FinRL_multiple_stock_trading.ipynb

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   "source": [
    "<a href=\"https://colab.research.google.com/github/AI4Finance-LLC/FinRL-Library/blob/master/FinRL_multiple_stock_trading.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
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   "source": [
    "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading\n",
    "\n",
    "Tutorials to use OpenAI DRL to trade multiple stocks in one Jupyter Notebook | Presented at NeurIPS 2020: Deep RL Workshop\n",
    "\n",
    "* This blog is based on our paper: FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, presented at NeurIPS 2020: Deep RL Workshop.\n",
    "* Check out medium blog for detailed explanations: https://towardsdatascience.com/finrl-for-quantitative-finance-tutorial-for-multiple-stock-trading-7b00763b7530\n",
    "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
    "* **Pytorch Version** \n",
    "\n"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "# Content"
   ]
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   "source": [
    "* [1. Problem Definition](#0)\n",
    "* [2. Getting Started - Load Python packages](#1)\n",
    "    * [2.1. Install Packages](#1.1)    \n",
    "    * [2.2. Check Additional Packages](#1.2)\n",
    "    * [2.3. Import Packages](#1.3)\n",
    "    * [2.4. Create Folders](#1.4)\n",
    "* [3. Download Data](#2)\n",
    "* [4. Preprocess Data](#3)        \n",
    "    * [4.1. Technical Indicators](#3.1)\n",
    "    * [4.2. Perform Feature Engineering](#3.2)\n",
    "* [5.Build Environment](#4)  \n",
    "    * [5.1. Training & Trade Data Split](#4.1)\n",
    "    * [5.2. User-defined Environment](#4.2)   \n",
    "    * [5.3. Initialize Environment](#4.3)    \n",
    "* [6.Implement DRL Algorithms](#5)  \n",
    "* [7.Backtesting Performance](#6)  \n",
    "    * [7.1. BackTestStats](#6.1)\n",
    "    * [7.2. BackTestPlot](#6.2)   \n",
    "    * [7.3. Baseline Stats](#6.3)   \n",
    "    * [7.3. Compare to Stock Market Index](#6.4)             "
   ]
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  {
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   "source": [
    "<a id='0'></a>\n",
    "# Part 1. Problem Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HjLD2TZSLKZ-"
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   "source": [
    "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
    "\n",
    "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\n",
    "\n",
    "\n",
    "* Action: The action space describes the allowed actions that the agent interacts with the\n",
    "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
    "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
    "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
    "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
    "\n",
    "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
    "values at state s′ and s, respectively\n",
    "\n",
    "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
    "our trading agent observes many different features to better learn in an interactive environment.\n",
    "\n",
    "* Environment: Dow 30 consituents\n",
    "\n",
    "\n",
    "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ffsre789LY08"
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   "source": [
    "<a id='1'></a>\n",
    "# Part 2. Getting Started- Load Python Packages"
   ]
  },
  {
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   "metadata": {
    "id": "Uy5_PTmOh1hj"
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   "source": [
    "<a id='1.1'></a>\n",
    "## 2.1. Install all the packages through FinRL library\n"
   ]
  },
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      "  Stored in directory: /root/.cache/pip/wheels/04/98/cc/2702a4242d60bdc14f48b4557c427ded1fe92aedf257d4565c\n",
      "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-cp36-none-any.whl size=75764 sha256=d386c94dd6aa49b4acd82579c5e23f839043337a87eea7f28a1a9c56f7f0b1c0\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-cesdfnqn/wheels/43/ce/d9/6752fb6e03205408773235435205a0519d2c608a94f1976e56\n",
      "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for empyrical: filename=empyrical-0.5.5-cp36-none-any.whl size=39765 sha256=fbecbe48a3eb6e2d7ad06f9f3de71b0cd0a03d8b4d93092ab2ed9dab47cd8ef6\n",
      "  Stored in directory: /root/.cache/pip/wheels/ea/b2/c8/6769d8444d2f2e608fae2641833110668d0ffd1abeb2e9f3fc\n",
      "Successfully built finrl yfinance pyfolio empyrical\n",
      "Installing collected packages: int-date, stockstats, lxml, yfinance, stable-baselines3, empyrical, pyfolio, finrl\n",
      "  Found existing installation: lxml 4.2.6\n",
      "    Uninstalling lxml-4.2.6:\n",
      "      Successfully uninstalled lxml-4.2.6\n",
      "Successfully installed empyrical-0.5.5 finrl-0.0.2 int-date-0.1.8 lxml-4.6.2 pyfolio-0.9.2+75.g4b901f6 stable-baselines3-0.10.0 stockstats-0.3.2 yfinance-0.1.55\n"
     ]
    }
   ],
   "source": [
    "## install finrl library\n",
    "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "osBHhVysOEzi"
   },
   "source": [
    "\n",
    "<a id='1.2'></a>\n",
    "## 2.2. Check if the additional packages needed are present, if not install them. \n",
    "* Yahoo Finance API\n",
    "* pandas\n",
    "* numpy\n",
    "* matplotlib\n",
    "* stockstats\n",
    "* OpenAI gym\n",
    "* stable-baselines\n",
    "* tensorflow\n",
    "* pyfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nGv01K8Sh1hn"
   },
   "source": [
    "<a id='1.3'></a>\n",
    "## 2.3. Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lPqeTTwoh1hn",
    "outputId": "c437c266-2780-4c50-af8b-6868e7fdaa1f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/pyfolio/pos.py:27: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
      "  'Module \"zipline.assets\" not found; multipliers will not be applied'\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "# matplotlib.use('Agg')\n",
    "import datetime\n",
    "\n",
    "%matplotlib inline\n",
    "from finrl.config import config\n",
    "from finrl.marketdata.yahoodownloader import YahooDownloader\n",
    "from finrl.preprocessing.preprocessors import FeatureEngineer\n",
    "from finrl.preprocessing.data import data_split\n",
    "from finrl.env.env_stocktrading import StockTradingEnv\n",
    "from finrl.model.models import DRLAgent\n",
    "from finrl.trade.backtest import BackTestStats, BaselineStats, BackTestPlot\n",
    "\n",
    "from pprint import pprint\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"../FinRL-Library\")\n",
    "\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T2owTj985RW4"
   },
   "source": [
    "<a id='1.4'></a>\n",
    "## 2.4. Create Folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "w9A8CN5R5PuZ"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
    "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
    "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
    "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
    "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
    "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
    "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
    "    os.makedirs(\"./\" + config.RESULTS_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A289rQWMh1hq"
   },
   "source": [
    "<a id='2'></a>\n",
    "# Part 3. Download Data\n",
    "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
    "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
    "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NPeQ7iS-LoMm"
   },
   "source": [
    "\n",
    "\n",
    "-----\n",
    "class YahooDownloader:\n",
    "    Provides methods for retrieving daily stock data from\n",
    "    Yahoo Finance API\n",
    "\n",
    "    Attributes\n",
    "    ----------\n",
    "        start_date : str\n",
    "            start date of the data (modified from config.py)\n",
    "        end_date : str\n",
    "            end date of the data (modified from config.py)\n",
    "        ticker_list : list\n",
    "            a list of stock tickers (modified from config.py)\n",
    "\n",
    "    Methods\n",
    "    -------\n",
    "    fetch_data()\n",
    "        Fetches data from yahoo API\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "h3XJnvrbLp-C",
    "outputId": "87dea23f-469d-4e9d-de91-0f8a74929de2"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'2009-01-01'"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from config.py start_date is a string\n",
    "config.START_DATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "FUnY8WEfLq3C",
    "outputId": "c635ae69-a13e-408f-d932-9d386d1d6dcf"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'2020-12-01'"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from config.py end_date is a string\n",
    "config.END_DATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JzqRRTOX6aFu",
    "outputId": "d3baf63f-948a-49f9-f6f2-b7241971b8ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AAPL', 'MSFT', 'JPM', 'V', 'RTX', 'PG', 'GS', 'NKE', 'DIS', 'AXP', 'HD', 'INTC', 'WMT', 'IBM', 'MRK', 'UNH', 'KO', 'CAT', 'TRV', 'JNJ', 'CVX', 'MCD', 'VZ', 'CSCO', 'XOM', 'BA', 'MMM', 'PFE', 'WBA', 'DD']\n"
     ]
    }
   ],
   "source": [
    "print(config.DOW_30_TICKER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yCKm4om-s9kE",
    "outputId": "932583d8-f98b-4243-c02d-375f7272db1a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (90630, 7)\n"
     ]
    }
   ],
   "source": [
    "df = YahooDownloader(start_date = '2009-01-01',\n",
    "                     end_date = '2021-01-01',\n",
    "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CV3HrZHLh1hy",
    "outputId": "b7b78172-8c8a-41c9-c8a6-0167edb9bd11"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90630, 7)"
      ]
     },
     "execution_count": 62,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "id": "4hYkeaPiICHS",
    "outputId": "ce9d7463-a74c-4917-c96d-848a1e8ad493"
   },
   "outputs": [
    {
     "data": {
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       "      <th>high</th>\n",
       "      <th>low</th>\n",
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       "      <td>2009-01-02</td>\n",
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       "      <td>3.251429</td>\n",
       "      <td>3.041429</td>\n",
       "      <td>2.795913</td>\n",
       "      <td>746015200.0</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>18.570000</td>\n",
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       "      <td>18.400000</td>\n",
       "      <td>15.800624</td>\n",
       "      <td>10955700.0</td>\n",
       "      <td>AXP</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>42.799999</td>\n",
       "      <td>45.560001</td>\n",
       "      <td>42.779999</td>\n",
       "      <td>33.680935</td>\n",
       "      <td>7010200.0</td>\n",
       "      <td>BA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>44.910000</td>\n",
       "      <td>46.980000</td>\n",
       "      <td>44.709999</td>\n",
       "      <td>32.514400</td>\n",
       "      <td>7117200.0</td>\n",
       "      <td>CAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>16.410000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>12.786087</td>\n",
       "      <td>40980600.0</td>\n",
       "      <td>CSCO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date       open       high        low      close       volume   tic\n",
       "0  2009-01-02   3.067143   3.251429   3.041429   2.795913  746015200.0  AAPL\n",
       "1  2009-01-02  18.570000  19.520000  18.400000  15.800624   10955700.0   AXP\n",
       "2  2009-01-02  42.799999  45.560001  42.779999  33.680935    7010200.0    BA\n",
       "3  2009-01-02  44.910000  46.980000  44.709999  32.514400    7117200.0   CAT\n",
       "4  2009-01-02  16.410000  17.000000  16.250000  12.786087   40980600.0  CSCO"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "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",
    "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
    "* Add technical indicators. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
    "* Add turbulence index. Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "fe = FeatureEngineer(\n",
    "                    use_technical_indicator=True,\n",
    "                    tech_indicator_list = config.TECHNICAL_INDICATORS_LIST,\n",
    "                    use_turbulence=True,\n",
    "                    user_defined_feature = False)\n",
    "\n",
    "processed = fe.preprocess_data(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": 340
    },
    "id": "grvhGJJII3Xn",
    "outputId": "91d09c37-b0e9-4c5c-d532-967e40d11f41"
   },
   "outputs": [
    {
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       "      <td>42.779999</td>\n",
       "      <td>33.680935</td>\n",
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       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>44.910000</td>\n",
       "      <td>46.980000</td>\n",
       "      <td>44.709999</td>\n",
       "      <td>32.514400</td>\n",
       "      <td>7117200.0</td>\n",
       "      <td>CAT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>16.410000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>12.786087</td>\n",
       "      <td>40980600.0</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>74.230003</td>\n",
       "      <td>77.300003</td>\n",
       "      <td>73.580002</td>\n",
       "      <td>48.043262</td>\n",
       "      <td>13695900.0</td>\n",
       "      <td>CVX</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>21.605234</td>\n",
       "      <td>22.060680</td>\n",
       "      <td>20.993229</td>\n",
       "      <td>14.527276</td>\n",
       "      <td>13251000.0</td>\n",
       "      <td>DD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009-01-02</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>DIS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>84.019997</td>\n",
       "      <td>87.620003</td>\n",
       "      <td>82.190002</td>\n",
       "      <td>72.844467</td>\n",
       "      <td>14088500.0</td>\n",
       "      <td>GS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>23.070000</td>\n",
       "      <td>24.190001</td>\n",
       "      <td>22.959999</td>\n",
       "      <td>17.909452</td>\n",
       "      <td>14902500.0</td>\n",
       "      <td>HD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date       open       high  ...     cci_30  dx_30  turbulence\n",
       "0  2009-01-02   3.067143   3.251429  ...  66.666667  100.0         0.0\n",
       "1  2009-01-02  18.570000  19.520000  ...  66.666667  100.0         0.0\n",
       "2  2009-01-02  42.799999  45.560001  ...  66.666667  100.0         0.0\n",
       "3  2009-01-02  44.910000  46.980000  ...  66.666667  100.0         0.0\n",
       "4  2009-01-02  16.410000  17.000000  ...  66.666667  100.0         0.0\n",
       "5  2009-01-02  74.230003  77.300003  ...  66.666667  100.0         0.0\n",
       "6  2009-01-02  21.605234  22.060680  ...  66.666667  100.0         0.0\n",
       "7  2009-01-02  22.760000  24.030001  ...  66.666667  100.0         0.0\n",
       "8  2009-01-02  84.019997  87.620003  ...  66.666667  100.0         0.0\n",
       "9  2009-01-02  23.070000  24.190001  ...  66.666667  100.0         0.0\n",
       "\n",
       "[10 rows x 12 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "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. Design Environment\n",
    "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
    "\n",
    "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
    "\n",
    "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5TOhcryx44bb"
   },
   "source": [
    "## Training data split: 2009-01-01 to 2018-12-31\n",
    "## Trade data split: 2019-01-01 to 2020-09-30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "W0qaVGjLtgbI",
    "outputId": "c98aeb90-84e3-4b83-9671-d679f3fe148f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75480\n",
      "15150\n"
     ]
    }
   ],
   "source": [
    "train = data_split(processed_full, '2009-01-01','2019-01-01')\n",
    "trade = data_split(processed_full, '2019-01-01','2021-01-01')\n",
    "print(len(train))\n",
    "print(len(trade))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "p52zNCOhTtLR",
    "outputId": "c41f9be0-a99f-4108-a427-3112b6bd4129"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>tic</th>\n",
       "      <th>macd</th>\n",
       "      <th>rsi_30</th>\n",
       "      <th>cci_30</th>\n",
       "      <th>dx_30</th>\n",
       "      <th>turbulence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>3.067143</td>\n",
       "      <td>3.251429</td>\n",
       "      <td>3.041429</td>\n",
       "      <td>2.795913</td>\n",
       "      <td>746015200.0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>18.570000</td>\n",
       "      <td>19.520000</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>15.800624</td>\n",
       "      <td>10955700.0</td>\n",
       "      <td>AXP</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>42.799999</td>\n",
       "      <td>45.560001</td>\n",
       "      <td>42.779999</td>\n",
       "      <td>33.680935</td>\n",
       "      <td>7010200.0</td>\n",
       "      <td>BA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>44.910000</td>\n",
       "      <td>46.980000</td>\n",
       "      <td>44.709999</td>\n",
       "      <td>32.514400</td>\n",
       "      <td>7117200.0</td>\n",
       "      <td>CAT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>16.410000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>12.786087</td>\n",
       "      <td>40980600.0</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date       open       high  ...     cci_30  dx_30  turbulence\n",
       "0  2009-01-02   3.067143   3.251429  ...  66.666667  100.0         0.0\n",
       "0  2009-01-02  18.570000  19.520000  ...  66.666667  100.0         0.0\n",
       "0  2009-01-02  42.799999  45.560001  ...  66.666667  100.0         0.0\n",
       "0  2009-01-02  44.910000  46.980000  ...  66.666667  100.0         0.0\n",
       "0  2009-01-02  16.410000  17.000000  ...  66.666667  100.0         0.0\n",
       "\n",
       "[5 rows x 12 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "k9zU9YaTTvFq",
    "outputId": "705f46e4-0529-4ef5-d182-c2a1337397a4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>tic</th>\n",
       "      <th>macd</th>\n",
       "      <th>rsi_30</th>\n",
       "      <th>cci_30</th>\n",
       "      <th>dx_30</th>\n",
       "      <th>turbulence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>38.722500</td>\n",
       "      <td>39.712502</td>\n",
       "      <td>38.557499</td>\n",
       "      <td>38.562561</td>\n",
       "      <td>148158800.0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>-2.019903</td>\n",
       "      <td>37.867349</td>\n",
       "      <td>-91.567852</td>\n",
       "      <td>42.250808</td>\n",
       "      <td>119.879197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>93.910004</td>\n",
       "      <td>96.269997</td>\n",
       "      <td>93.769997</td>\n",
       "      <td>92.643311</td>\n",
       "      <td>4175400.0</td>\n",
       "      <td>AXP</td>\n",
       "      <td>-3.426008</td>\n",
       "      <td>41.204982</td>\n",
       "      <td>-97.742269</td>\n",
       "      <td>26.709417</td>\n",
       "      <td>119.879197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>316.190002</td>\n",
       "      <td>323.950012</td>\n",
       "      <td>313.709991</td>\n",
       "      <td>314.645142</td>\n",
       "      <td>3292200.0</td>\n",
       "      <td>BA</td>\n",
       "      <td>-5.550592</td>\n",
       "      <td>47.010000</td>\n",
       "      <td>-21.712382</td>\n",
       "      <td>13.611972</td>\n",
       "      <td>119.879197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>124.029999</td>\n",
       "      <td>127.879997</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>119.302582</td>\n",
       "      <td>4783200.0</td>\n",
       "      <td>CAT</td>\n",
       "      <td>-0.686759</td>\n",
       "      <td>48.229089</td>\n",
       "      <td>-5.091209</td>\n",
       "      <td>0.873482</td>\n",
       "      <td>119.879197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>42.279999</td>\n",
       "      <td>43.200001</td>\n",
       "      <td>42.209999</td>\n",
       "      <td>40.382099</td>\n",
       "      <td>23833500.0</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>-0.960061</td>\n",
       "      <td>44.872557</td>\n",
       "      <td>-87.496850</td>\n",
       "      <td>29.529377</td>\n",
       "      <td>119.879197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date        open        high  ...     cci_30      dx_30  turbulence\n",
       "0  2019-01-02   38.722500   39.712502  ... -91.567852  42.250808  119.879197\n",
       "0  2019-01-02   93.910004   96.269997  ... -97.742269  26.709417  119.879197\n",
       "0  2019-01-02  316.190002  323.950012  ... -21.712382  13.611972  119.879197\n",
       "0  2019-01-02  124.029999  127.879997  ...  -5.091209   0.873482  119.879197\n",
       "0  2019-01-02   42.279999   43.200001  ... -87.496850  29.529377  119.879197\n",
       "\n",
       "[5 rows x 12 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zYN573SOHhxG",
    "outputId": "187c6d1b-3e91-40f8-dafd-230d787f2ee1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['macd', 'rsi_30', 'cci_30', 'dx_30']"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config.TECHNICAL_INDICATORS_LIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q2zqII8rMIqn",
    "outputId": "8a2c943b-1be4-4b8d-b64f-666e0852b7e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stock Dimension: 30, State Space: 181\n"
     ]
    }
   ],
   "source": [
    "stock_dimension = len(train.tic.unique())\n",
    "state_space = 1 + 2*stock_dimension + len(config.TECHNICAL_INDICATORS_LIST)*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": [
    "env_kwargs = {\n",
    "    \"hmax\": 100, \n",
    "    \"initial_amount\": 1000000, \n",
    "    \"buy_cost_pct\": 0.001,\n",
    "    \"sell_cost_pct\": 0.001,\n",
    "    \"state_space\": state_space, \n",
    "    \"stock_dim\": stock_dimension, \n",
    "    \"tech_indicator_list\": config.TECHNICAL_INDICATORS_LIST, \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": "406e5ec3-28ba-4a72-9b22-0d031f7bf9a6"
   },
   "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: Implement DRL Algorithms\n",
    "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
    "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
    "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
    "design their own DRL algorithms by adapting these DRL algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "364PsqckttcQ"
   },
   "outputs": [],
   "source": [
    "agent = DRLAgent(env = env_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YDmqOyF9h1iz"
   },
   "source": [
    "### Model Training: 5 models, A2C DDPG, PPO, TD3, SAC\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uijiWgkuh1jB"
   },
   "source": [
    "### Model 1: A2C\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GUCnkn-HIbmj",
    "outputId": "2fdb297a-8d35-4c7e-806f-de859d70e19e"
   },
   "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": "9eb09ba2-fc4b-46a1-ea3d-bd9b3bfefffd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logging to tensorboard_log/a2c/a2c_1\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 131      |\n",
      "|    iterations         | 100      |\n",
      "|    time_elapsed       | 3        |\n",
      "|    total_timesteps    | 500      |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 99       |\n",
      "|    policy_loss        | -14.9    |\n",
      "|    std                | 1        |\n",
      "|    value_loss         | 0.362    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 177      |\n",
      "|    iterations         | 200      |\n",
      "|    time_elapsed       | 5        |\n",
      "|    total_timesteps    | 1000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.7    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 199      |\n",
      "|    policy_loss        | -52      |\n",
      "|    std                | 1        |\n",
      "|    value_loss         | 2.03     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 199      |\n",
      "|    iterations         | 300      |\n",
      "|    time_elapsed       | 7        |\n",
      "|    total_timesteps    | 1500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.7    |\n",
      "|    explained_variance | -754     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 299      |\n",
      "|    policy_loss        | -379     |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 72.5     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 213      |\n",
      "|    iterations         | 400      |\n",
      "|    time_elapsed       | 9        |\n",
      "|    total_timesteps    | 2000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.8    |\n",
      "|    explained_variance | -899     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 399      |\n",
      "|    policy_loss        | -50.2    |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 2.23     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 222       |\n",
      "|    iterations         | 500       |\n",
      "|    time_elapsed       | 11        |\n",
      "|    total_timesteps    | 2500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.8     |\n",
      "|    explained_variance | -5.49e+03 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 499       |\n",
      "|    policy_loss        | 863       |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 470       |\n",
      "-------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:5069607.313605958\n",
      "total_reward:4069607.3136059577\n",
      "total_cost:  67556.9160195016\n",
      "total_trades:  54955\n",
      "Sharpe:  1.034955174352521\n",
      "=================================\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 225       |\n",
      "|    iterations         | 600       |\n",
      "|    time_elapsed       | 13        |\n",
      "|    total_timesteps    | 3000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.8     |\n",
      "|    explained_variance | -1.13e+03 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 599       |\n",
      "|    policy_loss        | -56.4     |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 3.43      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 229       |\n",
      "|    iterations         | 700       |\n",
      "|    time_elapsed       | 15        |\n",
      "|    total_timesteps    | 3500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.8     |\n",
      "|    explained_variance | -3.16e+03 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 699       |\n",
      "|    policy_loss        | 93.9      |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 8.12      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 232      |\n",
      "|    iterations         | 800      |\n",
      "|    time_elapsed       | 17       |\n",
      "|    total_timesteps    | 4000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.8    |\n",
      "|    explained_variance | -3.3e+11 |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 799      |\n",
      "|    policy_loss        | 65.4     |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 3.13     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 236       |\n",
      "|    iterations         | 900       |\n",
      "|    time_elapsed       | 19        |\n",
      "|    total_timesteps    | 4500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.8     |\n",
      "|    explained_variance | -1.57e+04 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 899       |\n",
      "|    policy_loss        | 628       |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 222       |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 239      |\n",
      "|    iterations         | 1000     |\n",
      "|    time_elapsed       | 20       |\n",
      "|    total_timesteps    | 5000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 999      |\n",
      "|    policy_loss        | 283      |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 51.9     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4806928.073206688\n",
