Stock_NeurIPS2018.ipynb

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        "<a href=\"https://colab.research.google.com/github/AI4Finance-Foundation/FinRL/blob/master/Stock_NeurIPS2018.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading\n",
        "\n",
        "* **Pytorch Version** \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
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      },
      "source": [
        "# Content"
      ]
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      "source": [
        "* [1. Task Description](#0)\n",
        "* [2. Install Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. A List of Python Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download and Preprocess Data](#2)\n",
        "* [4. Preprocess Data](#3)        \n",
        "    * [4.1. Technical Indicators](#3.1)\n",
        "    * [4.2. Perform Feature Engineering](#3.2)\n",
        "* [5. Build Market Environment in OpenAI Gym-style](#4)  \n",
        "    * [5.1. Data Split](#4.1)  \n",
        "    * [5.3. Environment for Training](#4.2)    \n",
        "* [6. Train DRL Agents](#5)\n",
        "* [7. Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "  "
      ]
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      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Task Discription"
      ]
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      "source": [
        "We train a DRL agent for stock trading. This task is modeled as a Markov Decision Process (MDP), and the objective function is maximizing (expected) cumulative return.\n",
        "\n",
        "We specify the state-action-reward as follows:\n",
        "\n",
        "* **State s**: The state space represents an agent's perception of the market environment. Just like a human trader analyzing various information, here our agent passively observes many features and learns by interacting with the market environment (usually by replaying historical data).\n",
        "\n",
        "* **Action a**: The action space includes allowed actions that an agent can take at each state. For example, a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
        "selling, holding, and buying. When an action operates multiple shares, a ∈{−k, ..., −1, 0, 1, ..., k}, e.g.. \"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′)**: Reward is an incentive for an agent to learn a better policy. For example, it can be the change of the portfolio value when taking a at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio values at state s′ and s, respectively\n",
        "\n",
        "\n",
        "**Market environment**: 30 consituent stocks of Dow Jones Industrial Average (DJIA) index. Accessed at the starting date of the testing period.\n",
        "\n",
        "\n",
        "The data for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
      ]
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      "source": [
        "<a id='1'></a>\n",
        "# Part 2. Install Python Packages"
      ]
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      "source": [
        "<a id='1.1'></a>\n",
        "## 2.1. Install packages\n"
      ]
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      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-Foundation/FinRL.git"
      ]
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      "source": [
        "\n",
        "<a id='1.2'></a>\n",
        "## 2.2. A list of Python packages \n",
        "* Yahoo Finance API\n",
        "* pandas\n",
        "* numpy\n",
        "* matplotlib\n",
        "* stockstats\n",
        "* OpenAI gym\n",
        "* stable-baselines\n",
        "* tensorflow\n",
        "* pyfolio"
      ]
    },
    {
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      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
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      "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.meta.preprocessor.yahoodownloader import YahooDownloader\n",
        "from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
        "from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
        "from finrl.agents.stablebaselines3.models import DRLAgent\n",
        "from finrl.meta.data_processor import DataProcessor\n",
        "\n",
        "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
        "from pprint import pprint\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"../FinRL\")\n",
        "\n",
        "import itertools"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
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        "id": "RtUc_ofKmpdy"
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      "source": [
        "from finrl import config\n",
        "from finrl import config_tickers\n",
        "import os\n",
        "from finrl.main import check_and_make_directories\n",
        "from finrl.config import (\n",
        "    DATA_SAVE_DIR,\n",
        "    TRAINED_MODEL_DIR,\n",
        "    TENSORBOARD_LOG_DIR,\n",
        "    RESULTS_DIR,\n",
        "    INDICATORS,\n",
        "    TRAIN_START_DATE,\n",
        "    TRAIN_END_DATE,\n",
        "    TEST_START_DATE,\n",
        "    TEST_END_DATE,\n",
        "    TRADE_START_DATE,\n",
        "    TRADE_END_DATE,\n",
        ")\n",
        "check_and_make_directories([DATA_SAVE_DIR, TRAINED_MODEL_DIR, TENSORBOARD_LOG_DIR, RESULTS_DIR])\n",
        "\n"
      ]
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      "source": [
        "<a id='2'></a>\n",
        "# Part 3. Download Data\n",
        "Yahoo Finance provides stock data, financial news, financial reports, etc. Yahoo Finance is free.\n",
        "* FinRL uses a class **YahooDownloader** in FinRL-Meta to fetch data via 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)."
      ]
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      "source": [
        "\n",
        "\n",
        "-----\n",
        "class YahooDownloader:\n",
        "    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"
      ]
    },
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            "text/plain": [
              "'2014-01-01'"
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          "execution_count": 4,
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      "source": [
        "# from config.py, TRAIN_START_DATE is a string\n",
        "TRAIN_START_DATE\n",
        "# from config.py, TRAIN_END_DATE is a string\n",
        "TRAIN_END_DATE"
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            "text/plain": [
              "'2020-07-31'"
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      "source": [
        "TRAIN_START_DATE = '2009-01-01'\n",
        "TRAIN_END_DATE = '2020-07-01'\n",
        "TRADE_START_DATE = '2020-07-01'\n",
        "TRADE_END_DATE = '2021-10-31'\n"
      ]
    },
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          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
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            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (94331, 8)\n"
          ]
        }
      ],
      "source": [
        "df = YahooDownloader(start_date = TRAIN_START_DATE,\n",
        "                     end_date = TRADE_END_DATE,\n",
        "                     ticker_list = config_tickers.DOW_30_TICKER).fetch_data()"
      ]
    },
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        "id": "JzqRRTOX6aFu",
        "outputId": "7d6323e0-230c-47c4-b154-2f15b0630bf5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['AXP', 'AMGN', 'AAPL', 'BA', 'CAT', 'CSCO', 'CVX', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'KO', 'JPM', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PG', 'TRV', 'UNH', 'CRM', 'VZ', 'V', 'WBA', 'WMT', 'DIS', 'DOW']\n"
          ]
        }
      ],
      "source": [
        "print(config_tickers.DOW_30_TICKER)"
      ]
    },
    {
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      "source": [
        "df.shape"
      ]
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         date       open       high        low      close     volume   tic  \\\n",
              "0  2009-01-02   3.067143   3.251429   3.041429   2.771174  746015200  AAPL   \n",
              "1  2009-01-02  58.590000  59.080002  57.750000  44.867603    6547900  AMGN   \n",
              "2  2009-01-02  18.570000  19.520000  18.400000  15.535339   10955700   AXP   \n",
              "3  2009-01-02  42.799999  45.560001  42.779999  33.941097    7010200    BA   \n",
              "4  2009-01-02  44.910000  46.980000  44.709999  32.164722    7117200   CAT   \n",
              "\n",
              "   day  \n",
              "0    4  \n",
              "1    4  \n",
              "2    4  \n",
              "3    4  \n",
              "4    4  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.sort_values(['date','tic'],ignore_index=True).head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqC6c40Zh1iH"
      },
      "source": [
        "# Part 4: Preprocess Data\n",
        "We need to check for missing data and do feature engineering to convert the data point into a state.\n",
        "* **Adding technical indicators**. In practical trading, various information needs to be taken into account, such as historical prices, current holding shares, technical indicators, etc. Here, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
        "* **Adding turbulence index**. Risk-aversion reflects whether an investor prefers to protect the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the turbulence index that measures extreme fluctuation of asset price."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PmKP-1ii3RLS",
        "outputId": "c158433e-5323-4cab-c0d3-a2f40d425f6b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Successfully added technical indicators\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (3229, 8)\n",
            "Successfully added vix\n",
            "Successfully added turbulence index\n"
          ]
        }
      ],
      "source": [
        "fe = FeatureEngineer(\n",
        "                    use_technical_indicator=True,\n",
        "                    tech_indicator_list = INDICATORS,\n",
        "                    use_vix=True,\n",
        "                    use_turbulence=True,\n",
        "                    user_defined_feature = False)\n",
        "\n",
        "processed = fe.preprocess_data(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kixon2tR3RLT"
      },
      "outputs": [],
      "source": [
        "list_ticker = processed[\"tic\"].unique().tolist()\n",
        "list_date = list(pd.date_range(processed['date'].min(),processed['date'].max()).astype(str))\n",
        "combination = list(itertools.product(list_date,list_ticker))\n",
        "\n",
        "processed_full = pd.DataFrame(combination,columns=[\"date\",\"tic\"]).merge(processed,on=[\"date\",\"tic\"],how=\"left\")\n",
        "processed_full = processed_full[processed_full['date'].isin(processed['date'])]\n",
        "processed_full = processed_full.sort_values(['date','tic'])\n",
        "\n",
        "processed_full = processed_full.fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "grvhGJJII3Xn",
        "outputId": "84e8bfe9-3ab9-4934-b697-6f54f01d65cf"
      },
      "outputs": [
        {
          "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>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>boll_ub</th>\n",
              "      <th>boll_lb</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>close_30_sma</th>\n",
              "      <th>close_60_sma</th>\n",
              "      <th>vix</th>\n",
              "      <th>turbulence</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>2.771174</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.99505</td>\n",
              "      <td>2.664252</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2.771174</td>\n",
              "      <td>2.771174</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AMGN</td>\n",
              "      <td>58.590000</td>\n",
              "      <td>59.080002</td>\n",
              "      <td>57.750000</td>\n",
              "      <td>44.867603</td>\n",
              "      <td>6547900.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.99505</td>\n",
              "      <td>2.664252</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>44.867603</td>\n",
              "      <td>44.867603</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</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>15.535339</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.99505</td>\n",
              "      <td>2.664252</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>15.535339</td>\n",
              "      <td>15.535339</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</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>33.941097</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.99505</td>\n",
              "      <td>2.664252</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>33.941097</td>\n",
              "      <td>33.941097</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</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>32.164722</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.99505</td>\n",
              "      <td>2.664252</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>32.164722</td>\n",
              "      <td>32.164722</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CRM</td>\n",
              "      <td>8.025000</td>\n",
              "      <td>8.550000</td>\n",
              "      <td>7.912500</td>\n",
              "      <td>8.505000</td>\n",
              "      <td>4069200.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>8.505000</td>\n",
              "      <td>8.505000</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>6</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>12.265081</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>2.664252</td>\n",
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              "      <td>100.0</td>\n",
              "      <td>12.265081</td>\n",
              "      <td>12.265081</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>7</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>44.807625</td>\n",
              "      <td>13695900.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.99505</td>\n",
              "      <td>2.664252</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>44.807625</td>\n",
              "      <td>44.807625</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</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>4.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>9</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>70.735085</td>\n",
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              "      <td>100.0</td>\n",
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              "      <td>70.735085</td>\n",
              "      <td>39.189999</td>\n",
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            "text/plain": [
              "         date   tic       open       high        low      close       volume  \\\n",
              "0  2009-01-02  AAPL   3.067143   3.251429   3.041429   2.771174  746015200.0   \n",
              "1  2009-01-02  AMGN  58.590000  59.080002  57.750000  44.867603    6547900.0   \n",
              "2  2009-01-02   AXP  18.570000  19.520000  18.400000  15.535339   10955700.0   \n",
              "3  2009-01-02    BA  42.799999  45.560001  42.779999  33.941097    7010200.0   \n",
              "4  2009-01-02   CAT  44.910000  46.980000  44.709999  32.164722    7117200.0   \n",
              "5  2009-01-02   CRM   8.025000   8.550000   7.912500   8.505000    4069200.0   \n",
              "6  2009-01-02  CSCO  16.410000  17.000000  16.250000  12.265081   40980600.0   \n",
              "7  2009-01-02   CVX  74.230003  77.300003  73.580002  44.807625   13695900.0   \n",
              "8  2009-01-02   DIS  22.760000  24.030001  22.500000  20.597496    9796600.0   \n",
              "9  2009-01-02    GS  84.019997  87.620003  82.190002  70.735085   14088500.0   \n",
              "\n",
              "   day  macd  boll_ub   boll_lb  rsi_30     cci_30  dx_30  close_30_sma  \\\n",
              "0  4.0   0.0  2.99505  2.664252   100.0  66.666667  100.0      2.771174   \n",
              "1  4.0   0.0  2.99505  2.664252   100.0  66.666667  100.0     44.867603   \n",
              "2  4.0   0.0  2.99505  2.664252   100.0  66.666667  100.0     15.535339   \n",
              "3  4.0   0.0  2.99505  2.664252   100.0  66.666667  100.0     33.941097   \n",
              "4  4.0   0.0  2.99505  2.664252   100.0  66.666667  100.0     32.164722   \n",
              "5  4.0   0.0  2.99505  2.664252   100.0  66.666667  100.0      8.505000   \n",
              "6  4.0   0.0  2.99505  2.664252   100.0  66.666667  100.0     12.265081   \n",
              "7  4.0   0.0  2.99505  2.664252   100.0  66.666667  100.0     44.807625   \n",
              "8  4.0   0.0  2.99505  2.664252   100.0  66.666667  100.0     20.597496   \n",
              "9  4.0   0.0  2.99505  2.664252   100.0  66.666667  100.0     70.735085   \n",
              "\n",
              "   close_60_sma        vix  turbulence  \n",
              "0      2.771174  39.189999         0.0  \n",
              "1     44.867603  39.189999         0.0  \n",
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              "4     32.164722  39.189999         0.0  \n",
              "5      8.505000  39.189999         0.0  \n",
              "6     12.265081  39.189999         0.0  \n",
              "7     44.807625  39.189999         0.0  \n",
              "8     20.597496  39.189999         0.0  \n",
              "9     70.735085  39.189999         0.0  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "processed_full.sort_values(['date','tic'],ignore_index=True).head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QsYaY0Dh1iw"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Build A Market Environment in OpenAI Gym-style\n",
        "The training process involves observing stock price change, taking an action and reward's calculation. By interacting with the market environment, the agent will eventually derive a trading strategy that may maximize (expected) rewards.\n",
        "\n",
        "Our market environment, based on OpenAI Gym, simulates stock markets with historical market data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5TOhcryx44bb"
      },
      "source": [
        "## Data Split\n",
        "We split the data into training set and testing set as follows:\n",
        "\n",
        "Training data period: 2009-01-01 to 2020-07-01\n",
        "\n",
        "Trading data period: 2020-07-01 to 2021-10-31\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0qaVGjLtgbI",
        "outputId": "7e32c3df-763b-44df-e292-aa9ab2975a96"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "83897\n",
            "9744\n"
          ]
        }
      ],
      "source": [
        "train = data_split(processed_full, TRAIN_START_DATE,TRAIN_END_DATE)\n",
        "trade = data_split(processed_full, TRADE_START_DATE,TRADE_END_DATE)\n",
        "print(len(train))\n",
        "print(len(trade))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "p52zNCOhTtLR",
        "outputId": "58501086-c2fb-400b-bc62-f8ba107ac330"
      },
      "outputs": [
        {
