef_loss   | -3.87    |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 58315    |\n",
            "---------------------------------\n",
            "day: 3650, episode: 20\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3389166.27\n",
            "total_reward: 2389166.27\n",
            "total_cost: 1895.61\n",
            "total_trades: 62481\n",
            "Sharpe: 0.623\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 20       |\n",
            "|    fps             | 20       |\n",
            "|    time_elapsed    | 3585     |\n",
            "|    total timesteps | 73020    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -4.82    |\n",
            "|    critic_loss     | 12.4     |\n",
            "|    ent_coef        | 0.00159  |\n",
            "|    ent_coef_loss   | -4.38    |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 72919    |\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2wZgkQXh1jE"
      },
      "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": "markdown",
      "metadata": {
        "id": "U5mmgQF_h1jQ"
      },
      "source": [
        "### Trade\n",
        "\n",
        "DRL model needs to update periodically in order to take full advantage of the data, ideally we need to retrain our model yearly, quarterly, or monthly. We also need to tune the parameters along the way, in this notebook I only use the in-sample data from 2009-01 to 2018-12 to tune the parameters once, so there is some alpha decay here as the length of trade date extends. \n",
        "\n",
        "Numerous hyperparameters – e.g. the learning rate, the total number of samples to train on – influence the learning process and are usually determined by testing some variations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIqoV0GSI52v"
      },
      "source": [
        "trade = data_split(processed_full, '2019-01-01','2021-01-01')\n",
        "e_trade_gym = StockTradingEnv(df = trade, **env_kwargs)\n",
        "# env_trade, obs_trade = e_trade_gym.get_sb_env()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "W_XNgGsBMeVw",
        "outputId": "87d3c734-ca43-4b2e-e4a7-195b5bdf93e3"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>OPM</th>\n",
              "      <th>NPM</th>\n",
              "      <th>ROA</th>\n",
              "      <th>ROE</th>\n",
              "      <th>cur_ratio</th>\n",
              "      <th>quick_ratio</th>\n",
              "      <th>cash_ratio</th>\n",
              "      <th>inv_turnover</th>\n",
              "      <th>acc_rec_turnover</th>\n",
              "      <th>acc_pay_turnover</th>\n",
              "      <th>debt_ratio</th>\n",
              "      <th>debt_to_equity</th>\n",
              "      <th>PE</th>\n",
              "      <th>PB</th>\n",
              "      <th>Div_yield</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>38.722500</td>\n",
              "      <td>39.712502</td>\n",
              "      <td>38.557499</td>\n",
              "      <td>38.382229</td>\n",
              "      <td>148158800.0</td>\n",
              "      <td>0.258891</td>\n",
              "      <td>0.227773</td>\n",
              "      <td>0.133360</td>\n",
              "      <td>0.430843</td>\n",
              "      <td>1.315382</td>\n",
              "      <td>1.134347</td>\n",
              "      <td>0.854114</td>\n",
              "      <td>23.571867</td>\n",
              "      <td>7.620024</td>\n",
              "      <td>3.781658</td>\n",
              "      <td>0.690466</td>\n",
              "      <td>2.230663</td>\n",
              "      <td>5.728691</td>\n",
              "      <td>1.670488</td>\n",
              "      <td>0.019019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>AXP</td>\n",
              "      <td>93.910004</td>\n",
              "      <td>96.269997</td>\n",
              "      <td>93.769997</td>\n",
              "      <td>91.803406</td>\n",
              "      <td>4175400.0</td>\n",
              "      <td>0.203479</td>\n",
              "      <td>0.160494</td>\n",
              "      <td>0.026811</td>\n",
              "      <td>0.237960</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.231669</td>\n",
              "      <td>0.279424</td>\n",
              "      <td>0.887329</td>\n",
              "      <td>7.875371</td>\n",
              "      <td>50.720114</td>\n",
              "      <td>3.458432</td>\n",
              "      <td>0.004248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>BA</td>\n",
              "      <td>316.190002</td>\n",
              "      <td>323.950012</td>\n",
              "      <td>313.709991</td>\n",
              "      <td>314.645142</td>\n",
              "      <td>3292200.0</td>\n",
              "      <td>0.116496</td>\n",
              "      <td>0.102682</td>\n",
              "      <td>0.066409</td>\n",
              "      <td>34.409483</td>\n",
              "      <td>1.070490</td>\n",
              "      <td>0.262465</td>\n",
              "      <td>0.092436</td>\n",
              "      <td>0.933164</td>\n",
              "      <td>5.468453</td>\n",
              "      <td>4.151637</td>\n",
              "      <td>0.998070</td>\n",
              "      <td>517.142241</td>\n",
              "      <td>83.019826</td>\n",
              "      <td>1418.196271</td>\n",
              "      <td>0.006531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>CAT</td>\n",
              "      <td>124.029999</td>\n",
              "      <td>127.879997</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>117.506577</td>\n",
              "      <td>4783200.0</td>\n",
              "      <td>0.186871</td>\n",
              "      <td>0.107064</td>\n",
              "      <td>0.056932</td>\n",