      "total_reward:3806928.0732066883\n",
      "total_cost:  29371.967713621536\n",
      "total_trades:  48579\n",
      "Sharpe:  0.9611082492472007\n",
      "=================================\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 241       |\n",
      "|    iterations         | 1100      |\n",
      "|    time_elapsed       | 22        |\n",
      "|    total_timesteps    | 5500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.8     |\n",
      "|    explained_variance | -1.34e+04 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 1099      |\n",
      "|    policy_loss        | -9.16     |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 5.7       |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 243      |\n",
      "|    iterations         | 1200     |\n",
      "|    time_elapsed       | 24       |\n",
      "|    total_timesteps    | 6000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 1199     |\n",
      "|    policy_loss        | -169     |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 35       |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 244       |\n",
      "|    iterations         | 1300      |\n",
      "|    time_elapsed       | 26        |\n",
      "|    total_timesteps    | 6500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.9     |\n",
      "|    explained_variance | -8.12e+05 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 1299      |\n",
      "|    policy_loss        | 796       |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 360       |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 246      |\n",
      "|    iterations         | 1400     |\n",
      "|    time_elapsed       | 28       |\n",
      "|    total_timesteps    | 7000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 1399     |\n",
      "|    policy_loss        | -31.3    |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 0.783    |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 248       |\n",
      "|    iterations         | 1500      |\n",
      "|    time_elapsed       | 30        |\n",
      "|    total_timesteps    | 7500      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.9     |\n",
      "|    explained_variance | -3.62e+14 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 1499      |\n",
      "|    policy_loss        | -693      |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 542       |\n",
      "-------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:5032249.439668636\n",
      "total_reward:4032249.439668636\n",
      "total_cost:  27369.775673342636\n",
      "total_trades:  46757\n",
      "Sharpe:  0.9689568826715832\n",
      "=================================\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 249       |\n",
      "|    iterations         | 1600      |\n",
      "|    time_elapsed       | 32        |\n",
      "|    total_timesteps    | 8000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.8     |\n",
      "|    explained_variance | -4.17e+11 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 1599      |\n",
      "|    policy_loss        | -12.2     |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 0.468     |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 250      |\n",
      "|    iterations         | 1700     |\n",
      "|    time_elapsed       | 33       |\n",
      "|    total_timesteps    | 8500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 1699     |\n",
      "|    policy_loss        | 87.7     |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 4.56     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 251       |\n",
      "|    iterations         | 1800      |\n",
      "|    time_elapsed       | 35        |\n",
      "|    total_timesteps    | 9000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.8     |\n",
      "|    explained_variance | -4.62e+05 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 1799      |\n",
      "|    policy_loss        | -255      |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 40.4      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 252      |\n",
      "|    iterations         | 1900     |\n",
      "|    time_elapsed       | 37       |\n",
      "|    total_timesteps    | 9500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 1899     |\n",
      "|    policy_loss        | -127     |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 16.6     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 253       |\n",
      "|    iterations         | 2000      |\n",
      "|    time_elapsed       | 39        |\n",
      "|    total_timesteps    | 10000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.8     |\n",
      "|    explained_variance | -1.97e+13 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 1999      |\n",
      "|    policy_loss        | 406       |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 95.1      |\n",
      "-------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3904628.721074527\n",
      "total_reward:2904628.721074527\n",
      "total_cost:  32800.81143295443\n",
      "total_trades:  45335\n",
      "Sharpe:  0.8354269955192407\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 253      |\n",
      "|    iterations         | 2100     |\n",
      "|    time_elapsed       | 41       |\n",
      "|    total_timesteps    | 10500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.8    |\n",
      "|    explained_variance | -10.3    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2099     |\n",
      "|    policy_loss        | 69.7     |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 2.66     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 253      |\n",
      "|    iterations         | 2200     |\n",
      "|    time_elapsed       | 43       |\n",
      "|    total_timesteps    | 11000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2199     |\n",
      "|    policy_loss        | -42.8    |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 1.92     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 253      |\n",
      "|    iterations         | 2300     |\n",
      "|    time_elapsed       | 45       |\n",
      "|    total_timesteps    | 11500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2299     |\n",
      "|    policy_loss        | 48.1     |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 9.7      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 254      |\n",
      "|    iterations         | 2400     |\n",
      "|    time_elapsed       | 47       |\n",
      "|    total_timesteps    | 12000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.7    |\n",
      "|    explained_variance | -49.7    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2399     |\n",
      "|    policy_loss        | 204      |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 24.4     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 255      |\n",
      "|    iterations         | 2500     |\n",
      "|    time_elapsed       | 49       |\n",
      "|    total_timesteps    | 12500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.7    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2499     |\n",
      "|    policy_loss        | 56.3     |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 3.8      |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3630490.4667401677\n",
      "total_reward:2630490.4667401677\n",
      "total_cost:  49957.625875016725\n",
      "total_trades:  49675\n",
      "Sharpe:  0.7870109277440298\n",
      "=================================\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 255       |\n",
      "|    iterations         | 2600      |\n",
      "|    time_elapsed       | 50        |\n",
      "|    total_timesteps    | 13000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.7     |\n",
      "|    explained_variance | -1.27e+12 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 2599      |\n",
      "|    policy_loss        | -122      |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 9.1       |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 255       |\n",
      "|    iterations         | 2700      |\n",
      "|    time_elapsed       | 52        |\n",
      "|    total_timesteps    | 13500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.7     |\n",
      "|    explained_variance | -3.15e+11 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 2699      |\n",
      "|    policy_loss        | 16.4      |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 0.422     |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 255      |\n",
      "|    iterations         | 2800     |\n",
      "|    time_elapsed       | 54       |\n",
      "|    total_timesteps    | 14000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2799     |\n",
      "|    policy_loss        | 119      |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 9.84     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 256      |\n",
      "|    iterations         | 2900     |\n",
      "|    time_elapsed       | 56       |\n",
      "|    total_timesteps    | 14500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.7    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2899     |\n",
      "|    policy_loss        | 230      |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 38.4     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 257      |\n",
      "|    iterations         | 3000     |\n",
      "|    time_elapsed       | 58       |\n",
      "|    total_timesteps    | 15000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2999     |\n",
      "|    policy_loss        | 54.7     |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 14.1     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4105857.0455575557\n",
      "total_reward:3105857.0455575557\n",
      "total_cost:  12537.663790287688\n",
      "total_trades:  43652\n",
      "Sharpe:  0.8861282120753707\n",
      "=================================\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 258       |\n",
      "|    iterations         | 3100      |\n",
      "|    time_elapsed       | 60        |\n",
      "|    total_timesteps    | 15500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.9     |\n",
      "|    explained_variance | -7.93e+04 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 3099      |\n",
      "|    policy_loss        | 99.6      |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 6.67      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 258       |\n",
      "|    iterations         | 3200      |\n",
      "|    time_elapsed       | 61        |\n",
      "|    total_timesteps    | 16000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.9     |\n",
      "|    explained_variance | -1.05e+05 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 3199      |\n",
      "|    policy_loss        | 190       |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 23.1      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 258      |\n",
      "|    iterations         | 3300     |\n",
      "|    time_elapsed       | 63       |\n",
      "|    total_timesteps    | 16500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 3299     |\n",
      "|    policy_loss        | 17.2     |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 2.04     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 259      |\n",
      "|    iterations         | 3400     |\n",
      "|    time_elapsed       | 65       |\n",
      "|    total_timesteps    | 17000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.9    |\n",
      "|    explained_variance | -9.4e+04 |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 3399     |\n",
      "|    policy_loss        | -46.1    |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 1.93     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 259      |\n",
      "|    iterations         | 3500     |\n",
      "|    time_elapsed       | 67       |\n",
      "|    total_timesteps    | 17500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 3499     |\n",
      "|    policy_loss        | -17.4    |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 5.37     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3307214.1514936504\n",
      "total_reward:2307214.1514936504\n",
      "total_cost:  23884.956163034414\n",
      "total_trades:  42682\n",
      "Sharpe:  0.7168631999656054\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 259      |\n",
      "|    iterations         | 3600     |\n",
      "|    time_elapsed       | 69       |\n",
      "|    total_timesteps    | 18000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.9    |\n",
      "|    explained_variance | -4.3e+03 |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 3599     |\n",
      "|    policy_loss        | 226      |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 28.9     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 260      |\n",
      "|    iterations         | 3700     |\n",
      "|    time_elapsed       | 71       |\n",
      "|    total_timesteps    | 18500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 3699     |\n",
      "|    policy_loss        | 59.8     |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 8.43     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 260       |\n",
      "|    iterations         | 3800      |\n",
      "|    time_elapsed       | 72        |\n",
      "|    total_timesteps    | 19000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -43       |\n",
      "|    explained_variance | -7.04e+04 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 3799      |\n",
      "|    policy_loss        | 50.8      |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 1.82      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 260       |\n",
      "|    iterations         | 3900      |\n",
      "|    time_elapsed       | 74        |\n",
      "|    total_timesteps    | 19500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.9     |\n",
      "|    explained_variance | -4.89e+08 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 3899      |\n",
      "|    policy_loss        | -457      |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 140       |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 261       |\n",
      "|    iterations         | 4000      |\n",
      "|    time_elapsed       | 76        |\n",
      "|    total_timesteps    | 20000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.9     |\n",
      "|    explained_variance | -2.78e+07 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 3999      |\n",
      "|    policy_loss        | -441      |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 143       |\n",
      "-------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4148540.1545087425\n",
      "total_reward:3148540.1545087425\n",
      "total_cost:  15764.782369253146\n",
      "total_trades:  38897\n",
      "Sharpe:  0.8610175924981203\n",
      "=================================\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 261       |\n",
      "|    iterations         | 4100      |\n",
      "|    time_elapsed       | 78        |\n",
      "|    total_timesteps    | 20500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -42.9     |\n",
      "|    explained_variance | -2.42e+04 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 4099      |\n",
      "|    policy_loss        | 76.1      |\n",
      "|    std                | 1.01      |\n",
      "|    value_loss         | 5.77      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 261      |\n",
      "|    iterations         | 4200     |\n",
      "|    time_elapsed       | 80       |\n",
      "|    total_timesteps    | 21000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -42.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4199     |\n",
      "|    policy_loss        | 143      |\n",
      "|    std                | 1.01     |\n",
      "|    value_loss         | 15.3     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 262      |\n",
      "|    iterations         | 4300     |\n",
      "|    time_elapsed       | 81       |\n",
      "|    total_timesteps    | 21500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4299     |\n",
      "|    policy_loss        | 29.3     |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 3.48     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 262      |\n",
      "|    iterations         | 4400     |\n",
      "|    time_elapsed       | 83       |\n",
      "|    total_timesteps    | 22000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4399     |\n",
      "|    policy_loss        | -52.3    |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 3.13     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 262      |\n",
      "|    iterations         | 4500     |\n",
      "|    time_elapsed       | 85       |\n",
      "|    total_timesteps    | 22500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4499     |\n",
      "|    policy_loss        | -53.7    |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 14.7     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4751485.433416299\n",
      "total_reward:3751485.4334162986\n",
      "total_cost:  15499.176757445255\n",
      "total_trades:  39836\n",
      "Sharpe:  0.9930905921879077\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 263      |\n",
      "|    iterations         | 4600     |\n",
      "|    time_elapsed       | 87       |\n",
      "|    total_timesteps    | 23000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4599     |\n",
      "|    policy_loss        | -62.3    |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 6.41     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 262      |\n",
      "|    iterations         | 4700     |\n",
      "|    time_elapsed       | 89       |\n",
      "|    total_timesteps    | 23500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4699     |\n",
      "|    policy_loss        | -86.6    |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 5.69     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 263      |\n",
      "|    iterations         | 4800     |\n",
      "|    time_elapsed       | 91       |\n",
      "|    total_timesteps    | 24000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4799     |\n",
      "|    policy_loss        | -160     |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 18.5     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 263      |\n",
      "|    iterations         | 4900     |\n",
      "|    time_elapsed       | 93       |\n",
      "|    total_timesteps    | 24500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4899     |\n",
      "|    policy_loss        | -162     |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 20.8     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 263      |\n",
      "|    iterations         | 5000     |\n",
      "|    time_elapsed       | 94       |\n",
      "|    total_timesteps    | 25000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4999     |\n",
      "|    policy_loss        | 481      |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 143      |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4724903.433106359\n",
      "total_reward:3724903.433106359\n",
      "total_cost:  8886.69877304687\n",
      "total_trades:  38303\n",
      "Sharpe:  0.9980131996548207\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 263      |\n",
      "|    iterations         | 5100     |\n",
      "|    time_elapsed       | 96       |\n",
      "|    total_timesteps    | 25500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5099     |\n",
      "|    policy_loss        | -139     |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 12.7     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 263       |\n",
      "|    iterations         | 5200      |\n",
      "|    time_elapsed       | 98        |\n",
      "|    total_timesteps    | 26000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -43.1     |\n",
      "|    explained_variance | -6.12e+04 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 5199      |\n",
      "|    policy_loss        | 128       |\n",
      "|    std                | 1.02      |\n",
      "|    value_loss         | 8.81      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 263      |\n",
      "|    iterations         | 5300     |\n",
      "|    time_elapsed       | 100      |\n",
      "|    total_timesteps    | 26500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5299     |\n",
      "|    policy_loss        | 5.06     |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 1.05     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 263      |\n",
      "|    iterations         | 5400     |\n",
      "|    time_elapsed       | 102      |\n",
      "|    total_timesteps    | 27000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5399     |\n",
      "|    policy_loss        | 190      |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 24.3     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 263      |\n",
      "|    iterations         | 5500     |\n",
      "|    time_elapsed       | 104      |\n",
      "|    total_timesteps    | 27500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5499     |\n",
      "|    policy_loss        | 42.8     |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 9        |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4783015.926924407\n",
      "total_reward:3783015.9269244066\n",
      "total_cost:  7815.295760473641\n",
      "total_trades:  36995\n",
      "Sharpe:  0.9898009778895888\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 263      |\n",
      "|    iterations         | 5600     |\n",
      "|    time_elapsed       | 106      |\n",
      "|    total_timesteps    | 28000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5599     |\n",
      "|    policy_loss        | -1.76    |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 0.422    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 262      |\n",
      "|    iterations         | 5700     |\n",
      "|    time_elapsed       | 108      |\n",
      "|    total_timesteps    | 28500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5699     |\n",
      "|    policy_loss        | -69.8    |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 2.85     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 262      |\n",
      "|    iterations         | 5800     |\n",
      "|    time_elapsed       | 110      |\n",
      "|    total_timesteps    | 29000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5799     |\n",
      "|    policy_loss        | 165      |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 15.5     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 263      |\n",
      "|    iterations         | 5900     |\n",
      "|    time_elapsed       | 112      |\n",
      "|    total_timesteps    | 29500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5899     |\n",
      "|    policy_loss        | 14.9     |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 2.67     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 263      |\n",
      "|    iterations         | 6000     |\n",
      "|    time_elapsed       | 113      |\n",
      "|    total_timesteps    | 30000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5999     |\n",
      "|    policy_loss        | -145     |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 21.6     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3767699.432239705\n",
      "total_reward:2767699.432239705\n",
      "total_cost:  3225.8563617229293\n",
      "total_trades:  31503\n",
      "Sharpe:  0.8438602812346044\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 263      |\n",
      "|    iterations         | 6100     |\n",
      "|    time_elapsed       | 115      |\n",
      "|    total_timesteps    | 30500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6099     |\n",
      "|    policy_loss        | 75       |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 3.38     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 263      |\n",
      "|    iterations         | 6200     |\n",
      "|    time_elapsed       | 117      |\n",
      "|    total_timesteps    | 31000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.3    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6199     |\n",
      "|    policy_loss        | 65.1     |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 4.46     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 264      |\n",
      "|    iterations         | 6300     |\n",
      "|    time_elapsed       | 119      |\n",
      "|    total_timesteps    | 31500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6299     |\n",
      "|    policy_loss        | 19.5     |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 4.29     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 264      |\n",
      "|    iterations         | 6400     |\n",
      "|    time_elapsed       | 121      |\n",
      "|    total_timesteps    | 32000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6399     |\n",
      "|    policy_loss        | 131      |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 15.4     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 264      |\n",
      "|    iterations         | 6500     |\n",
      "|    time_elapsed       | 122      |\n",
      "|    total_timesteps    | 32500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6499     |\n",
      "|    policy_loss        | 113      |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 38.6     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3966658.0536604635\n",
      "total_reward:2966658.0536604635\n",
      "total_cost:  7977.4614967514335\n",
      "total_trades:  34678\n",
      "Sharpe:  0.8831165688078209\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 264      |\n",
      "|    iterations         | 6600     |\n",
      "|    time_elapsed       | 124      |\n",
      "|    total_timesteps    | 33000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6599     |\n",
      "|    policy_loss        | 5.64     |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 0.305    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 264      |\n",
      "|    iterations         | 6700     |\n",
      "|    time_elapsed       | 126      |\n",
      "|    total_timesteps    | 33500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6699     |\n",
      "|    policy_loss        | 5.23     |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 0.54     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 265      |\n",
      "|    iterations         | 6800     |\n",
      "|    time_elapsed       | 128      |\n",
      "|    total_timesteps    | 34000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6799     |\n",
      "|    policy_loss        | 85.1     |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 6.29     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 265      |\n",
      "|    iterations         | 6900     |\n",
      "|    time_elapsed       | 130      |\n",
      "|    total_timesteps    | 34500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6899     |\n",
      "|    policy_loss        | -97.3    |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 9.65     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 265      |\n",
      "|    iterations         | 7000     |\n",
      "|    time_elapsed       | 131      |\n",
      "|    total_timesteps    | 35000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6999     |\n",
      "|    policy_loss        | -585     |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 198      |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3446294.959740542\n",
      "total_reward:2446294.959740542\n",
      "total_cost:  3397.7268977155813\n",
      "total_trades:  31617\n",
      "Sharpe:  0.7885649055566806\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 265      |\n",
      "|    iterations         | 7100     |\n",
      "|    time_elapsed       | 133      |\n",
      "|    total_timesteps    | 35500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7099     |\n",