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              "            date  tic        open        high         low       close  \\\n",
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              "2892  2020-06-30    V  191.490005  193.750000  190.160004  190.737228   \n",
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              "2892  2020-06-30  WMT  119.220001  120.129997  118.540001  116.121780   \n",
              "\n",
              "          volume  day      macd     boll_ub     boll_lb     rsi_30     cci_30  \\\n",
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          "execution_count": 14,
          "metadata": {},
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      ],
      "source": [
        "train.tail()"
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              "      <td>119.412837</td>\n",
              "      <td>113.646674</td>\n",
              "      <td>28.620001</td>\n",
              "      <td>53.068073</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-438520d1-e798-431b-affb-fd9233bb6aed')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-438520d1-e798-431b-affb-fd9233bb6aed button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-438520d1-e798-431b-affb-fd9233bb6aed');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         date   tic        open        high         low       close  \\\n",
              "0  2020-07-01  AAPL   91.279999   91.839996   90.977501   89.904617   \n",
              "0  2020-07-01  AMGN  235.520004  256.230011  232.580002  240.153961   \n",
              "0  2020-07-01   AXP   95.250000   96.959999   93.639999   92.086380   \n",
              "0  2020-07-01    BA  185.880005  190.610001  180.039993  180.320007   \n",
              "0  2020-07-01   CAT  129.380005  129.399994  125.879997  120.651634   \n",
              "\n",
              "        volume  day      macd     boll_ub     boll_lb     rsi_30      cci_30  \\\n",
              "0  110737200.0  2.0  3.014606   92.699321   80.179950  62.807170  107.491949   \n",
              "0    6575800.0  2.0  3.636392  232.397419  200.212942  61.279641  271.386208   \n",
              "0    3301000.0  2.0 -0.389162  110.423944   87.759333  48.504818  -66.328704   \n",
              "0   49036700.0  2.0  5.443193  220.721139  160.932863  50.925771   24.220608   \n",
              "0    2807800.0  2.0  1.272630  130.624272  113.353134  52.865419   35.597297   \n",
              "\n",
              "       dx_30  close_30_sma  close_60_sma        vix  turbulence  \n",
              "0  29.730532     83.933767     77.717542  28.620001   53.068073  \n",
              "0  46.806139    214.858661    215.931666  28.620001   53.068073  \n",
              "0   3.142448     97.244637     90.695525  28.620001   53.068073  \n",
              "0  15.932920    176.472335    155.614168  28.620001   53.068073  \n",
              "0  14.457404    119.412837    113.646674  28.620001   53.068073  "
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trade.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYN573SOHhxG",
        "outputId": "c23ff5eb-0a6f-4888-fbf5-55fba4d917e5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['macd',\n",
              " 'boll_ub',\n",
              " 'boll_lb',\n",
              " 'rsi_30',\n",
              " 'cci_30',\n",
              " 'dx_30',\n",
              " 'close_30_sma',\n",
              " 'close_60_sma']"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "INDICATORS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2zqII8rMIqn",
        "outputId": "2f923ca2-14ba-4b4c-def2-255c541f33fb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Stock Dimension: 29, State Space: 291\n"
          ]
        }
      ],
      "source": [
        "stock_dimension = len(train.tic.unique())\n",
        "state_space = 1 + 2*stock_dimension + len(INDICATORS)*stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AWyp84Ltto19"
      },
      "outputs": [],
      "source": [
        "buy_cost_list = sell_cost_list = [0.001] * stock_dimension\n",
        "num_stock_shares = [0] * stock_dimension\n",
        "\n",
        "env_kwargs = {\n",
        "    \"hmax\": 100,\n",
        "    \"initial_amount\": 1000000,\n",
        "    \"num_stock_shares\": num_stock_shares,\n",
        "    \"buy_cost_pct\": buy_cost_list,\n",
        "    \"sell_cost_pct\": sell_cost_list,\n",
        "    \"state_space\": state_space,\n",
        "    \"stock_dim\": stock_dimension,\n",
        "    \"tech_indicator_list\": INDICATORS,\n",
        "    \"action_space\": stock_dimension,\n",
        "    \"reward_scaling\": 1e-4\n",
        "}\n",
        "\n",
        "\n",
        "e_train_gym = StockTradingEnv(df = train, **env_kwargs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64EoqOrQjiVf"
      },
      "source": [
        "## Environment for Training\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwSvvPjutpqS",
        "outputId": "08154abe-994b-4ed2-b52c-498e811cb89c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv'>\n"
          ]
        }
      ],
      "source": [
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "print(type(env_train))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMNR5nHjh1iz"
      },
      "source": [
        "<a id='5'></a>\n",
        "# Part 6: Train DRL Agents\n",
        "* The DRL algorithms are from **Stable Baselines 3**. Users are also encouraged to try **ElegantRL** and **Ray RLlib**.\n",
        "* FinRL includes fine-tuned standard DRL algorithms, such as DQN, DDPG, 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",
      "execution_count": null,
      "metadata": {
        "id": "364PsqckttcQ"
      },
      "outputs": [],
      "source": [
        "agent = DRLAgent(env = env_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDmqOyF9h1iz"
      },
      "source": [
        "### Agent Training: 5 algorithms (A2C, DDPG, PPO, TD3, SAC)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uijiWgkuh1jB"
      },
      "source": [
        "### Agent 1: A2C\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUCnkn-HIbmj",
        "outputId": "07ef8700-2e39-4128-cc4a-991f15849b95"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0007}\n",
            "Using cpu device\n"
          ]
        }
      ],
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "model_a2c = agent.get_model(\"a2c\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0GVpkWGqH4-D",
        "outputId": "208532db-f654-4cc4-9553-d753943e741a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 85          |\n",
            "|    iterations         | 100         |\n",
            "|    time_elapsed       | 5           |\n",
            "|    total_timesteps    | 500         |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.1       |\n",
            "|    explained_variance | 0.225       |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 99          |\n",
            "|    policy_loss        | -3.3        |\n",
            "|    reward             | -0.38245595 |\n",
            "|    std                | 0.997       |\n",
            "|    value_loss         | 0.81        |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 200        |\n",
            "|    time_elapsed       | 11         |\n",
            "|    total_timesteps    | 1000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.1      |\n",
            "|    explained_variance | -0.00998   |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 199        |\n",
            "|    policy_loss        | -16.6      |\n",
            "|    reward             | -2.0795436 |\n",
            "|    std                | 0.998      |\n",
            "|    value_loss         | 0.521      |\n",
            "--------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 300      |\n",
            "|    time_elapsed       | 17       |\n",
            "|    total_timesteps    | 1500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | -0.307   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 299      |\n",
            "|    policy_loss        | -185     |\n",
            "|    reward             | 3.66972  |\n",
            "|    std                | 0.997    |\n",
            "|    value_loss         | 23.4     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 400       |\n",
            "|    time_elapsed       | 22        |\n",
            "|    total_timesteps    | 2000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | -0.0719   |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 399       |\n",
            "|    policy_loss        | 64.3      |\n",
            "|    reward             | 1.3105918 |\n",
            "|    std                | 0.998     |\n",
            "|    value_loss         | 4.65      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 500       |\n",
            "|    time_elapsed       | 28        |\n",
            "|    total_timesteps    | 2500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | -0.0128   |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 499       |\n",
            "|    policy_loss        | 562       |\n",
            "|    reward             | -13.59298 |\n",
            "|    std                | 0.996     |\n",
            "|    value_loss         | 235       |\n",
            "-------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 87          |\n",
            "|    iterations         | 600         |\n",
            "|    time_elapsed       | 34          |\n",
            "|    total_timesteps    | 3000        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41         |\n",
            "|    explained_variance | -0.612      |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 599         |\n",
            "|    policy_loss        | 157         |\n",
            "|    reward             | -0.20955521 |\n",
            "|    std                | 0.995       |\n",
            "|    value_loss         | 16.5        |\n",
            "---------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 700       |\n",
            "|    time_elapsed       | 40        |\n",
            "|    total_timesteps    | 3500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | -0.00822  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 699       |\n",
            "|    policy_loss        | -150      |\n",
            "|    reward             | -4.177808 |\n",
            "|    std                | 0.995     |\n",
            "|    value_loss         | 13.7      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 800        |\n",
            "|    time_elapsed       | 46         |\n",
            "|    total_timesteps    | 4000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41        |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 799        |\n",
            "|    policy_loss        | -18.7      |\n",
            "|    reward             | -2.8758662 |\n",
            "|    std                | 0.996      |\n",
            "|    value_loss         | 8.18       |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 86          |\n",
            "|    iterations         | 900         |\n",
            "|    time_elapsed       | 52          |\n",
            "|    total_timesteps    | 4500        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41         |\n",
            "|    explained_variance | -1.19e-07   |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 899         |\n",
            "|    policy_loss        | 278         |\n",
            "|    reward             | -0.08112537 |\n",
            "|    std                | 0.997       |\n",
            "|    value_loss         | 51.8        |\n",
            "---------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 1000     |\n",
            "|    time_elapsed       | 57       |\n",
            "|    total_timesteps    | 5000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 999      |\n",
            "|    policy_loss        | -6.5     |\n",
            "|    reward             | -8.42856 |\n",
            "|    std                | 0.996    |\n",
            "|    value_loss         | 2.46     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 1100      |\n",
            "|    time_elapsed       | 63        |\n",
            "|    total_timesteps    | 5500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | 5.96e-08  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1099      |\n",
            "|    policy_loss        | -784      |\n",
            "|    reward             | 5.4715543 |\n",
            "|    std                | 0.996     |\n",
            "|    value_loss         | 442       |\n",
            "-------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 86          |\n",
            "|    iterations         | 1200        |\n",
            "|    time_elapsed       | 69          |\n",
            "|    total_timesteps    | 6000        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41         |\n",
            "|    explained_variance | 0.0212      |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 1199        |\n",
            "|    policy_loss        | -173        |\n",
            "|    reward             | 0.077639945 |\n",
            "|    std                | 0.995       |\n",
            "|    value_loss         | 19.8        |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 1300       |\n",
            "|    time_elapsed       | 74         |\n",
            "|    total_timesteps    | 6500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41        |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1299       |\n",
            "|    policy_loss        | -62        |\n",
            "|    reward             | -4.6043253 |\n",
            "|    std                | 0.994      |\n",
            "|    value_loss         | 7.45       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 1400      |\n",
            "|    time_elapsed       | 80        |\n",
            "|    total_timesteps    | 7000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | 1.79e-07  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1399      |\n",
            "|    policy_loss        | 145       |\n",
            "|    reward             | 1.2491926 |\n",
            "|    std                | 0.995     |\n",
            "|    value_loss         | 19.3      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 1500      |\n",
            "|    time_elapsed       | 86        |\n",
            "|    total_timesteps    | 7500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | 0.000278  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1499      |\n",
            "|    policy_loss        | 290       |\n",
            "|    reward             | 5.5308213 |\n",
            "|    std                | 0.995     |\n",
            "|    value_loss         | 70.4      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 1600       |\n",
            "|    time_elapsed       | 91         |\n",
            "|    total_timesteps    | 8000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.1      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1599       |\n",
            "|    policy_loss        | -261       |\n",
            "|    reward             | 0.46437263 |\n",
            "|    std                | 0.997      |\n",
            "|    value_loss         | 45.7       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 1700      |\n",
            "|    time_elapsed       | 97        |\n",
            "|    total_timesteps    | 8500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1699      |\n",
            "|    policy_loss        | 169       |\n",
            "|    reward             | 4.5881724 |\n",
            "|    std                | 0.998     |\n",
            "|    value_loss         | 87.3      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 1800      |\n",
            "|    time_elapsed       | 103       |\n",
            "|    total_timesteps    | 9000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | 1.19e-07  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1799      |\n",
            "|    policy_loss        | -13.6     |\n",
            "|    reward             | 1.2186269 |\n",
            "|    std                | 0.998     |\n",
            "|    value_loss         | 0.621     |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 87         |\n",
            "|    iterations         | 1900       |\n",
            "|    time_elapsed       | 108        |\n",
            "|    total_timesteps    | 9500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.1      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1899       |\n",
            "|    policy_loss        | -23.2      |\n",
            "|    reward             | 0.86510557 |\n",
            "|    std                | 0.999      |\n",
            "|    value_loss         | 1.49       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 87         |\n",
            "|    iterations         | 2000       |\n",
            "|    time_elapsed       | 114        |\n",
            "|    total_timesteps    | 10000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1999       |\n",
            "|    policy_loss        | -62.9      |\n",
            "|    reward             | -3.6844032 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 34.7       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 2100      |\n",
            "|    time_elapsed       | 119       |\n",
            "|    total_timesteps    | 10500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 2099      |\n",
            "|    policy_loss        | 592       |\n",
            "|    reward             | 0.5913914 |\n",
            "|    std                | 0.999     |\n",
            "|    value_loss         | 210       |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 2200      |\n",
            "|    time_elapsed       | 125       |\n",
            "|    total_timesteps    | 11000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 2199      |\n",
            "|    policy_loss        | -197      |\n",
            "|    reward             | -18.43632 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 28.6      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 87         |\n",
            "|    iterations         | 2300       |\n",
            "|    time_elapsed       | 131        |\n",
            "|    total_timesteps    | 11500      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.1      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 2299       |\n",
            "|    policy_loss        | -2.89e+03  |\n",
            "|    reward             | -15.708329 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 5.45e+03   |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 2400      |\n",
            "|    time_elapsed       | 136       |\n",
            "|    total_timesteps    | 12000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 2399      |\n",
            "|    policy_loss        | 101       |\n",
            "|    reward             | 1.3252198 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 12.9      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 2500      |\n",
            "|    time_elapsed       | 142       |\n",
            "|    total_timesteps    | 12500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 2499      |\n",
            "|    policy_loss        | -154      |\n",
