Algorithms/Train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/opt/anaconda3/lib/python3.9/site-packages/pyfolio/pos.py:26: UserWarning: Module \"zipline.assets\" not found; mutltipliers will not be applied to position notionals.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "# matplotlib.use('Agg')\n",
    "import datetime\n",
    "\n",
    "%matplotlib inline\n",
    "from finrl.meta.preprocessor.yahoodownloader import YahooDownloader\n",
    "from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
    "from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
    "from finrl.agents.stablebaselines3.models import DRLAgent\n",
    "from stable_baselines3.common.logger import configure\n",
    "from finrl.meta.data_processor import DataProcessor\n",
    "\n",
    "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
    "from pprint import pprint\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"../FinRL\")\n",
    "\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from finrl import config\n",
    "from finrl import config_tickers\n",
    "import os\n",
    "from finrl.main import check_and_make_directories\n",
    "from finrl.config import (\n",
    "    DATA_SAVE_DIR,\n",
    "    TRAINED_MODEL_DIR,\n",
    "    TENSORBOARD_LOG_DIR,\n",
    "    RESULTS_DIR,\n",
    "    INDICATORS,\n",
    "    TRAIN_START_DATE,\n",
    "    TRAIN_END_DATE,\n",
    "    TEST_START_DATE,\n",
    "    TEST_END_DATE,\n",
    "    TRADE_START_DATE,\n",
    "    TRADE_END_DATE,\n",
    ")\n",
    "check_and_make_directories([DATA_SAVE_DIR, TRAINED_MODEL_DIR, TENSORBOARD_LOG_DIR, RESULTS_DIR])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-07-31'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from config.py, TRAIN_START_DATE is a string\n",
    "TRAIN_START_DATE\n",
    "# from config.py, TRAIN_END_DATE is a string\n",
    "TRAIN_END_DATE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. GET data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAIN_START_DATE = '2009-01-01'\n",
    "TRAIN_END_DATE = '2020-07-01'\n",
    "TRADE_START_DATE = '2020-07-01'\n",
    "TRADE_END_DATE = '2021-10-31'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished AMGN\n",
      "[*********************100%***********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "finished AAPL\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished AMAT\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished INTC\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished PCAR\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished PAYX\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished MSFT\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished ADBE\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished CSCO\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished SGEN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished QCOM\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished COST\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished SBUX\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished FISV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished CTXS\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished INTU\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished AMZN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished EBAY\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished BIIB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished CHKP\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished GILD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished NLOK\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished CMCSA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished FAST\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished ADSK\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished CTSH\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished NVDA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished GOOGL\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished ISRG\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished VRTX\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished HSIC\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished BIDU\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished ATVI\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished ADP\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished ROST\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished ORLY\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished FTNT\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished BKNG\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished KDP\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished MU\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished DLTR\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished LCID\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished SIRI\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished MNST\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished AVGO\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished TXN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished MDLZ\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished META\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished ADI\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished WDC\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished REGN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished LBTYK\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished VRSK\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished NFLX\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished TSLA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished CHTR\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished MAR\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished ILMN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished LRCX\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished EA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished AAL\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished WBA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished KHC\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished BMRN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished JD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished SWKS\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished INCY\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished PYPL\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished CDW\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished FOXA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished PDD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished TMUS\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished EXPE\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished TCOM\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished ULTA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished CSX\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished NTES\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished MCHP\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished CTAS\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished KLAC\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished HAS\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished JBHT\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished IDXX\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished WYNN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished MELI\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished ALGN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished CDNS\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished WDAY\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished SNPS\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished ASML\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished TTWO\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished PEP\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished NXPI\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished XEL\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished AMD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished NTAP\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished VRSN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished LULU\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished SPLK\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished UAL\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of DataFrame:  (305102, 8)\n"
     ]
    }
   ],
   "source": [
    "df = YahooDownloader(start_date = TRAIN_START_DATE,\n",
    "                     end_date = TRADE_END_DATE,\n",
    "                     ticker_list = config_tickers.NAS_100_TICKER).fetch_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AMGN', 'AAPL', 'AMAT', 'INTC', 'PCAR', 'PAYX', 'MSFT', 'ADBE', 'CSCO', 'SGEN', 'QCOM', 'COST', 'SBUX', 'FISV', 'CTXS', 'INTU', 'AMZN', 'EBAY', 'BIIB', 'CHKP', 'GILD', 'NLOK', 'CMCSA', 'FAST', 'ADSK', 'CTSH', 'NVDA', 'GOOGL', 'ISRG', 'VRTX', 'HSIC', 'BIDU', 'ATVI', 'ADP', 'ROST', 'ORLY', 'FTNT', 'BKNG', 'KDP', 'MU', 'DLTR', 'LCID', 'SIRI', 'MNST', 'AVGO', 'TXN', 'MDLZ', 'META', 'ADI', 'WDC', 'REGN', 'LBTYK', 'VRSK', 'NFLX', 'TSLA', 'CHTR', 'MAR', 'ILMN', 'LRCX', 'EA', 'AAL', 'WBA', 'KHC', 'BMRN', 'JD', 'SWKS', 'INCY', 'PYPL', 'CDW', 'FOXA', 'PDD', 'TMUS', 'EXPE', 'TCOM', 'ULTA', 'CSX', 'NTES', 'MCHP', 'CTAS', 'KLAC', 'HAS', 'JBHT', 'IDXX', 'WYNN', 'MELI', 'ALGN', 'CDNS', 'WDAY', 'SNPS', 'ASML', 'TTWO', 'PEP', 'NXPI', 'XEL', 'AMD', 'NTAP', 'VRSN', 'LULU', 'SPLK', 'UAL']\n"
     ]
    }
   ],
   "source": [
    "print(config_tickers.NAS_100_TICKER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305102, 8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>tic</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>7.730000</td>\n",
       "      <td>8.480000</td>\n",
       "      <td>7.670000</td>\n",
       "      <td>7.909601</td>\n",
       "      <td>5167000</td>\n",
       "      <td>AAL</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>3.067143</td>\n",
       "      <td>3.251429</td>\n",