      "|    policy_loss        | -23.1    |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 2.04     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 264       |\n",
      "|    iterations         | 7200      |\n",
      "|    time_elapsed       | 135       |\n",
      "|    total_timesteps    | 36000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -43.2     |\n",
      "|    explained_variance | -1.25e+03 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 7199      |\n",
      "|    policy_loss        | 25.2      |\n",
      "|    std                | 1.02      |\n",
      "|    value_loss         | 1.22      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 264      |\n",
      "|    iterations         | 7300     |\n",
      "|    time_elapsed       | 137      |\n",
      "|    total_timesteps    | 36500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7299     |\n",
      "|    policy_loss        | -86.7    |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 6.06     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 265      |\n",
      "|    iterations         | 7400     |\n",
      "|    time_elapsed       | 139      |\n",
      "|    total_timesteps    | 37000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7399     |\n",
      "|    policy_loss        | -371     |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 82.5     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 265      |\n",
      "|    iterations         | 7500     |\n",
      "|    time_elapsed       | 141      |\n",
      "|    total_timesteps    | 37500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7499     |\n",
      "|    policy_loss        | -34.4    |\n",
      "|    std                | 1.02     |\n",
      "|    value_loss         | 2.71     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3344736.938978183\n",
      "total_reward:2344736.938978183\n",
      "total_cost:  2206.6413143639265\n",
      "total_trades:  31325\n",
      "Sharpe:  0.7692258924747282\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 265      |\n",
      "|    iterations         | 7600     |\n",
      "|    time_elapsed       | 143      |\n",
      "|    total_timesteps    | 38000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.3    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7599     |\n",
      "|    policy_loss        | 49.6     |\n",
      "|    std                | 1.03     |\n",
      "|    value_loss         | 1.61     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 265      |\n",
      "|    iterations         | 7700     |\n",
      "|    time_elapsed       | 144      |\n",
      "|    total_timesteps    | 38500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.3    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7699     |\n",
      "|    policy_loss        | -50.2    |\n",
      "|    std                | 1.03     |\n",
      "|    value_loss         | 2.28     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 265      |\n",
      "|    iterations         | 7800     |\n",
      "|    time_elapsed       | 146      |\n",
      "|    total_timesteps    | 39000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.3    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7799     |\n",
      "|    policy_loss        | 92.3     |\n",
      "|    std                | 1.03     |\n",
      "|    value_loss         | 5.65     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 265      |\n",
      "|    iterations         | 7900     |\n",
      "|    time_elapsed       | 148      |\n",
      "|    total_timesteps    | 39500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.3    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7899     |\n",
      "|    policy_loss        | -82.3    |\n",
      "|    std                | 1.03     |\n",
      "|    value_loss         | 20.1     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 265      |\n",
      "|    iterations         | 8000     |\n",
      "|    time_elapsed       | 150      |\n",
      "|    total_timesteps    | 40000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.3    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7999     |\n",
      "|    policy_loss        | 144      |\n",
      "|    std                | 1.03     |\n",
      "|    value_loss         | 15.5     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3405743.1783298114\n",
      "total_reward:2405743.1783298114\n",
      "total_cost:  2954.0446352297254\n",
      "total_trades:  33773\n",
      "Sharpe:  0.8134505006039155\n",
      "=================================\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 266       |\n",
      "|    iterations         | 8100      |\n",
      "|    time_elapsed       | 152       |\n",
      "|    total_timesteps    | 40500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -43.4     |\n",
      "|    explained_variance | -3.13e+12 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 8099      |\n",
      "|    policy_loss        | 70.7      |\n",
      "|    std                | 1.03      |\n",
      "|    value_loss         | 5.87      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 266      |\n",
      "|    iterations         | 8200     |\n",
      "|    time_elapsed       | 154      |\n",
      "|    total_timesteps    | 41000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.4    |\n",
      "|    explained_variance | -4.64    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8199     |\n",
      "|    policy_loss        | 171      |\n",
      "|    std                | 1.03     |\n",
      "|    value_loss         | 17       |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 266      |\n",
      "|    iterations         | 8300     |\n",
      "|    time_elapsed       | 155      |\n",
      "|    total_timesteps    | 41500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.4    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8299     |\n",
      "|    policy_loss        | -160     |\n",
      "|    std                | 1.03     |\n",
      "|    value_loss         | 23.3     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 266      |\n",
      "|    iterations         | 8400     |\n",
      "|    time_elapsed       | 157      |\n",
      "|    total_timesteps    | 42000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.5    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8399     |\n",
      "|    policy_loss        | -85.1    |\n",
      "|    std                | 1.03     |\n",
      "|    value_loss         | 3.98     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 266      |\n",
      "|    iterations         | 8500     |\n",
      "|    time_elapsed       | 159      |\n",
      "|    total_timesteps    | 42500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.5    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8499     |\n",
      "|    policy_loss        | 63.9     |\n",
      "|    std                | 1.03     |\n",
      "|    value_loss         | 5.08     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3319582.998510127\n",
      "total_reward:2319582.998510127\n",
      "total_cost:  12366.33568307691\n",
      "total_trades:  37206\n",
      "Sharpe:  0.7728922919437156\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 266      |\n",
      "|    iterations         | 8600     |\n",
      "|    time_elapsed       | 161      |\n",
      "|    total_timesteps    | 43000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8599     |\n",
      "|    policy_loss        | -62.1    |\n",
      "|    std                | 1.04     |\n",
      "|    value_loss         | 2.26     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 266       |\n",
      "|    iterations         | 8700      |\n",
      "|    time_elapsed       | 163       |\n",
      "|    total_timesteps    | 43500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -43.6     |\n",
      "|    explained_variance | -2.19e+13 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 8699      |\n",
      "|    policy_loss        | -27.8     |\n",
      "|    std                | 1.04      |\n",
      "|    value_loss         | 5.62      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 266      |\n",
      "|    iterations         | 8800     |\n",
      "|    time_elapsed       | 164      |\n",
      "|    total_timesteps    | 44000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8799     |\n",
      "|    policy_loss        | 59.2     |\n",
      "|    std                | 1.04     |\n",
      "|    value_loss         | 2.79     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 266      |\n",
      "|    iterations         | 8900     |\n",
      "|    time_elapsed       | 166      |\n",
      "|    total_timesteps    | 44500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.7    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8899     |\n",
      "|    policy_loss        | 40.6     |\n",
      "|    std                | 1.04     |\n",
      "|    value_loss         | 1.43     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 267      |\n",
      "|    iterations         | 9000     |\n",
      "|    time_elapsed       | 168      |\n",
      "|    total_timesteps    | 45000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.7    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8999     |\n",
      "|    policy_loss        | -86.3    |\n",
      "|    std                | 1.04     |\n",
      "|    value_loss         | 6.33     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:2904244.1476431573\n",
      "total_reward:1904244.1476431573\n",
      "total_cost:  15007.745762967967\n",
      "total_trades:  37861\n",
      "Sharpe:  0.7277540513736201\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 267      |\n",
      "|    iterations         | 9100     |\n",
      "|    time_elapsed       | 170      |\n",
      "|    total_timesteps    | 45500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.7    |\n",
      "|    explained_variance | -37.3    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9099     |\n",
      "|    policy_loss        | -252     |\n",
      "|    std                | 1.04     |\n",
      "|    value_loss         | 35.1     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 267      |\n",
      "|    iterations         | 9200     |\n",
      "|    time_elapsed       | 172      |\n",
      "|    total_timesteps    | 46000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9199     |\n",
      "|    policy_loss        | 129      |\n",
      "|    std                | 1.04     |\n",
      "|    value_loss         | 10.8     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 267      |\n",
      "|    iterations         | 9300     |\n",
      "|    time_elapsed       | 173      |\n",
      "|    total_timesteps    | 46500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9299     |\n",
      "|    policy_loss        | 57.2     |\n",
      "|    std                | 1.04     |\n",
      "|    value_loss         | 3.01     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 267      |\n",
      "|    iterations         | 9400     |\n",
      "|    time_elapsed       | 175      |\n",
      "|    total_timesteps    | 47000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9399     |\n",
      "|    policy_loss        | -63.5    |\n",
      "|    std                | 1.04     |\n",
      "|    value_loss         | 2.74     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 267      |\n",
      "|    iterations         | 9500     |\n",
      "|    time_elapsed       | 177      |\n",
      "|    total_timesteps    | 47500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9499     |\n",
      "|    policy_loss        | 17.2     |\n",
      "|    std                | 1.04     |\n",
      "|    value_loss         | 3.6      |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3181599.2553931386\n",
      "total_reward:2181599.2553931386\n",
      "total_cost:  6695.658203102723\n",
      "total_trades:  37040\n",
      "Sharpe:  0.7662862328769516\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 267      |\n",
      "|    iterations         | 9600     |\n",
      "|    time_elapsed       | 179      |\n",
      "|    total_timesteps    | 48000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -43.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9599     |\n",
      "|    policy_loss        | 87       |\n",
      "|    std                | 1.04     |\n",
      "|    value_loss         | 5.95     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 267       |\n",
      "|    iterations         | 9700      |\n",
      "|    time_elapsed       | 181       |\n",
      "|    total_timesteps    | 48500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -43.9     |\n",
      "|    explained_variance | -4.02e+12 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 9699      |\n",
      "|    policy_loss        | 65        |\n",
      "|    std                | 1.05      |\n",
      "|    value_loss         | 5.72      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 267       |\n",
      "|    iterations         | 9800      |\n",
      "|    time_elapsed       | 183       |\n",
      "|    total_timesteps    | 49000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -44       |\n",
      "|    explained_variance | -4.34e+12 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 9799      |\n",
      "|    policy_loss        | -82.4     |\n",
      "|    std                | 1.05      |\n",
      "|    value_loss         | 7.55      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 267      |\n",
      "|    iterations         | 9900     |\n",
      "|    time_elapsed       | 184      |\n",
      "|    total_timesteps    | 49500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9899     |\n",
      "|    policy_loss        | -233     |\n",
      "|    std                | 1.05     |\n",
      "|    value_loss         | 34.3     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 267      |\n",
      "|    iterations         | 10000    |\n",
      "|    time_elapsed       | 186      |\n",
      "|    total_timesteps    | 50000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44      |\n",
      "|    explained_variance | -212     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9999     |\n",
      "|    policy_loss        | 125      |\n",
      "|    std                | 1.05     |\n",
      "|    value_loss         | 15.3     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3163155.7293832605\n",
      "total_reward:2163155.7293832605\n",
      "total_cost:  2870.1664502791505\n",
      "total_trades:  34933\n",
      "Sharpe:  0.7643903649884202\n",
      "=================================\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 267       |\n",
      "|    iterations         | 10100     |\n",
      "|    time_elapsed       | 188       |\n",
      "|    total_timesteps    | 50500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -44       |\n",
      "|    explained_variance | -6.08e+03 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 10099     |\n",
      "|    policy_loss        | 128       |\n",
      "|    std                | 1.05      |\n",
      "|    value_loss         | 12.8      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 267      |\n",
      "|    iterations         | 10200    |\n",
      "|    time_elapsed       | 190      |\n",
      "|    total_timesteps    | 51000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 10199    |\n",
      "|    policy_loss        | -39.2    |\n",
      "|    std                | 1.05     |\n",
      "|    value_loss         | 10.6     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 10300    |\n",
      "|    time_elapsed       | 192      |\n",
      "|    total_timesteps    | 51500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 10299    |\n",
      "|    policy_loss        | 74.1     |\n",
      "|    std                | 1.06     |\n",
      "|    value_loss         | 2.81     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 268       |\n",
      "|    iterations         | 10400     |\n",
      "|    time_elapsed       | 193       |\n",
      "|    total_timesteps    | 52000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -44.1     |\n",
      "|    explained_variance | -1.17e+04 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 10399     |\n",
      "|    policy_loss        | 241       |\n",
      "|    std                | 1.05      |\n",
      "|    value_loss         | 53.4      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 10500    |\n",
      "|    time_elapsed       | 195      |\n",
      "|    total_timesteps    | 52500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 10499    |\n",
      "|    policy_loss        | -66.3    |\n",
      "|    std                | 1.06     |\n",
      "|    value_loss         | 6.42     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3196491.408967822\n",
      "total_reward:2196491.408967822\n",
      "total_cost:  4270.783389629947\n",
      "total_trades:  41108\n",
      "Sharpe:  0.7902910911867141\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 10600    |\n",
      "|    time_elapsed       | 197      |\n",
      "|    total_timesteps    | 53000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 10599    |\n",
      "|    policy_loss        | 22.2     |\n",
      "|    std                | 1.06     |\n",
      "|    value_loss         | 2.71     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 268       |\n",
      "|    iterations         | 10700     |\n",
      "|    time_elapsed       | 199       |\n",
      "|    total_timesteps    | 53500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -44.2     |\n",
      "|    explained_variance | -3.64e+03 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 10699     |\n",
      "|    policy_loss        | 246       |\n",
      "|    std                | 1.06      |\n",
      "|    value_loss         | 43.3      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 10800    |\n",
      "|    time_elapsed       | 201      |\n",
      "|    total_timesteps    | 54000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 10799    |\n",
      "|    policy_loss        | -146     |\n",
      "|    std                | 1.06     |\n",
      "|    value_loss         | 12       |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 10900    |\n",
      "|    time_elapsed       | 203      |\n",
      "|    total_timesteps    | 54500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 10899    |\n",
      "|    policy_loss        | -263     |\n",
      "|    std                | 1.06     |\n",
      "|    value_loss         | 37.1     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 11000    |\n",
      "|    time_elapsed       | 205      |\n",
      "|    total_timesteps    | 55000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 10999    |\n",
      "|    policy_loss        | 114      |\n",
      "|    std                | 1.06     |\n",
      "|    value_loss         | 12.2     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3849179.1372045293\n",
      "total_reward:2849179.1372045293\n",
      "total_cost:  14247.086195249696\n",
      "total_trades:  45210\n",
      "Sharpe:  0.9919759691333234\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 11100    |\n",
      "|    time_elapsed       | 207      |\n",
      "|    total_timesteps    | 55500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11099    |\n",
      "|    policy_loss        | -54.8    |\n",
      "|    std                | 1.06     |\n",
      "|    value_loss         | 3.89     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 11200    |\n",
      "|    time_elapsed       | 208      |\n",
      "|    total_timesteps    | 56000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.3    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11199    |\n",
      "|    policy_loss        | 105      |\n",
      "|    std                | 1.06     |\n",
      "|    value_loss         | 7.82     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 11300    |\n",
      "|    time_elapsed       | 210      |\n",
      "|    total_timesteps    | 56500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11299    |\n",
      "|    policy_loss        | 51.1     |\n",
      "|    std                | 1.06     |\n",
      "|    value_loss         | 2.34     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 268       |\n",
      "|    iterations         | 11400     |\n",
      "|    time_elapsed       | 212       |\n",
      "|    total_timesteps    | 57000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -44.2     |\n",
      "|    explained_variance | -7.43e+12 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 11399     |\n",
      "|    policy_loss        | 126       |\n",
      "|    std                | 1.06      |\n",
      "|    value_loss         | 15.9      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 11500    |\n",
      "|    time_elapsed       | 214      |\n",
      "|    total_timesteps    | 57500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.2    |\n",
      "|    explained_variance | -11.7    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11499    |\n",
      "|    policy_loss        | -122     |\n",
      "|    std                | 1.06     |\n",
      "|    value_loss         | 9.54     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3576028.4597782856\n",
      "total_reward:2576028.4597782856\n",
      "total_cost:  9016.778400975834\n",
      "total_trades:  42915\n",
      "Sharpe:  0.8953228502423565\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 11600    |\n",
      "|    time_elapsed       | 216      |\n",
      "|    total_timesteps    | 58000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.3    |\n",
      "|    explained_variance | -425     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11599    |\n",
      "|    policy_loss        | -120     |\n",
      "|    std                | 1.06     |\n",
      "|    value_loss         | 10.6     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 11700    |\n",
      "|    time_elapsed       | 218      |\n",
      "|    total_timesteps    | 58500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.3    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11699    |\n",
      "|    policy_loss        | 46.7     |\n",
      "|    std                | 1.06     |\n",
      "|    value_loss         | 3.25     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 11800    |\n",
      "|    time_elapsed       | 219      |\n",
      "|    total_timesteps    | 59000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.4    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11799    |\n",
      "|    policy_loss        | -16.5    |\n",
      "|    std                | 1.06     |\n",
      "|    value_loss         | 7.51     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 11900    |\n",
      "|    time_elapsed       | 221      |\n",
      "|    total_timesteps    | 59500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.4    |\n",
      "|    explained_variance | -62.2    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11899    |\n",
      "|    policy_loss        | 115      |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 9.24     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 12000    |\n",
      "|    time_elapsed       | 223      |\n",
      "|    total_timesteps    | 60000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.4    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11999    |\n",
      "|    policy_loss        | 0.0658   |\n",
      "|    std                | 1.06     |\n",
      "|    value_loss         | 4.37     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3436426.812452521\n",
      "total_reward:2436426.812452521\n",
      "total_cost:  6259.129675209552\n",
      "total_trades:  41073\n",
      "Sharpe:  0.8546131042738302\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 12100    |\n",
      "|    time_elapsed       | 225      |\n",
      "|    total_timesteps    | 60500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.4    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12099    |\n",
      "|    policy_loss        | -14.7    |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 0.461    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 12200    |\n",
      "|    time_elapsed       | 227      |\n",
      "|    total_timesteps    | 61000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.5    |\n",
      "|    explained_variance | -32.5    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12199    |\n",
      "|    policy_loss        | -114     |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 14       |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 12300    |\n",
      "|    time_elapsed       | 229      |\n",
      "|    total_timesteps    | 61500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.5    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12299    |\n",
      "|    policy_loss        | -42.1    |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 4.82     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 12400    |\n",
      "|    time_elapsed       | 231      |\n",
      "|    total_timesteps    | 62000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.5    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12399    |\n",
      "|    policy_loss        | -34.7    |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 1.68     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 12500    |\n",
      "|    time_elapsed       | 232      |\n",
      "|    total_timesteps    | 62500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12499    |\n",
      "|    policy_loss        | 76.1     |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 8.46     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3018532.345473118\n",
      "total_reward:2018532.3454731181\n",
      "total_cost:  6047.126481140976\n",
      "total_trades:  42707\n",
      "Sharpe:  0.7384948297244762\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 12600    |\n",
      "|    time_elapsed       | 234      |\n",
      "|    total_timesteps    | 63000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.6    |\n",
      "|    explained_variance | -553     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12599    |\n",
      "|    policy_loss        | -18.4    |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 4.33     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 12700    |\n",
      "|    time_elapsed       | 236      |\n",
      "|    total_timesteps    | 63500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12699    |\n",
      "|    policy_loss        | -156     |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 16.8     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 12800    |\n",
      "|    time_elapsed       | 238      |\n",
      "|    total_timesteps    | 64000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12799    |\n",
      "|    policy_loss        | 86       |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 4.19     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 12900    |\n",
      "|    time_elapsed       | 240      |\n",
      "|    total_timesteps    | 64500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12899    |\n",
      "|    policy_loss        | -77.7    |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 5.54     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 13000    |\n",
      "|    time_elapsed       | 241      |\n",
      "|    total_timesteps    | 65000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12999    |\n",
      "|    policy_loss        | -48.1    |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 3.39     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3005454.017886528\n",
      "total_reward:2005454.0178865278\n",
      "total_cost:  5775.348413782655\n",
      "total_trades:  37868\n",
      "Sharpe:  0.6834871369231124\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 13100    |\n",
      "|    time_elapsed       | 243      |\n",
      "|    total_timesteps    | 65500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13099    |\n",
      "|    policy_loss        | -41.1    |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 0.966    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 13200    |\n",