            "|    reward             | 1.1985308 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 16.8      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 87         |\n",
            "|    iterations         | 2600       |\n",
            "|    time_elapsed       | 148        |\n",
            "|    total_timesteps    | 13000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.1      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 2599       |\n",
            "|    policy_loss        | 297        |\n",
            "|    reward             | -0.3930558 |\n",
            "|    std                | 0.999      |\n",
            "|    value_loss         | 72.5       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 87         |\n",
            "|    iterations         | 2700       |\n",
            "|    time_elapsed       | 154        |\n",
            "|    total_timesteps    | 13500      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.1      |\n",
            "|    explained_variance | -1.19e-07  |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 2699       |\n",
            "|    policy_loss        | 39.9       |\n",
            "|    reward             | -2.0099065 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 3.71       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 87         |\n",
            "|    iterations         | 2800       |\n",
            "|    time_elapsed       | 160        |\n",
            "|    total_timesteps    | 14000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0.000893   |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 2799       |\n",
            "|    policy_loss        | 390        |\n",
            "|    reward             | 0.57461935 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 91.2       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 2900      |\n",
            "|    time_elapsed       | 165       |\n",
            "|    total_timesteps    | 14500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | -0.0166   |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 2899      |\n",
            "|    policy_loss        | -159      |\n",
            "|    reward             | 5.5083456 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 16.2      |\n",
            "-------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 87          |\n",
            "|    iterations         | 3000        |\n",
            "|    time_elapsed       | 171         |\n",
            "|    total_timesteps    | 15000       |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.3       |\n",
            "|    explained_variance | 0.0163      |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 2999        |\n",
            "|    policy_loss        | 35.1        |\n",
            "|    reward             | -0.57458067 |\n",
            "|    std                | 1           |\n",
            "|    value_loss         | 0.858       |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 87         |\n",
            "|    iterations         | 3100       |\n",
            "|    time_elapsed       | 177        |\n",
            "|    total_timesteps    | 15500      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 3099       |\n",
            "|    policy_loss        | 66.7       |\n",
            "|    reward             | 0.51296186 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 3.13       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 3200      |\n",
            "|    time_elapsed       | 183       |\n",
            "|    total_timesteps    | 16000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3199      |\n",
            "|    policy_loss        | -332      |\n",
            "|    reward             | -2.820065 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 107       |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 3300      |\n",
            "|    time_elapsed       | 189       |\n",
            "|    total_timesteps    | 16500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 6.74e-05  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3299      |\n",
            "|    policy_loss        | -13.8     |\n",
            "|    reward             | 1.1075561 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 3.38      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 87         |\n",
            "|    iterations         | 3400       |\n",
            "|    time_elapsed       | 194        |\n",
            "|    total_timesteps    | 17000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 3399       |\n",
            "|    policy_loss        | 117        |\n",
            "|    reward             | -12.577578 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 28         |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 87         |\n",
            "|    iterations         | 3500       |\n",
            "|    time_elapsed       | 200        |\n",
            "|    total_timesteps    | 17500      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 3499       |\n",
            "|    policy_loss        | 139        |\n",
            "|    reward             | 0.79640126 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 14.7       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 3600      |\n",
            "|    time_elapsed       | 206       |\n",
            "|    total_timesteps    | 18000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | -0.00612  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3599      |\n",
            "|    policy_loss        | -110      |\n",
            "|    reward             | 1.3899982 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 8.92      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 3700      |\n",
            "|    time_elapsed       | 211       |\n",
            "|    total_timesteps    | 18500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3699      |\n",
            "|    policy_loss        | 233       |\n",
            "|    reward             | -0.111542 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 41.5      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 3800      |\n",
            "|    time_elapsed       | 217       |\n",
            "|    total_timesteps    | 19000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3799      |\n",
            "|    policy_loss        | 133       |\n",
            "|    reward             | 4.2435246 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 10.8      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 3900      |\n",
            "|    time_elapsed       | 223       |\n",
            "|    total_timesteps    | 19500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3899      |\n",
            "|    policy_loss        | -110      |\n",
            "|    reward             | 1.5189852 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 15.4      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 4000     |\n",
            "|    time_elapsed       | 229      |\n",
            "|    total_timesteps    | 20000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3999     |\n",
            "|    policy_loss        | -162     |\n",
            "|    reward             | 6.805601 |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 29       |\n",
            "------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 87          |\n",
            "|    iterations         | 4100        |\n",
            "|    time_elapsed       | 235         |\n",
            "|    total_timesteps    | 20500       |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.3       |\n",
            "|    explained_variance | 5.96e-08    |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 4099        |\n",
            "|    policy_loss        | 14.3        |\n",
            "|    reward             | -0.06070591 |\n",
            "|    std                | 1.01        |\n",
            "|    value_loss         | 1.95        |\n",
            "---------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 4200      |\n",
            "|    time_elapsed       | 241       |\n",
            "|    total_timesteps    | 21000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 4199      |\n",
            "|    policy_loss        | -155      |\n",
            "|    reward             | 2.1403897 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 20.2      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 4300     |\n",
            "|    time_elapsed       | 247      |\n",
            "|    total_timesteps    | 21500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4299     |\n",
            "|    policy_loss        | -73.2    |\n",
            "|    reward             | 4.132366 |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 7.15     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 87        |\n",
            "|    iterations         | 4400      |\n",
            "|    time_elapsed       | 252       |\n",
            "|    total_timesteps    | 22000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 4399      |\n",
            "|    policy_loss        | -41.7     |\n",
            "|    reward             | 4.682027  |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 29.2      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 4500      |\n",
            "|    time_elapsed       | 258       |\n",
            "|    total_timesteps    | 22500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 4499      |\n",
            "|    policy_loss        | 706       |\n",
            "|    reward             | -0.782581 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 299       |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 4600      |\n",
            "|    time_elapsed       | 264       |\n",
            "|    total_timesteps    | 23000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 4599      |\n",
            "|    policy_loss        | 471       |\n",
            "|    reward             | 1.6243507 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 140       |\n",
            "-------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 86          |\n",
            "|    iterations         | 4700        |\n",
            "|    time_elapsed       | 270         |\n",
            "|    total_timesteps    | 23500       |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.3       |\n",
            "|    explained_variance | 0           |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 4699        |\n",
            "|    policy_loss        | -135        |\n",
            "|    reward             | -0.09023765 |\n",
            "|    std                | 1.01        |\n",
            "|    value_loss         | 16.8        |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 4800       |\n",
            "|    time_elapsed       | 275        |\n",
            "|    total_timesteps    | 24000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 4799       |\n",
            "|    policy_loss        | -278       |\n",
            "|    reward             | -1.1204126 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 50.1       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 4900       |\n",
            "|    time_elapsed       | 283        |\n",
            "|    total_timesteps    | 24500      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 4899       |\n",
            "|    policy_loss        | -16.2      |\n",
            "|    reward             | 0.57891697 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 2.09       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 5000       |\n",
            "|    time_elapsed       | 289        |\n",
            "|    total_timesteps    | 25000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 4999       |\n",
            "|    policy_loss        | -77.8      |\n",
            "|    reward             | -0.8018503 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 10.8       |\n",
            "--------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 5100     |\n",
            "|    time_elapsed       | 294      |\n",
            "|    total_timesteps    | 25500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5099     |\n",
            "|    policy_loss        | 204      |\n",
            "|    reward             | 3.961386 |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 37.9     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 5200      |\n",
            "|    time_elapsed       | 300       |\n",
            "|    total_timesteps    | 26000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 5199      |\n",
            "|    policy_loss        | -365      |\n",
            "|    reward             | 13.346269 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 166       |\n",
            "-------------------------------------\n",
            "day: 2892, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 7613512.38\n",
            "total_reward: 6613512.38\n",
            "total_cost: 6267.13\n",
            "total_trades: 41022\n",
            "Sharpe: 0.963\n",
            "=================================\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 5300       |\n",
            "|    time_elapsed       | 306        |\n",
            "|    total_timesteps    | 26500      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 5299       |\n",
            "|    policy_loss        | -76.9      |\n",
            "|    reward             | 0.22087732 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 3.92       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 5400       |\n",
            "|    time_elapsed       | 312        |\n",
            "|    total_timesteps    | 27000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 5399       |\n",
            "|    policy_loss        | -42.2      |\n",
            "|    reward             | -0.7331574 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 4.88       |\n",
            "--------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 5500     |\n",
            "|    time_elapsed       | 318      |\n",
            "|    total_timesteps    | 27500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5499     |\n",
            "|    policy_loss        | 98       |\n",
            "|    reward             | 1.317904 |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 24.7     |\n",
            "------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 5600       |\n",
            "|    time_elapsed       | 324        |\n",
            "|    total_timesteps    | 28000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 5599       |\n",
            "|    policy_loss        | -259       |\n",
            "|    reward             | -2.1360464 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 51.1       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 5700       |\n",
            "|    time_elapsed       | 330        |\n",
            "|    total_timesteps    | 28500      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | -1.19e-07  |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 5699       |\n",
            "|    policy_loss        | -276       |\n",
            "|    reward             | -4.0962267 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 75.3       |\n",
            "--------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 5800     |\n",
            "|    time_elapsed       | 336      |\n",
            "|    total_timesteps    | 29000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | 0.0146   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5799     |\n",
            "|    policy_loss        | -27.5    |\n",
            "|    reward             | 1.270464 |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 3.49     |\n",
            "------------------------------------\n",
            "----------------------------------------\n",
            "| time/                 |              |\n",
            "|    fps                | 86           |\n",
            "|    iterations         | 5900         |\n",
            "|    time_elapsed       | 341          |\n",
            "|    total_timesteps    | 29500        |\n",
            "| train/                |              |\n",
            "|    entropy_loss       | -41.2        |\n",
            "|    explained_variance | -1.19e-07    |\n",
            "|    learning_rate      | 0.0007       |\n",
            "|    n_updates          | 5899         |\n",
            "|    policy_loss        | -1.33        |\n",
            "|    reward             | 0.0148807345 |\n",
            "|    std                | 1            |\n",
            "|    value_loss         | 0.381        |\n",
            "----------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 6000       |\n",
            "|    time_elapsed       | 347        |\n",
            "|    total_timesteps    | 30000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 5999       |\n",
            "|    policy_loss        | 174        |\n",
            "|    reward             | -1.4712937 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 20         |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 6100      |\n",
            "|    time_elapsed       | 353       |\n",
            "|    total_timesteps    | 30500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 6099      |\n",
            "|    policy_loss        | -141      |\n",
            "|    reward             | -5.030463 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 14.9      |\n",
            "-------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 86          |\n",
            "|    iterations         | 6200        |\n",
            "|    time_elapsed       | 359         |\n",
            "|    total_timesteps    | 31000       |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.2       |\n",
            "|    explained_variance | 1.19e-07    |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 6199        |\n",
            "|    policy_loss        | -262        |\n",
            "|    reward             | -0.94790137 |\n",
            "|    std                | 1           |\n",
            "|    value_loss         | 41.7        |\n",
            "---------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 6300      |\n",
            "|    time_elapsed       | 365       |\n",
            "|    total_timesteps    | 31500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 6299      |\n",
            "|    policy_loss        | -375      |\n",
            "|    reward             | 3.6837375 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 129       |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 6400      |\n",
            "|    time_elapsed       | 371       |\n",
            "|    total_timesteps    | 32000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 6399      |\n",
            "|    policy_loss        | 97.1      |\n",
            "|    reward             | 1.8584676 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 5.02      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 6500       |\n",
            "|    time_elapsed       | 377        |\n",
            "|    total_timesteps    | 32500      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 6499       |\n",
            "|    policy_loss        | 1.77       |\n",
            "|    reward             | -3.7367923 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 4.41       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 6600       |\n",
            "|    time_elapsed       | 383        |\n",