       "      <td>3.041429</td>\n",
       "      <td>2.767330</td>\n",
       "      <td>746015200</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>21.110001</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>21.070000</td>\n",
       "      <td>23.020000</td>\n",
       "      <td>6670700</td>\n",
       "      <td>ADBE</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.780001</td>\n",
       "      <td>18.760000</td>\n",
       "      <td>13.920574</td>\n",
       "      <td>3264900</td>\n",
       "      <td>ADI</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>34.784901</td>\n",
       "      <td>35.548725</td>\n",
       "      <td>34.205444</td>\n",
       "      <td>25.174133</td>\n",
       "      <td>4021809</td>\n",
       "      <td>ADP</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date       open       high        low      close     volume   tic  \\\n",
       "0  2009-01-02   7.730000   8.480000   7.670000   7.909601    5167000   AAL   \n",
       "1  2009-01-02   3.067143   3.251429   3.041429   2.767330  746015200  AAPL   \n",
       "2  2009-01-02  21.110001  23.100000  21.070000  23.020000    6670700  ADBE   \n",
       "3  2009-01-02  19.000000  19.780001  18.760000  13.920574    3264900   ADI   \n",
       "4  2009-01-02  34.784901  35.548725  34.205444  25.174133    4021809   ADP   \n",
       "\n",
       "   day  \n",
       "0    4  \n",
       "1    4  \n",
       "2    4  \n",
       "3    4  \n",
       "4    4  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(['date','tic'],ignore_index=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "print(len(df.tic.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Preprocess Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/preprocessors.py:158: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  indicator_df = indicator_df.append(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully added technical indicators\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished ^VIX\n",
      "Shape of DataFrame:  (3229, 8)\n",
      "Successfully added vix\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully added turbulence index\n"
     ]
    }
   ],
   "source": [
    "fe = FeatureEngineer(\n",
    "                    use_technical_indicator=True,\n",
    "                    tech_indicator_list = INDICATORS,\n",
    "                    use_vix=True,\n",
    "                    use_turbulence=True,\n",
    "                    user_defined_feature = False)\n",
    "\n",
    "processed = fe.preprocess_data(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "print(len(processed[\"tic\"].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>tic</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>day</th>\n",
       "      <th>macd</th>\n",
       "      <th>boll_ub</th>\n",
       "      <th>boll_lb</th>\n",
       "      <th>rsi_30</th>\n",
       "      <th>cci_30</th>\n",
       "      <th>dx_30</th>\n",
       "      <th>close_30_sma</th>\n",
       "      <th>close_60_sma</th>\n",
       "      <th>vix</th>\n",
       "      <th>turbulence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>AAL</td>\n",
       "      <td>7.730000</td>\n",
       "      <td>8.480000</td>\n",
       "      <td>7.670000</td>\n",
       "      <td>7.909601</td>\n",
       "      <td>5167000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.961314</td>\n",
       "      <td>7.801324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.909601</td>\n",
       "      <td>7.909601</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>3.067143</td>\n",
       "      <td>3.251429</td>\n",
       "      <td>3.041429</td>\n",
       "      <td>2.767330</td>\n",
       "      <td>746015200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.961314</td>\n",
       "      <td>7.801324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.767330</td>\n",
       "      <td>2.767330</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>ADBE</td>\n",
       "      <td>21.110001</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>21.070000</td>\n",
       "      <td>23.020000</td>\n",
       "      <td>6670700.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.961314</td>\n",
       "      <td>7.801324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>23.020000</td>\n",
       "      <td>23.020000</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>ADI</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.780001</td>\n",
       "      <td>18.760000</td>\n",
       "      <td>13.920574</td>\n",
       "      <td>3264900.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.961314</td>\n",
       "      <td>7.801324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>13.920574</td>\n",
       "      <td>13.920574</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>ADP</td>\n",
       "      <td>34.784901</td>\n",
       "      <td>35.548725</td>\n",
       "      <td>34.205444</td>\n",
       "      <td>25.174133</td>\n",
       "      <td>4021809.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.961314</td>\n",