              "      <td>0.289572</td>\n",
              "      <td>1.428582</td>\n",
              "      <td>0.919490</td>\n",
              "      <td>0.266175</td>\n",
              "      <td>2.135008</td>\n",
              "      <td>2.339630</td>\n",
              "      <td>3.660183</td>\n",
              "      <td>0.803394</td>\n",
              "      <td>4.086316</td>\n",
              "      <td>35.716285</td>\n",
              "      <td>4.351801</td>\n",
              "      <td>0.007319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>42.279999</td>\n",
              "      <td>43.200001</td>\n",
              "      <td>42.209999</td>\n",
              "      <td>39.496738</td>\n",
              "      <td>23833500.0</td>\n",
              "      <td>0.263373</td>\n",
              "      <td>0.261680</td>\n",
              "      <td>0.098017</td>\n",
              "      <td>0.246218</td>\n",
              "      <td>1.801859</td>\n",
              "      <td>1.677431</td>\n",
              "      <td>1.370671</td>\n",
              "      <td>7.722516</td>\n",
              "      <td>4.244056</td>\n",
              "      <td>7.937160</td>\n",
              "      <td>0.601911</td>\n",
              "      <td>1.512001</td>\n",
              "      <td>28.011871</td>\n",
              "      <td>4.283841</td>\n",
              "      <td>0.008355</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date   tic        open  ...         PE           PB  Div_yield\n",
              "0 2019-01-01  AAPL   38.722500  ...   5.728691     1.670488   0.019019\n",
              "0 2019-01-01   AXP   93.910004  ...  50.720114     3.458432   0.004248\n",
              "0 2019-01-01    BA  316.190002  ...  83.019826  1418.196271   0.006531\n",
              "0 2019-01-01   CAT  124.029999  ...  35.716285     4.351801   0.007319\n",
              "0 2019-01-01  CSCO   42.279999  ...  28.011871     4.283841   0.008355\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLOnL5eYh1jR",
        "outputId": "3f32a755-8f10-4187-b629-ec2f42ac6c64"
      },
      "source": [
        "df_account_value, df_actions = DRLAgent.DRL_prediction(\n",
        "    model=trained_ddpg, \n",
        "    environment = e_trade_gym)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hit end!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERxw3KqLkcP4",
        "outputId": "68cd7119-6ecf-4501-da74-02ca635dcddf"
      },
      "source": [
        "df_account_value.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(731, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "2yRkNguY5yvp",
        "outputId": "f122be3c-233f-49bd-a043-79de0e0e8021"
      },
      "source": [
        "df_account_value.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>account_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>726</th>\n",
              "      <td>2020-12-27</td>\n",
              "      <td>1.338573e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>727</th>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>1.338573e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>728</th>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>1.334876e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>729</th>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>1.340975e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>730</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>1.350643e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          date  account_value\n",
              "726 2020-12-27   1.338573e+06\n",
              "727 2020-12-28   1.338573e+06\n",
              "728 2020-12-29   1.334876e+06\n",
              "729 2020-12-30   1.340975e+06\n",
              "730 2020-12-31   1.350643e+06"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "nFlK5hNbWVFk",
        "outputId": "8e8cccc0-e0d7-4d39-ca7e-d36026a4ba10"
      },
      "source": [
        "df_actions.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "      <th></th>\n",
              "      <th>AAPL</th>\n",
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              "      <th>INTC</th>\n",
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              "      <th>V</th>\n",
              "      <th>VZ</th>\n",
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              "      <th>WMT</th>\n",
              "      <th>XOM</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>2019-01-01</th>\n",
              "      <td>0</td>\n",
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              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-02</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
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              "      <td>100</td>\n",
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              "      <td>100</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2019-01-03</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
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              "      <td>100</td>\n",
              "      <td>100</td>\n",
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              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-04</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
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              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
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              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            AAPL  AXP  BA  CAT  CSCO  CVX  ...  UNH    V   VZ  WBA  WMT  XOM\n",
              "date                                       ...                              \n",
              "2019-01-01     0    0   0  100   100    0  ...    0  100  100  100  100    0\n",