            "|    total_timesteps    | 33000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 5.96e-08   |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 6599       |\n",
            "|    policy_loss        | -46.6      |\n",
            "|    reward             | -1.8607585 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 8.77       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 6700      |\n",
            "|    time_elapsed       | 388       |\n",
            "|    total_timesteps    | 33500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 6699      |\n",
            "|    policy_loss        | -1.09e+03 |\n",
            "|    reward             | -8.768982 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 916       |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 6800       |\n",
            "|    time_elapsed       | 394        |\n",
            "|    total_timesteps    | 34000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 6799       |\n",
            "|    policy_loss        | -403       |\n",
            "|    reward             | 0.51975477 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 107        |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 86          |\n",
            "|    iterations         | 6900        |\n",
            "|    time_elapsed       | 400         |\n",
            "|    total_timesteps    | 34500       |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.2       |\n",
            "|    explained_variance | 0           |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 6899        |\n",
            "|    policy_loss        | -119        |\n",
            "|    reward             | -0.71556073 |\n",
            "|    std                | 1           |\n",
            "|    value_loss         | 109         |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 86         |\n",
            "|    iterations         | 7000       |\n",
            "|    time_elapsed       | 406        |\n",
            "|    total_timesteps    | 35000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.1      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 6999       |\n",
            "|    policy_loss        | 6.97       |\n",
            "|    reward             | 0.07734101 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 0.193      |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 7100      |\n",
            "|    time_elapsed       | 412       |\n",
            "|    total_timesteps    | 35500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 5.96e-08  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 7099      |\n",
            "|    policy_loss        | -6.05     |\n",
            "|    reward             | 1.7844425 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 0.513     |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 86        |\n",
            "|    iterations         | 7200      |\n",
            "|    time_elapsed       | 418       |\n",
            "|    total_timesteps    | 36000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 7199      |\n",
            "|    policy_loss        | -343      |\n",
            "|    reward             | 2.7030456 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 65.9      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 7300       |\n",
            "|    time_elapsed       | 424        |\n",
            "|    total_timesteps    | 36500      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 7299       |\n",
            "|    policy_loss        | -445       |\n",
            "|    reward             | -1.2324498 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 127        |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 7400       |\n",
            "|    time_elapsed       | 430        |\n",
            "|    total_timesteps    | 37000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.1      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 7399       |\n",
            "|    policy_loss        | 380        |\n",
            "|    reward             | -17.135262 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 132        |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 7500       |\n",
            "|    time_elapsed       | 436        |\n",
            "|    total_timesteps    | 37500      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.1      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 7499       |\n",
            "|    policy_loss        | 217        |\n",
            "|    reward             | -11.758028 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 42         |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 85          |\n",
            "|    iterations         | 7600        |\n",
            "|    time_elapsed       | 442         |\n",
            "|    total_timesteps    | 38000       |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.1       |\n",
            "|    explained_variance | -1.19e-07   |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 7599        |\n",
            "|    policy_loss        | -87.6       |\n",
            "|    reward             | 0.032885257 |\n",
            "|    std                | 1           |\n",
            "|    value_loss         | 4.97        |\n",
            "---------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 7700      |\n",
            "|    time_elapsed       | 447       |\n",
            "|    total_timesteps    | 38500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 7699      |\n",
            "|    policy_loss        | -42.8     |\n",
            "|    reward             | 2.4885812 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 2.28      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 7800       |\n",
            "|    time_elapsed       | 453        |\n",
            "|    total_timesteps    | 39000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.1      |\n",
            "|    explained_variance | -2.38e-07  |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 7799       |\n",
            "|    policy_loss        | -86.3      |\n",
            "|    reward             | -2.6571417 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 9.24       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 7900      |\n",
            "|    time_elapsed       | 459       |\n",
            "|    total_timesteps    | 39500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | -0.000151 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 7899      |\n",
            "|    policy_loss        | 335       |\n",
            "|    reward             | 8.218592  |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 113       |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 8000       |\n",
            "|    time_elapsed       | 466        |\n",
            "|    total_timesteps    | 40000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0.0108     |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 7999       |\n",
            "|    policy_loss        | -180       |\n",
            "|    reward             | 0.58800954 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 25.8       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 8100      |\n",
            "|    time_elapsed       | 472       |\n",
            "|    total_timesteps    | 40500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 1.79e-07  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 8099      |\n",
            "|    policy_loss        | 259       |\n",
            "|    reward             | 1.3651996 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 147       |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 8200       |\n",
            "|    time_elapsed       | 477        |\n",
            "|    total_timesteps    | 41000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | -0.000263  |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 8199       |\n",
            "|    policy_loss        | 94         |\n",
            "|    reward             | -0.5384578 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 7.34       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 8300       |\n",
            "|    time_elapsed       | 483        |\n",
            "|    total_timesteps    | 41500      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 8299       |\n",
            "|    policy_loss        | 84.1       |\n",
            "|    reward             | -1.9264724 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 6.88       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 8400      |\n",
            "|    time_elapsed       | 489       |\n",
            "|    total_timesteps    | 42000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 5.96e-08  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 8399      |\n",
            "|    policy_loss        | 150       |\n",
            "|    reward             | -6.429836 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 19.9      |\n",
            "-------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 85          |\n",
            "|    iterations         | 8500        |\n",
            "|    time_elapsed       | 495         |\n",
            "|    total_timesteps    | 42500       |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.3       |\n",
            "|    explained_variance | 0           |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 8499        |\n",
            "|    policy_loss        | -139        |\n",
            "|    reward             | -0.73073864 |\n",
            "|    std                | 1.01        |\n",
            "|    value_loss         | 14.4        |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 8600       |\n",
            "|    time_elapsed       | 501        |\n",
            "|    total_timesteps    | 43000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 8599       |\n",
            "|    policy_loss        | 207        |\n",
            "|    reward             | -16.898623 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 47.9       |\n",
            "--------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 8700     |\n",
            "|    time_elapsed       | 507      |\n",
            "|    total_timesteps    | 43500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | 2.32e-05 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8699     |\n",
            "|    policy_loss        | 16.6     |\n",
            "|    reward             | 1.095771 |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.45     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 8800      |\n",
            "|    time_elapsed       | 512       |\n",
            "|    total_timesteps    | 44000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | 0.0328    |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 8799      |\n",
            "|    policy_loss        | -68       |\n",
            "|    reward             | 0.8716334 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 2.92      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 8900       |\n",
            "|    time_elapsed       | 518        |\n",
            "|    total_timesteps    | 44500      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.4      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 8899       |\n",
            "|    policy_loss        | -26.3      |\n",
            "|    reward             | 0.13365027 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 2.09       |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 85          |\n",
            "|    iterations         | 9000        |\n",
            "|    time_elapsed       | 524         |\n",
            "|    total_timesteps    | 45000       |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.5       |\n",
            "|    explained_variance | -1.19e-07   |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 8999        |\n",
            "|    policy_loss        | 7.9         |\n",
            "|    reward             | 0.061435416 |\n",
            "|    std                | 1.01        |\n",
            "|    value_loss         | 6.97        |\n",
            "---------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 9100      |\n",
            "|    time_elapsed       | 530       |\n",
            "|    total_timesteps    | 45500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.5     |\n",
            "|    explained_variance | -0.00365  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 9099      |\n",
            "|    policy_loss        | 15.7      |\n",
            "|    reward             | 2.2866766 |\n",
            "|    std                | 1.02      |\n",
            "|    value_loss         | 6.09      |\n",
            "-------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                 |               |\n",
            "|    fps                | 85            |\n",
            "|    iterations         | 9200          |\n",
            "|    time_elapsed       | 536           |\n",
            "|    total_timesteps    | 46000         |\n",
            "| train/                |               |\n",
            "|    entropy_loss       | -41.6         |\n",
            "|    explained_variance | 0             |\n",
            "|    learning_rate      | 0.0007        |\n",
            "|    n_updates          | 9199          |\n",
            "|    policy_loss        | -273          |\n",
            "|    reward             | -0.0070014545 |\n",
            "|    std                | 1.02          |\n",
            "|    value_loss         | 91.8          |\n",
            "-----------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 85         |\n",
            "|    iterations         | 9300       |\n",
            "|    time_elapsed       | 542        |\n",
            "|    total_timesteps    | 46500      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.5      |\n",
            "|    explained_variance | -0.0327    |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 9299       |\n",
            "|    policy_loss        | -204       |\n",
            "|    reward             | 0.07887427 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 29.3       |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 85          |\n",
            "|    iterations         | 9400        |\n",
            "|    time_elapsed       | 547         |\n",
            "|    total_timesteps    | 47000       |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.6       |\n",
            "|    explained_variance | 0           |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 9399        |\n",
            "|    policy_loss        | 234         |\n",
            "|    reward             | -0.45618594 |\n",
            "|    std                | 1.02        |\n",
            "|    value_loss         | 33.7        |\n",
            "---------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 9500      |\n",
            "|    time_elapsed       | 553       |\n",
            "|    total_timesteps    | 47500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.6     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 9499      |\n",
            "|    policy_loss        | 157       |\n",
            "|    reward             | 0.6259964 |\n",
            "|    std                | 1.02      |\n",
            "|    value_loss         | 18.6      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 9600      |\n",
            "|    time_elapsed       | 559       |\n",
            "|    total_timesteps    | 48000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.6     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 9599      |\n",
            "|    policy_loss        | -124      |\n",
            "|    reward             | 1.2416445 |\n",
            "|    std                | 1.02      |\n",
            "|    value_loss         | 13.5      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 85        |\n",
            "|    iterations         | 9700      |\n",
            "|    time_elapsed       | 565       |\n",
            "|    total_timesteps    | 48500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.7     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 9699      |\n",
            "|    policy_loss        | 18        |\n",
            "|    reward             | 2.1162922 |\n",
            "|    std                | 1.02      |\n",
            "|    value_loss         | 3.82      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 9800     |\n",
            "|    time_elapsed       | 571      |\n",
            "|    total_timesteps    | 49000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9799     |\n",
            "|    policy_loss        | -140     |\n",
            "|    reward             | 8.202153 |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 89.4     |\n",
            "------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 85          |\n",
            "|    iterations         | 9900        |\n",
            "|    time_elapsed       | 577         |\n",
            "|    total_timesteps    | 49500       |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.7       |\n",
            "|    explained_variance | 5.96e-08    |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 9899        |\n",
            "|    policy_loss        | -11.6       |\n",
            "|    reward             | -0.64417005 |\n",
            "|    std                | 1.02        |\n",
            "|    value_loss         | 0.478       |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 85          |\n",
            "|    iterations         | 10000       |\n",
            "|    time_elapsed       | 583         |\n",
            "|    total_timesteps    | 50000       |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.9       |\n",
            "|    explained_variance | -0.0549     |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 9999        |\n",
            "|    policy_loss        | 26          |\n",
            "|    reward             | -0.55169517 |\n",
            "|    std                | 1.03        |\n",
            "|    value_loss         | 1.77        |\n",
            "---------------------------------------\n"
          ]
        }
      ],
      "source": [
        "trained_a2c = agent.train_model(model=model_a2c, \n",
        "                             tb_log_name='a2c',\n",
        "                             total_timesteps=50000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRiOtrywfAo1"
      },
      "source": [
        "### Agent 2: DDPG"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2YadjfnLwgt",
        "outputId": "328b5097-e8ad-441d-d1a0-cbfef345fd46"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 50000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ]
        }
      ],
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "model_ddpg = agent.get_model(\"ddpg\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tCDa78rqfO_a",
        "outputId": "a2f106cf-ed1b-4584-c258-2ed52f774019"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "day: 2892, episode: 20\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4817633.79\n",
            "total_reward: 3817633.79\n",
            "total_cost: 6109.81\n",
            "total_trades: 54806\n",
            "Sharpe: 0.927\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 4         |\n",
            "|    fps             | 29        |\n",