       "      <td>7.801324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25.174133</td>\n",
       "      <td>25.174133</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>ADSK</td>\n",
       "      <td>19.549999</td>\n",
       "      <td>20.760000</td>\n",
       "      <td>19.549999</td>\n",
       "      <td>20.680000</td>\n",
       "      <td>2845600.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.961314</td>\n",
       "      <td>7.801324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>20.680000</td>\n",
       "      <td>20.680000</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>ALGN</td>\n",
       "      <td>8.520000</td>\n",
       "      <td>9.060000</td>\n",
       "      <td>8.520000</td>\n",
       "      <td>8.890000</td>\n",
       "      <td>538900.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.961314</td>\n",
       "      <td>7.801324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>8.890000</td>\n",
       "      <td>8.890000</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>AMAT</td>\n",
       "      <td>10.120000</td>\n",
       "      <td>10.720000</td>\n",
       "      <td>10.010000</td>\n",
       "      <td>8.303118</td>\n",
       "      <td>10074600.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.961314</td>\n",
       "      <td>7.801324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>8.303118</td>\n",
       "      <td>8.303118</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>AMD</td>\n",
       "      <td>2.190000</td>\n",
       "      <td>2.430000</td>\n",
       "      <td>2.170000</td>\n",
       "      <td>2.380000</td>\n",
       "      <td>13832100.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.961314</td>\n",
       "      <td>7.801324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.380000</td>\n",
       "      <td>2.380000</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>AMGN</td>\n",
       "      <td>58.590000</td>\n",
       "      <td>59.080002</td>\n",
       "      <td>57.750000</td>\n",
       "      <td>44.523758</td>\n",
       "      <td>6547900.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.961314</td>\n",
       "      <td>7.801324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>44.523758</td>\n",
       "      <td>44.523758</td>\n",
       "      <td>39.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   tic       open       high        low      close       volume  \\\n",
       "0  2009-01-02   AAL   7.730000   8.480000   7.670000   7.909601    5167000.0   \n",
       "1  2009-01-02  AAPL   3.067143   3.251429   3.041429   2.767330  746015200.0   \n",
       "2  2009-01-02  ADBE  21.110001  23.100000  21.070000  23.020000    6670700.0   \n",
       "3  2009-01-02   ADI  19.000000  19.780001  18.760000  13.920574    3264900.0   \n",
       "4  2009-01-02   ADP  34.784901  35.548725  34.205444  25.174133    4021809.0   \n",
       "5  2009-01-02  ADSK  19.549999  20.760000  19.549999  20.680000    2845600.0   \n",
       "6  2009-01-02  ALGN   8.520000   9.060000   8.520000   8.890000     538900.0   \n",
       "7  2009-01-02  AMAT  10.120000  10.720000  10.010000   8.303118   10074600.0   \n",
       "8  2009-01-02   AMD   2.190000   2.430000   2.170000   2.380000   13832100.0   \n",
       "9  2009-01-02  AMGN  58.590000  59.080002  57.750000  44.523758    6547900.0   \n",
       "\n",
       "   day  macd   boll_ub   boll_lb  rsi_30     cci_30  dx_30  close_30_sma  \\\n",
       "0  4.0   0.0  7.961314  7.801324     0.0  66.666667  100.0      7.909601   \n",
       "1  4.0   0.0  7.961314  7.801324     0.0  66.666667  100.0      2.767330   \n",
       "2  4.0   0.0  7.961314  7.801324     0.0  66.666667  100.0     23.020000   \n",
       "3  4.0   0.0  7.961314  7.801324     0.0  66.666667  100.0     13.920574   \n",
       "4  4.0   0.0  7.961314  7.801324     0.0  66.666667  100.0     25.174133   \n",
       "5  4.0   0.0  7.961314  7.801324     0.0  66.666667  100.0     20.680000   \n",
       "6  4.0   0.0  7.961314  7.801324     0.0  66.666667  100.0      8.890000   \n",
       "7  4.0   0.0  7.961314  7.801324     0.0  66.666667  100.0      8.303118   \n",
       "8  4.0   0.0  7.961314  7.801324     0.0  66.666667  100.0      2.380000   \n",
       "9  4.0   0.0  7.961314  7.801324     0.0  66.666667  100.0     44.523758   \n",
       "\n",
       "   close_60_sma        vix  turbulence  \n",
       "0      7.909601  39.189999         0.0  \n",
       "1      2.767330  39.189999         0.0  \n",
       "2     23.020000  39.189999         0.0  \n",
       "3     13.920574  39.189999         0.0  \n",
       "4     25.174133  39.189999         0.0  \n",
       "5     20.680000  39.189999         0.0  \n",
       "6      8.890000  39.189999         0.0  \n",
       "7      8.303118  39.189999         0.0  \n",
       "8      2.380000  39.189999         0.0  \n",
       "9     44.523758  39.189999         0.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_full.sort_values(['date','tic'],ignore_index=True).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Build A Market Environment in OpenAI Gym-style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "289300\n",