      "|    time_elapsed       | 245      |\n",
      "|    total_timesteps    | 66000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.6    |\n",
      "|    explained_variance | -20.2    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13199    |\n",
      "|    policy_loss        | -51.7    |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 5.59     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 268       |\n",
      "|    iterations         | 13300     |\n",
      "|    time_elapsed       | 247       |\n",
      "|    total_timesteps    | 66500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -44.6     |\n",
      "|    explained_variance | -6.77e+12 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 13299     |\n",
      "|    policy_loss        | -257      |\n",
      "|    std                | 1.07      |\n",
      "|    value_loss         | 43.6      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 13400    |\n",
      "|    time_elapsed       | 249      |\n",
      "|    total_timesteps    | 67000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13399    |\n",
      "|    policy_loss        | 101      |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 5.95     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 13500    |\n",
      "|    time_elapsed       | 251      |\n",
      "|    total_timesteps    | 67500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.6    |\n",
      "|    explained_variance | -103     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13499    |\n",
      "|    policy_loss        | -60.1    |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 2.95     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:2861607.410381282\n",
      "total_reward:1861607.4103812822\n",
      "total_cost:  5185.6480773171215\n",
      "total_trades:  32918\n",
      "Sharpe:  0.627333223770252\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 13600    |\n",
      "|    time_elapsed       | 252      |\n",
      "|    total_timesteps    | 68000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.6    |\n",
      "|    explained_variance | -15      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13599    |\n",
      "|    policy_loss        | 291      |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 51.8     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 13700    |\n",
      "|    time_elapsed       | 254      |\n",
      "|    total_timesteps    | 68500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13699    |\n",
      "|    policy_loss        | 13.2     |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 0.659    |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 268       |\n",
      "|    iterations         | 13800     |\n",
      "|    time_elapsed       | 256       |\n",
      "|    total_timesteps    | 69000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -44.6     |\n",
      "|    explained_variance | -1.15e+12 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 13799     |\n",
      "|    policy_loss        | 11.6      |\n",
      "|    std                | 1.07      |\n",
      "|    value_loss         | 1.12      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 268       |\n",
      "|    iterations         | 13900     |\n",
      "|    time_elapsed       | 258       |\n",
      "|    total_timesteps    | 69500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -44.6     |\n",
      "|    explained_variance | -8.56e+08 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 13899     |\n",
      "|    policy_loss        | 150       |\n",
      "|    std                | 1.07      |\n",
      "|    value_loss         | 13.7      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 14000    |\n",
      "|    time_elapsed       | 260      |\n",
      "|    total_timesteps    | 70000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13999    |\n",
      "|    policy_loss        | -43.5    |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 1.76     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3191285.6374592897\n",
      "total_reward:2191285.6374592897\n",
      "total_cost:  4185.107238528008\n",
      "total_trades:  33416\n",
      "Sharpe:  0.715991374478748\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 14100    |\n",
      "|    time_elapsed       | 262      |\n",
      "|    total_timesteps    | 70500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.6    |\n",
      "|    explained_variance | -31.8    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14099    |\n",
      "|    policy_loss        | 1.39e+03 |\n",
      "|    std                | 1.07     |\n",
      "|    value_loss         | 944      |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 269       |\n",
      "|    iterations         | 14200     |\n",
      "|    time_elapsed       | 263       |\n",
      "|    total_timesteps    | 71000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -44.6     |\n",
      "|    explained_variance | -2.69e+12 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 14199     |\n",
      "|    policy_loss        | -96.5     |\n",
      "|    std                | 1.07      |\n",
      "|    value_loss         | 6.91      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 269       |\n",
      "|    iterations         | 14300     |\n",
      "|    time_elapsed       | 265       |\n",
      "|    total_timesteps    | 71500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -44.6     |\n",
      "|    explained_variance | -3.11e+12 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 14299     |\n",
      "|    policy_loss        | 94.2      |\n",
      "|    std                | 1.07      |\n",
      "|    value_loss         | 7.25      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 14400    |\n",
      "|    time_elapsed       | 267      |\n",
      "|    total_timesteps    | 72000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.7    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14399    |\n",
      "|    policy_loss        | 21       |\n",
      "|    std                | 1.08     |\n",
      "|    value_loss         | 1.09     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 269       |\n",
      "|    iterations         | 14500     |\n",
      "|    time_elapsed       | 269       |\n",
      "|    total_timesteps    | 72500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -44.7     |\n",
      "|    explained_variance | -1.56e+12 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 14499     |\n",
      "|    policy_loss        | 114       |\n",
      "|    std                | 1.08      |\n",
      "|    value_loss         | 6.86      |\n",
      "-------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3276649.777189667\n",
      "total_reward:2276649.777189667\n",
      "total_cost:  3942.9014864051105\n",
      "total_trades:  34694\n",
      "Sharpe:  0.7189915467634915\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 14600    |\n",
      "|    time_elapsed       | 271      |\n",
      "|    total_timesteps    | 73000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14599    |\n",
      "|    policy_loss        | -80.3    |\n",
      "|    std                | 1.08     |\n",
      "|    value_loss         | 4.13     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 14700    |\n",
      "|    time_elapsed       | 272      |\n",
      "|    total_timesteps    | 73500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.8    |\n",
      "|    explained_variance | -42.9    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14699    |\n",
      "|    policy_loss        | 5.46     |\n",
      "|    std                | 1.08     |\n",
      "|    value_loss         | 1.23     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 14800    |\n",
      "|    time_elapsed       | 274      |\n",
      "|    total_timesteps    | 74000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14799    |\n",
      "|    policy_loss        | -41.4    |\n",
      "|    std                | 1.08     |\n",
      "|    value_loss         | 1.92     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 14900    |\n",
      "|    time_elapsed       | 276      |\n",
      "|    total_timesteps    | 74500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14899    |\n",
      "|    policy_loss        | 69.1     |\n",
      "|    std                | 1.08     |\n",
      "|    value_loss         | 9.59     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 15000    |\n",
      "|    time_elapsed       | 278      |\n",
      "|    total_timesteps    | 75000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14999    |\n",
      "|    policy_loss        | -10.7    |\n",
      "|    std                | 1.08     |\n",
      "|    value_loss         | 0.911    |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3508348.255896097\n",
      "total_reward:2508348.255896097\n",
      "total_cost:  11208.941549323808\n",
      "total_trades:  37043\n",
      "Sharpe:  0.8124699557413589\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 15100    |\n",
      "|    time_elapsed       | 280      |\n",
      "|    total_timesteps    | 75500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15099    |\n",
      "|    policy_loss        | 2.28     |\n",
      "|    std                | 1.08     |\n",
      "|    value_loss         | 0.074    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 15200    |\n",
      "|    time_elapsed       | 281      |\n",
      "|    total_timesteps    | 76000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.8    |\n",
      "|    explained_variance | -1.87    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15199    |\n",
      "|    policy_loss        | -80      |\n",
      "|    std                | 1.08     |\n",
      "|    value_loss         | 3.56     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 15300    |\n",
      "|    time_elapsed       | 283      |\n",
      "|    total_timesteps    | 76500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15299    |\n",
      "|    policy_loss        | 8.44     |\n",
      "|    std                | 1.08     |\n",
      "|    value_loss         | 1.24     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 15400    |\n",
      "|    time_elapsed       | 285      |\n",
      "|    total_timesteps    | 77000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15399    |\n",
      "|    policy_loss        | 276      |\n",
      "|    std                | 1.08     |\n",
      "|    value_loss         | 57.5     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 15500    |\n",
      "|    time_elapsed       | 287      |\n",
      "|    total_timesteps    | 77500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15499    |\n",
      "|    policy_loss        | 160      |\n",
      "|    std                | 1.08     |\n",
      "|    value_loss         | 16.4     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4416862.49751315\n",
      "total_reward:3416862.49751315\n",
      "total_cost:  18937.26260040585\n",
      "total_trades:  37061\n",
      "Sharpe:  0.9703548552780149\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 15600    |\n",
      "|    time_elapsed       | 289      |\n",
      "|    total_timesteps    | 78000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15599    |\n",
      "|    policy_loss        | 577      |\n",
      "|    std                | 1.09     |\n",
      "|    value_loss         | 273      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 15700    |\n",
      "|    time_elapsed       | 290      |\n",
      "|    total_timesteps    | 78500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -44.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15699    |\n",
      "|    policy_loss        | -72.5    |\n",
      "|    std                | 1.09     |\n",
      "|    value_loss         | 3.3      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 15800    |\n",
      "|    time_elapsed       | 292      |\n",
      "|    total_timesteps    | 79000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45      |\n",
      "|    explained_variance | -271     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15799    |\n",
      "|    policy_loss        | -63.8    |\n",
      "|    std                | 1.09     |\n",
      "|    value_loss         | 3.84     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 15900    |\n",
      "|    time_elapsed       | 294      |\n",
      "|    total_timesteps    | 79500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15899    |\n",
      "|    policy_loss        | -514     |\n",
      "|    std                | 1.09     |\n",
      "|    value_loss         | 170      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 16000    |\n",
      "|    time_elapsed       | 296      |\n",
      "|    total_timesteps    | 80000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15999    |\n",
      "|    policy_loss        | 293      |\n",
      "|    std                | 1.09     |\n",
      "|    value_loss         | 53.8     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 16100    |\n",
      "|    time_elapsed       | 298      |\n",
      "|    total_timesteps    | 80500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16099    |\n",
      "|    policy_loss        | -312     |\n",
      "|    std                | 1.09     |\n",
      "|    value_loss         | 109      |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:6572073.540279714\n",
      "total_reward:5572073.540279714\n",
      "total_cost:  25558.900906312338\n",
      "total_trades:  38195\n",
      "Sharpe:  1.1694339512811986\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 16200    |\n",
      "|    time_elapsed       | 299      |\n",
      "|    total_timesteps    | 81000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45      |\n",
      "|    explained_variance | -509     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16199    |\n",
      "|    policy_loss        | 257      |\n",
      "|    std                | 1.09     |\n",
      "|    value_loss         | 32.5     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 16300    |\n",
      "|    time_elapsed       | 301      |\n",
      "|    total_timesteps    | 81500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16299    |\n",
      "|    policy_loss        | 117      |\n",
      "|    std                | 1.09     |\n",
      "|    value_loss         | 9.1      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 16400    |\n",
      "|    time_elapsed       | 303      |\n",
      "|    total_timesteps    | 82000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16399    |\n",
      "|    policy_loss        | 262      |\n",
      "|    std                | 1.09     |\n",
      "|    value_loss         | 35.9     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 16500    |\n",
      "|    time_elapsed       | 305      |\n",
      "|    total_timesteps    | 82500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16499    |\n",
      "|    policy_loss        | -45      |\n",
      "|    std                | 1.09     |\n",
      "|    value_loss         | 2.27     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 16600    |\n",
      "|    time_elapsed       | 307      |\n",
      "|    total_timesteps    | 83000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16599    |\n",
      "|    policy_loss        | -561     |\n",
      "|    std                | 1.09     |\n",
      "|    value_loss         | 236      |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:5698994.463846846\n",
      "total_reward:4698994.463846846\n",
      "total_cost:  17337.4506195575\n",
      "total_trades:  36912\n",
      "Sharpe:  1.0295608824494007\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 16700    |\n",
      "|    time_elapsed       | 308      |\n",
      "|    total_timesteps    | 83500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16699    |\n",
      "|    policy_loss        | -54.8    |\n",
      "|    std                | 1.09     |\n",
      "|    value_loss         | 2.36     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 16800    |\n",
      "|    time_elapsed       | 310      |\n",
      "|    total_timesteps    | 84000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16799    |\n",
      "|    policy_loss        | 56.3     |\n",
      "|    std                | 1.09     |\n",
      "|    value_loss         | 4.36     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 270       |\n",
      "|    iterations         | 16900     |\n",
      "|    time_elapsed       | 312       |\n",
      "|    total_timesteps    | 84500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -45.2     |\n",
      "|    explained_variance | -7.42e+03 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 16899     |\n",
      "|    policy_loss        | 20.5      |\n",
      "|    std                | 1.1       |\n",
      "|    value_loss         | 6.59      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 17000    |\n",
      "|    time_elapsed       | 314      |\n",
      "|    total_timesteps    | 85000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16999    |\n",
      "|    policy_loss        | 306      |\n",
      "|    std                | 1.1      |\n",
      "|    value_loss         | 66.7     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 17100    |\n",
      "|    time_elapsed       | 316      |\n",
      "|    total_timesteps    | 85500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.3    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17099    |\n",
      "|    policy_loss        | -195     |\n",
      "|    std                | 1.1      |\n",
      "|    value_loss         | 66.1     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:6381904.8543528775\n",
      "total_reward:5381904.8543528775\n",
      "total_cost:  12508.200039626663\n",
      "total_trades:  35689\n",
      "Sharpe:  1.1424293800622989\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 17200    |\n",
      "|    time_elapsed       | 317      |\n",
      "|    total_timesteps    | 86000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.3    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17199    |\n",
      "|    policy_loss        | 30       |\n",
      "|    std                | 1.1      |\n",
      "|    value_loss         | 0.588    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 17300    |\n",
      "|    time_elapsed       | 319      |\n",
      "|    total_timesteps    | 86500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17299    |\n",
      "|    policy_loss        | -206     |\n",
      "|    std                | 1.1      |\n",
      "|    value_loss         | 21.8     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 270       |\n",
      "|    iterations         | 17400     |\n",
      "|    time_elapsed       | 321       |\n",
      "|    total_timesteps    | 87000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -45.3     |\n",
      "|    explained_variance | -5.48e+03 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 17399     |\n",
      "|    policy_loss        | 215       |\n",
      "|    std                | 1.1       |\n",
      "|    value_loss         | 25.9      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 17500    |\n",
      "|    time_elapsed       | 323      |\n",
      "|    total_timesteps    | 87500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.3    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17499    |\n",
      "|    policy_loss        | -28.9    |\n",
      "|    std                | 1.1      |\n",
      "|    value_loss         | 4.87     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 17600    |\n",
      "|    time_elapsed       | 325      |\n",
      "|    total_timesteps    | 88000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.3    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17599    |\n",
      "|    policy_loss        | -75.1    |\n",
      "|    std                | 1.1      |\n",
      "|    value_loss         | 6.57     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:5436034.522246395\n",
      "total_reward:4436034.522246395\n",
      "total_cost:  15350.251113259093\n",
      "total_trades:  38300\n",
      "Sharpe:  1.111300596501636\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 17700    |\n",
      "|    time_elapsed       | 327      |\n",
      "|    total_timesteps    | 88500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.4    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17699    |\n",
      "|    policy_loss        | 131      |\n",
      "|    std                | 1.1      |\n",
      "|    value_loss         | 8.69     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 17800    |\n",
      "|    time_elapsed       | 329      |\n",
      "|    total_timesteps    | 89000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.4    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17799    |\n",
      "|    policy_loss        | 37.7     |\n",
      "|    std                | 1.1      |\n",
      "|    value_loss         | 1.64     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 17900    |\n",
      "|    time_elapsed       | 330      |\n",
      "|    total_timesteps    | 89500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.4    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17899    |\n",
      "|    policy_loss        | 14.6     |\n",
      "|    std                | 1.1      |\n",
      "|    value_loss         | 2.22     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 18000    |\n",
      "|    time_elapsed       | 332      |\n",
      "|    total_timesteps    | 90000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.4    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17999    |\n",
      "|    policy_loss        | -304     |\n",
      "|    std                | 1.1      |\n",
      "|    value_loss         | 49.1     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 18100    |\n",
      "|    time_elapsed       | 334      |\n",
      "|    total_timesteps    | 90500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.4    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 18099    |\n",
      "|    policy_loss        | -370     |\n",
      "|    std                | 1.1      |\n",
      "|    value_loss         | 72.5     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:5112916.556362064\n",
      "total_reward:4112916.5563620636\n",
      "total_cost:  15612.707192791122\n",
      "total_trades:  37413\n",
      "Sharpe:  1.0611073756631733\n",
      "=================================\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 270       |\n",
      "|    iterations         | 18200     |\n",
      "|    time_elapsed       | 336       |\n",
      "|    total_timesteps    | 91000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -45.5     |\n",
      "|    explained_variance | -6.66e+03 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 18199     |\n",
      "|    policy_loss        | 74.9      |\n",
      "|    std                | 1.11      |\n",
      "|    value_loss         | 3.92      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 18300    |\n",
      "|    time_elapsed       | 338      |\n",
      "|    total_timesteps    | 91500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.5    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 18299    |\n",
      "|    policy_loss        | -133     |\n",
      "|    std                | 1.11     |\n",
      "|    value_loss         | 13.7     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 18400    |\n",
      "|    time_elapsed       | 339      |\n",
      "|    total_timesteps    | 92000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.5    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 18399    |\n",
      "|    policy_loss        | 73       |\n",
      "|    std                | 1.11     |\n",
      "|    value_loss         | 3.98     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 18500    |\n",
      "|    time_elapsed       | 341      |\n",
      "|    total_timesteps    | 92500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 18499    |\n",
      "|    policy_loss        | 4.46     |\n",
      "|    std                | 1.11     |\n",
      "|    value_loss         | 0.844    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 18600    |\n",
      "|    time_elapsed       | 343      |\n",
      "|    total_timesteps    | 93000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 18599    |\n",
      "|    policy_loss        | -214     |\n",
      "|    std                | 1.11     |\n",
      "|    value_loss         | 26.6     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4986097.277640037\n",
      "total_reward:3986097.2776400372\n",
      "total_cost:  13702.647875393004\n",
      "total_trades:  35305\n",
      "Sharpe:  1.0387271032164815\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 18700    |\n",
      "|    time_elapsed       | 345      |\n",
      "|    total_timesteps    | 93500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.5    |\n",
      "|    explained_variance | -26.8    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 18699    |\n",
      "|    policy_loss        | -40.8    |\n",
      "|    std                | 1.11     |\n",
      "|    value_loss         | 0.888    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 18800    |\n",
      "|    time_elapsed       | 347      |\n",
      "|    total_timesteps    | 94000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 18799    |\n",
      "|    policy_loss        | -114     |\n",
      "|    std                | 1.11     |\n",
      "|    value_loss         | 9.15     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 18900    |\n",
      "|    time_elapsed       | 348      |\n",
      "|    total_timesteps    | 94500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 18899    |\n",
      "|    policy_loss        | -360     |\n",
      "|    std                | 1.11     |\n",
      "|    value_loss         | 58.3     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 19000    |\n",
      "|    time_elapsed       | 350      |\n",
      "|    total_timesteps    | 95000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 18999    |\n",
      "|    policy_loss        | 94.4     |\n",
      "|    std                | 1.11     |\n",
      "|    value_loss         | 8.57     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 19100    |\n",
      "|    time_elapsed       | 352      |\n",
      "|    total_timesteps    | 95500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19099    |\n",
      "|    policy_loss        | -4.65    |\n",
      "|    std                | 1.11     |\n",
      "|    value_loss         | 2.46     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:5478501.103530731\n",
      "total_reward:4478501.103530731\n",
      "total_cost:  10256.280938558313\n",
      "total_trades:  37074\n",
      "Sharpe:  1.1342798023300105\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 19200    |\n",
      "|    time_elapsed       | 354      |\n",
      "|    total_timesteps    | 96000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.6    |\n",
      "|    explained_variance | -2.2e+03 |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19199    |\n",
      "|    policy_loss        | -52.2    |\n",
      "|    std                | 1.11     |\n",
      "|    value_loss         | 3.13     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 19300    |\n",
      "|    time_elapsed       | 356      |\n",
      "|    total_timesteps    | 96500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.7    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19299    |\n",
      "|    policy_loss        | -221     |\n",
      "|    std                | 1.11     |\n",
      "|    value_loss         | 29.8     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 19400    |\n",
      "|    time_elapsed       | 358      |\n",
      "|    total_timesteps    | 97000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.7    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19399    |\n",
      "|    policy_loss        | 2.54     |\n",
      "|    std                | 1.11     |\n",