            "|    time_elapsed    | 394       |\n",
            "|    total_timesteps | 11572     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -41.3     |\n",
            "|    critic_loss     | 101       |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 8679      |\n",
            "|    reward          | 1.4751048 |\n",
            "----------------------------------\n"
          ]
        }
      ],
      "source": [
        "trained_ddpg = agent.train_model(model=model_ddpg, \n",
        "                             tb_log_name='ddpg',\n",
        "                             total_timesteps=50000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gDkU-j-fCmZ"
      },
      "source": [
        "### Agent 3: PPO"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5D5PFUhMzSV",
        "outputId": "0f5f5f4c-845a-4654-beb6-56fe37a8db67"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'n_steps': 2048, 'ent_coef': 0.01, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n"
          ]
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gt8eIQKYM4G3",
        "outputId": "7f5bf8cb-8fda-4b14-ce65-5c35d43b7b08"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-----------------------------------\n",
            "| time/              |            |\n",
            "|    fps             | 96         |\n",
            "|    iterations      | 1          |\n",
            "|    time_elapsed    | 21         |\n",
            "|    total_timesteps | 2048       |\n",
            "| train/             |            |\n",
            "|    reward          | 0.30886105 |\n",
            "-----------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 94          |\n",
            "|    iterations           | 2           |\n",
            "|    time_elapsed         | 43          |\n",
            "|    total_timesteps      | 4096        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.015826263 |\n",
            "|    clip_fraction        | 0.205       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | -0.0394     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.77        |\n",
            "|    n_updates            | 10          |\n",
            "|    policy_gradient_loss | -0.0202     |\n",
            "|    reward               | 0.71778566  |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 13.9        |\n",
            "-----------------------------------------\n",
            "day: 2892, episode: 40\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3441481.08\n",
            "total_reward: 2441481.08\n",
            "total_cost: 353770.58\n",
            "total_trades: 80749\n",
            "Sharpe: 0.741\n",
            "=================================\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 94         |\n",
            "|    iterations           | 3          |\n",
            "|    time_elapsed         | 65         |\n",
            "|    total_timesteps      | 6144       |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.01884521 |\n",
            "|    clip_fraction        | 0.247      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -41.2      |\n",
            "|    explained_variance   | -0.00243   |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 30.3       |\n",
            "|    n_updates            | 20         |\n",
            "|    policy_gradient_loss | -0.0181    |\n",
            "|    reward               | -1.3031422 |\n",
            "|    std                  | 1          |\n",
            "|    value_loss           | 67.9       |\n",
            "----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 92         |\n",
            "|    iterations           | 4          |\n",
            "|    time_elapsed         | 88         |\n",
            "|    total_timesteps      | 8192       |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.01728037 |\n",
            "|    clip_fraction        | 0.193      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -41.3      |\n",
            "|    explained_variance   | -0.00394   |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 24.1       |\n",
            "|    n_updates            | 30         |\n",
            "|    policy_gradient_loss | -0.0192    |\n",
            "|    reward               | 2.4198737  |\n",
            "|    std                  | 1          |\n",
            "|    value_loss           | 43         |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 92          |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 110         |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020282106 |\n",
            "|    clip_fraction        | 0.24        |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.3       |\n",
            "|    explained_variance   | -0.00879    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.86        |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0247     |\n",
            "|    reward               | 2.4776623   |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 15.8        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 92         |\n",
            "|    iterations           | 6          |\n",
            "|    time_elapsed         | 132        |\n",
            "|    total_timesteps      | 12288      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.01991706 |\n",
            "|    clip_fraction        | 0.248      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -41.3      |\n",
            "|    explained_variance   | 0.000112   |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 30.8       |\n",
            "|    n_updates            | 50         |\n",
            "|    policy_gradient_loss | -0.0173    |\n",
            "|    reward               | 1.5963827  |\n",
            "|    std                  | 1.01       |\n",
            "|    value_loss           | 33.7       |\n",
            "----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 92         |\n",
            "|    iterations           | 7          |\n",
            "|    time_elapsed         | 154        |\n",
            "|    total_timesteps      | 14336      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02312897 |\n",
            "|    clip_fraction        | 0.197      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -41.4      |\n",
            "|    explained_variance   | -0.00486   |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 16.6       |\n",
            "|    n_updates            | 60         |\n",
            "|    policy_gradient_loss | -0.0161    |\n",
            "|    reward               | 1.1714903  |\n",
            "|    std                  | 1.01       |\n",
            "|    value_loss           | 38.8       |\n",
            "----------------------------------------\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 92           |\n",
            "|    iterations           | 8            |\n",
            "|    time_elapsed         | 177          |\n",
            "|    total_timesteps      | 16384        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.018019047  |\n",
            "|    clip_fraction        | 0.156        |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -41.4        |\n",
            "|    explained_variance   | 0.0234       |\n",
            "|    learning_rate        | 0.00025      |\n",
            "|    loss                 | 3.73         |\n",
            "|    n_updates            | 70           |\n",
            "|    policy_gradient_loss | -0.0139      |\n",
            "|    reward               | 0.0071891127 |\n",
            "|    std                  | 1.01         |\n",
            "|    value_loss           | 11.8         |\n",
            "------------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 92          |\n",
            "|    iterations           | 9           |\n",
            "|    time_elapsed         | 200         |\n",
            "|    total_timesteps      | 18432       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.022401342 |\n",
            "|    clip_fraction        | 0.195       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.4       |\n",
            "|    explained_variance   | 0.00658     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 14          |\n",
            "|    n_updates            | 80          |\n",
            "|    policy_gradient_loss | -0.0161     |\n",
            "|    reward               | -0.16097178 |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 25.2        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 91          |\n",
            "|    iterations           | 10          |\n",
            "|    time_elapsed         | 223         |\n",
            "|    total_timesteps      | 20480       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.024039125 |\n",
            "|    clip_fraction        | 0.233       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.5       |\n",
            "|    explained_variance   | -0.000831   |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 20.2        |\n",
            "|    n_updates            | 90          |\n",
            "|    policy_gradient_loss | -0.0172     |\n",
            "|    reward               | 0.48095208  |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 43.5        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 91          |\n",
            "|    iterations           | 11          |\n",
            "|    time_elapsed         | 247         |\n",
            "|    total_timesteps      | 22528       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.021172967 |\n",
            "|    clip_fraction        | 0.232       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.6       |\n",
            "|    explained_variance   | -8.09e-05   |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 23.7        |\n",
            "|    n_updates            | 100         |\n",
            "|    policy_gradient_loss | -0.0184     |\n",
            "|    reward               | 2.8099735   |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 53.6        |\n",
            "-----------------------------------------\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 90             |\n",
            "|    iterations           | 12             |\n",
            "|    time_elapsed         | 270            |\n",
            "|    total_timesteps      | 24576          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | 0.016935166    |\n",
            "|    clip_fraction        | 0.174          |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -41.6          |\n",
            "|    explained_variance   | -0.0446        |\n",
            "|    learning_rate        | 0.00025        |\n",
            "|    loss                 | 9.64           |\n",
            "|    n_updates            | 110            |\n",
            "|    policy_gradient_loss | -0.0148        |\n",
            "|    reward               | -0.00097534724 |\n",
            "|    std                  | 1.02           |\n",
            "|    value_loss           | 23             |\n",
            "--------------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 13          |\n",
            "|    time_elapsed         | 293         |\n",
            "|    total_timesteps      | 26624       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.021523612 |\n",
            "|    clip_fraction        | 0.202       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.7       |\n",
            "|    explained_variance   | -0.0117     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 31.9        |\n",
            "|    n_updates            | 120         |\n",
            "|    policy_gradient_loss | -0.0181     |\n",
            "|    reward               | -0.15636031 |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 84.2        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 14          |\n",
            "|    time_elapsed         | 316         |\n",
            "|    total_timesteps      | 28672       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.021340657 |\n",
            "|    clip_fraction        | 0.237       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.7       |\n",
            "|    explained_variance   | -0.00332    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 20.1        |\n",
            "|    n_updates            | 130         |\n",
            "|    policy_gradient_loss | -0.0115     |\n",
            "|    reward               | 0.24301007  |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 58.2        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 15          |\n",
            "|    time_elapsed         | 338         |\n",
            "|    total_timesteps      | 30720       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.018273095 |\n",
            "|    clip_fraction        | 0.183       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.8       |\n",
            "|    explained_variance   | 0.0126      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 21.1        |\n",
            "|    n_updates            | 140         |\n",
            "|    policy_gradient_loss | -0.0128     |\n",
            "|    reward               | 4.9345737   |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 40.1        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 16          |\n",
            "|    time_elapsed         | 361         |\n",
            "|    total_timesteps      | 32768       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.018592542 |\n",
            "|    clip_fraction        | 0.235       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.8       |\n",
            "|    explained_variance   | -0.00808    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 20.1        |\n",
            "|    n_updates            | 150         |\n",
            "|    policy_gradient_loss | -0.0226     |\n",
            "|    reward               | -0.36810476 |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 59.5        |\n",
            "-----------------------------------------\n",
            "day: 2892, episode: 50\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4017317.43\n",
            "total_reward: 3017317.43\n",
            "total_cost: 321478.58\n",
            "total_trades: 77582\n",
            "Sharpe: 0.720\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 17          |\n",
            "|    time_elapsed         | 385         |\n",
            "|    total_timesteps      | 34816       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.022970995 |\n",
            "|    clip_fraction        | 0.235       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.8       |\n",
            "|    explained_variance   | -0.00651    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 49.3        |\n",
            "|    n_updates            | 160         |\n",
            "|    policy_gradient_loss | -0.0122     |\n",
            "|    reward               | 0.37088677  |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 116         |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 90          |\n",
            "|    iterations           | 18          |\n",
            "|    time_elapsed         | 408         |\n",
            "|    total_timesteps      | 36864       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.014596333 |\n",
            "|    clip_fraction        | 0.145       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.8       |\n",
            "|    explained_variance   | 0.024       |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 33.2        |\n",
            "|    n_updates            | 170         |\n",
            "|    policy_gradient_loss | -0.00831    |\n",
            "|    reward               | -0.32497942 |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 78.7        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 89          |\n",
            "|    iterations           | 19          |\n",
            "|    time_elapsed         | 432         |\n",
            "|    total_timesteps      | 38912       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.026338592 |\n",
            "|    clip_fraction        | 0.223       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.8       |\n",
            "|    explained_variance   | -0.008      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.47        |\n",
            "|    n_updates            | 180         |\n",
            "|    policy_gradient_loss | -0.02       |\n",
            "|    reward               | -0.13824873 |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 11.6        |\n",
            "-----------------------------------------\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 89           |\n",
            "|    iterations           | 20           |\n",
            "|    time_elapsed         | 455          |\n",
            "|    total_timesteps      | 40960        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.024078036  |\n",
            "|    clip_fraction        | 0.255        |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -41.9        |\n",
            "|    explained_variance   | 0.00203      |\n",
            "|    learning_rate        | 0.00025      |\n",
            "|    loss                 | 28.4         |\n",
            "|    n_updates            | 190          |\n",
            "|    policy_gradient_loss | -0.0176      |\n",
            "|    reward               | -0.077695966 |\n",
            "|    std                  | 1.03         |\n",
            "|    value_loss           | 52.8         |\n",
            "------------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 89          |\n",
            "|    iterations           | 21          |\n",
            "|    time_elapsed         | 478         |\n",
            "|    total_timesteps      | 43008       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.031172728 |\n",
            "|    clip_fraction        | 0.329       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42         |\n",
            "|    explained_variance   | 0.00693     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 27.1        |\n",
            "|    n_updates            | 200         |\n",
            "|    policy_gradient_loss | -0.00453    |\n",
            "|    reward               | -8.163727   |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 76.4        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 89          |\n",
            "|    iterations           | 22          |\n",
            "|    time_elapsed         | 500         |\n",
            "|    total_timesteps      | 45056       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.018378198 |\n",
            "|    clip_fraction        | 0.197       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42         |\n",
            "|    explained_variance   | -0.022      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 16.3        |\n",
            "|    n_updates            | 210         |\n",
            "|    policy_gradient_loss | -0.0145     |\n",
            "|    reward               | 3.1000922   |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 25.6        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 89          |\n",
            "|    iterations           | 23          |\n",
            "|    time_elapsed         | 523         |\n",