              "2019-01-02     0    0   0  100   100    0  ...    0  100  100  100  100    0\n",
              "2019-01-03     0    0   0  100   100    0  ...    0  100  100  100  100    0\n",
              "2019-01-04     0    0   0  100   100    0  ...    0  100  100  100  100    0\n",
              "2019-01-05     0    0   0  100   100    0  ...    0  100  100  100  100    0\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6vvNSC6h1jZ"
      },
      "source": [
        "<a id='6'></a>\n",
        "# Part 7: Backtest Our Strategy\n",
        "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lr2zX7ZxNyFQ"
      },
      "source": [
        "<a id='6.1'></a>\n",
        "## 7.1 BackTestStats\n",
        "pass in df_account_value, this information is stored in env class\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nzkr9yv-AdV_",
        "outputId": "357f4fb1-ddf1-4a51-f1d1-40fc03ba2de4"
      },
      "source": [
        "print(\"==============Get Backtest Results===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "\n",
        "perf_stats_all = backtest_stats(account_value=df_account_value)\n",
        "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
        "perf_stats_all.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Backtest Results===========\n",
            "Annual return          0.109179\n",
            "Cumulative returns     0.350643\n",
            "Annual volatility      0.210154\n",
            "Sharpe ratio           0.599338\n",
            "Calmar ratio           0.339833\n",
            "Stability              0.296554\n",
            "Max drawdown          -0.321272\n",
            "Omega ratio            1.164859\n",
            "Sortino ratio          0.841479\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.899170\n",
            "Daily value at risk   -0.025977\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QkV-LB66iwhD",
        "outputId": "dc264452-6208-41a0-f26e-ccada8c0a27e"
      },
      "source": [
        "#baseline stats\n",
        "print(\"==============Get Baseline Stats===========\")\n",
        "baseline_df = get_baseline(\n",
        "        ticker=\"^DJI\", \n",
        "        start = '2019-01-01',\n",
        "        end = '2021-01-01')\n",
        "\n",
        "stats = backtest_stats(baseline_df, value_col_name = 'close')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (505, 8)\n",
            "Annual return          0.144674\n",
            "Cumulative returns     0.310981\n",
            "Annual volatility      0.274619\n",
            "Sharpe ratio           0.631418\n",
            "Calmar ratio           0.390102\n",
            "Stability              0.116677\n",
            "Max drawdown          -0.370862\n",
            "Omega ratio            1.149365\n",
            "Sortino ratio          0.870084\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.860710\n",
            "Daily value at risk   -0.033911\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U6Suru3h1jc"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lKRGftSS7pNM",
        "outputId": "dd1bf0e3-c5d8-4764-f92c-fd4f962ea83e"
      },
      "source": [
        "print(\"==============Compare to DJIA===========\")\n",
        "%matplotlib inline\n",
        "# S&P 500: ^GSPC\n",
        "# Dow Jones Index: ^DJI\n",
        "# NASDAQ 100: ^NDX\n",
        "backtest_plot(df_account_value, \n",
        "             baseline_ticker = '^DJI', \n",
        "             baseline_start = '2019-01-01',\n",
        "             baseline_end = '2021-01-01')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Compare to DJIA===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (505, 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>2019-01-02</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-12-31</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>24</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>16.656%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>36.17%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>21.287%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>0.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>0.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-20.583%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>1.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>16.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>0.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-2.612%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.63</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
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        {
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          "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>20.58</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>2020-04-29</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.80</td>\n",
              "      <td>2020-06-08</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>2020-12-08</td>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.41</td>\n",
              "      <td>2020-04-29</td>\n",
              "      <td>2020-05-13</td>\n",
              "      <td>2020-06-03</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.21</td>\n",
              "      <td>2019-04-23</td>\n",
              "      <td>2019-05-31</td>\n",
              "      <td>2019-06-18</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.08</td>\n",
              "      <td>2019-09-16</td>\n",
              "      <td>2019-10-02</td>\n",
              "      <td>2019-11-01</td>\n",
              "      <td>35</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": "BzBaE63H3RLc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}