      "|    value_loss         | 0.552    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 19500    |\n",
      "|    time_elapsed       | 360      |\n",
      "|    total_timesteps    | 97500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.7    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19499    |\n",
      "|    policy_loss        | 324      |\n",
      "|    std                | 1.12     |\n",
      "|    value_loss         | 73.7     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 270       |\n",
      "|    iterations         | 19600     |\n",
      "|    time_elapsed       | 361       |\n",
      "|    total_timesteps    | 98000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -45.7     |\n",
      "|    explained_variance | -2.23e+04 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 19599     |\n",
      "|    policy_loss        | -546      |\n",
      "|    std                | 1.11      |\n",
      "|    value_loss         | 139       |\n",
      "-------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4206773.89180218\n",
      "total_reward:3206773.8918021796\n",
      "total_cost:  5223.3386326608415\n",
      "total_trades:  36723\n",
      "Sharpe:  0.9776063927933439\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 19700    |\n",
      "|    time_elapsed       | 363      |\n",
      "|    total_timesteps    | 98500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19699    |\n",
      "|    policy_loss        | -155     |\n",
      "|    std                | 1.12     |\n",
      "|    value_loss         | 12.4     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 19800    |\n",
      "|    time_elapsed       | 365      |\n",
      "|    total_timesteps    | 99000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19799    |\n",
      "|    policy_loss        | 73.5     |\n",
      "|    std                | 1.12     |\n",
      "|    value_loss         | 4.66     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 19900    |\n",
      "|    time_elapsed       | 367      |\n",
      "|    total_timesteps    | 99500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19899    |\n",
      "|    policy_loss        | -24.7    |\n",
      "|    std                | 1.12     |\n",
      "|    value_loss         | 2.18     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 20000    |\n",
      "|    time_elapsed       | 369      |\n",
      "|    total_timesteps    | 100000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19999    |\n",
      "|    policy_loss        | 42       |\n",
      "|    std                | 1.12     |\n",
      "|    value_loss         | 1.86     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 20100    |\n",
      "|    time_elapsed       | 371      |\n",
      "|    total_timesteps    | 100500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 20099    |\n",
      "|    policy_loss        | 279      |\n",
      "|    std                | 1.12     |\n",
      "|    value_loss         | 51.4     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4319570.605313044\n",
      "total_reward:3319570.605313044\n",
      "total_cost:  6777.852646750923\n",
      "total_trades:  38079\n",
      "Sharpe:  0.9793624584136245\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 20200    |\n",
      "|    time_elapsed       | 373      |\n",
      "|    total_timesteps    | 101000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 20199    |\n",
      "|    policy_loss        | 94       |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 6.2      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 20300    |\n",
      "|    time_elapsed       | 375      |\n",
      "|    total_timesteps    | 101500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 20299    |\n",
      "|    policy_loss        | -23.3    |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 1.69     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 20400    |\n",
      "|    time_elapsed       | 376      |\n",
      "|    total_timesteps    | 102000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 20399    |\n",
      "|    policy_loss        | 33.9     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 2.74     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 20500    |\n",
      "|    time_elapsed       | 378      |\n",
      "|    total_timesteps    | 102500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 20499    |\n",
      "|    policy_loss        | -137     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 12       |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 20600    |\n",
      "|    time_elapsed       | 380      |\n",
      "|    total_timesteps    | 103000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 20599    |\n",
      "|    policy_loss        | 374      |\n",
      "|    std                | 1.12     |\n",
      "|    value_loss         | 99       |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:6257628.032702145\n",
      "total_reward:5257628.032702145\n",
      "total_cost:  15497.552403549977\n",
      "total_trades:  41618\n",
      "Sharpe:  1.1223670233311491\n",
      "=================================\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 270       |\n",
      "|    iterations         | 20700     |\n",
      "|    time_elapsed       | 382       |\n",
      "|    total_timesteps    | 103500    |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -45.9     |\n",
      "|    explained_variance | -1.38e+04 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 20699     |\n",
      "|    policy_loss        | -30.9     |\n",
      "|    std                | 1.12      |\n",
      "|    value_loss         | 21.9      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 20800    |\n",
      "|    time_elapsed       | 384      |\n",
      "|    total_timesteps    | 104000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 20799    |\n",
      "|    policy_loss        | -34      |\n",
      "|    std                | 1.12     |\n",
      "|    value_loss         | 1.24     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 20900    |\n",
      "|    time_elapsed       | 386      |\n",
      "|    total_timesteps    | 104500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 20899    |\n",
      "|    policy_loss        | 72.1     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 3.54     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 21000    |\n",
      "|    time_elapsed       | 388      |\n",
      "|    total_timesteps    | 105000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 20999    |\n",
      "|    policy_loss        | -385     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 89.4     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 21100    |\n",
      "|    time_elapsed       | 389      |\n",
      "|    total_timesteps    | 105500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 21099    |\n",
      "|    policy_loss        | 115      |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 32.1     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4738471.037828859\n",
      "total_reward:3738471.037828859\n",
      "total_cost:  7014.150195751989\n",
      "total_trades:  41430\n",
      "Sharpe:  0.9741579164389573\n",
      "=================================\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 270       |\n",
      "|    iterations         | 21200     |\n",
      "|    time_elapsed       | 391       |\n",
      "|    total_timesteps    | 106000    |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -46       |\n",
      "|    explained_variance | -4.84e+10 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 21199     |\n",
      "|    policy_loss        | -199      |\n",
      "|    std                | 1.13      |\n",
      "|    value_loss         | 19.4      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 270       |\n",
      "|    iterations         | 21300     |\n",
      "|    time_elapsed       | 393       |\n",
      "|    total_timesteps    | 106500    |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -46.1     |\n",
      "|    explained_variance | -2.18e+03 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 21299     |\n",
      "|    policy_loss        | -306      |\n",
      "|    std                | 1.13      |\n",
      "|    value_loss         | 45.8      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 270       |\n",
      "|    iterations         | 21400     |\n",
      "|    time_elapsed       | 395       |\n",
      "|    total_timesteps    | 107000    |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -46       |\n",
      "|    explained_variance | -1.53e+05 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 21399     |\n",
      "|    policy_loss        | -210      |\n",
      "|    std                | 1.13      |\n",
      "|    value_loss         | 24.8      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 21500    |\n",
      "|    time_elapsed       | 397      |\n",
      "|    total_timesteps    | 107500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 21499    |\n",
      "|    policy_loss        | 126      |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 9.59     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 21600    |\n",
      "|    time_elapsed       | 399      |\n",
      "|    total_timesteps    | 108000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 21599    |\n",
      "|    policy_loss        | -214     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 96.2     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4857941.929380179\n",
      "total_reward:3857941.9293801794\n",
      "total_cost:  4300.517490341594\n",
      "total_trades:  39933\n",
      "Sharpe:  1.0101593537518043\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 21700    |\n",
      "|    time_elapsed       | 401      |\n",
      "|    total_timesteps    | 108500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 21699    |\n",
      "|    policy_loss        | -26.1    |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 0.598    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 21800    |\n",
      "|    time_elapsed       | 402      |\n",
      "|    total_timesteps    | 109000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 21799    |\n",
      "|    policy_loss        | 81.4     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 6.68     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 21900    |\n",
      "|    time_elapsed       | 404      |\n",
      "|    total_timesteps    | 109500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 21899    |\n",
      "|    policy_loss        | -198     |\n",
      "|    std                | 1.12     |\n",
      "|    value_loss         | 18.1     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 22000    |\n",
      "|    time_elapsed       | 406      |\n",
      "|    total_timesteps    | 110000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 21999    |\n",
      "|    policy_loss        | -107     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 6.12     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 22100    |\n",
      "|    time_elapsed       | 408      |\n",
      "|    total_timesteps    | 110500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -45.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 22099    |\n",
      "|    policy_loss        | -209     |\n",
      "|    std                | 1.12     |\n",
      "|    value_loss         | 74       |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3889237.068636508\n",
      "total_reward:2889237.068636508\n",
      "total_cost:  2349.804122118537\n",
      "total_trades:  40372\n",
      "Sharpe:  0.8843985305523498\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 22200    |\n",
      "|    time_elapsed       | 410      |\n",
      "|    total_timesteps    | 111000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 22199    |\n",
      "|    policy_loss        | 29.7     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 0.671    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 22300    |\n",
      "|    time_elapsed       | 412      |\n",
      "|    total_timesteps    | 111500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 22299    |\n",
      "|    policy_loss        | 78.5     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 3.36     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 22400    |\n",
      "|    time_elapsed       | 414      |\n",
      "|    total_timesteps    | 112000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 22399    |\n",
      "|    policy_loss        | 33.8     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 1.25     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 22500    |\n",
      "|    time_elapsed       | 416      |\n",
      "|    total_timesteps    | 112500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 22499    |\n",
      "|    policy_loss        | 221      |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 29.2     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 270       |\n",
      "|    iterations         | 22600     |\n",
      "|    time_elapsed       | 418       |\n",
      "|    total_timesteps    | 113000    |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -46.1     |\n",
      "|    explained_variance | nan       |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 22599     |\n",
      "|    policy_loss        | -1.03e+03 |\n",
      "|    std                | 1.13      |\n",
      "|    value_loss         | 551       |\n",
      "-------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4224562.913610662\n",
      "total_reward:3224562.9136106623\n",
      "total_cost:  7311.709253680451\n",
      "total_trades:  39684\n",
      "Sharpe:  0.908724330269282\n",
      "=================================\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 270       |\n",
      "|    iterations         | 22700     |\n",
      "|    time_elapsed       | 420       |\n",
      "|    total_timesteps    | 113500    |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -46.2     |\n",
      "|    explained_variance | -2.31e+04 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 22699     |\n",
      "|    policy_loss        | -135      |\n",
      "|    std                | 1.13      |\n",
      "|    value_loss         | 11.1      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 22800    |\n",
      "|    time_elapsed       | 422      |\n",
      "|    total_timesteps    | 114000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 22799    |\n",
      "|    policy_loss        | -74.1    |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 3.66     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 270       |\n",
      "|    iterations         | 22900     |\n",
      "|    time_elapsed       | 424       |\n",
      "|    total_timesteps    | 114500    |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -46.2     |\n",
      "|    explained_variance | -1.82e+10 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 22899     |\n",
      "|    policy_loss        | 44.3      |\n",
      "|    std                | 1.13      |\n",
      "|    value_loss         | 5.06      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 270       |\n",
      "|    iterations         | 23000     |\n",
      "|    time_elapsed       | 425       |\n",
      "|    total_timesteps    | 115000    |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -46.1     |\n",
      "|    explained_variance | -2.81e+05 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 22999     |\n",
      "|    policy_loss        | 98.9      |\n",
      "|    std                | 1.13      |\n",
      "|    value_loss         | 14.7      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 23100    |\n",
      "|    time_elapsed       | 427      |\n",
      "|    total_timesteps    | 115500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 23099    |\n",
      "|    policy_loss        | 252      |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 39.1     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4058599.1541633434\n",
      "total_reward:3058599.1541633434\n",
      "total_cost:  4712.075511668796\n",
      "total_trades:  39992\n",
      "Sharpe:  0.9184456466750243\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 23200    |\n",
      "|    time_elapsed       | 429      |\n",
      "|    total_timesteps    | 116000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.1    |\n",
      "|    explained_variance | -19.7    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 23199    |\n",
      "|    policy_loss        | 34.4     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 1.2      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 23300    |\n",
      "|    time_elapsed       | 431      |\n",
      "|    total_timesteps    | 116500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 23299    |\n",
      "|    policy_loss        | 79.1     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 7.28     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 23400    |\n",
      "|    time_elapsed       | 433      |\n",
      "|    total_timesteps    | 117000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 23399    |\n",
      "|    policy_loss        | -95.2    |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 5.33     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 23500    |\n",
      "|    time_elapsed       | 435      |\n",
      "|    total_timesteps    | 117500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 23499    |\n",
      "|    policy_loss        | 138      |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 15.2     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 270      |\n",
      "|    iterations         | 23600    |\n",
      "|    time_elapsed       | 436      |\n",
      "|    total_timesteps    | 118000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 23599    |\n",
      "|    policy_loss        | 211      |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 28.6     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4447977.909194936\n",
      "total_reward:3447977.909194936\n",
      "total_cost:  4003.027452147933\n",
      "total_trades:  41100\n",
      "Sharpe:  0.9956972796668654\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 23700    |\n",
      "|    time_elapsed       | 438      |\n",
      "|    total_timesteps    | 118500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.1    |\n",
      "|    explained_variance | -6.88    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 23699    |\n",
      "|    policy_loss        | -68.4    |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 2.73     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 23800    |\n",
      "|    time_elapsed       | 440      |\n",
      "|    total_timesteps    | 119000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.1    |\n",
      "|    explained_variance | -186     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 23799    |\n",
      "|    policy_loss        | -106     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 6.79     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 23900    |\n",
      "|    time_elapsed       | 442      |\n",
      "|    total_timesteps    | 119500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 23899    |\n",
      "|    policy_loss        | 30.7     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 1.59     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 24000    |\n",
      "|    time_elapsed       | 444      |\n",
      "|    total_timesteps    | 120000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 23999    |\n",
      "|    policy_loss        | 69.7     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 5.71     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 24100    |\n",
      "|    time_elapsed       | 446      |\n",
      "|    total_timesteps    | 120500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 24099    |\n",
      "|    policy_loss        | 224      |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 22.6     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3735086.557625893\n",
      "total_reward:2735086.557625893\n",
      "total_cost:  2757.089181630181\n",
      "total_trades:  40506\n",
      "Sharpe:  0.8851253072732341\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 24200    |\n",
      "|    time_elapsed       | 448      |\n",
      "|    total_timesteps    | 121000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.2    |\n",
      "|    explained_variance | -2.36    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 24199    |\n",
      "|    policy_loss        | 5.03     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 0.398    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 24300    |\n",
      "|    time_elapsed       | 450      |\n",
      "|    total_timesteps    | 121500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 24299    |\n",
      "|    policy_loss        | -225     |\n",
      "|    std                | 1.14     |\n",
      "|    value_loss         | 28.7     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 24400    |\n",
      "|    time_elapsed       | 452      |\n",
      "|    total_timesteps    | 122000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 24399    |\n",
      "|    policy_loss        | 65.6     |\n",
      "|    std                | 1.13     |\n",
      "|    value_loss         | 2.32     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 24500    |\n",
      "|    time_elapsed       | 454      |\n",
      "|    total_timesteps    | 122500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.3    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 24499    |\n",
      "|    policy_loss        | -163     |\n",
      "|    std                | 1.14     |\n",
      "|    value_loss         | 16.1     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 24600    |\n",
      "|    time_elapsed       | 456      |\n",
      "|    total_timesteps    | 123000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.3    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 24599    |\n",
      "|    policy_loss        | 53.5     |\n",
      "|    std                | 1.14     |\n",
      "|    value_loss         | 2.15     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3941900.9207990007\n",
      "total_reward:2941900.9207990007\n",
      "total_cost:  3208.161901015157\n",
      "total_trades:  39655\n",
      "Sharpe:  0.916833519860494\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 24700    |\n",
      "|    time_elapsed       | 458      |\n",
      "|    total_timesteps    | 123500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.3    |\n",
      "|    explained_variance | -10.9    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 24699    |\n",
      "|    policy_loss        | -21.7    |\n",
      "|    std                | 1.14     |\n",
      "|    value_loss         | 1.55     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 24800    |\n",
      "|    time_elapsed       | 459      |\n",
      "|    total_timesteps    | 124000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.3    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 24799    |\n",
      "|    policy_loss        | 274      |\n",
      "|    std                | 1.14     |\n",
      "|    value_loss         | 37.5     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 24900    |\n",
      "|    time_elapsed       | 461      |\n",
      "|    total_timesteps    | 124500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.4    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 24899    |\n",
      "|    policy_loss        | -99.3    |\n",
      "|    std                | 1.14     |\n",
      "|    value_loss         | 5.44     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 25000    |\n",
      "|    time_elapsed       | 463      |\n",
      "|    total_timesteps    | 125000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.4    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 24999    |\n",
      "|    policy_loss        | 73.4     |\n",
      "|    std                | 1.14     |\n",
      "|    value_loss         | 2.62     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 25100    |\n",
      "|    time_elapsed       | 465      |\n",
      "|    total_timesteps    | 125500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.5    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 25099    |\n",
      "|    policy_loss        | 85.4     |\n",
      "|    std                | 1.14     |\n",
      "|    value_loss         | 4.21     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3918748.3829585924\n",
      "total_reward:2918748.3829585924\n",
      "total_cost:  7273.962180458869\n",
      "total_trades:  40377\n",
      "Sharpe:  0.9114365429898307\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 25200    |\n",
      "|    time_elapsed       | 467      |\n",
      "|    total_timesteps    | 126000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.4    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 25199    |\n",
      "|    policy_loss        | 78.4     |\n",
      "|    std                | 1.14     |\n",
      "|    value_loss         | 3.83     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 25300    |\n",
      "|    time_elapsed       | 469      |\n",
      "|    total_timesteps    | 126500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.5    |\n",
      "|    explained_variance | -359     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 25299    |\n",
      "|    policy_loss        | 43.3     |\n",
      "|    std                | 1.14     |\n",
      "|    value_loss         | 11.3     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 25400    |\n",
      "|    time_elapsed       | 471      |\n",
      "|    total_timesteps    | 127000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.5    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 25399    |\n",
      "|    policy_loss        | -117     |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 8.74     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 25500    |\n",
      "|    time_elapsed       | 473      |\n",
      "|    total_timesteps    | 127500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.5    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 25499    |\n",
      "|    policy_loss        | -334     |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 55.2     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 25600    |\n",
      "|    time_elapsed       | 475      |\n",
      "|    total_timesteps    | 128000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.5    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 25599    |\n",
      "|    policy_loss        | 80.1     |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 7.16     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3416634.0516581917\n",
      "total_reward:2416634.0516581917\n",
      "total_cost:  4919.955620021787\n",
      "total_trades:  38886\n",
      "Sharpe:  0.7925876800612837\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 25700    |\n",
      "|    time_elapsed       | 476      |\n",
      "|    total_timesteps    | 128500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 25699    |\n",
      "|    policy_loss        | 74.3     |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 4.39     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 25800    |\n",
      "|    time_elapsed       | 478      |\n",
      "|    total_timesteps    | 129000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.5    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 25799    |\n",
      "|    policy_loss        | -45.1    |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 7.72     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 269       |\n",
      "|    iterations         | 25900     |\n",
      "|    time_elapsed       | 480       |\n",
      "|    total_timesteps    | 129500    |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -46.6     |\n",
      "|    explained_variance | -2.03e+08 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 25899     |\n",
      "|    policy_loss        | 237       |\n",