            "|    total_timesteps      | 47104       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017813593 |\n",
            "|    clip_fraction        | 0.131       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.1       |\n",
            "|    explained_variance   | 0.00422     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 15.6        |\n",
            "|    n_updates            | 220         |\n",
            "|    policy_gradient_loss | -0.011      |\n",
            "|    reward               | 0.88160783  |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 61          |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 89          |\n",
            "|    iterations           | 24          |\n",
            "|    time_elapsed         | 546         |\n",
            "|    total_timesteps      | 49152       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.025783975 |\n",
            "|    clip_fraction        | 0.135       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.1       |\n",
            "|    explained_variance   | 0.00921     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 30.7        |\n",
            "|    n_updates            | 230         |\n",
            "|    policy_gradient_loss | -0.0105     |\n",
            "|    reward               | 5.9369354   |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 81.8        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 89          |\n",
            "|    iterations           | 25          |\n",
            "|    time_elapsed         | 570         |\n",
            "|    total_timesteps      | 51200       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.038430214 |\n",
            "|    clip_fraction        | 0.323       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.1       |\n",
            "|    explained_variance   | 0.00663     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 35.5        |\n",
            "|    n_updates            | 240         |\n",
            "|    policy_gradient_loss | -0.0147     |\n",
            "|    reward               | -0.13486888 |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 73.8        |\n",
            "-----------------------------------------\n"
          ]
        }
      ],
      "source": [
        "trained_ppo = agent.train_model(model=model_ppo, \n",
        "                             tb_log_name='ppo',\n",
        "                             total_timesteps=50000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Zpv4S0-fDBv"
      },
      "source": [
        "### Agent 4: TD3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSAHhV4Xc-bh",
        "outputId": "5e0056f3-fbf0-4490-de8b-030b1c41d46e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'batch_size': 100, 'buffer_size': 1000000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ]
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSRxNYAxdKpU",
        "outputId": "b5a01333-2393-4353-f605-8f4ac0a5a8ee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "day: 2892, episode: 60\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3941856.98\n",
            "total_reward: 2941856.98\n",
            "total_cost: 999.00\n",
            "total_trades: 49164\n",
            "Sharpe: 0.718\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 4         |\n",
            "|    fps             | 31        |\n",
            "|    time_elapsed    | 370       |\n",
            "|    total_timesteps | 11572     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | 55.7      |\n",
            "|    critic_loss     | 3.7e+03   |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 8679      |\n",
            "|    reward          | 1.2822058 |\n",
            "----------------------------------\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 8         |\n",
            "|    fps             | 27        |\n",
            "|    time_elapsed    | 844       |\n",
            "|    total_timesteps | 23144     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | 21.4      |\n",
            "|    critic_loss     | 932       |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 20251     |\n",
            "|    reward          | 1.2822058 |\n",
            "----------------------------------\n"
          ]
        }
      ],
      "source": [
        "trained_td3 = agent.train_model(model=model_td3, \n",
        "                             tb_log_name='td3',\n",
        "                             total_timesteps=30000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dr49PotrfG01"
      },
      "source": [
        "### Agent 5: SAC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwOhVjqRkCdM",
        "outputId": "d285c73b-0647-451f-a9b2-b3319c80acc7"
      },
      "outputs": [
        {
          "name": "stdout",
          "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"
          ]
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8RSdKCckJyH",
        "outputId": "fcdb52e3-ccd6-4fcc-c565-36f590c1820f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "day: 2892, episode: 70\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 5483466.70\n",
            "total_reward: 4483466.70\n",
            "total_cost: 162323.27\n",
            "total_trades: 50926\n",
            "Sharpe: 0.929\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 4         |\n",
            "|    fps             | 24        |\n",
            "|    time_elapsed    | 467       |\n",
            "|    total_timesteps | 11572     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | 246       |\n",
            "|    critic_loss     | 639       |\n",
            "|    ent_coef        | 0.115     |\n",
            "|    ent_coef_loss   | -96.9     |\n",
            "|    learning_rate   | 0.0001    |\n",
            "|    n_updates       | 11471     |\n",
            "|    reward          | 7.5468073 |\n",
            "----------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 24       |\n",
            "|    time_elapsed    | 945      |\n",
            "|    total_timesteps | 23144    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 97.3     |\n",
            "|    critic_loss     | 61.2     |\n",
            "|    ent_coef        | 0.0362   |\n",
            "|    ent_coef_loss   | -144     |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 23043    |\n",
            "|    reward          | 10.76891 |\n",
            "---------------------------------\n",
            "day: 2892, episode: 80\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 8867966.41\n",
            "total_reward: 7867966.41\n",
            "total_cost: 8154.17\n",
            "total_trades: 47568\n",
            "Sharpe: 1.037\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 12        |\n",
            "|    fps             | 24        |\n",
            "|    time_elapsed    | 1423      |\n",
            "|    total_timesteps | 34716     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | 34.3      |\n",
            "|    critic_loss     | 64.6      |\n",
            "|    ent_coef        | 0.0117    |\n",
            "|    ent_coef_loss   | -142      |\n",
            "|    learning_rate   | 0.0001    |\n",
            "|    n_updates       | 34615     |\n",
            "|    reward          | 13.044359 |\n",
            "----------------------------------\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 16        |\n",
            "|    fps             | 24        |\n",
            "|    time_elapsed    | 1904      |\n",
            "|    total_timesteps | 46288     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | 17.4      |\n",
            "|    critic_loss     | 22.7      |\n",
            "|    ent_coef        | 0.00413   |\n",
            "|    ent_coef_loss   | -49.7     |\n",
            "|    learning_rate   | 0.0001    |\n",
            "|    n_updates       | 46187     |\n",
            "|    reward          | 10.832052 |\n",
            "----------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 20       |\n",
            "|    fps             | 24       |\n",
            "|    time_elapsed    | 2390     |\n",
            "|    total_timesteps | 57860    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 11.2     |\n",
            "|    critic_loss     | 11.8     |\n",
            "|    ent_coef        | 0.00281  |\n",
            "|    ent_coef_loss   | -9.17    |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 57759    |\n",
            "|    reward          | 8.068603 |\n",
            "---------------------------------\n"
          ]
        }
      ],
      "source": [
        "trained_sac = agent.train_model(model=model_sac, \n",
        "                             tb_log_name='sac',\n",
        "                             total_timesteps=60000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2wZgkQXh1jE"
      },
      "source": [
        "## In-sample Performance\n",
        "\n",
        "Assume that the initial capital is $1,000,000."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bEv5KGC8h1jE"
      },
      "source": [
        "### Set turbulence threshold\n",
        "Set the turbulence threshold to be greater than the maximum of insample turbulence data. If current turbulence index is greater than the threshold, then we assume that the current market is volatile"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "efwBi84ch1jE"
      },
      "outputs": [],
      "source": [
        "data_risk_indicator = processed_full[(processed_full.date<TRAIN_END_DATE) & (processed_full.date>=TRAIN_START_DATE)]\n",
        "insample_risk_indicator = data_risk_indicator.drop_duplicates(subset=['date'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VHZMBpSqh1jG",
        "outputId": "6620b13a-81cf-4ed4-a6bc-81e8b5ce58ca"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count    2893.000000\n",
              "mean       18.824245\n",
              "std         8.489311\n",
              "min         9.140000\n",
              "25%        13.330000\n",
              "50%        16.139999\n",
              "75%        21.309999\n",
              "max        82.690002\n",
              "Name: vix, dtype: float64"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "insample_risk_indicator.vix.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BDkszkMloRWT",
        "outputId": "cb657a17-738f-4848-a64c-db7dabb6d12d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "57.40400183105453"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "insample_risk_indicator.vix.quantile(0.996)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AL7hs7svnNWT",
        "outputId": "38a82127-684d-4287-b354-fca00aa58d85"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count    2893.000000\n",
              "mean       34.567957\n",
              "std        43.790802\n",
              "min         0.000000\n",
              "25%        14.962679\n",
              "50%        24.124183\n",
              "75%        39.162269\n",
              "max       652.502726\n",
              "Name: turbulence, dtype: float64"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "insample_risk_indicator.turbulence.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N78hfHckoqJ9",
        "outputId": "65542595-696e-4e1a-d720-7a3ad142116e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "276.4542576252012"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "insample_risk_indicator.turbulence.quantile(0.996)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5mmgQF_h1jQ"
      },
      "source": [
        "### Trading (Out-of-sample Performance)\n",
        "\n",
        "We update periodically in order to take full advantage of the data, e.g., retrain quarterly, monthly or weekly. We also tune the parameters along the way, in this notebook we use the in-sample data from 2009-01 to 2020-07 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",
      "execution_count": null,
      "metadata": {
        "id": "cIqoV0GSI52v"
      },
      "outputs": [],
      "source": [
        "e_trade_gym = StockTradingEnv(df = trade, turbulence_threshold = 70,risk_indicator_col='vix', **env_kwargs)\n",
        "# env_trade, obs_trade = e_trade_gym.get_sb_env()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "W_XNgGsBMeVw",
        "outputId": "131828a9-4b9f-45d2-bb81-b600f356b5fb"
      },
      "outputs": [
        {
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              "      <th>date</th>\n",
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              "      <td>AAPL</td>\n",
              "      <td>91.279999</td>\n",
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              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>AXP</td>\n",
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              "      <td>3.142448</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>BA</td>\n",
              "      <td>185.880005</td>\n",
              "      <td>190.610001</td>\n",
              "      <td>180.039993</td>\n",
              "      <td>180.320007</td>\n",
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              "      <td>5.443193</td>\n",
              "      <td>220.721139</td>\n",
              "      <td>160.932863</td>\n",
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              "      <td>2020-07-01</td>\n",
              "      <td>CAT</td>\n",
              "      <td>129.380005</td>\n",
              "      <td>129.399994</td>\n",
              "      <td>125.879997</td>\n",
              "      <td>120.651634</td>\n",
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            "text/plain": [
              "         date   tic        open        high         low       close  \\\n",
              "0  2020-07-01  AAPL   91.279999   91.839996   90.977501   89.904617   \n",
              "0  2020-07-01  AMGN  235.520004  256.230011  232.580002  240.153961   \n",
              "0  2020-07-01   AXP   95.250000   96.959999   93.639999   92.086380   \n",
              "0  2020-07-01    BA  185.880005  190.610001  180.039993  180.320007   \n",
              "0  2020-07-01   CAT  129.380005  129.399994  125.879997  120.651634   \n",
              "\n",
              "        volume  day      macd     boll_ub     boll_lb     rsi_30      cci_30  \\\n",
              "0  110737200.0  2.0  3.014606   92.699321   80.179950  62.807170  107.491949   \n",
              "0    6575800.0  2.0  3.636392  232.397419  200.212942  61.279641  271.386208   \n",
              "0    3301000.0  2.0 -0.389162  110.423944   87.759333  48.504818  -66.328704   \n",
              "0   49036700.0  2.0  5.443193  220.721139  160.932863  50.925771   24.220608   \n",
              "0    2807800.0  2.0  1.272630  130.624272  113.353134  52.865419   35.597297   \n",
              "\n",
              "       dx_30  close_30_sma  close_60_sma        vix  turbulence  \n",
              "0  29.730532     83.933767     77.717542  28.620001   53.068073  \n",
              "0  46.806139    214.858661    215.931666  28.620001   53.068073  \n",
              "0   3.142448     97.244637     90.695525  28.620001   53.068073  \n",
              "0  15.932920    176.472335    155.614168  28.620001   53.068073  \n",
              "0  14.457404    119.412837    113.646674  28.620001   53.068073  "
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trade.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "eLOnL5eYh1jR",
        "outputId": "c97abb86-9c4a-4824-c946-7cf3d4a20ee0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "hit end!\n"
          ]
        }
      ],
      "source": [
        "df_account_value, df_actions = DRLAgent.DRL_prediction(\n",
        "    model=trained_sac, \n",
        "    environment = e_trade_gym)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "ERxw3KqLkcP4",
        "outputId": "39aace5e-97df-40bd-ff9a-559649b07300"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(336, 2)"
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          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "df_account_value.shape"
      ]
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              "      <td>2021-10-25</td>\n",
              "      <td>1.399896e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>333</th>\n",
              "      <td>2021-10-26</td>\n",
              "      <td>1.404413e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>334</th>\n",
              "      <td>2021-10-27</td>\n",
              "      <td>1.402845e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>335</th>\n",
              "      <td>2021-10-28</td>\n",
              "      <td>1.417062e+06</td>\n",
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              "\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-2c9e07c8-0a75-44c7-a243-44945f39ec06 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2c9e07c8-0a75-44c7-a243-44945f39ec06');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "           date  account_value\n",
              "331  2021-10-22   1.400329e+06\n",
              "332  2021-10-25   1.399896e+06\n",
              "333  2021-10-26   1.404413e+06\n",
              "334  2021-10-27   1.402845e+06\n",
              "335  2021-10-28   1.417062e+06"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_account_value.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "nFlK5hNbWVFk",
        "outputId": "94277979-b8f3-491d-fdb1-16cb0237b013"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ],
            "text/plain": [
              "            AAPL  AMGN  AXP  BA  CAT  CRM  CSCO  CVX  DIS  GS  ...  MRK  MSFT  \\\n",
              "date                                                           ...              \n",
              "2020-07-01    86    66    0  18    0   19    89   32    5  87  ...    0     0   \n",
              "2020-07-02    88    74    0  36    0   35    90    9   26  88  ...    0     0   \n",
              "2020-07-06    90    80    0  53    0   51    92  -16   45  88  ...    0     0   \n",
              "2020-07-07    92    85    0  67    0   64    93  -25   61  89  ...    0     0   \n",
              "2020-07-08    94    90    0  79    0   76    94    0   75  90  ...    0    29   \n",
              "\n",
              "            NKE  PG  TRV  UNH   V  VZ  WBA  WMT  \n",
              "date                                             \n",
              "2020-07-01    0   0    0   93  93   0    0    0  \n",
              "2020-07-02    0   0    0   93  94   0    0    0  \n",
              "2020-07-06    0   0    0   94  94   0    0    0  \n",
              "2020-07-07    0   0    0   95  95   0    0    0  \n",
              "2020-07-08    0   0    0   96  96   0    0    0  \n",
              "\n",
              "[5 rows x 29 columns]"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_actions.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6vvNSC6h1jZ"
      },
      "source": [
        "<a id='6'></a>\n",
        "# Part 7: Backtesting Results\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",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nzkr9yv-AdV_",
        "outputId": "0392eaa4-17da-453e-cc6f-07c9d06edae3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==============Get Backtest Results===========\n",
            "Annual return          0.298798\n",
            "Cumulative returns     0.417062\n",
            "Annual volatility      0.170675\n",
            "Sharpe ratio           1.622374\n",
            "Calmar ratio           2.599563\n",