      "33600\n"
     ]
    }
   ],
   "source": [
    "train = data_split(processed_full, TRAIN_START_DATE,TRAIN_END_DATE)\n",
    "trade = data_split(processed_full, TRADE_START_DATE,TRADE_END_DATE)\n",
    "print(len(train))\n",
    "print(len(trade))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th>date</th>\n",
       "      <th>tic</th>\n",
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       "      <th>boll_lb</th>\n",
       "      <th>rsi_30</th>\n",
       "      <th>cci_30</th>\n",
       "      <th>dx_30</th>\n",
       "      <th>close_30_sma</th>\n",
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       "      <th>2892</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>WBA</td>\n",
       "      <td>42.119999</td>\n",
       "      <td>42.580002</td>\n",
       "      <td>41.759998</td>\n",
       "      <td>38.577274</td>\n",
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       "      <td>36.058569</td>\n",
       "      <td>48.830181</td>\n",
       "      <td>-14.542137</td>\n",
       "      <td>1.500723</td>\n",
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       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>WDAY</td>\n",
       "      <td>186.179993</td>\n",
       "      <td>188.690002</td>\n",
       "      <td>185.020004</td>\n",
       "      <td>187.360001</td>\n",
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       "      <td>96.267627</td>\n",
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       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>WDC</td>\n",
       "      <td>43.220001</td>\n",
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       "      <td>-15.706761</td>\n",
       "      <td>6.421213</td>\n",
       "      <td>44.104666</td>\n",
       "      <td>43.083167</td>\n",
       "      <td>30.43</td>\n",
       "      <td>115.917386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>WYNN</td>\n",
       "      <td>73.570000</td>\n",
       "      <td>74.860001</td>\n",
       "      <td>72.120003</td>\n",
       "      <td>74.489998</td>\n",
       "      <td>4590400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-3.358382</td>\n",
       "      <td>109.820764</td>\n",
       "      <td>66.555236</td>\n",
       "      <td>45.028611</td>\n",
       "      <td>-128.071085</td>\n",
       "      <td>26.381041</td>\n",
       "      <td>86.764000</td>\n",
       "      <td>81.579667</td>\n",
       "      <td>30.43</td>\n",
       "      <td>115.917386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>XEL</td>\n",
       "      <td>62.660000</td>\n",
       "      <td>63.169998</td>\n",
       "      <td>62.250000</td>\n",
       "      <td>58.787342</td>\n",
       "      <td>4116800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.018074</td>\n",
       "      <td>62.933629</td>\n",
       "      <td>58.016337</td>\n",
       "      <td>49.015898</td>\n",
       "      <td>-40.847104</td>\n",
       "      <td>7.978643</td>\n",
       "      <td>59.755552</td>\n",
       "      <td>59.169271</td>\n",
       "      <td>30.43</td>\n",
       "      <td>115.917386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date   tic        open        high         low       close  \\\n",
       "2892  2020-06-30   WBA   42.119999   42.580002   41.759998   38.577274   \n",
       "2892  2020-06-30  WDAY  186.179993  188.690002  185.020004  187.360001   \n",
       "2892  2020-06-30   WDC   43.220001   44.310001   42.820000   44.150002   \n",
       "2892  2020-06-30  WYNN   73.570000   74.860001   72.120003   74.489998   \n",
       "2892  2020-06-30   XEL   62.660000   63.169998   62.250000   58.787342   \n",
       "\n",
       "         volume  day      macd     boll_ub     boll_lb     rsi_30      cci_30  \\\n",
       "2892  4782100.0  1.0 -0.082999   42.108878   36.058569  48.830181  -14.542137   \n",
       "2892  1929100.0  1.0  5.163632  190.785409  172.961589  57.951937   96.267627   \n",
       "2892  6924800.0  1.0 -0.295696   48.804006   39.911994  49.805174  -15.706761   \n",
       "2892  4590400.0  1.0 -3.358382  109.820764   66.555236  45.028611 -128.071085   \n",
       "2892  4116800.0  1.0 -0.018074   62.933629   58.016337  49.015898  -40.847104   \n",
       "\n",
       "          dx_30  close_30_sma  close_60_sma    vix  turbulence  \n",
       "2892   1.500723     38.675563     38.477853  30.43  115.917386  \n",
       "2892  14.442128    178.263666    163.655833  30.43  115.917386  \n",
       "2892   6.421213     44.104666     43.083167  30.43  115.917386  \n",
       "2892  26.381041     86.764000     81.579667  30.43  115.917386  \n",
       "2892   7.978643     59.755552     59.169271  30.43  115.917386  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>tic</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>day</th>\n",
       "      <th>macd</th>\n",
       "      <th>boll_ub</th>\n",
       "      <th>boll_lb</th>\n",
       "      <th>rsi_30</th>\n",