      "|    std                | 1.15      |\n",
      "|    value_loss         | 24.9      |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 269       |\n",
      "|    iterations         | 26000     |\n",
      "|    time_elapsed       | 482       |\n",
      "|    total_timesteps    | 130000    |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -46.6     |\n",
      "|    explained_variance | -2.15e+03 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 25999     |\n",
      "|    policy_loss        | -103      |\n",
      "|    std                | 1.15      |\n",
      "|    value_loss         | 9.79      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 26100    |\n",
      "|    time_elapsed       | 484      |\n",
      "|    total_timesteps    | 130500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.6    |\n",
      "|    explained_variance | -3.4e+11 |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 26099    |\n",
      "|    policy_loss        | 43.2     |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 1.28     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3276619.3638079385\n",
      "total_reward:2276619.3638079385\n",
      "total_cost:  5264.404229684018\n",
      "total_trades:  38979\n",
      "Sharpe:  0.7353175977211657\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 26200    |\n",
      "|    time_elapsed       | 486      |\n",
      "|    total_timesteps    | 131000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.6    |\n",
      "|    explained_variance | -908     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 26199    |\n",
      "|    policy_loss        | 60       |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 3.88     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 269       |\n",
      "|    iterations         | 26300     |\n",
      "|    time_elapsed       | 488       |\n",
      "|    total_timesteps    | 131500    |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -46.6     |\n",
      "|    explained_variance | -2.84e+12 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 26299     |\n",
      "|    policy_loss        | -556      |\n",
      "|    std                | 1.15      |\n",
      "|    value_loss         | 149       |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 26400    |\n",
      "|    time_elapsed       | 489      |\n",
      "|    total_timesteps    | 132000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 26399    |\n",
      "|    policy_loss        | -144     |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 10.9     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 26500    |\n",
      "|    time_elapsed       | 491      |\n",
      "|    total_timesteps    | 132500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 26499    |\n",
      "|    policy_loss        | 68.5     |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 4.74     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 26600    |\n",
      "|    time_elapsed       | 493      |\n",
      "|    total_timesteps    | 133000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 26599    |\n",
      "|    policy_loss        | 2.66     |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 0.188    |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3639991.011414096\n",
      "total_reward:2639991.011414096\n",
      "total_cost:  5876.438289118703\n",
      "total_trades:  39596\n",
      "Sharpe:  0.792662828054479\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 26700    |\n",
      "|    time_elapsed       | 495      |\n",
      "|    total_timesteps    | 133500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.6    |\n",
      "|    explained_variance | -22.5    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 26699    |\n",
      "|    policy_loss        | 114      |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 7.19     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 26800    |\n",
      "|    time_elapsed       | 497      |\n",
      "|    total_timesteps    | 134000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 26799    |\n",
      "|    policy_loss        | -227     |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 28.2     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 26900    |\n",
      "|    time_elapsed       | 499      |\n",
      "|    total_timesteps    | 134500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.5    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 26899    |\n",
      "|    policy_loss        | -99.1    |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 5.7      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 27000    |\n",
      "|    time_elapsed       | 501      |\n",
      "|    total_timesteps    | 135000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 26999    |\n",
      "|    policy_loss        | -50.5    |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 1.92     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 27100    |\n",
      "|    time_elapsed       | 503      |\n",
      "|    total_timesteps    | 135500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 27099    |\n",
      "|    policy_loss        | 86.8     |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 4.17     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3312273.2546917126\n",
      "total_reward:2312273.2546917126\n",
      "total_cost:  6513.921766223839\n",
      "total_trades:  39866\n",
      "Sharpe:  0.7669939696087845\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 27200    |\n",
      "|    time_elapsed       | 505      |\n",
      "|    total_timesteps    | 136000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.6    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 27199    |\n",
      "|    policy_loss        | 83.3     |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 4.52     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 27300    |\n",
      "|    time_elapsed       | 507      |\n",
      "|    total_timesteps    | 136500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.7    |\n",
      "|    explained_variance | -242     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 27299    |\n",
      "|    policy_loss        | 196      |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 27.6     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 27400    |\n",
      "|    time_elapsed       | 509      |\n",
      "|    total_timesteps    | 137000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.7    |\n",
      "|    explained_variance | -256     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 27399    |\n",
      "|    policy_loss        | -14.1    |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 0.802    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 27500    |\n",
      "|    time_elapsed       | 510      |\n",
      "|    total_timesteps    | 137500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.7    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 27499    |\n",
      "|    policy_loss        | -133     |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 10.8     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 27600    |\n",
      "|    time_elapsed       | 512      |\n",
      "|    total_timesteps    | 138000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.7    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 27599    |\n",
      "|    policy_loss        | -216     |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 23.3     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3537920.924942015\n",
      "total_reward:2537920.924942015\n",
      "total_cost:  7636.677849389829\n",
      "total_trades:  39571\n",
      "Sharpe:  0.7721256456339295\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 27700    |\n",
      "|    time_elapsed       | 514      |\n",
      "|    total_timesteps    | 138500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.7    |\n",
      "|    explained_variance | -538     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 27699    |\n",
      "|    policy_loss        | -78.9    |\n",
      "|    std                | 1.15     |\n",
      "|    value_loss         | 5.95     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 27800    |\n",
      "|    time_elapsed       | 516      |\n",
      "|    total_timesteps    | 139000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 27799    |\n",
      "|    policy_loss        | -135     |\n",
      "|    std                | 1.16     |\n",
      "|    value_loss         | 11.2     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 27900    |\n",
      "|    time_elapsed       | 518      |\n",
      "|    total_timesteps    | 139500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.8    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 27899    |\n",
      "|    policy_loss        | -7.94    |\n",
      "|    std                | 1.16     |\n",
      "|    value_loss         | 2.54     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 28000    |\n",
      "|    time_elapsed       | 520      |\n",
      "|    total_timesteps    | 140000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 27999    |\n",
      "|    policy_loss        | -118     |\n",
      "|    std                | 1.16     |\n",
      "|    value_loss         | 7.13     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 28100    |\n",
      "|    time_elapsed       | 522      |\n",
      "|    total_timesteps    | 140500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.9    |\n",
      "|    explained_variance | -1.4e+12 |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 28099    |\n",
      "|    policy_loss        | 33.8     |\n",
      "|    std                | 1.16     |\n",
      "|    value_loss         | 1.74     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3335901.863268089\n",
      "total_reward:2335901.863268089\n",
      "total_cost:  6148.2616701473435\n",
      "total_trades:  38459\n",
      "Sharpe:  0.8009972305518047\n",
      "=================================\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 269       |\n",
      "|    iterations         | 28200     |\n",
      "|    time_elapsed       | 523       |\n",
      "|    total_timesteps    | 141000    |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -46.9     |\n",
      "|    explained_variance | -1.72e+07 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 28199     |\n",
      "|    policy_loss        | -75.4     |\n",
      "|    std                | 1.16      |\n",
      "|    value_loss         | 4.2       |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 28300    |\n",
      "|    time_elapsed       | 525      |\n",
      "|    total_timesteps    | 141500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 28299    |\n",
      "|    policy_loss        | 13.6     |\n",
      "|    std                | 1.16     |\n",
      "|    value_loss         | 3.07     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 28400    |\n",
      "|    time_elapsed       | 527      |\n",
      "|    total_timesteps    | 142000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -46.9    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 28399    |\n",
      "|    policy_loss        | -38.5    |\n",
      "|    std                | 1.16     |\n",
      "|    value_loss         | 0.936    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 28500    |\n",
      "|    time_elapsed       | 529      |\n",
      "|    total_timesteps    | 142500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -47      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 28499    |\n",
      "|    policy_loss        | -20.5    |\n",
      "|    std                | 1.16     |\n",
      "|    value_loss         | 1.02     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 28600    |\n",
      "|    time_elapsed       | 531      |\n",
      "|    total_timesteps    | 143000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -47      |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 28599    |\n",
      "|    policy_loss        | -95.6    |\n",
      "|    std                | 1.16     |\n",
      "|    value_loss         | 6.74     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3791388.9622966833\n",
      "total_reward:2791388.9622966833\n",
      "total_cost:  4739.291239631439\n",
      "total_trades:  36786\n",
      "Sharpe:  0.8352371557337978\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 28700    |\n",
      "|    time_elapsed       | 533      |\n",
      "|    total_timesteps    | 143500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -47      |\n",
      "|    explained_variance | -656     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 28699    |\n",
      "|    policy_loss        | 145      |\n",
      "|    std                | 1.17     |\n",
      "|    value_loss         | 10       |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 28800    |\n",
      "|    time_elapsed       | 535      |\n",
      "|    total_timesteps    | 144000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -47.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 28799    |\n",
      "|    policy_loss        | 195      |\n",
      "|    std                | 1.17     |\n",
      "|    value_loss         | 23       |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 28900    |\n",
      "|    time_elapsed       | 536      |\n",
      "|    total_timesteps    | 144500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -47.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 28899    |\n",
      "|    policy_loss        | -26      |\n",
      "|    std                | 1.17     |\n",
      "|    value_loss         | 2.42     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 29000    |\n",
      "|    time_elapsed       | 538      |\n",
      "|    total_timesteps    | 145000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -47.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 28999    |\n",
      "|    policy_loss        | 32.1     |\n",
      "|    std                | 1.17     |\n",
      "|    value_loss         | 3.84     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 269       |\n",
      "|    iterations         | 29100     |\n",
      "|    time_elapsed       | 540       |\n",
      "|    total_timesteps    | 145500    |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -47.1     |\n",
      "|    explained_variance | -1.11e+11 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 29099     |\n",
      "|    policy_loss        | -51.3     |\n",
      "|    std                | 1.17      |\n",
      "|    value_loss         | 1.21      |\n",
      "-------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3722466.511508156\n",
      "total_reward:2722466.511508156\n",
      "total_cost:  2619.4388887420964\n",
      "total_trades:  36838\n",
      "Sharpe:  0.8751149961312088\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 29200    |\n",
      "|    time_elapsed       | 542      |\n",
      "|    total_timesteps    | 146000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -47      |\n",
      "|    explained_variance | -37.7    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 29199    |\n",
      "|    policy_loss        | 97.3     |\n",
      "|    std                | 1.17     |\n",
      "|    value_loss         | 5.24     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 29300    |\n",
      "|    time_elapsed       | 544      |\n",
      "|    total_timesteps    | 146500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -47.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 29299    |\n",
      "|    policy_loss        | 63.7     |\n",
      "|    std                | 1.17     |\n",
      "|    value_loss         | 3.25     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 269      |\n",
      "|    iterations         | 29400    |\n",
      "|    time_elapsed       | 546      |\n",
      "|    total_timesteps    | 147000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -47.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 29399    |\n",
      "|    policy_loss        | 76.1     |\n",
      "|    std                | 1.17     |\n",
      "|    value_loss         | 3.03     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 29500    |\n",
      "|    time_elapsed       | 548      |\n",
      "|    total_timesteps    | 147500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -47.2    |\n",
      "|    explained_variance | -134     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 29499    |\n",
      "|    policy_loss        | -178     |\n",
      "|    std                | 1.17     |\n",
      "|    value_loss         | 15.8     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 29600    |\n",
      "|    time_elapsed       | 550      |\n",
      "|    total_timesteps    | 148000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -47.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 29599    |\n",
      "|    policy_loss        | -202     |\n",
      "|    std                | 1.17     |\n",
      "|    value_loss         | 15.6     |\n",
      "------------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3503288.5069474406\n",
      "total_reward:2503288.5069474406\n",
      "total_cost:  2306.8302833824664\n",
      "total_trades:  38804\n",
      "Sharpe:  0.8406587986683967\n",
      "=================================\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 29700    |\n",
      "|    time_elapsed       | 552      |\n",
      "|    total_timesteps    | 148500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -47.2    |\n",
      "|    explained_variance | -338     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 29699    |\n",
      "|    policy_loss        | 174      |\n",
      "|    std                | 1.17     |\n",
      "|    value_loss         | 17.4     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 29800    |\n",
      "|    time_elapsed       | 553      |\n",
      "|    total_timesteps    | 149000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -47.2    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 29799    |\n",
      "|    policy_loss        | -106     |\n",
      "|    std                | 1.17     |\n",
      "|    value_loss         | 7.64     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 29900    |\n",
      "|    time_elapsed       | 555      |\n",
      "|    total_timesteps    | 149500   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -47.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 29899    |\n",
      "|    policy_loss        | 67.9     |\n",
      "|    std                | 1.17     |\n",
      "|    value_loss         | 2.68     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 268      |\n",
      "|    iterations         | 30000    |\n",
      "|    time_elapsed       | 557      |\n",
      "|    total_timesteps    | 150000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -47.1    |\n",
      "|    explained_variance | nan      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 29999    |\n",
      "|    policy_loss        | -121     |\n",
      "|    std                | 1.17     |\n",
      "|    value_loss         | 8.47     |\n",
      "------------------------------------\n"
     ]
    }
   ],
   "source": [
    "trained_a2c = agent.train_model(model=model_a2c, \n",
    "                             tb_log_name='a2c',\n",
    "                             total_timesteps=100000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MRiOtrywfAo1"
   },
   "source": [
    "### Model 2: DDPG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M2YadjfnLwgt",
    "outputId": "3b2a8f89-0561-4083-a015-fbee11693037"
   },
   "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": {
     "background_save": true,
     "base_uri": "https://localhost:8080/"
    },
    "id": "tCDa78rqfO_a",
    "outputId": "f651f8be-4c93-4b1e-c88a-7e3a09976693"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logging to tensorboard_log/ddpg/ddpg_1\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:3761309.8057632465\n",
      "total_reward:2761309.8057632465\n",
      "total_cost:  6807.077776350557\n",
      "total_trades:  39070\n",
      "Sharpe:  1.0173492167488003\n",
      "=================================\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 4        |\n",
      "|    fps             | 38       |\n",
      "|    time_elapsed    | 258      |\n",
      "|    total timesteps | 10064    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -2.81    |\n",
      "|    critic_loss     | 272      |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    n_updates       | 7548     |\n",
      "---------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 8        |\n",
      "|    fps             | 33       |\n",
      "|    time_elapsed    | 604      |\n",
      "|    total timesteps | 20128    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -8.32    |\n",
      "|    critic_loss     | 12.8     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    n_updates       | 17612    |\n",
      "---------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 12       |\n",
      "|    fps             | 31       |\n",
      "|    time_elapsed    | 953      |\n",
      "|    total timesteps | 30192    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -9.46    |\n",
      "|    critic_loss     | 4.31     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    n_updates       | 27676    |\n",
      "---------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 16       |\n",
      "|    fps             | 30       |\n",
      "|    time_elapsed    | 1309     |\n",
      "|    total timesteps | 40256    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -10.2    |\n",
      "|    critic_loss     | 3.19     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    n_updates       | 37740    |\n",
      "---------------------------------\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "begin_total_asset:1000000\n",
      "end_total_asset:4423657.61673363\n",
      "total_reward:3423657.61673363\n",
      "total_cost:  1277.392035166502\n",
      "total_trades:  32819\n",
      "Sharpe:  0.8726982452731067\n",
      "=================================\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 20       |\n",
      "|    fps             | 30       |\n",
      "|    time_elapsed    | 1675     |\n",
      "|    total timesteps | 50320    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -11.1    |\n",
      "|    critic_loss     | 2.24     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    n_updates       | 47804    |\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": [
    "### Model 3: PPO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "y5D5PFUhMzSV",
    "outputId": "2716af5e-06e5-4eab-b071-a506c60a0475"
   },
   "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": "1016cc05-58b6-45dc-c871-a322f1c3dc89"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logging to tensorboard_log/ppo/ppo_2\n",
      "-----------------------------\n",
      "| time/              |      |\n",
      "|    fps             | 104  |\n",
      "|    iterations      | 1    |\n",
      "|    time_elapsed    | 19   |\n",
      "|    total_timesteps | 2048 |\n",
      "-----------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 102         |\n",
      "|    iterations           | 2           |\n",
      "|    time_elapsed         | 39          |\n",
      "|    total_timesteps      | 4096        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.014151055 |\n",
      "|    clip_fraction        | 0.212       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -42.6       |\n",
      "|    explained_variance   | -28.1       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 5.76        |\n",
      "|    n_updates            | 10          |\n",
      "|    policy_gradient_loss | -0.0277     |\n",
      "|    std                  | 1           |\n",
      "|    value_loss           | 12          |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 101         |\n",
      "|    iterations           | 3           |\n",
      "|    time_elapsed         | 60          |\n",
      "|    total_timesteps      | 6144        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.016467014 |\n",
      "|    clip_fraction        | 0.186       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -42.6       |\n",
      "|    explained_variance   | -176        |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 9.99        |\n",
      "|    n_updates            | 20          |\n",
      "|    policy_gradient_loss | -0.0275     |\n",
      "|    std                  | 1           |\n",
      "|    value_loss           | 18.9        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 101         |\n",
      "|    iterations           | 4           |\n",
      "|    time_elapsed         | 80          |\n",
      "|    total_timesteps      | 8192        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.020772668 |\n",
      "|    clip_fraction        | 0.191       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -42.6       |\n",
      "|    explained_variance   | -87.8       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 16.7        |\n",
      "|    n_updates            | 30          |\n",
      "|    policy_gradient_loss | -0.028      |\n",
      "|    std                  | 1           |\n",
      "|    value_loss           | 32.2        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 101         |\n",
      "|    iterations           | 5           |\n",
      "|    time_elapsed         | 101         |\n",
      "|    total_timesteps      | 10240       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.019156657 |\n",
      "|    clip_fraction        | 0.225       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -42.7       |\n",
      "|    explained_variance   | -81.3       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 11          |\n",
      "|    n_updates            | 40          |\n",
      "|    policy_gradient_loss | -0.0184     |\n",
      "|    std                  | 1           |\n",
      "|    value_loss           | 26.6        |\n",
      "-----------------------------------------\n",
      "----------------------------------------\n",
      "| time/                   |            |\n",
      "|    fps                  | 100        |\n",
      "|    iterations           | 6          |\n",
      "|    time_elapsed         | 122        |\n",
      "|    total_timesteps      | 12288      |\n",
      "| train/                  |            |\n",
      "|    approx_kl            | 0.02388929 |\n",
      "|    clip_fraction        | 0.223      |\n",
      "|    clip_range           | 0.2        |\n",
      "|    entropy_loss         | -42.7      |\n",
      "|    explained_variance   | -67        |\n",
      "|    learning_rate        | 0.00025    |\n",
      "|    loss                 | 7.86       |\n",
      "|    n_updates            | 50         |\n",
      "|    policy_gradient_loss | -0.0269    |\n",
      "|    std                  | 1.01       |\n",
      "|    value_loss           | 23         |\n",
      "----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 7           |\n",
      "|    time_elapsed         | 142         |\n",
      "|    total_timesteps      | 14336       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.023960019 |\n",
      "|    clip_fraction        | 0.21        |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -42.8       |\n",
      "|    explained_variance   | -58.1       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 6.32        |\n",
      "|    n_updates            | 60          |\n",
      "|    policy_gradient_loss | -0.0234     |\n",
      "|    std                  | 1.01        |\n",
      "|    value_loss           | 12          |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 8           |\n",
      "|    time_elapsed         | 163         |\n",
      "|    total_timesteps      | 16384       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.021991765 |\n",
      "|    clip_fraction        | 0.212       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -42.8       |\n",
      "|    explained_variance   | -36.4       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 9.39        |\n",