            "Stability              0.917383\n",
            "Max drawdown          -0.114942\n",
            "Omega ratio            1.313801\n",
            "Sortino ratio          2.377919\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.035998\n",
            "Daily value at risk   -0.020404\n",
            "dtype: float64\n"
          ]
        }
      ],
      "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(\"./\"+RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QkV-LB66iwhD",
        "outputId": "c2ece831-5648-45cf-e2fe-0bc316fe5add"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (335, 8)\n",
            "Annual return          0.273520\n",
            "Cumulative returns     0.379084\n",
            "Annual volatility      0.139248\n",
            "Sharpe ratio           1.811893\n",
            "Calmar ratio           3.062662\n",
            "Stability              0.918651\n",
            "Max drawdown          -0.089308\n",
            "Omega ratio            1.351851\n",
            "Sortino ratio          2.684720\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.051856\n",
            "Daily value at risk   -0.016542\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "#baseline stats\n",
        "print(\"==============Get Baseline Stats===========\")\n",
        "baseline_df = get_baseline(\n",
        "        ticker=\"^DJI\", \n",
        "        start = df_account_value.loc[0,'date'],\n",
        "        end = df_account_value.loc[len(df_account_value)-1,'date'])\n",
        "\n",
        "stats = backtest_stats(baseline_df, value_col_name = 'close')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "qg1kvfemrrQH",
        "outputId": "9ebfc506-8e87-42db-b4f8-78b0d9c41a98"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2020-07-01'"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_account_value.loc[0,'date']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "tt1bzL5OrsTa",
        "outputId": "e817cbbb-2fcf-407d-85f4-365d70aad28f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2021-10-28'"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_account_value.loc[len(df_account_value)-1,'date']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U6Suru3h1jc"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lKRGftSS7pNM",
        "outputId": "696b9b4d-5417-4527-c0fc-05a55353fa67"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==============Compare to DJIA===========\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (335, 8)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2020-07-01</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2021-10-28</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>16</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>29.88%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>41.706%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>17.067%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>1.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>2.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-11.494%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>2.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>1.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-2.04%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>1.09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Worst drawdown periods</th>\n",
              "      <th>Net drawdown in %</th>\n",
              "      <th>Peak date</th>\n",
              "      <th>Valley date</th>\n",
              "      <th>Recovery date</th>\n",
              "      <th>Duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11.49</td>\n",
              "      <td>2020-09-02</td>\n",
              "      <td>2020-10-30</td>\n",
              "      <td>2020-11-11</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.69</td>\n",
              "      <td>2021-09-02</td>\n",
              "      <td>2021-10-04</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.95</td>\n",
              "      <td>2021-04-16</td>\n",
              "      <td>2021-05-12</td>\n",
              "      <td>2021-06-24</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.44</td>\n",
              "      <td>2020-07-15</td>\n",
              "      <td>2020-07-30</td>\n",
              "      <td>2020-08-06</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.44</td>\n",
              "      <td>2021-01-25</td>\n",
              "      <td>2021-01-29</td>\n",
              "      <td>2021-02-09</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
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        },
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            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Stress Events</th>\n",
              "      <th>mean</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>New Normal</th>\n",
              "      <td>0.11%</td>\n",
              "      <td>-4.00%</td>\n",
              "      <td>3.73%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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33ntv3jX/+Z//mbKyMmpqavIqO3784x/n9ttv55prrsHv97Nq1SpaWlr44he/SGlpKaeccgqvvz74NfXBBx9kwYIFBAIBTj31VJ544gn7s7Vr13Leeedx9913U15ezhe/+MVh9/61r32Ns88+m46OjuP+e1RMffb37Gddwzo2HdqElNIe74/38/3Xvk8sFeNg30Ey0mrMkus+gcHuQ3OClqD39PTg6hsU52xp3KMx2mbl8bame3n/y7zd+jZIiLfGicfjrFq1iooKy1qfDJSgTzF++ctf8uSTT9LY2EhTUxNf/epXaW5u5v3vfz/33nsvnZ2dfPe73+UjH/kIra2t9nE///nP+f73v093dzf19fW2aIdCIS6//HJWr17NoUOH2L9/P9dff7193Jtvvkl1dTXt7e088sgj/MM//ANdXYOWyaOPPsr9999PV1cXgUCACy+8kEWLFnH48GFuvvlm7rjjDnvuggULeOmll+jr6+MrX/kKN910E+3t7XnXqq2tpa2tLa/LkpSSO+64g/Xr1/Piiy+quu0nAa82vcq3XvwWj29/nB++8UPebnvb/uwXW35Bd7QbsH43srXKcwVdCEGZtwyn7mRh2UKSySSbNm1C69FgoDFX1kI/GrlWfS653wjGy47DO/j5Wz8H4Oraq4l0R/D5fJSUlOB2u0kmk5PSQeykdrls376dvr7JbRBbXFzMaaedNub5n/nMZ+wSul/5ylf4xCc+QUVFBVdeeSXXXnstAKtXr+aCCy7giSee4NOf/jQAn/jEJ1i2bBkAH/vYx7jzzjsBePLJJyktLeWee+6xr7Fq1WDKcU1NjS3K119/PX6/n507d3LRRRcB8IEPfIBzzjkHgA9+8IM8+OCDfPKTVi/WG264gTVr1pDJZNA0jb/927+1z3vTTTexZs0aNm3axDXXXANAVVUVd911F0IIHA7rVy+dTnPLLbfQ29vL008/jcczciSCYuYQTob56ab8sMDG7kZWzLKilPd27QWsMMNIMkJ3rJtid3GeoJf7yrn/vfcTT8XxO/1s3ryZeDyOQ3OgZTQyWmbMWZ+jCfrxWOhb27YipeSyBZex1LmUvYf32gEA2TLXiURiwn/fT2pBn4pkS+ACzJkzh7a2Nvbv38/jjz9OMDhYtiaVStlCC9hdjgB8Pp9dcrepqemIVRhzjxt6LJDXss7j8Qx7n0qlSCaTuN1u1q5dy0MPPcSBAwcACIfDeZ2UamtrhxVH2rdvH9u2beOll15SYn6S8Lt3hqeUZGuEJ9IJwokwDt1BfbCenYd38sD6B7h2ybW26wVgln+W5SoxYePGjbS3t1NeXo52WEOYAhxjt9BzXS4LyxZSHajm5f0v0xvvPeZ7zDbZmBOcQ6g9RFFREV6vVS43K+jvvPMO55577jFfYyROakEfj+VcKHJbyzU1NVFdXU19fT033ngjP/3p+JMd6urq2Ldv30QucUQOHDjApz71KV544QVWrVqFrussW7Yszzc6UqG1RYsW8YUvfIHrrruO5557jtNPP33S16o4cSTSCV5reg1NaFyz5Br+uPOPAERSlqBnNyLLvGWUeErs4/6060/MKx2sph30WMbNwYMHaW9vZ+nSpVRUVLBuyzo0t4aJaYcpSinZunUrwWCQ+vp6YrEYPT091NTUAPmCfvG8i1lVt4rXml4jmoySNJPHlBCUvR+v4aU71E1JyeC9FBcX4/P5SCYnPjNV+dCnGI888ghNTU309PTwzW9+kxtuuIFbbrmFdevWsW7dOkzTJJFI8Je//MW2hI/EtddeS0dHB9/+9reJx+NEo1Fee+21CV93JBJBCGH7v3/84x+za9fY0gc+9KEP8dBDD3HFFVewffv2CV+bYuoQSUaQUlLkLuL6pdfzuQs+BwxatNlU/jJP2TALO9u6DQbjuqPRKIZhsHDhQlwuF26HG2EK5pfORwhBKpXirbfeoqmpia1bt/Lmm2/yxhtvsHnzZjvgINflUltkfYvMumv64n3EU3Ge3/u83ThjrPcJ4NbdxGIxfL7BLFWPx8Pq1attt+ZEogR9inHzzTdz9dVXM2/ePGpra/mXf/kX6urqeOKJJ3jwwQepqKigtraWf/3Xf8U0zaOeLxAI8Nxzz/HMM88wa9Ys5s2bx5/+9KcJX/epp57K3Xffzfnnn091dTW7du3ivPPOG/PxN954I9/+9rd53/vex86dO49+gGJakhXlbNZmNh0/O57NrCz3lbOsalnesdmY7mJ3MZfMvQSAWCxmu+pcLheLKhaxsnold15wJ1JK3njjDZqbrQSh6upqWltbCYWsmoAtLVYN9GzYoiY0ZgVm2dcAq/vRa02v8dutv+WLT33RLilw1PsceEA5Mg6klLa7ZbIRuV+JZwJCiLlAY2NjI3Pnzs37rKWlhdmzJ7famWLyUP9+0589nXt48C8PsqBsAV96z5doC7Vx33P3UeGvYM0Va/jJpp/wWtNr3LTiJi6bfxl/PfhXfrzxx/bxhm7wvWu/h6EbAGzYsAGPx2P7orOGy/Lly2lpaeHNNwe7Fl1yySU0Njbabk2/38+ll14KwI82/ohSTykfOv1DADzy10fY3LyZT5/7afZ07eGFd1+wz3Ploiv50LIPHfE+v7DuC/TF+rjnnHvY+fZOLrjgAsrKyo7772///v3MmzcPYN5AMcI8TmofukKhKCyxlFWSaZiFPmDRHui13IjZ2PLz6s6j0l/Ja02v4Xf6WVy+2BZzgHg8Tmlpqf3e6XSSSlk1WJqamvB6vbZrxe12s2TJErq7uwkGgzQ3N9Pb20tJSQmfOvdTeesMui0ffU+8x848zUbdvHLglaMKejwSJ9gWZF+DZdEXykJXgq5QKAqG7XIxPHl/RlNRUmaK1lCr3XQiy7ySecwrGdwQlVLamcbJZDIvOsowDFKpFIlEgs7OThYuXMiePVYpXafTKt61evVq0uk0bW1tHDx4MG/DMktW0PtifYQTVtTXR5d/lP/a9F9HLdqVNJNkEhkEAjNt4nQ6cbtHriMz0SgfukKhKAjpTJqfbrYitbyGZbE6NAduhxspJS2hFqSUBN3BI0aWtLe388wzz9hBAX7/YJ3zrKA3NzcjpaSmpoZAwMoYzY2ycjgczJo1i+bm5hH3ooo9Az70eK/d+KLUa30TOJqgR5NRREZg6Abvec97uOyyywrW71dZ6AqFYlJp6m1CImnsbrSzI7OWOVi9PuPpOAd6LIEu9ZYipWT//v34fL5hRay6u7uRUtqb50MFPRKJ0NzcTHFxMYFAgIsuumhE0a6vr+fQoUO0trZSW5tfuCtroffGewklQvZYtjyvmTHtWjJDiafjaFJDFzoulwtdH3neZHDSCbqUsmBPS8XEMdM2708WemO9rFm/BjNj5v2/y836DLgCdEY6bf95qaeUPXv20NDQgN/vx+124/f7SaVSPPvss4AV0ZL1leeGBGYFHazIK7Cs8Wxmci6lpaV4vV4OHjx4REHPWuhFriKcupNEOkHKTOUJekNHA682vcqHT/8wSTOJyAh0XS+omMNJJuiapmGa5oj/uIqpjWmaaJryEE43DkcO20W0ch/K2TrhMJjRmRV0d8xNQ3sDXq+XcDjMhg0bqK+vz4twqqiooLy8nFAolPegMIzBDdPi4iOn/gshqKuro6GhgWg0mrdxmRX0jnAHGZlB13RcDhcOzUGCBKlMCjeWXzyUCPHvL/27fdxpVadZgu4orJjDSeZD93q99Pf3K2tvmiGlpL+/v2CRAoqJI+uuWF69nH+66J/wOq1/w1X1qwiFQpimaff6zHYf0mN6XigiWH7z3GJWPp+Puro62wrP4nQO+t7HUkoia5lnY9LtYw0Phm7Y5Qa8hhchhO3bz/WjH+wbzO5+49AbJNKJEyboJ5WpGggE6O7uzqtSqJgeuFwue3NLMX3IuiuK3cUsrVzK/3n//6Ez0onbdLNhwwaKi4sJNYbQ3BoZhyWewhR4i7x5vnEgr4Z4th7KUHIt9LFElni9Xrxe77AifUIIgp6gnczkNqxzOXRLMpPmYNp+NuQSrEzXHYd3IDICh1F4eT2pBF0IMSHB/QqFYmxkLfSAy3oYOzQHVf4qXn75ZaSU9Pb2YugGekq3BT2TyuB2u/NcKalUys7whNEFPdcqH6v/OhAI5J07S9A9KOjZqJyshZ7ODDZoCSXzj93QuAF3xo3hMCg0J5XLRaFQFJasoBe5B+uyHDhwgN7eXtsCd+pORGZAvCWYKdO2rs877zzmzLFazOUWmRvN+j4Wgy0QCBCJRMhkMnR1ddkx7rnld7OFvhxJB1paG9FCXz5rOWC5Y4QUee6fQqEEXaFQTBq2he4cdJc1NTURDAZZuXIl8+fPx9ANW9CFFGhotmBXVlayfPnyYVU4R7PQhRBccMEFXHDBBWNeo8/ns8X81VdfZf/+/XY8fBa3w22VDDgE7n53ng89W1lxUdkiyn3l1joyIs/9UyiUoCsUM5hshc0TRVe0C0fcgSszKMCpVAq/308gEOC0007D7XSjZSwpcoVdaGjD/Od1dXUsXrzYfj+aoINlpY/HUs8+PJqarE3ZnTt38tprr9klegH0kM6WLVvQ0BAZkWehZysr+pw+u+SvkGLU5tOTiRJ0hWIGs3nzZl544QU2btx4TMIeDod54403jqmpcdJMcrD3IP5uP12NXXZ0WTqdzgsd9nv8VlMKwIgbBMuCI7YhXLRoEStXrmT27NkTGsKafTi0tbXZY11dXQQcAxmmpiDRmqCyshLDZyCkYH3jentuOBnG0+uhv7nf+iYyEETnNJSgKxSKCSQcDuP1emlra+PQoUPjPr6jo4P29nZ27Ngx7mMP9BxAJiU+w0ckFOHw4cNkMhlSqVSeO8Ln8dkWumZq+Ly+UZP/Zs2axdlnnz3utRyJrIU+tMenI2k9dLS0lfU5f/58dIeOkIKtrVttd1I4HsYVcdHd0o0n7UFIa+1K0BUKxYSRyWRIJpPU1NTgcDjszMrxkM26PHToUF7z8LHQHm5HT+v4nX4cDge7d+/mySefREqZZ6G7XW5cwgXS8j173YXNN8gW7RqKDysDVUhh98E1hWkL9r3P3Es4GaY7bDW0djlcaNHBJtVuZ2EKcuWiBF2hmKHE43GklHg8HhwOhx29MVZM06Sjo4NAIIDX62Xr1q3jeij0xHvQU1aG5ZIlS+jtHez4MzRe3CVd9sZooQVdCCsixTCMvAdNJjkQF58RpDOWmyhuxm2XSiKd4Pfbfk9/tB9d6LgdbszwoOC7jNH9/JOFEnSFYoYSi1m1x91u97gEXUpJd3c3r776KuFwGLfbzYoVK4hEIjQ2No75+r2xXvSUjt/nZ86cOXkx4nkWutuNUzjRTEuOvJ7CZwQXFxfn+eazZQfuuvAu6orqqCuuQ9d1ounoYIgl8E7bO2gZDZ/TZx2f1mxXTcBT+ES4kyqxSKE4mYjHrcYM47HQw+Ewb731Fr29vei6jtvtZu7cuZSVleHz+YZlVA6lJ9bDf2/9b+aXzqc72o2e0ikJlqBpGsuWLWPjxo1AvqB7PB4MzUouAsunXmiyZQbq6+s5ePAgqVSKnp4eVlWtwrPMw/bt23E4HEhN2hY4WD1HjYyB1/BSV1dHS0sLK90rkYZkQfmCgt+HEnSFYoaSjUxxuVwYhjEmQW9oaCAcDrN8+XJqa2vzsi0DgQB9fX0cOHDA9ssP5Webf8b29u1sbt4MGQiaQcpLrNjs6upqu4PQUJeLQ3fYgn4iLPSsDz0YDBIMBtm1axctLS15zTQcDgdXLb6KJ1990nK7DOh6tvZ5UVGR1SyjB/Ch4tAVCsXopDNp3mx+046uOBrJZBIhhO0bPpr/O9vFp66ujjlz5gxLnQ8EAkSjUbZu3cqmTZuGRYWAVajKEXcgTIFuWsdXlVTZn2fPmXtut9uNLnT0tDU2Faqher1epJTEYjHS6TS6rqNpGvUl9VT5q/KsdC1jRcEYhkF5ebk9XujSuaAsdIVi2vC7d37HC+++QE1RDV++7MtH7OoDlqBnIziyLpd0Oo1pmsMSczo6OgiFQmQymTxRyqWmpoZoNIrP56OhoYGtW7eyYsUK+3MpJeF4mKKuIq49+1raRTv9mX4qgoMx5RUVFYRCoV3WH+sAACAASURBVGE+dIc2aKFPBUHP+vuzgp5dk/1nwkHKYz0ghSkwXAa6rue1szsR96EsdIVimvDy/pcBaO5v5rdbf3vU+VlBB2xB3759O88//zz79u3LS/R5/fXX2b59OwBFRUUjns/v93PmmWeyaNEiFi5cyMGDB/OSlRLpBKRAFzr9Xf0s8iyiyl+Vtxm6dOlSLrnkkrymFNkGFNnNxhNh2Q4lW6o5Go1immbeNwspJb7unPUnHfgDVmarEnSFQnFUpJR2urmu6fyl8S/s6th1xGOSyST9qX4aexptQY9Go0gp2b59O++++y4wQi3wMdQRr6urAyzLPks4GUYzNRyaJWQHDx5E1/W8IlWapg1rPCGEyCu2NRUamXg8HoQQRKPRPAs9t5dC4HDAqnue0gmWWmUCnE4nF110ERdeeKESdIVCMTLxtBWx4nK4uHjuxcBgh59Rj0nE2dC0gYdefgihC6SUxONxKioq8Pv99Pb22r07XS4X5eXlrFixYkwtGn0+Hy6Xi+7ubnssnAyjZTQM3bAFPyuMR2NexTxcDhe1JbVHnVsINM0qEDbU5eL3+21R11M6Rsza+AwWDdZ9KSkpobS0tPCLRvnQFYppgS3omssuRRtLWXHmUkp6Yj34Xf48v3okFiFFilgqRm/CSuqJxWIEg0EymQyJRILe3l76+vpYvnw5c+bMGfN6ntj5BBsPbWS1e7U99sqBV2wL/bTTTqO4uHjEmiwjEfAFOK/2vDF9OygUHo/HttCzew7BYBBXuQusOl62oPu8hQ+1HAkl6ArFNCCejiMyAl+zj2SJ5XqJJqPs7drL49sfZ3fnboKeIN+64ls4dSfRaJRoPErGsCJROqKWa8Q0TQzDsB4CPT00Nzej6zo1NTVjXktDRwN/2vUnfKaPfR37WM1qwskw6/etx2N6kJrEMAzmzZs35nNmXS5TYUM0i9frtb+B5HbLqiyq5BBWXRwjYQn6iUgiGomp87enUChGJZ6Oo6d0dHT6mvvQ0HjtrddYv2c9UrdcAL2xXnpjvVT6K2lrayOdSZNyWZEYbZE2qrDCBx0OB5qmkUgk6OnpIRgMjiqkjd2N/HHXHyl2F9Of6Oes2WfxZMOTAGS0DB39HfY8sIprZTsPjYesoE+FDdEsHo+HWCyGpml5+wAr61fS19KHoRv2PkbAqwRdoVCMkUQ6YaWVaw4cwkFRm+V20TIaFfMrCCVCdEe7SaQTxGIxdu7cieExyMQtcW0Nt9qC7nQ6kVJimia9vb0sXLhw1Ouub1zPO23v2O+3tm4FwOv0ktEzhCJWqOO+bqubkMgIVtSuGPFcR2KqWujZv6dcQXc73dQU1ZDKDMb1TxULXW2KKhTTgLZQG3paR9d16ufX2+POqJNyRzklnhKMmMGbf32TlpYWMpkMlQsq7WzGtkib3cHe4XDkxaEPjTrJ0hvr5a8H/wrAmbPPzPvsrgvvwuF0kMqk6Ap10RKyImXOrj6b2SWzx31/Wd/5VBP0LLmCnv0WYWgGAWcAv8ePy1H4QlwjMXX+9hQKRR77uvfxg9d/wIeWfYhfbfkVvrQPp89JZVll3rx4Yxy9RMfX7aPf6KexsZGioiLSxmCqvylNIqkIAWcAwzDyBD03JjyX+56/DzNjAvD+xe9n9YLVvLjvRc6tPZd5JfMoC5TR19XHoa5DdEWt0rpO4TymXppT1eWSJfeesg8dr9fLeXPPw+VyjSmSpxAoQVcopijb2rfRH+/nN2//BrD808IhKPbnW9ROh5PEYatuiylNYrEY9fX1NKQaBicJq79nVtBzW7xlLdGUmeJ/tv8PZ84+kwpfBfFU3J5T7C5mrmcuSyqW2GMVxRX00cdT25+iU3Ra6f6afkyCnhXFqWSh5wp6bl2W7ENHCMHcuXPzYtNPNFPnb0+hUOSR7VWZDU8UGUHYDFMSKMmbV7Oohn179kEU26KeNWsWbzW+BUCZt4yeVA/hRBgCljjlim5WrDY0buD5vc/z/N7nqSnOj3oJuIb7iJfXLWfv23tp6W4h4U9Q2l6Kc86xWehCCObMmTPmMMdCkJvgNJKFLoTI63M6FVA+dIViihJN5fcAFRmBy+nC4/TgdgxmVhb5iyiqtjZJzYyJ3+/H6/PaD4RTyk9BapJY2nowjFQFMJ1J52WeNvc1532ezf7MZdW8VVbEjKnZjSxgeCu3sXL66adTXV19TMdOFpdccgnV1dV5bqmpkMk6GspCVyimKNFkjqBnrFZoly26DCEE59Sew0v7XwKspBaP13IPmNLEV+XjH//4j6RNy4de7i0HYYk2DAr65ZdfbpWHzaT56vNfpSM8mMa/rGoZV5xyBd95+TtHXKPhMuymDm7dTVFREbNnj39TdKpSXFzMOeeckzeWFfSplASVRQm6QjFFCcfCFLUXkfAmSHlSnFJ2ClXFVuhhSXDQ7aJpmu0T35HZgQgJW8wByn3lSCRmxszzU2cFqaGjwRZzXdO59axbOa/uPDShcceqO6j052/C5mI4DbSkhpbW8Lg8XHLJJVNmg3Cy8Pl8rFixgqqqqqNPLjBK0BWKKUo8EkdLawQjQeLhOJ5Kjx2dcvHFF/Pzwz8HCZX+SnriPfTWWOn9m5s3552nzFtmW+gOh2OY4G5r3wbA5Qsv5yOnfyTv8+Wzlh9xjYZhIDICzdTwF/tnvJhnydaqmWooQVcopiixuOXzvvTMS2k60ETQHcz7mv/1932dw5HD1Afr2du1d9Tz2IIu0yP6z7cftsrmnl59+rgF2Wk4EVJYD55A8OgHKCYVJegKxRQlmUxiYHDu8nOJdcYwDCOvpsjsotnMLrL81d3R7tFOQ5GrCKfuRCIRer5g98X7ONh7EEM3WFg2esboaDgNJ0jQTZ2yorJxH6+YWKbudq1CcRKTMlOYKcvn7ff4ufTSS7n00ktHtaDPrj171HM5dScewyqatbNrJ2s3r7XDG7PW+eLyxUftgDTiuQcsdCR5nYkUJ4aCCboQIiiEeFQIERJCNAshPjPKvPcKId4RQvQKIbqEEI8LIcZeCk6hmAGEEiG0jIbDsAppBQKBvCYQQ5lXMo/bz7t9xM+EEJagC8nunt28sv8V3jj0BgDb2iz/+bLqZce0ztwQPiXoJ55CWugPY7l4ZgPXAF8XQlw2wrztwJVSyuDA3D3Ajwq2SoXiBNPV1cWOXTsQpsDpGrvVvLx6OQvLFrJ6wWo0Yf3XtkPsDA+R0gixYssv/2TDk2Rkht2duwFYWrH0mNaayCTs16OVEFAUjoL40IUQPuDDwJlSyhCwRQjxE+A24MXcuVLKtiGHm8D4nXsKxTTllVdfoSvahZE0cAdGt8qHYugG97znHsDym/9h5x84v+58ADwOj10bHaA91M5ze5+jL96Hx/AwKzDrmNYazwyWB8gtZqU4MRRqU3QRIKSUO3LGtgBXjDRZCFEPbAWKsAR9xO+SQoggMHRrfWr0sFIojgHTNNl1eJfdkKKofOSGzUfjmiXXcPnCy22/uNsY/mB47J3HAJhfOv+Yww3Pm3Mef3j3D9SW1k6pwlonK4USdD/QP2SsFxixiLCUsgkICiFKgU9iuWFG4i7gaxO1SIXiRNPV3WWLecqTorTy2HtT5pZ0zd3w9Dq9eVmoVf5jT5BZXLmY8+vOp6p86iXZnIwUStDDWNZ2LsVA6EgHSSm7hRA/A94WQtRIKdNDpnwXWDtkrBZ46TjWqlCcMPY07QGgd1YvaFaVw4nA0Abjz0s9pQiEXevF5zx237fD4cCpO5X/fIpQKEHfDUghxFIp5c6BsRXAtjEc6wAqsR4IecG2UspeLEvf5mTJVFPMTPY37yftTNvhCtmG0MeLoQ8KutvhpsJXMWGCDsp/PlUoSJSLlDICPAZ8QwgREEIsx9oQ/cnQuUKIvxVCnCIsKoGHgLeklKNnTigUM4BEIkFnTycp92Brs8XlE1OedSRBz+Izjl3QXS4XmqZRVDQxDx7F8VHIsMXPAhJoBZ4G7pdSviiEqBdChAc2QgHqgGex3DRvY22KfrCA61QoTggdHR2Ek2HSLsuzuKRyCeW+8gk5d64P3W248wpuHY+F7nQ6ufzyy6dc2duTlYKl/g+4Rz48wngT1qZp9v13sXzjCsVJQUdHB6lUisOHD9Of7sc0TO5bfR/VgYkTyVwf+lAL3e/0j3TImMltZ6c4sahaLgpFAci2KRu6x9Pf38/GjRsRQhA348SNOMWeYuqD9SOd5pgZ6nKp9E2Mha6YWqhaLgpFAXjolYf4xovfyOs/mUql2LRpE5lMhnQ6TU+4B9MwmVMyZ8KvfyQfutepNjRnCkrQFYpJJpFOsPPwTg72HiScDNvjzc3NRCIRzj77bIQQhBIhMo4Mc4ITL+hD49BzwyG9hhL0mYJyuSgUk8z+nv34un0IUxBLxeyGy8lkEoCqqiqKi4sJt4Yx3eakCHquD91reBFC8NC1DyGltOu+KKY/StAVikmkI9LBfzz9H3hjlhUcSUZ4ef/LGLpBIB1A13U0TaOyspLQO4Wx0LM+8+PdDFVMPZSgKxSTyKObH8XbO+jSONh9kF9s/QUO3cHtc2/H4XAQT8VxlDvoLu+myFM0YdmhueT60D2OqdfcWDExKEFXKCaJvV17adjbgIvBsL639r8FQNpME0/G0XSNNevX0BpqBQcsLFs4KdnOuYKuolpmLsp5plBMEo+/8zjOiJO64jrcDqva4Z6WPfbnsUSMg6GDlpgPsKh80aSsJVfQ1SbozEUJukIxSbS1tCGk4KpLrqJmltV0S5iD1nd/rJ9dXbvyjplfOn9S1pK7Kaos9JmLEnSFYhKQUmJGTTKODPPr5zN78WwyegbN1HDEHegJnZ1tO/M6/gDMDsyelPXkunGOpXeoYnqgfOgKxSSQSCcQpkA4BA7NgdfwktEzOJIOnDEnaWeaw+ZhpEviNtzEU1bnn9wa5hNJbgEuVZF05qIEXaGYBKKpKJqpYbgtV0fQHUTqEi2pIRBoGQ2REUghmROcQ0NHw6Sup8hdxOcv+rwKVZzhKJeLQjEJxNNxREbgdFrujTNmnUF9RT1O3UlZaZllvUuB1CQfOf0jLK9ezh0X3DGpazq18tQJrxGjmFooC12hmATC8TBCCgynZaEbusEd77+DaDTK717/HZ3dnQBk9AzVgepJF3PFyYGy0BWKSaA/arXQdbsHmzMbhkFxcTFu1+CY0+VUm5SKCUMJukIxCYSiVrvcXPHO4vMMblB6fSomXDFxKEFXKCaBUMQSdJ93eMx3wBuwX/u9apNSMXEoQVcoJoFIZKAB8wiCXh602sqZDpNSb2lB16WY2ahNUYViEohGo0hN4nOPIOhF5fTO7gVQgq6YUJSgKxSTQCwaI+PIjFg3pdhdDAO5PUFPsMArU8xklMtFoZgEEokEGS2Dxxheqja3lkqxa+JL5SpOXpSgKxSTQCqVQmpyRAs9t0OQQ1dfkhUThxJ0hWISSKfTSM2q0zISF869kDJvGadVnlbglSlmMso8UCgmGCklqXQK6RjZQgf4+FkfR0qpCmUpJhRloSsUE0w6ncbMmEhNjuhDz6LEXDHRKEFXKCaYdDpNOpNGitEtdIViMlCCrlBMMIlkAlOaoGG3nlMoCoESdIVinEgpefbZZ2lubh7x83A8DIDTcCq3iqKgKEFXKMZJMpkkkUiwbdu2ET/PVlr0uEf3nysUk4ESdIVinKTTaWD0Tc1wzLLQVTNmRaFRgq5QjJNUKnXEz7v7u4H8MrkKRSFQgq5QjJMjWejpdJrmA82k3ClVGldRcJSgKxTjJGuhjyTo3d3dJNNJEr4EAVdg2OcKxWSiMkUVJxWJdIJUJoXfObr13NTbhNvhxpQmZd6yYS3ijiToXV1dpDNp0p70Ea+hUEwGStAVJw1SSr790rc5HD7M1y//OiWekmFzOiOdrFm/BjNjAjC7aDafPf+zVPor7Tm5LpfOSCcPv/4w71/0fs6tO5fOzk6kS4IGfpcSdEVhGbPLRQhRLITwDLwWQohbhRC3TN7SFIqJI5QIsa19Gwd6DhBLxfjzu38ecd6Gxg22mAO09LfwrT9/i//+w3/T3W1tdkbjUXZ37qYj0sHjOx6nua+ZH238Eel0mr6+PjLuDAA+Q22KKgrLeCz0PwFfAP4K3Ad8BkgLIRZLKe+bjMUpFBNBZ6STe5+5N2+suW/kpKC3W98G4KNnfJT5pfP5064/sWvPLraEtlC/u54Lzr+Atw69RVu4jdZoK3PL5yJMgdQk3d3dSCktQQ+Dy+Ga9HtTKHIZz6boUuDNgdc3A1cAFwN/N9GLUigmku2Htw8bS5rJYWO9sV5aQ624HC7eM+89zCuZx23Lb6PcLMfMmBxoPcD27dvpaOmwDpAgMoLitmKcMSednZ1omkbSsM6tBF1RaMZjoetSyrQQYjZQJKXcCiCEKJucpSkUE4Mu9GFjCTOR937joY1sat4EwKLyRehC591336WhoYGgHiSiReiN9LJ//36MIoNUKoURN2DgNI6Eg76+PoqLi0kmLUFXdVwUhWY8FvpeIcStwO3ACwBCiHIgMhkLUygmilAiZL++acVNACTTgxZ60kzyn2/8J5ubNwOwwL+AdevWsWPHDioqKlh+7nL6qvrQ6jSuuOIK/HP8pJ3WxmgqbkW86CmdeCKO0+kkno4DykJXFJ7xWOj/L/ALLJvk+oGxa4FNE70ohWIiCSetVPyrFl3F6VWnA/kWen+8336tJ3VCe0L4nD5OOeUUFi9ezDtt74AGMUcMwzCs0riaBCAWiVnHpXQi8QhFgSISaevcLl0JuqKwjNlCl1K+KKWslVIukFJmnZK/Aj44OUtTKCaGrIVe5a+yreZcH3ooOWjBu8IufE4fixYtYsmSJQghCLitBKHt7dt5Zs8zZDIZGAhBj0fi9rH94X4cDoct6Mrloig0484UFUKUCCHqhRD1wKyBH4ViypK10AOugJ0klOtysV0yEnwpH3V1dSxevNj+vMhVZL9+7J3HMKWJFJaFnoqnbGs9YSbQdd0WdKcjPyFJoZhsxuxyEUKswnK5zMsdBiQwfNdJoZgiZAXb7/QPCrqZtHt6Zj93JBysqF7BrFn5NsrQFP7uWLct4umUlRVqxA06I520RloBcOpONKEqaygKy3h86I8A64AfAuHJWY5CMfFEEhG0tIbP6UMIgaEbpMwUSTOJy+GyBf2sorMI6AEqKiryjh+a+n+g54AdOSORmLqJrukcjhzmiV1PQEBtiCpODOMR9AXAWVLKzGQtRqGYDFIdKYp6i+jv7Kc6UI1Tdw4XdAmZcIaqRVVo2tEt66zLBSDjyAwbV4KuOBGM5zvhVqB+shaiUBwPmUzGrrGSi5SSdMIaD/VYlnhWbLO+7gO9B3AkHRgYw9wtWd678L3559VyBF3P2EKe/bMz0nk8t6NQHBPjEfRfAo8JIW4QQlyS+zNZi1Moxsqbm9/k6Wefpq+vj3fffZdw2PIKJs0kIi3QhEY8ZkWk5PrRe2O97O7cjSvpotRbSnl5+Yjn/+jyj/LAlQ/Y73Mt9IA/YEe9ZMdVYS7FiWA8LpcfDPz5myHjalNUcULp6Ovgj5v+SNJMkk6n7SzP973vfURTUURG4NAcRCJWDlxW0BNmgkgygpSSMq2M8tJyDMMY9Tpl3jICroDlosmpnFtRXEFLawsAlUWVLJu/jGVVyybvhhWKURiPhR6QUmoj/IxJzIUQQSHEo0KIkBCiWQjxmVHm3SqEeFMI0T8w7ztCCBX/pRiV1/a8RiwVIxwI0xPvIZ6O0xPuobOzk2gyimZqODQHiUSCxsZG+mNWItH3XvkesbSVGGRIA4/nyE2dhRC4jYHYcmH9ZPQMVYEqW+C9Ti8fWvYhllQsmazbVShGZUyCLoTQga7jFNaHsb4RzAauAb4uhLhshHle4C6gAliJVQDsy8dxXcUMpz9sCXTSk0TWSTY7N7OpeRP7W/fTH7U+M8oNSktL2bZtG0WHixCmIJKMEE9Zbhg9o+N0Hv3X+7TK0+zXUkgyeoYidxGSAReMHOVAhaIAjMnlIqU0hRAHscR2eJm6oyCE8AEfBs6UUoaALUKInwC3AS8OudYjOW9bhRC/AK4b5bxBIDhkuHa861NMb+JxS5SlJtnSuYVYKkaxVszmA5uZE5kDgDfg5cILL+Tw4cOEXwrTHm5HlAqiqShI0OXYBP1vTvsbgu4gf373z5iGSdpI43P6SLvSGAkDqStFV5w4xuNy+Qrwn0KIucdwnUWAkFLuyBnbAozF0XgJMLz+qcVdQOOQn5eOYX2KaUwiniCjW+n4sZTlQjEdJt0d3TQfaCbhTeALWs0mKioq8BpehBSYGZN4Oo7ICHRtbILuMTxcs+Qagp4g4fIw8aI4PqePhD9Bf2U/ptM86jkUisliPJui2c3Qvx3aS3EMfnQ/0D9krBc4YhddIcTHgIuAFaNM+S