      "|    n_updates            | 70          |\n",
      "|    policy_gradient_loss | -0.0243     |\n",
      "|    std                  | 1.01        |\n",
      "|    value_loss           | 19.9        |\n",
      "-----------------------------------------\n",
      "----------------------------------------\n",
      "| time/                   |            |\n",
      "|    fps                  | 100        |\n",
      "|    iterations           | 9          |\n",
      "|    time_elapsed         | 183        |\n",
      "|    total_timesteps      | 18432      |\n",
      "| train/                  |            |\n",
      "|    approx_kl            | 0.01857267 |\n",
      "|    clip_fraction        | 0.205      |\n",
      "|    clip_range           | 0.2        |\n",
      "|    entropy_loss         | -42.9      |\n",
      "|    explained_variance   | -59.3      |\n",
      "|    learning_rate        | 0.00025    |\n",
      "|    loss                 | 8.22       |\n",
      "|    n_updates            | 80         |\n",
      "|    policy_gradient_loss | -0.0235    |\n",
      "|    std                  | 1.01       |\n",
      "|    value_loss           | 20.5       |\n",
      "----------------------------------------\n",
      "day: 2515, episode: 130\n",
      "begin_total_asset:1000000.00\n",
      "end_total_asset:3383653.45\n",
      "total_reward:2383653.45\n",
      "total_cost: 255155.22\n",
      "total_trades: 72649\n",
      "Sharpe: 0.863\n",
      "=================================\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 10          |\n",
      "|    time_elapsed         | 203         |\n",
      "|    total_timesteps      | 20480       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.022291362 |\n",
      "|    clip_fraction        | 0.213       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -42.9       |\n",
      "|    explained_variance   | -70.1       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 12.4        |\n",
      "|    n_updates            | 90          |\n",
      "|    policy_gradient_loss | -0.019      |\n",
      "|    std                  | 1.01        |\n",
      "|    value_loss           | 34.1        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 11          |\n",
      "|    time_elapsed         | 224         |\n",
      "|    total_timesteps      | 22528       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.017316487 |\n",
      "|    clip_fraction        | 0.22        |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -42.9       |\n",
      "|    explained_variance   | -159        |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 21.4        |\n",
      "|    n_updates            | 100         |\n",
      "|    policy_gradient_loss | -0.0182     |\n",
      "|    std                  | 1.01        |\n",
      "|    value_loss           | 38.8        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 12          |\n",
      "|    time_elapsed         | 244         |\n",
      "|    total_timesteps      | 24576       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.018951386 |\n",
      "|    clip_fraction        | 0.179       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -43         |\n",
      "|    explained_variance   | -25.3       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 11.4        |\n",
      "|    n_updates            | 110         |\n",
      "|    policy_gradient_loss | -0.0135     |\n",
      "|    std                  | 1.02        |\n",
      "|    value_loss           | 29.9        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 13          |\n",
      "|    time_elapsed         | 265         |\n",
      "|    total_timesteps      | 26624       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.033302963 |\n",
      "|    clip_fraction        | 0.298       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -43.1       |\n",
      "|    explained_variance   | -58.1       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 9.17        |\n",
      "|    n_updates            | 120         |\n",
      "|    policy_gradient_loss | -0.0236     |\n",
      "|    std                  | 1.02        |\n",
      "|    value_loss           | 28.3        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 14          |\n",
      "|    time_elapsed         | 285         |\n",
      "|    total_timesteps      | 28672       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.027676268 |\n",
      "|    clip_fraction        | 0.278       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -43.1       |\n",
      "|    explained_variance   | -91.7       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 12.8        |\n",
      "|    n_updates            | 130         |\n",
      "|    policy_gradient_loss | -0.0192     |\n",
      "|    std                  | 1.02        |\n",
      "|    value_loss           | 32.7        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 15          |\n",
      "|    time_elapsed         | 306         |\n",
      "|    total_timesteps      | 30720       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.027800845 |\n",
      "|    clip_fraction        | 0.233       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -43.2       |\n",
      "|    explained_variance   | -85.9       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 26          |\n",
      "|    n_updates            | 140         |\n",
      "|    policy_gradient_loss | -0.0217     |\n",
      "|    std                  | 1.02        |\n",
      "|    value_loss           | 40.1        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 16          |\n",
      "|    time_elapsed         | 326         |\n",
      "|    total_timesteps      | 32768       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.016968882 |\n",
      "|    clip_fraction        | 0.219       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -43.3       |\n",
      "|    explained_variance   | -71.3       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 10.2        |\n",
      "|    n_updates            | 150         |\n",
      "|    policy_gradient_loss | -0.0209     |\n",
      "|    std                  | 1.02        |\n",
      "|    value_loss           | 26.9        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 17          |\n",
      "|    time_elapsed         | 347         |\n",
      "|    total_timesteps      | 34816       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.022131229 |\n",
      "|    clip_fraction        | 0.215       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -43.3       |\n",
      "|    explained_variance   | -15.7       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 20.6        |\n",
      "|    n_updates            | 160         |\n",
      "|    policy_gradient_loss | -0.0153     |\n",
      "|    std                  | 1.03        |\n",
      "|    value_loss           | 49.1        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 18          |\n",
      "|    time_elapsed         | 368         |\n",
      "|    total_timesteps      | 36864       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.029286291 |\n",
      "|    clip_fraction        | 0.266       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -43.4       |\n",
      "|    explained_variance   | -43.9       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 13.2        |\n",
      "|    n_updates            | 170         |\n",
      "|    policy_gradient_loss | -0.015      |\n",
      "|    std                  | 1.03        |\n",
      "|    value_loss           | 19.9        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 19          |\n",
      "|    time_elapsed         | 388         |\n",
      "|    total_timesteps      | 38912       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.027719798 |\n",
      "|    clip_fraction        | 0.24        |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -43.4       |\n",
      "|    explained_variance   | -131        |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 16.8        |\n",
      "|    n_updates            | 180         |\n",
      "|    policy_gradient_loss | -0.0183     |\n",
      "|    std                  | 1.03        |\n",
      "|    value_loss           | 34          |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 20          |\n",
      "|    time_elapsed         | 409         |\n",
      "|    total_timesteps      | 40960       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.022764063 |\n",
      "|    clip_fraction        | 0.217       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -43.5       |\n",
      "|    explained_variance   | -63.1       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 22.5        |\n",
      "|    n_updates            | 190         |\n",
      "|    policy_gradient_loss | -0.0186     |\n",
      "|    std                  | 1.03        |\n",
      "|    value_loss           | 37.9        |\n",
      "-----------------------------------------\n",
      "----------------------------------------\n",
      "| time/                   |            |\n",
      "|    fps                  | 100        |\n",
      "|    iterations           | 21         |\n",
      "|    time_elapsed         | 429        |\n",
      "|    total_timesteps      | 43008      |\n",
      "| train/                  |            |\n",
      "|    approx_kl            | 0.02734076 |\n",
      "|    clip_fraction        | 0.208      |\n",
      "|    clip_range           | 0.2        |\n",
      "|    entropy_loss         | -43.5      |\n",
      "|    explained_variance   | -113       |\n",
      "|    learning_rate        | 0.00025    |\n",
      "|    loss                 | 21         |\n",
      "|    n_updates            | 200        |\n",
      "|    policy_gradient_loss | -0.0201    |\n",
      "|    std                  | 1.03       |\n",
      "|    value_loss           | 60.7       |\n",
      "----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 22          |\n",
      "|    time_elapsed         | 450         |\n",
      "|    total_timesteps      | 45056       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.023378888 |\n",
      "|    clip_fraction        | 0.277       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -43.6       |\n",
      "|    explained_variance   | -57         |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 19.6        |\n",
      "|    n_updates            | 210         |\n",
      "|    policy_gradient_loss | -0.0153     |\n",
      "|    std                  | 1.03        |\n",
      "|    value_loss           | 38.9        |\n",
      "-----------------------------------------\n",
      "day: 2515, episode: 140\n",
      "begin_total_asset:1000000.00\n",
      "end_total_asset:5223199.40\n",
      "total_reward:4223199.40\n",
      "total_cost: 235269.98\n",
      "total_trades: 71552\n",
      "Sharpe: 1.128\n",
      "=================================\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 23          |\n",
      "|    time_elapsed         | 470         |\n",
      "|    total_timesteps      | 47104       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.025331508 |\n",
      "|    clip_fraction        | 0.29        |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -43.6       |\n",
      "|    explained_variance   | -61.4       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 19.7        |\n",
      "|    n_updates            | 220         |\n",
      "|    policy_gradient_loss | -0.0119     |\n",
      "|    std                  | 1.04        |\n",
      "|    value_loss           | 34.8        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 24          |\n",
      "|    time_elapsed         | 491         |\n",
      "|    total_timesteps      | 49152       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.025766762 |\n",
      "|    clip_fraction        | 0.231       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -43.7       |\n",
      "|    explained_variance   | -64.7       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 20.4        |\n",
      "|    n_updates            | 230         |\n",
      "|    policy_gradient_loss | -0.0187     |\n",
      "|    std                  | 1.04        |\n",
      "|    value_loss           | 47.4        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 25          |\n",
      "|    time_elapsed         | 511         |\n",
      "|    total_timesteps      | 51200       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.041917183 |\n",
      "|    clip_fraction        | 0.278       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -43.8       |\n",
      "|    explained_variance   | -34         |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 22.2        |\n",
      "|    n_updates            | 240         |\n",
      "|    policy_gradient_loss | -0.0164     |\n",
      "|    std                  | 1.04        |\n",
      "|    value_loss           | 48          |\n",
      "-----------------------------------------\n",
      "---------------------------------------\n",
      "| time/                   |           |\n",
      "|    fps                  | 100       |\n",
      "|    iterations           | 26        |\n",
      "|    time_elapsed         | 531       |\n",
      "|    total_timesteps      | 53248     |\n",
      "| train/                  |           |\n",
      "|    approx_kl            | 0.0367468 |\n",
      "|    clip_fraction        | 0.273     |\n",
      "|    clip_range           | 0.2       |\n",
      "|    entropy_loss         | -43.8     |\n",
      "|    explained_variance   | -48.1     |\n",
      "|    learning_rate        | 0.00025   |\n",
      "|    loss                 | 21.5      |\n",
      "|    n_updates            | 250       |\n",
      "|    policy_gradient_loss | -0.00821  |\n",
      "|    std                  | 1.04      |\n",
      "|    value_loss           | 39.5      |\n",
      "---------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 27          |\n",
      "|    time_elapsed         | 552         |\n",
      "|    total_timesteps      | 55296       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.024581099 |\n",
      "|    clip_fraction        | 0.211       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -43.9       |\n",
      "|    explained_variance   | -198        |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 32.1        |\n",
      "|    n_updates            | 260         |\n",
      "|    policy_gradient_loss | -0.0106     |\n",
      "|    std                  | 1.05        |\n",
      "|    value_loss           | 58.2        |\n",
      "-----------------------------------------\n",
      "----------------------------------------\n",
      "| time/                   |            |\n",
      "|    fps                  | 100        |\n",
      "|    iterations           | 28         |\n",
      "|    time_elapsed         | 573        |\n",
      "|    total_timesteps      | 57344      |\n",
      "| train/                  |            |\n",
      "|    approx_kl            | 0.02569989 |\n",
      "|    clip_fraction        | 0.209      |\n",
      "|    clip_range           | 0.2        |\n",
      "|    entropy_loss         | -43.9      |\n",
      "|    explained_variance   | -161       |\n",
      "|    learning_rate        | 0.00025    |\n",
      "|    loss                 | 25.1       |\n",
      "|    n_updates            | 270        |\n",
      "|    policy_gradient_loss | -0.0137    |\n",
      "|    std                  | 1.05       |\n",
      "|    value_loss           | 55         |\n",
      "----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 29          |\n",
      "|    time_elapsed         | 593         |\n",
      "|    total_timesteps      | 59392       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.032340243 |\n",
      "|    clip_fraction        | 0.252       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44         |\n",
      "|    explained_variance   | -24.1       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 9.23        |\n",
      "|    n_updates            | 280         |\n",
      "|    policy_gradient_loss | -0.0167     |\n",
      "|    std                  | 1.05        |\n",
      "|    value_loss           | 34          |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 30          |\n",
      "|    time_elapsed         | 613         |\n",
      "|    total_timesteps      | 61440       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.018233867 |\n",
      "|    clip_fraction        | 0.239       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44         |\n",
      "|    explained_variance   | -34.1       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 28.1        |\n",
      "|    n_updates            | 290         |\n",
      "|    policy_gradient_loss | -0.0158     |\n",
      "|    std                  | 1.05        |\n",
      "|    value_loss           | 41.2        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 31          |\n",
      "|    time_elapsed         | 634         |\n",
      "|    total_timesteps      | 63488       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.030068567 |\n",
      "|    clip_fraction        | 0.152       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44         |\n",
      "|    explained_variance   | -26.1       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 19.2        |\n",
      "|    n_updates            | 300         |\n",
      "|    policy_gradient_loss | -0.0121     |\n",
      "|    std                  | 1.05        |\n",
      "|    value_loss           | 64.9        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 32          |\n",
      "|    time_elapsed         | 654         |\n",
      "|    total_timesteps      | 65536       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.024889158 |\n",
      "|    clip_fraction        | 0.27        |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44.1       |\n",
      "|    explained_variance   | -31.2       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 37.3        |\n",
      "|    n_updates            | 310         |\n",
      "|    policy_gradient_loss | -0.0148     |\n",
      "|    std                  | 1.05        |\n",
      "|    value_loss           | 58          |\n",
      "-----------------------------------------\n",
      "----------------------------------------\n",
      "| time/                   |            |\n",
      "|    fps                  | 100        |\n",
      "|    iterations           | 33         |\n",
      "|    time_elapsed         | 674        |\n",
      "|    total_timesteps      | 67584      |\n",
      "| train/                  |            |\n",
      "|    approx_kl            | 0.03883523 |\n",
      "|    clip_fraction        | 0.234      |\n",
      "|    clip_range           | 0.2        |\n",
      "|    entropy_loss         | -44.1      |\n",
      "|    explained_variance   | -39.9      |\n",
      "|    learning_rate        | 0.00025    |\n",
      "|    loss                 | 24.5       |\n",
      "|    n_updates            | 320        |\n",
      "|    policy_gradient_loss | -0.0121    |\n",
      "|    std                  | 1.05       |\n",
      "|    value_loss           | 84.4       |\n",
      "----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 34          |\n",
      "|    time_elapsed         | 695         |\n",
      "|    total_timesteps      | 69632       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.024309162 |\n",
      "|    clip_fraction        | 0.225       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44.2       |\n",
      "|    explained_variance   | -12.9       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 8.79        |\n",
      "|    n_updates            | 330         |\n",
      "|    policy_gradient_loss | -0.015      |\n",
      "|    std                  | 1.06        |\n",
      "|    value_loss           | 23.8        |\n",
      "-----------------------------------------\n",
      "day: 2515, episode: 150\n",
      "begin_total_asset:1000000.00\n",
      "end_total_asset:6320097.75\n",
      "total_reward:5320097.75\n",
      "total_cost: 222029.44\n",
      "total_trades: 69973\n",
      "Sharpe: 1.250\n",
      "=================================\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 35          |\n",
      "|    time_elapsed         | 715         |\n",
      "|    total_timesteps      | 71680       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.024664927 |\n",
      "|    clip_fraction        | 0.183       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44.2       |\n",
      "|    explained_variance   | -17.2       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 15.3        |\n",
      "|    n_updates            | 340         |\n",
      "|    policy_gradient_loss | -0.0141     |\n",
      "|    std                  | 1.06        |\n",
      "|    value_loss           | 48.7        |\n",
      "-----------------------------------------\n",
      "----------------------------------------\n",
      "| time/                   |            |\n",
      "|    fps                  | 100        |\n",
      "|    iterations           | 36         |\n",
      "|    time_elapsed         | 735        |\n",
      "|    total_timesteps      | 73728      |\n",
      "| train/                  |            |\n",
      "|    approx_kl            | 0.03882557 |\n",
      "|    clip_fraction        | 0.207      |\n",
      "|    clip_range           | 0.2        |\n",
      "|    entropy_loss         | -44.3      |\n",
      "|    explained_variance   | -27.1      |\n",
      "|    learning_rate        | 0.00025    |\n",
      "|    loss                 | 50.3       |\n",
      "|    n_updates            | 350        |\n",
      "|    policy_gradient_loss | -0.0141    |\n",
      "|    std                  | 1.06       |\n",
      "|    value_loss           | 93.7       |\n",
      "----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 37          |\n",
      "|    time_elapsed         | 756         |\n",
      "|    total_timesteps      | 75776       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.022156972 |\n",
      "|    clip_fraction        | 0.214       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44.3       |\n",
      "|    explained_variance   | -23.9       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 26.5        |\n",
      "|    n_updates            | 360         |\n",
      "|    policy_gradient_loss | -0.0161     |\n",
      "|    std                  | 1.06        |\n",
      "|    value_loss           | 71.7        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 38          |\n",
      "|    time_elapsed         | 776         |\n",
      "|    total_timesteps      | 77824       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.022767432 |\n",
      "|    clip_fraction        | 0.223       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44.4       |\n",
      "|    explained_variance   | -17.5       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 23.8        |\n",
      "|    n_updates            | 370         |\n",
      "|    policy_gradient_loss | -0.0154     |\n",
      "|    std                  | 1.06        |\n",
      "|    value_loss           | 38.7        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 39          |\n",
      "|    time_elapsed         | 797         |\n",
      "|    total_timesteps      | 79872       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.020827759 |\n",
      "|    clip_fraction        | 0.178       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44.4       |\n",
      "|    explained_variance   | -56         |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 36.3        |\n",
      "|    n_updates            | 380         |\n",
      "|    policy_gradient_loss | -0.00964    |\n",
      "|    std                  | 1.07        |\n",
      "|    value_loss           | 82.1        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 40          |\n",
      "|    time_elapsed         | 817         |\n",
      "|    total_timesteps      | 81920       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.013000591 |\n",
      "|    clip_fraction        | 0.132       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44.5       |\n",
      "|    explained_variance   | -23         |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 14          |\n",
      "|    n_updates            | 390         |\n",
      "|    policy_gradient_loss | -0.0162     |\n",
      "|    std                  | 1.07        |\n",
      "|    value_loss           | 63.1        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 41          |\n",
      "|    time_elapsed         | 837         |\n",
      "|    total_timesteps      | 83968       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.021172233 |\n",
      "|    clip_fraction        | 0.19        |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44.5       |\n",
      "|    explained_variance   | -26.6       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 47.3        |\n",
      "|    n_updates            | 400         |\n",
      "|    policy_gradient_loss | -0.0191     |\n",
      "|    std                  | 1.07        |\n",
      "|    value_loss           | 98          |\n",
      "-----------------------------------------\n",
      "----------------------------------------\n",
      "| time/                   |            |\n",
      "|    fps                  | 100        |\n",
      "|    iterations           | 42         |\n",
      "|    time_elapsed         | 858        |\n",
      "|    total_timesteps      | 86016      |\n",
      "| train/                  |            |\n",
      "|    approx_kl            | 0.02925424 |\n",
      "|    clip_fraction        | 0.16       |\n",
      "|    clip_range           | 0.2        |\n",
      "|    entropy_loss         | -44.6      |\n",
      "|    explained_variance   | -33.8      |\n",
      "|    learning_rate        | 0.00025    |\n",
      "|    loss                 | 59.2       |\n",
      "|    n_updates            | 410        |\n",
      "|    policy_gradient_loss | -0.0117    |\n",
      "|    std                  | 1.07       |\n",
      "|    value_loss           | 163        |\n",
      "----------------------------------------\n",
      "----------------------------------------\n",
      "| time/                   |            |\n",
      "|    fps                  | 100        |\n",
      "|    iterations           | 43         |\n",
      "|    time_elapsed         | 878        |\n",
      "|    total_timesteps      | 88064      |\n",
      "| train/                  |            |\n",
      "|    approx_kl            | 0.01635669 |\n",
      "|    clip_fraction        | 0.138      |\n",
      "|    clip_range           | 0.2        |\n",
      "|    entropy_loss         | -44.6      |\n",
      "|    explained_variance   | -28.9      |\n",
      "|    learning_rate        | 0.00025    |\n",
      "|    loss                 | 57.9       |\n",
      "|    n_updates            | 420        |\n",
      "|    policy_gradient_loss | -0.0135    |\n",
      "|    std                  | 1.07       |\n",
      "|    value_loss           | 122        |\n",
      "----------------------------------------\n",
      "----------------------------------------\n",
      "| time/                   |            |\n",
      "|    fps                  | 100        |\n",
      "|    iterations           | 44         |\n",
      "|    time_elapsed         | 898        |\n",
      "|    total_timesteps      | 90112      |\n",
      "| train/                  |            |\n",
      "|    approx_kl            | 0.03150232 |\n",
      "|    clip_fraction        | 0.188      |\n",
      "|    clip_range           | 0.2        |\n",
      "|    entropy_loss         | -44.6      |\n",
      "|    explained_variance   | -20.9      |\n",
      "|    learning_rate        | 0.00025    |\n",
      "|    loss                 | 45.1       |\n",
      "|    n_updates            | 430        |\n",
      "|    policy_gradient_loss | -0.0222    |\n",
      "|    std                  | 1.07       |\n",
      "|    value_loss           | 84.9       |\n",