6wdshYLUrUTyqSiaQl6DlkHBmSqSTSK4kZMTvqRAiBw+FASEHKTOVtmo5F0LMY2sDmqQC/4bf86UYGKZWFrjhxjEfQR/J3j5UwUDRkrBgIjTAXACHE9cC/A1dIKdtGmiOl7MV6MOQedxzLVExHUskUUpcsrlhMQ0cDYPnTo6koO8QOEHB2zdn2fIfuQBv4chpKhNAy2vgFXR+MhskNUTSlstAVJ44xC7qUcsNxXGc3IIUQS6WUOwfGVgDbRposhLgK+AlwrZRyy3FcVzHD6I/3E3AF8h7c6XQaKSRnzj7TFvS0O023uxtSMLdkLovLB4tt6bqOQ1i/+v2J/nG5XLLYFjrgc/qoLa7lUN8hllYsPd5bVCiOmfEU5xo1gUhK+ZcjHSuljAghHgO+IYT4BFaBr9uAG0a4zmqssrx/I6V8fazrU8x8GnsaeWD9A1w450JuPetWe9xMm0iHZE5wDretvI2gO8gPXv+BnQl65SlX5j0A8gQ93o8r7MJZ7sTnG3tT5yL34BdOn9PHXRfexZbWLZxfd/7x3qZCccyMx+WyfoSxrMNwLLHonwV+BLRi+dPvl1K+OFCGdwdwqpSyCasBdTHwZM5/wgNSytNGOKfiJGLToU1IKXl5/8s0dDZw90V3U+IuwcxY5WydupNV9asAq0JiIp2g3FfOmbPPzDuPruvoA9s+/fF+HEkHs2pm4XCM/b9DbfFgMJXPsIp+vWfeeybgLhWKY2c8Lpe8iJiB3qIPAP8zxuN7sUIXh443YW2aZt8fj69eMYM51HcIAC2lkXg3wW+9v+Xvz/17MpkMUpN5BbGK3cV0Rjq5YuEV6Fq+vaHrOo64A5fmopdeiigi4Dni/vwwqv3V9mu1b6OYKozHQs9DStkihPgc8Abwh4lbkkIxHCkljT2NABgJAy2t0dfdRyqVshtO5LZ8++CpH2Rnx04umnvRsHPpuo4mNDx9HpKeJJrQqAhUDJt3JJZWLmVZ1TJqimuO78YUignkmAV9AInqWKQoAK2hVmKpGIZuoJnWl8V4X5x0Om25XIZY6IsrFrO4YvGI5xJC2M0nhBT4nX5czvGVu3VoDu688M5jvBuFYnIYz6box4YM+YCbgFcndEUKxQjs694HwPLq5ayoXsHv3/g90f4o0XiUjBzucjkSpmkOCnpGEHAFxuU/VyimKuP5Lf76kPchYBNWBqlCMalkBX2Wcxax3hgew0MsFWPngZ1IJLpDH3PLt2xFRhi00JWgK2YC49kUnXf0WQrF5LCvZx96Uif8bhjNpVFUXkSsNcbm3ZsBqC8Ze++VYRa6R1noipnBmGu5CCF+O8r4ryduOQrFcOKpOC39LXhDXvxOKyCqtq4W0zBp72kH4LSasUe1mqZpt5kzhIHX8CpBV8wIxlOc6+pRxq+ciIUoFFn27NnDk08+addFaexpREtoBDNBambXMH/+fOZUzyHtslrLZfQMNcGxR5t4vV7LQhdQ5a0COGJjC4ViunBUsyQnQ1QXQlxMXq8WFmPVaVEoxkUmkxm1GfOuXbsAiEQi+P1+9nXvw9PvIegLsmLFChwOB029TaRcKVxhF6Zh2pb7WFi5ciWbmzYjw5IqXxXEURa6YkYwlt/i9QN/SiC3novEyvq8d4LXpJjhxONxXnjhBc4880yqq60EnWxyTnt7uz3vrbfeIhwOszO9E0fCwYLlC2zhrQ5UY7qt+PPxCrrT6WTJvCV09XWxMLiQUHto1IeLQjGdOKqgZzNEhRDbpJSqUaLiuAmHw5imyc6dO9mxYweBQIBzzz0XgCdeeIKDvQcpchexRFuCaZp0NHUghWTVslX2OZy6kwp/BZ2VnWS0zLibMs8pnUNsdgwZkQSDQZXtqZgRjNksUWKumCiyHYYikQjRaJT29nbbX97Q3UAsHWOfsY95Z86jN95LRmYIlAUo9ZfmnafaX03GkQENvIZ3XGvQdStsMRQKMXv27Am4K4XixDOeKBdNCHGvEGKPEKJvYOxKIcQnJ295iulOOp0e1vQhK+hFRYMVC3t6ekib1iZn0psk5U2RIEFrfysAp88/fdi5cysejjUGPUtW0AFmzVLJzoqZwXj+F9yPVVzrnxmssrgX+IcJXpNihpDJZHjqqafYsWNH3ng8HsfhcHDGGWeQNtKEk2FeeeUVdu3ehcgIpGb9ejX1NtEd60YguHjJxcPOX+Qa2jNl7GQFvbS0FI/Hc8znUSimEuMR9L8DPiClfBTI9vtqBOZO9KIUM4NUKgXAvn378sbj8Thut5u0kWZddB1vtr0JQEOD1Zwio1m/Xi/ue5G4P05lUSUVxcOLZ2ULb51Te86415YVdOVuUcwkxhOrFQAODRnTgfTELUcxndmyZQumaXL22Va7t3R68FdDSmlvPIbDYbxeL2+1vgVAqCLEvPnz2NFgWfJZCz2ajEIxXHfxdSNer8JXwXev/S4eY/wWdklJCbNmzaKmRlVLVMwcxmOhvwN8cMjYdcBbE7ccxXTm4MGDtLS02O+zFjrAM888QygUSGrmkQAAIABJREFUIhqNEgqFSDgT/GGHVXU548gwe95sdKdlNWcFHaAqUMWi8kWjXtPn9I3bfw7gdrtZuXLluNrOKRRTnfFY6F8CnhNCfABwCyH+L/ARVKaoAiudPks6ncbhcORZ6KlUitZWa4OzO9bNs43PkiBhfx5OhimtKoXGQZcLwMVzL1YhhQrFGBlP2OJfgZVAL1aykQH8P8C1k7IyxbQiEonYr8NhK3k4K+hnnnkmwWCQw4cP09TUxPbQduLEOb/+fLtmeX+in2B1kFhxDKfPspp1TeeC+gsKfCcKxfRlTIIuhLhICPFPwEIp5Z1Yrpa3gcewrHTFSU4oFLJfZ8U9K+glJSVUVlbS29tLf6SfqCOKruncdvZtdqTK261vkxZpEv4Ep1ScwuKKxVy/9HoCrvG1hlMoTmbGUsvl74EfAt1AqRDiy8DlwDzgi8AvJnWFimlBrqAnk0lg0IfucDiorKxk9+7dJNNJMo4Mlb7KPFfK+n3raeptAqwGz3esuqOAq1coZgZjsdDvBD4qpazACl38Jla44qlSyp9JKTNHPFpxUhAOh/H5fAghbEHPWuiGYRAMBjEMg1QmRUbP2ElB80vn2+fINrHI7Q2qUCjGzlgEvU5K+buB19ma6J+XUiYnaU2KaUgoFCIQCGAYRp6FrmkamqYhhKCiooJkOonpMG1XysVzL+a2lbdxxqwz7HM5dRV5olAcC2MRdHuOlNIEQlLKyBHmK04yMpkMkUjEFvSsqyWdTufVGT/llFPw1/hBg2J3MQAuh4tV9av+//bOPE6uq7rz31NrL9W7urValmTLWmzLizBO8IYhFgIHhxnGLCYfxoR1MHzw4IRJAgkQkgAJgw0hMckQD5kQCGCHzRgwtsExXiSDLQlhW7Il2ZYsuVvqvau7q7qq7vxx3339aulF3VXVpfb5fj796ar7Xr369euq884795xzuX7r9ZPH05s+RZkTs0lbjIvInwee1xU8xxjzF+WVpZxKJJNJjDEkEglisZjvoQ8PD9PQMNk0q7m5mXBbGE4Ul+0nYgluuuwmfvDkD7jk9Euqql9RFguzMegPA1cGnu8oeG4ANegvYVyaovPQ+/r6SKfTDAwMsHZt/lK0IxN238ZYY9FxNnZuZGPnxsoLVpRFymz6ob+yCjqUUxhn0BOJBJlMhkwmw0MPPUQul6O9Pb/lbWrCFhOdbLtbRVFmRtfdUubM2NgYv/rVr8jlckQiEcLhMC0tLfT19flpjG1tbfmvyYwBUBepq7peRVns6LpbypwZHBykv7+fwcFBf2m4TZs20dRkM1gSiQTxeH4K4tiENehzaailKMr0qEFX5kwqNdmLxWWzhMNhWltbAYrCLQDjGbu4hXroilJ+1KArcyZo0J2HDvgLRpQy6M5DV4OuKOVHDboyZ9xSckDeMnPNzc2ICB0dHcWv8Tx0DbkoSvnRSVFlzgQ99KBBX7ZsGa9+9auLlnYzxmjIRVEqiHroypxJpVJ+g62gQReRkut0prNpjDHEwjHCoXDRdkVR5oca9Bqmr6+Pvr6+hZYxJalUys9oCRr0qfDj51H1zhWlEqhBr2EefPBBHnzwwYWWURJjTJ5Bd7+nw+Wga/xcUSqDxtCVOZHJZMhms7S0tLB69Wo/VXE6xie8CdGIGnRFqQRq0JU54SZE4/E4S5YsmdVrnIcej2i/c0WpBBpyUeaEM+h1dbOPh2vKoqJUFjXoNcpsJhkXkqCHPlv8sn8NuShKRVCDXqNks9mKH//QoUPkcnNbTMIVFQUN+q07buVzD3xuygUq/Bx0zXJRlIqgBr1GcetxVoojR46wd+9eTpw4MafXp1IpQqGQ38NlND3KYy88xr7j+9jbvbfka3RSVFEqixr0GsUt41Ypuru7gcle5idLKpUiHo/7hUXHR4/72/Yc21PyNdo6V1Eqi2a51ChBD90Y4xvOch37+HFrgF3f8pPFGXTH8ZFJg55M5y85m8qk+PWLv2ZofAjQkIuiVAo16DXG+Pg42Ww2z6Bns9m8bobz5fjx4/6iFPPx0IMZLseTkwbdxcod33/y+9z99N3+cw25KEpl0JBLjfHrX/+ahx9+2F9oGco/Qdrd3U00GmX58uWMjIwwPj5+0jH7Qg+9J9njP3ahFbD9Wx45/EjeazVtUVEqgxr0GmNoaIixsTGOHj3qj801E6UUxhi6u7tZunQpzc3NpNNpfvrTn/Loo4+e1DGKQi5BD92b/Owd7eWG793A0PgQzXXNfkOuUgtEK4oyfzTkUkNks1nGxqx36yYtobwGfWxsjHQ6TUdHR17I5GSyXdJp2zUx+PqekWIP/VDfIX/s8jWXc9aSs3jq+FOsa183nz9BUZQpUINeQ4yMjGCMIRqN5mW5lDPk4mLmiUSiZL/y2Uy+FhYVTWQnGBgf8Le7GHoqO9kv/VVnvIqmeBObujbN7w9QFGVKNORSQzhju25dvgdbTg/dZbU4g75lyxZCIfsxSCaT073Up9Cgnxg9gTGGtvo2wIZcgotZXLnuSpriM3djVBRlflTNoItIq4h8S0SGReQFEXn/FPudIyI/EZFeEant+vcyMzIygoiwdu1aYrEYLS0tQPk99Hg8TiwWA+D000/nsssuA2z8fja4cJAz6C5lcVnTMqLhKDmTI51N+7F0TVNUlOpQTQ/9S9gQzwrgauCTInJlif0mgG8Bf1BFbTXByMgIDQ0NRKNRLr/8cjZu3AiU10MfGBjwLxSORCJBKBRicHCQkZER9uzZM2Vh0/DwMIcO2di4M+guw6WrscsvGkplUtq7RVGqTFVi6CLSCFwLXGCMGQZ2ichtWKP9s+C+xph9wD4ROXMWx20FChtxryqP6urS09NDb2+v31e8vr7eT10sl4eeyWQYHh5m2bJleeOhUIhEIkF3d7e/SlI2m+WCCy4oOkYwndLlxrsMl85EJ3XROoZTw4xOjE5WhqqHrihVoVqTomcBYox5IjC2C9g2z+PeCHx8nsdYcFKpFDt27ACgo6PDH3ex7XIZdDfp2tzcXLStpaWFw4cP5+1bCqdl+fLl/tiJpM2Q6WzspDnezPGR4zx+9HH10BWlylQr5JIACgO0A8B8Z8puAdYW/Fw2z2NWncHBQf/x0qVL/ccuC2W2k5Uz4YqHXPw8SKGRd+mThTiDvmHDBn9sOG0nWpvjzVy5zkbRHn3hUVIZO3mqhUSKUh2q5aGPAIVuYQswt0YiHsaYAeyFwaecPU+qxcCA/RO2bt1KIpHwxyORCA0NDXMuzy/EGfRSbQQaG/OLfVKpFNlslnA4XPIYwXHniTdEG2httyGj4dQw8bCNsWszLkWpDtXy0PcDRkSCScjnA6X7rL7EGBgYoKmpiRUrVhRtSyQSc26gFWRkZMSvBi000gCdnZ2sX7+eSy+9lC1btgCTPc+DlLooOINeF6nz0xOHU8O6KLSiVJmqGHRjTBK4HfiUiDSJyBbshOhthfuKpQ6Iec/rvOeLlsHBwaLME4DfdP+GvkwfyWRy3isYHTt2zH9cykMPhUJs3LiRtrY2/y6hVNjFhVzcReEfd/wjg+M2ZNQQbSAeiRMLx8jmsn46o5b6K0p1qGba4g2AAY4BPwY+YYz5mYisFpEREVnt7Xc6MAb8xns+5v0sSsbHxxkfH/ezWxzGGG558BbuPHgn2WyW0dHRotcaY2bdVCvo5c/UudHF7ksZ9Ewmg4gQCoUYSY/wyxd+6W9ziz87Lz2dTbO2fS0dDR1Fx1EUpfxUrfTfi3dfW2L8eeykqXv+LHDqBcLniIufFxp0F8bIRrLkTI6RkZG8OHc6nWbnzp0MDw+zffv2GecOgga9VMglSF1dHSIypUGPRCKICM/0PpO3zWloijfRO9oLwLb1801kUhRltmjpf5XI5XLs2LGD3t7evPGBgQFEpCjLZHTCeuS5aA5jTFEcfdeuXfT395PJZGacNDXG5O0zk/EPhULE4/EpQy7Ow3+u/7mSr0/E7PW5o6GDC5YX57IrilIZ1KBXifHxcXp6enjooYd49tln/fHBwUGampqKvGbnoZuQIRKzC1E8/vjj3H333Rhj6O3t9bsdBtMeSzE6OnrS1ab19fVTeuhOa/94f8nXdiW6AOudu5a5iqJUHu22WCVcQyuwi1iEw2FOO+00RkZGaGtrK9rfeegADY0NDA8P++GZoaEhMpkM5557Lrt3756xB8tcsmTq6+tLHteFXAAGx0pfSF6/6fWc3XU25y4796TfV1GUuaMeepVwBv2MM86gs7OT3bt309vby9jYWFEOOOSv+lPXUJcXMnFhm46Ojik9aUcmk6G/v7QnPR3uuIXZNcHc9MFUaYOeiCXYsnzLKVkToCinMuqhV4FMJsOePXsAWLt2LdFolHvuuYcnnngCYwwNDQ1FrwkutBxviOdls5w4cYL6+nrq6+upq6srmS8O1vj+6Ec/AqYOoUxFfX092WyWdDqdtzLRxMSEr9elK7bVt3HtuUXz3YqiVBn10KvA888/n9dDPBKJsHLlSj+EUsqguxg6QLw+nretu7vb7/kynUHv6ZlcRShYgQqQyWU40HuAbK50n5ipUhcnJiaIRqNkc1mGU8OICJ9+zae5aNVFJY+jKEr1UINeBYI55K7hVrC5VVNTcUubYAw9Wh8t2t7e3g5MGvRShUfBzogiwlVXXcVrXvMaAO7Yewefuf8z3LX/rpKapzLo6XSaWCzG4Pggxhia4k068akoNYIa9CrgPPEg7e3tRCIRVq9eXbJZVtBDD0VCRKP5Rj1o0HO5XMn+5UGD3traSl1dnf9e9zxzDwB3P313Sc2lDHo2myWbzRKNRv08cy0aUpTaQWPoFSaXyzE4OMjpp5+e16EwFAqxbds232MvJLhGZ9ZkaWpqoq+vzx9zIRRneEdHR4suDOl0mkgkwsUXX1xUuOSIhYsvJgDRaJRwOJxn0N1FIxaLcWzUthLoqFeDrii1gnroFWZoaIhcLkdnZ2fe5CLYis3CTJAjg0fYf2K/7wGDXYS5MAbuXufGC1vs9vT0cPDgQWKxGO3t7VNeOKYy6CJCQ0NDSYOuHrqi1CbqoVcYlzI4lYccxPVvGRwfzItLZ02W5qbJStJgL5bGxkZEpKha1C2YEQy7OCayk+GZ6eLfhZkx7lhq0BWlNlGDXmEGBgaoq6vzqzqnYzg17KcCBrNPMtmM74knEgm2bt3qbwuFQtTX1xcZdBGZsnlX0PsfSU3dNqC+vj6vClU9dEWpbdSgV5j+/n5aW1tnVWRzbPhYyfFMLkNnZyfnnXceq1atKgqfJBKJvJDLTGX+J0ZP+I+T6SSZXIZIqPijUFdXRyqVIpfLEQqF8g36mBp0Rak1NIZeQfr6bC/z4Dqh0/HiyIsAXLTqIraftd3vI57J2Za1q1evLhkLdwbdpS66tUO7urq47LLiFfncGqBTPXe40I7rgR7shd43aido1aArSu2gBr2CPP/880SjUVavXj3zzsCLw9agn9ZyGm88541sXWlDK5nc9D3PGxsbyWQyfoGRC5OcffbZJWP3QQ8d4HMPfI4dh3cU7ecuHs7jd+Gb8dw4E9kJGmIN1EUX9dojinJKoQa9giSTSZqbm2dcUMLhDPqypmUAfhhkJoPu4uvDw8MYYxgaGiIcDpfsEQPQm7ThkrZ62xRscHyQrzz6laL9Shl0EaFvTL1zRalF1KBXkNHR0ZJl/VPhQi7Lm2wVqTPoU5XnO5zh3rFjB/v27WNwcJDm5uYp4/bOQz9/xfl54yPp/AnSQoPuGnM5g95e3z67P0xRlKqgBr1CuBDIVF5yIelsmt7RXkKhEEsalwCTBn0iV1wFGiSYQXPkyBGGhoaKFswIcjxp1/rcduY2NnZt9MdfGHwhb79SBl1Cwnef/C6Ar1NRlNpADXqFcPnbs/XQ9x3fhzGGlc0rfUPucsRn8tCDnriIMDExUXLRaYDxiXGS6STRcJSOhg4+fMmH/cZaLwxNb9AzmQzPDT1H93A3oCEXRak11KBXCFeEU1gdOhW7j+0G4Pzlk2GQaMj2b5kphh7EZaIUVpY6XLhlScMSRAQRYW37WmB2Bn0wPZmXriEXRakt1KBXiGDO9mw42H8QgE2dm/wx56HPxqBfccUVwORCGqUafkHAoAfCJauaVwG27UAQt5BFMOTSXDcZymmpK30XoCjKwqAGvUIEy+RnIpvL+kVFK5tX+uPOQ3fVo9PR3Nzsd2CEqQ26i58vaQgY9BZr0I8OHc1rw+s8dOf1ZzIZcjJZtHRG+xkz6lIUpXqoQZ8jo6OjPPnkkyX7kEN+Z8KZ6En2kMlm6GjooCE2GXNfv2Q9IsIvX/ilX8gzHcGFpgsvJMeTx/nJ/p+w7/g+ADoaJ+PfTfEmmuJNjGfGueH7N/Bkz5PAZGw+6KFnscb97Re+XZeYU5QaQw36HHnhhRd45pln8havCDIxMYGI5BnZqTjYZ8MtzlN2rGpZxfqO9Rhj/JTG6XD57pFIpKii9M6n7uT2vbf7sfqghw6wNLHU6s5O8PlffJ6dh3cWhVwymQwZbPgnHp7d3ICiKNVDDfoccc2wSi0sAZMr+8zGi33q+FMAbOjcULStvcGGUWbjoTuDXirMU9gnprOxM+95YWrkfQfvK5m2mDXWQ49H1KArSq2hBn2WJJNJDh8+7D93Br1Ue1qYXHvTkc1luf/Q/UV9U4wxvkEPTog6XCaJK+aZDmfQS4V5Ct83GKsHeMPmN+Q9T2fTeQZ9YmKCdDpNJux56GrQFaXm0G6Ls2Tnzp2MjIywdOlSotHoSRv0h59/mK89/jUAtp+1nTee80YAuke6GRgboCneVGRkYbI8fzYG3YVIgu+byqRIZVIMp4b9sbec95aiPujnLD2HV657JT8/+HPAXgCCMfShoSEAMpEMpDXkoii1iHros8SlAw4NDTE+Pu43qpoq5DI+Pp6Xg354cNK7//H+H/uZK08etxOQGzo3lAzPnEzIxb3eva8xhj/+yR9z01035e03lTG+Yu0V/uOxiTHGsrY4KmjQ02Evv149dEWpOdSgzxIXxhgcHMxbTKKUh57L5Ugmk35xz0h6hPsO3Je3z84jO4HJ+PnGzo2UwoVc+sf6Z9ToQiSuu2NPsqfkAhZTLTu3qmUVf7Xtr+hM2Pi6W8TCGfRYLMa4GZ/2GIqiLBxq0GeJmxjs7u72F2t2ZfaFjI6OksvlfIP+pYe/VLTPw88/jDHGTyMsFT+HfIM+VYqkY926dVxyySUsWWIzWFz2TCHTedddiS5Wt9gLwokxG3d3Br25uZl0Tj10RalVNIY+C4wxjI+PIyL09vbS29tLPB4nHA6X9NCdB+8M+oHeA0X7HB44zO4Xd5NMJ2muay7KOnHUR+uJR+KkMimSE0kSsdIl/WAnRYPFRYWVn46ZjLHTcnzUFiHt22cvOuvWrSPVk5rVMRRFqT5q0GdBKpXCGMPmzZsJh8PEYjFaWlrYtWuXv6iEMcaPYff09BCJRGhubs6bjAT44G9/kD0v7uH+Q/fz3d/YroXLm5ZPmd4oIrTXt3Ns+Bj9o/3TGvRCCt/bMVO4pCvRBcDxkeN0MnmhaUw0ksnanuiuilVRlNpBQy6zwE2AxuNx1qxZw4oVK2hsbKShoYHR0VEOHDjAPffcQy6Xs0VAL75IV1cX4XCYfSf25R0rEU/wslUvAyabYa1oXjHt+7uJ0Z5kD6NpW8h0qP8Q9x641w/ZlCI5kSw5PlOGivPQe5I9eeONTbYVcCQU0SpRRalB1EOfBa6XSeHKQ42NjRw5coSnnnqKXC7HD3/4Q7Zs2UIqlWLFihWMpEd44NADea+Jh+OsallFNBxlImvj725Bi6lwqYtf3vFlAG753Vv4woNfIJm2Bvvjr/54UZUp2EyVUsw65OL1ffFf12BfV2pBaUVRFh710GeB89BLGXQgb7Jy7969hMNhOjs7ufWRW3mi5wnAhk5a6lroSnQRC8fYsGSyKvTM9jOnfX/noTv2vLjHN+YAzw08V/J1zpsvZCaD3l7fTiQcYWh8iCWdky0CcsZODEfDGm5RlFpEXa1ZEFztPkhrayvhcJhNmzaxd+9ewGaELF++nEgkwv4T+/19v3SNzXRxxnBT1yb2dtvXlPKugxT2HT86dDTvuVuLNMhjRx8r6m/umCmGLiIsaVjCi8MvsmrjKi44/wJyuRyjOXuBUA9dUWoT9dBnwXQe+mtf+1rWrFmTN75s2bI87/hlq15GLBzLM6SXr7mc85afx3XnXzdjPLrQoD878CwArfWtACUbd936yK1THm82OeRdjXZitCfZQ11dHQ0NDX5fdjXoilKbqEGfBdlslhOjJ/j6nq+TzuanKbpVf4K0t7f7pfrhUJjrL7y+6Jh10To+8Nsf4Mp1V874/i6G7nBpkBesuAAojnUX5qtvWbaFGy+5MU/zTLjiomAPGDXoilLbqEEvQTqdzisYymQyPNHzBL86+it2HN4BwH0H7uNDd37ID3e87nWv8/evr6/3S/U3LNkw75ztwhi6m0x1xUiF1aCF2S0ffMUHOXvp2Xx2+2f5/NWfn9V7lsp0cR0Z1aArSm2iBr0EO3fuZM+ePf7ziYw1ZCZkGJ0YJWdyfGP3NxhNj/Lw8w8DNr5+8cUXs3XrVkSE/nFbqt/W0Fb8BidJqRCJiLB+yXrAthYIeuU9Iz1F+4O9MDTFm2b1nsGQiyOTVQ9dUWoZ/WYWkMlkGBgYyBvrT072URlNj/Ivj/2L/zy4wlBXV5f/2GWhNMVmZ0BPliWNS0jEEn4VaSqToi5aB9gOjgAhCfHRKz86p+P7qYsjk+Ec1wtds1wUpTZRg17AwMAAxpi8kEvvSC9GDAg8+NyDeWt8jk+MlzyOywGvj9ZXROeKJluM1BhrJJVJMZIe8Q26i6lvP2s7q1tXz+n4SxqXICL0jfWRyWWIhCJ+qEc9dEWpTTTkUkB/v/XGgwZ9ND0K3jyiM+aJuC3BH8uULt4Zz1hDXx+pjEF3vdNdK4Dh9GSZvwu5uBL+uRAJRWivb8cY40+M6qSootQ2atALcAY9lUqRTCY5ePAgwwPDmNBkjLqtvo1rNl0DTF2NOTph0xad1zxfrtl8Td7zQoMenBh1ce+pGn7NliWNtqjItdF1Bl37uChKbaKuVgBjjG/QAe67z/YwT6VSTNRNEA1HiYQiXL/1et9gO0+8EBeKKVfI5eoNVzM8PszPDv4MsIVJMHmnUMpDdws/z5W6iL0YuewW97twtSNFUWoD9dADJJNJ0uk0ra2t/piI0HVuF2MtY7x2w2v54uu/yOauzTRE7WSoM+yFlDvkEpIQ28/aTmOskWs2X+Nnqzgv3FWPJtNJkukk8Uic5njzvN7ThVZcdouf5RJWP0BRahH9ZgZw3nlXV5ef6RIOh/1FHZzHCpOGeqpJ0XKHXMCmHd589c15hUHr2tYB8Gz/s0B+uGW+HRGdJ+5CLdmcl+WiIRdFqUnUQw/Q399PNBqlqSk/1dB528G2s85QV8tDdxQa6bXtawFr0HMm56cZzmdC1OF76J5B18IiRaltqmbQRaRVRL4lIsMi8oKIvH+afT/g7TMsIt8UkfnFDmZJf38/jU2NfO3XX/PX+gRIZ6b20KeaFK102qKjKd5ER0MHqUyKY0PH/JTF+U6IQrFB1ywXRaltqumhfwkb4lkBXA18UkSKGpmIyFXAx719VgJR4O8qLS6TyTA0PMTOEzt5Ovk0z4Wf89vFpjJ22bVYZLJi0xnqUmmLxhh/PHgRqBRr26yX/ol7P8F9B+1ErovxzwdnuF1BkRp0RaltqmLQRaQRuBb4mDFm2BizC7gN+IMSu18P/F9jzC5jzBDwUeDNIjJ/CzUNJ06c4FDfIZ5JPgNAJp7xG3GlstagB41zLBwjFAqRyWb8ghvH4PgguVyO+mh9VaoqXdgFYGh8yNc3X4pCLlpYpCg1TbU89LMAMcY8ERjbBZxTYt9zgN3uiTHmSe/h+sIdvTDOmuAPMH1z8RIMjg9y23/exuGhw+TqcjTFmzAhw84jO8mZnO+hB2PoIjI5MVqQunigz3ZDXNO25mSlzIl17euKxspxISnKcslploui1DLVMugJYKhgbAAo1egkAQwWjA1Ose+NwKGCnwdK7DctTfEmhsaGyNXn+P0Lf581bWvIhWy4ZcSMTE6KFnRN9MMuBXH0Q32HgNKGthKc1nJa0Vg5PPTCLBcNuShKbVOtb+YIUDix2QKUWpa+1L7NU+x7C/DVgrFVnKRRD0mI917zXpY2LqUh1sA9z9wDIUi2Jbl39F5MylaJTmXQCzNdDvYfBCZTCitNPBJnadNSuoe7/bGKhFw0y0VRappqeej7ASMimwJj5wN7S+y7FzjPPRGRjdhOKk8X7miMGTDGPBv8AY7MReDatrV+58Rt67cBMNEwgQkbp6NootE9D4ZcMrmMnxMejG1XmhtfcWPe87KGXDyDXirbR1GU2qEqBt0YkwRuBz4lIk0isgU7IXpbid2/CrxDRLaISBPwl8A3jTGlE74rwCtWv8IvrQd42/lv430Xv68oBdEZtr3de7lj7x1kchleGHqBiewEnYnOWfceLweNsca85+X00F1BkZscnu+CHYqiVIZq3jvfAPwf4Bg2nv4JY8zPRGQ18ASw2RjzvDHmpyLyKeDH2FDLXcAHq6gTEeGM9jN4ssfOx16x9oqSVZfOwP9k/08AOwk6lLJTBWe0n1EltZZCj7wSMfRSk8OKotQOVTPoxpgBbOpi4fjz2InQ4NjfUYXc8+lwRgymXoOz0GM/OnzUr9SsVvzcEQlFCIVC5HJ2MresHrqXh+4bdPXQFaUm0dL/Kbhi7RXEI3G2n7V9yn0Ky/rv2ncX+07sA6obP3cEjXg5YuiuZ4vLP3cGXWPoilKbaLrCFCxpXMItv3vLtBkdbjWgC1ZcwONHHyeTzdA32kc0HGVVy0mnw8+bWDjmNwsrZ8jFeehu8rccx1YUpfyohz4NM6XVIJQ0AAAQHUlEQVTnbV25lZt/92beddG78sZPbz19QVL7KuWhu/4wfsVsGTtIKopSPtSgz5NELEEsHOM9L3+PP3Zaa3GhTzUIGvRyeuiHBw7z4HMP6qSootQ4atDLRFt9m/94WWLZgmgI3hWUo2d58Hh37L2DbC5LKBTSwiJFqVHUoJeJ1rrJVY7K0Yt8LgSzcea7uAWUXmouHo6X5diKopQfNehlorU+YNAbF8ag9432lfV4pTxxzXBRlNpFDXqZiIQiXLTqIjZ0bijL4hJzYUXzCgA2dG4oy/GCBt155ZqDrii1iwZDy0hwYnQheOuWt/LYscfYdua2shzPLfABdvFpYN4LTyuKUjnUoC8iVrasZGXLyrIdL9gfxvVz2dy1uWzHVxSlvGjIRZmSjoYO3rzlzXlj5y47d4HUKIoyE2rQlWm5cMWF/uPW+taSi2koilIbqEFXpiWY1XLu0nM1ZVFRahg16Mq0BDtKblm+ZQGVKIoyEzopqkyLiLCyZSXDqWE2dW6a+QWKoiwYatCVGfnIZR8ha7Kag64oNY4adGVG3FqriqLUNhpDVxRFWSSoQVcURVkkqEFXFEVZJKhBVxRFWSSoQVcURVkkqEFXFEVZJKhBVxRFWSSoQVcURVkkqEFXFEVZJKhBVxRFWSQsxtL/MMCRI0cWWoeiKEpZCdi1cKntYoypnpoqICKXAg8stA5FUZQKcpkx5heFg4vRoMeBi4BjQPYkXroKeyG4DKgF9171TE8t6VEtU1NreqC2NJ2sljCwHHjUGJMq3LjoQi7eH1l05ZqJwEo8R4wxz5ZT01xQPdNTS3pUy9TUmh6oLU1z1HJgqg06KaooirJIUIOuKIqySFCDriiKskhQgz7JAPBJ73ctoHqmp5b0qJapqTU9UFuayqpl0WW5KIqivFRRD11RFGWRoAZdURRlkfCSM+gisuhy75WXJhJIYl5oRCS20BqUl5BBF5EuEfk0sH2htQCISIOIRBdah8Nd6ERkwT8TNaalRURWL7QOh4gsF5H3AZgamADzvlc3A+9ZaC0AIpIQkZaF1rFQLPgXphqIyGeAZ4D/hS2bXVDvxtPzS+A7IvJ2EUkslBZPz58CXxaRFmNMboHPTS1p+TSwC/gnEfmUiKxdKC2ens8A+4DzvOcL6qEHvlcfAtq9sQWzKZ6ePcB3ReSPROQ0b3whP0Mx73dVzsuiNugi8mYR6QdeDmwA/hT4HVg470ZEvgC8ArgOeBi4CfiYiJTsnlZhLaeJyDeAG4F1wFtgYc5NjWk5R0Qewf6fXg3cDLwZuLDaWjw9F4nIQeAq4DxjzP+ABf0Mv0lEBrHfq9XAu4HXeJpyC6TpL4BLsd/vfwNeC3xORCILeJ4+BvxQRJZ4zknF7e2iNuhYr+HdxphXGWOOAQkgKyKN1RYiIiERWQ78FvAeY8wuY8xfAd/DGq83V1sTUAf8CrgG+DnwahE50+mtspb6GtISAm42xlxhjDkIpIBOFu77shRIAx8wxhwSkc0icvkC3jEY4F3e92oAyAEjIrKy2kJEJOyFWH4L+IQx5qAx5ivA/wNeBdzg7Ve1/50XhvoK8E7sd+xDUJ2L3aIy6F5ceot7boy51Rhze8D7/TW27WSy2nq8f+aL2C/nhsBujwKnAW8SkSUV1hP1foc9TU8DXzfGPALcDWSAtwX0VlJLo4hc4m5JjTH7gX+vES17gO+JSMQLu9wH3AOcISLXikhHlfTEPT13Ag8BHxSRH3la/gh4XETeWmkHpYSebxtjvh34Xr0AbKZKhTrB/5cxJmuMGQTWYzsXOp4CmoDrRWRFle8cIsBO7Of3K8CVInK+p72iNnfRGHQR+WPsB+s2EfmmiLzBG48YY1wb3Z3AsIhcvhB6vFu/bwF/IiLuw/dy4N+BCWBjBfV8GHhKRDYYY7Ju4tEYc9T7/Qj24nK+2J7yFfvwichNwFHgFuDOwCTfkRrQ8l5Pwzj2i7kDSBhjrsXOe/xX4AOV0FJCzw9E5AZv0xeBLcBh7Ofk94C/xd7ZvaKKet7rjYewnjnAz7Cf36u8bRWLWZf4f73f2/QPwGdF5HxP2zbgX7FzDq+slB5Pk4uTO0fpKPAfxpiHgP8EnsSGEivvpRtjTvkf4BLs7foG4Ezg40A/sNbb7ipiN2Dj1q+qsp5PeHpWA23A/dgWvweAHwNrgP3AyyugpRH4a+yH6kHgjhL7hALn55+Bvw9say6zns1Yb3OLdy7ehe1bf3nBfjWjJXiegH8EbgPqKvC/mkrPK73tFwENBXp2AddV6HM82//VUuAH2PBm2XXMQs9l3vZved+r/diLzJneZ357BTXdhL2Ynec9j5TY5w3ed/73vOfhiump5D+g0j8BQ/12YGfBttuB/wzu5z3eDXzEexyqop7/COhJAGcAlwa2/8L7wkqZNSWAa7GTexcDTwOvn+qDhfX4vgZ8DPgp8Edl1vM64DkgFhj7e+/vX1qLWgo+P98APlmhz/NUeh4ClgTGwoHH9wNvqbKeUv+re4DPTvW5KpOeq0vo+Qfv/CSwd1SdwEUF5+eqCmiJeZ/LX2KdxJ0l9nH2YCnwGeCuwLb2SpyjUzrkYrwzA7QCz4pIe2DzO4GXi8hrjTHGxf+AnwAXioiYMt/+zKDnHZ6e1xljRoBnjTG/EJGoiPw70AvsDhyjXJpGgLuNMfcCj2EN0p9727Lu9jhwm/wr4Ergz4Bdxpi/Lace7Iorj2Njno4bgdOxE6J+rL9GtISBZSJS7010XQjcVWYdM+k5DfgvAT0RbzLwK1jD8vMq6wmeH/e9+iF2IjtkJkOc5SZUQs+HsOfnOmNMBugzxjwqInUi8m/YCdxHKqAl5x33Y1gPfIOIvA0m6yjcd9kY04116MZE5BYR+QU2ilB+KnGVqNYPk1fATUAf3q0gkyGEvwHuL3jNLdiZ77J653PRg71lPIy9Xa3IFbuExrOw8embvOeRwLaXAc8Dd5ZbT+DcrMXeJf0++Z7mHwJP1ZoWrFH7AnZC+zsEPOUF0tMEfBobwvsPoGMh9QTGPoQ19kUhhwX47Pw37KToDypxfgLvUx94/GHgeKHmwPOVnqZxvDuZimiq1IHLfOLWAq0l/sECRL3H38BOQDQFtl2HjVF3uA8AEK8BPZ3e2Abg7ArrCRWMRbBVfQcC52SF93sNsGGeWmLe73DBePDcfB57i352YNvF2DuINd7YqhrQss4bOw84twz/p/noeTxwbq4ELlxgPf7/yhuP1oAeN2e2Glg/Xz0noVuwd0pPAZ8uPB/YO4p92OhARR23qvzB8zhRS7GTG09h41QfxJsYI2CYvRNaj539/jCwyhu/CbhN9eRPJmLz87+JnWf4OXBfGbSsAL4O/F2JbUFvKu59+B/E3rGc442/A/hmmc5LzWhRPaeensD7TXeByXOUvMevx6bbxr3nq73fnQQufpX8qfgbzPOE3gZ81Xv8P7G3UP9csM+XsJ5wFHgr9ir4CDb/cwR4a+GJrwU9VT4/95LvTdVjLwQZ4Atl0HGR9zfu8r5sv+ONh0poeRp7x3QVtqLvALYIJImXJTGf/1W5tSymc6N6Zq1ptheYtsDjiPf728AD3t/ydDk+QyelvdpvOMsTGgKasQUdb3YnElvaO4Qt723AzjD/HO9K6O23Bhub/mvgdNXDz/HuELz9WrEz/48CK8uk57exXtKFwOewObhum2D759zlve/pgW0JbF73R8p4bmpGi+o5JfWczAXmSWBTYCwMfBebSvn5cmk6Kf0L8aZTnMi1QQMDdAG/AbYV7Pe3wC+9xxsD42WdjFmEely8PIwXM5+vFiZvO+uxxTdg0yPvwZaGu/2j5N8hRChTemYtaVE9p56eEvpO5gJzWmBbyBvfRZkcpTnpX6g3DpyIDmz2wD5sPukX3YnC3vbc606m93s9toT/dYETWba8V9Vz0lpWFLx/G/An2LuHjoJtshi1qJ5TT09A13wvMOGg/nLrO9mfBc1D9/qc/BAYBM7GphQuB1w570eBy0XkNcY7Y9g49IvYE48xJmfKlPeqeuak5QbvfYz3ux/bi2UQr9wZ+2XEWBaVFtVz6unxNHWIyHewWWffBm7xer6MYWPyYDNn7gWuC/TvyRhjnhVL2NheMkH9C8pCFxZ1Yq/a7zDGZIwx38L2P3En6BB2NvvLInKGN3YMGwd+UfVUVc9UWgoLkwCewPanuURE/hrYLyJXL1ItqucU01OLF5hyUdXl2ETkHOAcbBGAm3R4yhhjRCRqjJnAtptscq8xxnxURC4Evia2R/WlwCh2xlv1VEjPyWoJ3CFgjBnzKhovxVYV/rEx5oeLQYvqOfX0lMBdYP7Ge+9vicgrCFxgAprcBea93gXmTSLyoQpoKg+mCnEdvP7S2HDAt7GZGDcxGUNz8aswdrLhavfc+92FbaB/C/CHqqdyeuaqJfBawZapTwAfWyxaVM+ppydw7HOwaw6c7z2vw5u4ZLJg6csUpPwGXn8dth/9AeDacumqxE913sTmdf4ML8UHW5r7U+DPCvZrxfbvCM4ex1RP9fSUQwt2oqlhMWlRPaeknpq8wFTyp2IxdBFplckG+BdhU+ie9CYSbge+j22SdU3gZZuBEWPMYRG5RkSeB/5A9VRWTxm1vBNsbN8YM3qqa1E9p56eApYB52O7L16L/a5sD7yXa87XhM1r3xN4rVu6bhfQYoz5yzJpqihlN+gisl5E7sb2MvmeiKzHNtQZFpFXmsmJhDuAHmyWhltxZRsQFbsqy5eBjxpjvqx6KqOnAlpuXQxaVM+ppyegq5YvMBWnrAZdRN6JzSF9DDsrXI9tE9mOvRW7zu1r7Koeu/FW6RHbcvIcbCOkR40xK4wx/6p6KqNHtaiexaLHO25NXmCqTjnjN8BfEuiBgU3YH8bGxd6Ezfm8LrD9HGx8y8W0XkcZu5GpHtWiel4Set6JbUP9GWwH03uxC6NciF1h6p8K9n8/ti1zIzbT73Zs7vlflEvTQv2U92C25alrDRsHWrBxqbOxqUKfwFaKbfH2+e/Yrn/zbmmrelSL6nnJ6qmpC8xC/lTmoJOlsOcBe5mcwW4BvootTX8UuwjEGyv+R6oe1aJ6Fq0eauwCs5A/FSksMt5Zwzbi32+MSXvjg8D1IrIa2GqM+U4l3l/1qBbV89LRY4w5An5BUEpENmLnB582xqRF5GZs19N/E5Fx7Hq+7zbGpCqpayGoiEH3ZpSz2JVEfuyNvQ+4AvhzY8zT2OXFqoLqUS2qZ/HrqZULzEJSKQ89681mtwNLROQB7EKu7/b+yVVF9agW1bP49dTaBWZBqFQsBzgXuzL2McpQHq96VIvqUT2z0BPBrhL2p9iVg54FrlpoXdX6cZMaZUdEYsAHgH8wxoxX5E1Uj2pRPaonX8+52PzzbuB/G2M+t8CSqkrFDLqiKEq1qbULTLVRg64oirJIWOgFLhRFUZQyoQZdURRlkaAGXVEUZZGgBl1RFGWRoAZdURRlkaAGXVEUZZGgBl1RFGWRoAZdURRlkfD/AZeZhcnKZUsIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "print(\"==============Compare to DJIA===========\")\n",
        "%matplotlib inline\n",
        "# S&P 500: ^GSPC\n",
        "# Dow Jones Index: ^DJI\n",
        "# NASDAQ 100: ^NDX\n",
        "backtest_plot(df_account_value, \n",
        "             baseline_ticker = '^DJI', \n",
        "             baseline_start = df_account_value.loc[0,'date'],\n",
        "             baseline_end = df_account_value.loc[len(df_account_value)-1,'date'])"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "_gDkU-j-fCmZ",
        "3Zpv4S0-fDBv"
      ],
      "name": "Stock_NeurIPS2018.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}