      "----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 45          |\n",
      "|    time_elapsed         | 919         |\n",
      "|    total_timesteps      | 92160       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.035686597 |\n",
      "|    clip_fraction        | 0.335       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44.6       |\n",
      "|    explained_variance   | -5.37       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 4.34        |\n",
      "|    n_updates            | 440         |\n",
      "|    policy_gradient_loss | -0.0119     |\n",
      "|    std                  | 1.07        |\n",
      "|    value_loss           | 14.2        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 46          |\n",
      "|    time_elapsed         | 940         |\n",
      "|    total_timesteps      | 94208       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.028425248 |\n",
      "|    clip_fraction        | 0.293       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44.7       |\n",
      "|    explained_variance   | -4.65       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 22.9        |\n",
      "|    n_updates            | 450         |\n",
      "|    policy_gradient_loss | -0.0184     |\n",
      "|    std                  | 1.07        |\n",
      "|    value_loss           | 26.4        |\n",
      "-----------------------------------------\n",
      "day: 2515, episode: 160\n",
      "begin_total_asset:1000000.00\n",
      "end_total_asset:5044806.56\n",
      "total_reward:4044806.56\n",
      "total_cost: 237117.70\n",
      "total_trades: 70270\n",
      "Sharpe: 1.271\n",
      "=================================\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 47          |\n",
      "|    time_elapsed         | 960         |\n",
      "|    total_timesteps      | 96256       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.034343738 |\n",
      "|    clip_fraction        | 0.299       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44.7       |\n",
      "|    explained_variance   | -3.42       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 17.6        |\n",
      "|    n_updates            | 460         |\n",
      "|    policy_gradient_loss | -0.0185     |\n",
      "|    std                  | 1.08        |\n",
      "|    value_loss           | 56.9        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 48          |\n",
      "|    time_elapsed         | 981         |\n",
      "|    total_timesteps      | 98304       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.017608875 |\n",
      "|    clip_fraction        | 0.231       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44.8       |\n",
      "|    explained_variance   | -17.7       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 17.6        |\n",
      "|    n_updates            | 470         |\n",
      "|    policy_gradient_loss | -0.0054     |\n",
      "|    std                  | 1.08        |\n",
      "|    value_loss           | 35.9        |\n",
      "-----------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 100         |\n",
      "|    iterations           | 49          |\n",
      "|    time_elapsed         | 1001        |\n",
      "|    total_timesteps      | 100352      |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.024408635 |\n",
      "|    clip_fraction        | 0.168       |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -44.8       |\n",
      "|    explained_variance   | -4.44       |\n",
      "|    learning_rate        | 0.00025     |\n",
      "|    loss                 | 16.2        |\n",
      "|    n_updates            | 480         |\n",
      "|    policy_gradient_loss | -0.00922    |\n",
      "|    std                  | 1.08        |\n",
      "|    value_loss           | 50.8        |\n",
      "-----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "trained_ppo = agent.train_model(model=model_ppo, \n",
    "                             tb_log_name='ppo',\n",
    "                             total_timesteps=100000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3Zpv4S0-fDBv"
   },
   "source": [
    "### Model 4: TD3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JSAHhV4Xc-bh",
    "outputId": "e531db14-aab4-47d1-cc15-02c893ec66c9"
   },
   "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": "ddc4193c-884b-4a2c-9e49-31397e2cfbec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logging to tensorboard_log/td3/td3_2\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 4        |\n",
      "|    fps             | 33       |\n",
      "|    time_elapsed    | 296      |\n",
      "|    total timesteps | 10064    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | 67.9     |\n",
      "|    critic_loss     | 979      |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    n_updates       | 7548     |\n",
      "---------------------------------\n",
      "day: 2515, episode: 10\n",
      "begin_total_asset:1000000.00\n",
      "end_total_asset:4438572.29\n",
      "total_reward:3438572.29\n",
      "total_cost: 1038.05\n",
      "total_trades: 40290\n",
      "Sharpe: 1.049\n",
      "=================================\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 8        |\n",
      "|    fps             | 30       |\n",
      "|    time_elapsed    | 669      |\n",
      "|    total timesteps | 20128    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | 54       |\n",
      "|    critic_loss     | 199      |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    n_updates       | 17612    |\n",
      "---------------------------------\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 12       |\n",
      "|    fps             | 28       |\n",
      "|    time_elapsed    | 1052     |\n",
      "|    total timesteps | 30192    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | 41.4     |\n",
      "|    critic_loss     | 25.2     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    n_updates       | 27676    |\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": [
    "### Model 5: SAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xwOhVjqRkCdM",
    "outputId": "5ad99882-367d-49ce-d83e-124396074c12"
   },
   "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": {
     "background_save": true,
     "base_uri": "https://localhost:8080/"
    },
    "id": "K8RSdKCckJyH",
    "outputId": "8dfca8da-65ea-4e61-f7c7-16094ea00cc0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logging to tensorboard_log/sac/sac_8\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 4        |\n",
      "|    fps             | 27       |\n",
      "|    time_elapsed    | 372      |\n",
      "|    total timesteps | 10064    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | 1.76e+03 |\n",
      "|    critic_loss     | 1.53e+03 |\n",
      "|    ent_coef        | 0.243    |\n",
      "|    ent_coef_loss   | 169      |\n",
      "|    learning_rate   | 0.0001   |\n",
      "|    n_updates       | 9963     |\n",
      "---------------------------------\n",
      "day: 2515, episode: 220\n",
      "begin_total_asset:1000000.00\n",
      "end_total_asset:4405540.86\n",
      "total_reward:3405540.86\n",
      "total_cost: 74774.48\n",
      "total_trades: 56475\n",
      "Sharpe: 0.954\n",
      "=================================\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 8        |\n",
      "|    fps             | 26       |\n",
      "|    time_elapsed    | 749      |\n",
      "|    total timesteps | 20128    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | 975      |\n",
      "|    critic_loss     | 480      |\n",
      "|    ent_coef        | 0.121    |\n",
      "|    ent_coef_loss   | -91.8    |\n",
      "|    learning_rate   | 0.0001   |\n",
      "|    n_updates       | 20027    |\n",
      "---------------------------------\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 12       |\n",
      "|    fps             | 26       |\n",
      "|    time_elapsed    | 1132     |\n",
      "|    total timesteps | 30192    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | 574      |\n",
      "|    critic_loss     | 4.49e+03 |\n",
      "|    ent_coef        | 0.0453   |\n",
      "|    ent_coef_loss   | -103     |\n",
      "|    learning_rate   | 0.0001   |\n",
      "|    n_updates       | 30091    |\n",
      "---------------------------------\n",
      "day: 2515, episode: 230\n",
      "begin_total_asset:1000000.00\n",
      "end_total_asset:4828464.65\n",
      "total_reward:3828464.65\n",
      "total_cost: 2997.76\n",
      "total_trades: 39564\n",
      "Sharpe: 0.994\n",
      "=================================\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 16       |\n",
      "|    fps             | 26       |\n",
      "|    time_elapsed    | 1517     |\n",
      "|    total timesteps | 40256    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | 348      |\n",
      "|    critic_loss     | 23.1     |\n",
      "|    ent_coef        | 0.0173   |\n",
      "|    ent_coef_loss   | -87.4    |\n",
      "|    learning_rate   | 0.0001   |\n",
      "|    n_updates       | 40155    |\n",
      "---------------------------------\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 20       |\n",
      "|    fps             | 26       |\n",
      "|    time_elapsed    | 1903     |\n",
      "|    total timesteps | 50320    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | 205      |\n",
      "|    critic_loss     | 10.6     |\n",
      "|    ent_coef        | 0.00687  |\n",
      "|    ent_coef_loss   | -45.2    |\n",
      "|    learning_rate   | 0.0001   |\n",
      "|    n_updates       | 50219    |\n",
      "---------------------------------\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 24       |\n",
      "|    fps             | 26       |\n",
      "|    time_elapsed    | 2291     |\n",
      "|    total timesteps | 60384    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | 127      |\n",
      "|    critic_loss     | 9.65     |\n",
      "|    ent_coef        | 0.00328  |\n",
      "|    ent_coef_loss   | -0.401   |\n",
      "|    learning_rate   | 0.0001   |\n",
      "|    n_updates       | 60283    |\n",
      "---------------------------------\n",
      "day: 2515, episode: 240\n",
      "begin_total_asset:1000000.00\n",
      "end_total_asset:5207375.51\n",
      "total_reward:4207375.51\n",
      "total_cost: 1768.49\n",
      "total_trades: 38369\n",
      "Sharpe: 1.077\n",
      "=================================\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 28       |\n",
      "|    fps             | 26       |\n",
      "|    time_elapsed    | 2687     |\n",
      "|    total timesteps | 70448    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | 86.5     |\n",
      "|    critic_loss     | 9.16     |\n",
      "|    ent_coef        | 0.00253  |\n",
      "|    ent_coef_loss   | 1.39     |\n",
      "|    learning_rate   | 0.0001   |\n",
      "|    n_updates       | 70347    |\n",
      "---------------------------------\n"
     ]
    }
   ],
   "source": [
    "trained_sac = agent.train_model(model=model_sac, \n",
    "                             tb_log_name='sac',\n",
    "                             total_timesteps=80000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f2wZgkQXh1jE"
   },
   "source": [
    "## Trading\n",
    "Assume that we have $1,000,000 initial capital at 2019-01-01. We use the DDPG model to trade Dow jones 30 stocks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "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_turbulence = processed_full[(processed_full.date<'2019-01-01') & (processed_full.date>='2009-01-01')]\n",
    "insample_turbulence = data_turbulence.drop_duplicates(subset=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VHZMBpSqh1jG",
    "outputId": "f750f515-9f4f-4adb-846e-ea0bdf15ea6b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2516.000000\n",
       "mean       33.277069\n",
       "std        33.988999\n",
       "min         0.000000\n",
       "25%        15.233886\n",
       "50%        25.180900\n",
       "75%        39.290836\n",
       "max       332.062743\n",
       "Name: turbulence, dtype: float64"
      ]
     },
     "execution_count": 199,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insample_turbulence.turbulence.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yuwDPkV9h1jL"
   },
   "outputs": [],
   "source": [
    "turbulence_threshold = np.quantile(insample_turbulence.turbulence.values,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wwoz_7VSh1jO",
    "outputId": "37894e93-d22e-4e3f-f23a-d3ca08bf8342"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "332.06274290226577"
      ]
     },
     "execution_count": 216,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turbulence_threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U5mmgQF_h1jQ"
   },
   "source": [
    "### Trade\n",
    "\n",
    "DRL model needs to update periodically in order to take full advantage of the data, ideally we need to retrain our model yearly, quarterly, or monthly. We also need to tune the parameters along the way, in this notebook I only use the in-sample data from 2009-01 to 2018-12 to tune the parameters once, so there is some alpha decay here as the length of trade date extends. \n",
    "\n",
    "Numerous hyperparameters – e.g. the learning rate, the total number of samples to train on – influence the learning process and are usually determined by testing some variations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "id": "eLOnL5eYh1jR"
   },
   "outputs": [],
   "source": [
    "trade = data_split(processed_full, '2019-01-01','2021-01-01')\n",
    "e_trade_gym = StockTradingEnv(df = trade, turbulence_threshold = 380, **env_kwargs)\n",
    "env_trade, obs_trade = e_trade_gym.get_sb_env()\n",
    "\n",
    "df_account_value, df_actions = DRLAgent.DRL_prediction(model=trained_sac,\n",
    "                        test_data = trade,\n",
    "                        test_env = env_trade,\n",
    "                        test_obs = obs_trade)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ERxw3KqLkcP4",
    "outputId": "cbb465c9-38dc-4d88-e79a-6ae29025164b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(505, 2)"
      ]
     },
     "execution_count": 218,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_account_value.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 194
    },
    "id": "2yRkNguY5yvp",
    "outputId": "53ec139f-88e7-4291-cf11-8e6766184265"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>account_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>1.000000e+06</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>9.956678e+05</td>\n",
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       "      <th>2</th>\n",
       "      <td>2019-01-04</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>1.006531e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-08</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  account_value\n",
       "0  2019-01-02   1.000000e+06\n",
       "1  2019-01-03   9.956678e+05\n",
       "2  2019-01-04   1.004994e+06\n",
       "3  2019-01-07   1.006531e+06\n",
       "4  2019-01-08   1.015034e+06"
      ]
     },
     "execution_count": 219,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_account_value.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>CVX</th>\n",
       "      <th>DD</th>\n",
       "      <th>DIS</th>\n",
       "      <th>GS</th>\n",
       "      <th>HD</th>\n",
       "      <th>IBM</th>\n",
       "      <th>INTC</th>\n",
       "      <th>JNJ</th>\n",
       "      <th>JPM</th>\n",
       "      <th>KO</th>\n",
       "      <th>MCD</th>\n",
       "      <th>MMM</th>\n",
       "      <th>MRK</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>NKE</th>\n",
       "      <th>PFE</th>\n",
       "      <th>PG</th>\n",
       "      <th>RTX</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>V</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>95.222878</td>\n",
       "      <td>-84.559822</td>\n",
       "      <td>97.749985</td>\n",
       "      <td>97.763374</td>\n",
       "      <td>-87.515053</td>\n",
       "      <td>-99.458527</td>\n",
       "      <td>96.835724</td>\n",
       "      <td>90.078880</td>\n",
       "      <td>-93.829109</td>\n",
       "      <td>72.826408</td>\n",
       "      <td>98.866104</td>\n",
       "      <td>38.124157</td>\n",
       "      <td>-93.015900</td>\n",
       "      <td>92.972443</td>\n",
       "      <td>53.967762</td>\n",
       "      <td>-96.257362</td>\n",
       "      <td>-92.545105</td>\n",
       "      <td>-96.774742</td>\n",
       "      <td>-91.884949</td>\n",
       "      <td>31.690144</td>\n",
       "      <td>44.689381</td>\n",
       "      <td>89.037033</td>\n",
       "      <td>-88.882904</td>\n",
       "      <td>93.311623</td>\n",
       "      <td>97.080673</td>\n",
       "      <td>-88.938042</td>\n",
       "      <td>30.129683</td>\n",
       "      <td>-57.037388</td>\n",
       "      <td>-94.728508</td>\n",
       "      <td>-89.945351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>84.561661</td>\n",
       "      <td>-57.813274</td>\n",
       "      <td>92.667747</td>\n",
       "      <td>93.314674</td>\n",
       "      <td>-95.521545</td>\n",
       "      <td>-98.473625</td>\n",
       "      <td>96.680275</td>\n",
       "      <td>75.064453</td>\n",
       "      <td>-78.636749</td>\n",
       "      <td>98.583076</td>\n",
       "      <td>97.697639</td>\n",
       "      <td>-86.169891</td>\n",
       "      <td>-64.665230</td>\n",
       "      <td>79.482666</td>\n",
       "      <td>90.556229</td>\n",
       "      <td>-88.396980</td>\n",
       "      <td>-97.012894</td>\n",
       "      <td>8.799195</td>\n",
       "      <td>-84.323334</td>\n",
       "      <td>79.341576</td>\n",
       "      <td>-44.265259</td>\n",
       "      <td>95.109200</td>\n",
       "      <td>-99.220215</td>\n",
       "      <td>48.526024</td>\n",
       "      <td>56.340240</td>\n",
       "      <td>-95.006638</td>\n",
       "      <td>75.963913</td>\n",
       "      <td>-88.629875</td>\n",
       "      <td>-95.672897</td>\n",
       "      <td>-90.759430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>84.307251</td>\n",
       "      <td>-81.543991</td>\n",
       "      <td>98.309349</td>\n",
       "      <td>98.947021</td>\n",
       "      <td>-95.404869</td>\n",
       "      <td>-97.587181</td>\n",
       "      <td>96.812141</td>\n",
       "      <td>70.461342</td>\n",
       "      <td>-74.659348</td>\n",
       "      <td>91.952255</td>\n",
       "      <td>98.680473</td>\n",
       "      <td>-84.412781</td>\n",
       "      <td>-92.700592</td>\n",
       "      <td>75.787292</td>\n",
       "      <td>79.312424</td>\n",
       "      <td>-93.347343</td>\n",
       "      <td>-93.876938</td>\n",
       "      <td>25.555515</td>\n",
       "      <td>-88.040054</td>\n",
       "      <td>98.596786</td>\n",
       "      <td>87.320900</td>\n",
       "      <td>95.363853</td>\n",
       "      <td>-89.884407</td>\n",
       "      <td>93.896423</td>\n",
       "      <td>32.470451</td>\n",
       "      <td>-99.463394</td>\n",
       "      <td>88.486122</td>\n",
       "      <td>-85.763313</td>\n",
       "      <td>-98.706146</td>\n",
       "      <td>-80.587807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>95.804977</td>\n",
       "      <td>-98.475685</td>\n",
       "      <td>99.250076</td>\n",
       "      <td>98.576164</td>\n",
       "      <td>-95.005562</td>\n",
       "      <td>-93.486473</td>\n",
       "      <td>83.131981</td>\n",
       "      <td>84.942986</td>\n",
       "      <td>-79.091629</td>\n",
       "      <td>93.921188</td>\n",
       "      <td>99.603210</td>\n",
       "      <td>-77.012062</td>\n",
       "      <td>-72.247765</td>\n",
       "      <td>84.351852</td>\n",
       "      <td>94.593575</td>\n",
       "      <td>-96.556320</td>\n",
       "      <td>-99.383171</td>\n",
       "      <td>37.927414</td>\n",
       "      <td>-93.105026</td>\n",
       "      <td>62.268997</td>\n",
       "      <td>80.980095</td>\n",
       "      <td>95.859444</td>\n",
       "      <td>-89.581955</td>\n",
       "      <td>77.660110</td>\n",
       "      <td>73.498856</td>\n",
       "      <td>-72.815468</td>\n",
       "      <td>89.848091</td>\n",
       "      <td>-79.640350</td>\n",
       "      <td>-95.003166</td>\n",
       "      <td>-91.969521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>50.825321</td>\n",
       "      <td>-86.742165</td>\n",
       "      <td>95.236656</td>\n",
       "      <td>95.626114</td>\n",
       "      <td>-91.030098</td>\n",
       "      <td>-79.496910</td>\n",
       "      <td>99.352173</td>\n",
       "      <td>66.041161</td>\n",
       "      <td>14.676512</td>\n",
       "      <td>97.160789</td>\n",
       "      <td>99.611877</td>\n",
       "      <td>-13.648521</td>\n",
       "      <td>-94.209785</td>\n",
       "      <td>76.994827</td>\n",
       "      <td>64.870956</td>\n",
       "      <td>-98.375610</td>\n",
       "      <td>-95.546661</td>\n",
       "      <td>57.064533</td>\n",
       "      <td>-87.114822</td>\n",
       "      <td>24.467575</td>\n",
       "      <td>86.504745</td>\n",
       "      <td>69.436409</td>\n",
       "      <td>-95.334755</td>\n",
       "      <td>17.407692</td>\n",
       "      <td>35.462414</td>\n",
       "      <td>-97.937355</td>\n",
       "      <td>82.119667</td>\n",
       "      <td>-79.810303</td>\n",
       "      <td>-99.528976</td>\n",
       "      <td>-48.367775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AAPL        AXP         BA  ...        WBA        WMT        XOM\n",
       "date                                         ...                                 \n",
       "2019-01-02  95.222878 -84.559822  97.749985  ... -57.037388 -94.728508 -89.945351\n",
       "2019-01-03  84.561661 -57.813274  92.667747  ... -88.629875 -95.672897 -90.759430\n",
       "2019-01-04  84.307251 -81.543991  98.309349  ... -85.763313 -98.706146 -80.587807\n",
       "2019-01-07  95.804977 -98.475685  99.250076  ... -79.640350 -95.003166 -91.969521\n",
       "2019-01-08  50.825321 -86.742165  95.236656  ... -79.810303 -99.528976 -48.367775\n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W6vvNSC6h1jZ"
   },
   "source": [
    "<a id='6'></a>\n",
    "# Part 7: Backtest Our Strategy\n",
    "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lr2zX7ZxNyFQ"
   },
   "source": [
    "<a id='6.1'></a>\n",
    "## 7.1 BackTestStats\n",
    "pass in df_account_value, this information is stored in env class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nzkr9yv-AdV_",
    "outputId": "1053083a-d74c-48b0-a623-de33282e2fff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Get Backtest Results===========\n",
      "annual return:  23.588340141653006\n",
      "sharpe ratio:  1.0074093133078277\n",
      "Annual return          0.208323\n",
      "Cumulative returns     0.461140\n",
      "Annual volatility      0.210317\n",
      "Sharpe ratio           1.007409\n",
      "Calmar ratio           0.869951\n",
      "Stability              0.419595\n",
      "Max drawdown          -0.239465\n",
      "Omega ratio            1.212383\n",
      "Sortino ratio          1.426763\n",
      "Skew                        NaN\n",
      "Kurtosis                    NaN\n",
      "Tail ratio             1.034439\n",
      "Daily value at risk   -0.025657\n",
      "Alpha                  0.000000\n",
      "Beta                   1.000000\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 = BackTestStats(account_value=df_account_value)\n",
    "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
    "perf_stats_all.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9U6Suru3h1jc"
   },
   "source": [
    "<a id='6.2'></a>\n",
    "## 7.2 BackTestPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "lKRGftSS7pNM",
    "outputId": "4f77cef2-3934-444a-cacc-4ed8f94514ae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Compare to DJIA===========\n",
      "annual return:  23.588340141653006\n",
      "sharpe ratio:  1.0074093133078277\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (505, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2019-01-03</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-12-31</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>24</td></tr>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Backtest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Annual return</th>\n",
       "      <td>20.878%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>46.114%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>21.032%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-23.946%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>1.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>-0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>7.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-2.566%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha</th>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Worst drawdown periods</th>\n",
       "      <th>Net drawdown in %</th>\n",
       "      <th>Peak date</th>\n",
       "      <th>Valley date</th>\n",
       "      <th>Recovery date</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.95</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>2020-06-05</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.30</td>\n",
       "      <td>2020-06-08</td>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>2020-08-10</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.42</td>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>2019-08-14</td>\n",
       "      <td>2019-09-11</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.79</td>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>2020-09-24</td>\n",
       "      <td>2020-10-12</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.93</td>\n",
       "      <td>2020-10-12</td>\n",
       "      <td>2020-10-28</td>\n",
       "      <td>2020-11-09</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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      ]
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/pyfolio/tears.py:907: UserWarning: Passed returns do not overlap with anyinteresting times.\n",
      "  'interesting times.', UserWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x5184 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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",
    "BackTestPlot(df_account_value, \n",
    "             baseline_ticker = '^DJI', \n",
    "             baseline_start = '2019-01-01',\n",
    "             baseline_end = '2021-01-01')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SlLT9_5WN478"
   },
   "source": [
    "<a id='6.3'></a>\n",
    "## 7.3 Baseline Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YktexHcqh1jc",
    "outputId": "38566531-a3a0-4705-db30-d437e8f8fc73"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Get Baseline Stats===========\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (505, 7)\n",
      "Annual return          0.144674\n",
      "Cumulative returns     0.310981\n",
      "Annual volatility      0.274619\n",
      "Sharpe ratio           0.631418\n",
      "Calmar ratio           0.390102\n",
      "Stability              0.116677\n",
      "Max drawdown          -0.370862\n",
      "Omega ratio            1.149365\n",
      "Sortino ratio          0.870084\n",
      "Skew                        NaN\n",
      "Kurtosis                    NaN\n",
      "Tail ratio             0.860710\n",
      "Daily value at risk   -0.033911\n",
      "Alpha                  0.000000\n",
      "Beta                   1.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"==============Get Baseline Stats===========\")\n",
    "baesline_perf_stats=BaselineStats('^DJI',\n",
    "                                  baseline_start = '2019-01-01',\n",
    "                                  baseline_end = '2021-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "A6W2J57ch1j9"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "uijiWgkuh1jB",
    "MRiOtrywfAo1",
    "_gDkU-j-fCmZ",
    "3Zpv4S0-fDBv"
   ],
   "include_colab_link": true,
   "name": "FinRL_multiple_stock_trading.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}