       "      <th>cci_30</th>\n",
       "      <th>dx_30</th>\n",
       "      <th>close_30_sma</th>\n",
       "      <th>close_60_sma</th>\n",
       "      <th>vix</th>\n",
       "      <th>turbulence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>AAL</td>\n",
       "      <td>13.480000</td>\n",
       "      <td>14.290000</td>\n",
       "      <td>12.630000</td>\n",
       "      <td>12.810000</td>\n",
       "      <td>116661700.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.016407</td>\n",
       "      <td>20.135254</td>\n",
       "      <td>11.086746</td>\n",
       "      <td>47.624796</td>\n",
       "      <td>-20.614394</td>\n",
       "      <td>5.717277</td>\n",
       "      <td>14.015333</td>\n",
       "      <td>12.287167</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>216.488637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>91.279999</td>\n",
       "      <td>91.839996</td>\n",
       "      <td>90.977501</td>\n",
       "      <td>89.779900</td>\n",
       "      <td>110737200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.010423</td>\n",
       "      <td>92.570735</td>\n",
       "      <td>80.068732</td>\n",
       "      <td>62.807150</td>\n",
       "      <td>107.494096</td>\n",
       "      <td>29.730532</td>\n",
       "      <td>83.817341</td>\n",
       "      <td>77.609739</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>216.488637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>ADBE</td>\n",
       "      <td>434.799988</td>\n",
       "      <td>442.049988</td>\n",
       "      <td>433.619995</td>\n",
       "      <td>439.809998</td>\n",
       "      <td>2581700.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.322330</td>\n",
       "      <td>453.002175</td>\n",
       "      <td>379.367829</td>\n",
       "      <td>62.646184</td>\n",
       "      <td>120.383181</td>\n",
       "      <td>29.868037</td>\n",
       "      <td>405.434336</td>\n",
       "      <td>376.088668</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>216.488637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>ADI</td>\n",
       "      <td>122.290001</td>\n",
       "      <td>122.419998</td>\n",
       "      <td>120.300003</td>\n",
       "      <td>115.601295</td>\n",
       "      <td>1587300.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.908520</td>\n",
       "      <td>120.817465</td>\n",
       "      <td>111.683555</td>\n",
       "      <td>54.658372</td>\n",
       "      <td>44.593547</td>\n",
       "      <td>20.817781</td>\n",
       "      <td>113.869423</td>\n",
       "      <td>106.687574</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>216.488637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>ADP</td>\n",
       "      <td>149.220001</td>\n",
       "      <td>150.449997</td>\n",
       "      <td>148.630005</td>\n",
       "      <td>142.569382</td>\n",
       "      <td>1381300.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.791761</td>\n",
       "      <td>152.470034</td>\n",
       "      <td>134.683512</td>\n",
       "      <td>51.721587</td>\n",
       "      <td>27.969733</td>\n",
       "      <td>1.842731</td>\n",
       "      <td>140.816518</td>\n",
       "      <td>137.127408</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>216.488637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   tic        open        high         low       close  \\\n",
       "0  2020-07-01   AAL   13.480000   14.290000   12.630000   12.810000   \n",
       "0  2020-07-01  AAPL   91.279999   91.839996   90.977501   89.779900   \n",
       "0  2020-07-01  ADBE  434.799988  442.049988  433.619995  439.809998   \n",
       "0  2020-07-01   ADI  122.290001  122.419998  120.300003  115.601295   \n",
       "0  2020-07-01   ADP  149.220001  150.449997  148.630005  142.569382   \n",
       "\n",
       "        volume  day       macd     boll_ub     boll_lb     rsi_30      cci_30  \\\n",
       "0  116661700.0  2.0  -0.016407   20.135254   11.086746  47.624796  -20.614394   \n",
       "0  110737200.0  2.0   3.010423   92.570735   80.068732  62.807150  107.494096   \n",
       "0    2581700.0  2.0  15.322330  453.002175  379.367829  62.646184  120.383181   \n",
       "0    1587300.0  2.0   1.908520  120.817465  111.683555  54.658372   44.593547   \n",
       "0    1381300.0  2.0   0.791761  152.470034  134.683512  51.721587   27.969733   \n",
       "\n",
       "       dx_30  close_30_sma  close_60_sma        vix  turbulence  \n",
       "0   5.717277     14.015333     12.287167  28.620001  216.488637  \n",
       "0  29.730532     83.817341     77.609739  28.620001  216.488637  \n",
       "0  29.868037    405.434336    376.088668  28.620001  216.488637  \n",
       "0  20.817781    113.869423    106.687574  28.620001  216.488637  \n",
       "0   1.842731    140.816518    137.127408  28.620001  216.488637  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['macd',\n",
       " 'boll_ub',\n",
       " 'boll_lb',\n",
       " 'rsi_30',\n",
       " 'cci_30',\n",
       " 'dx_30',\n",
       " 'close_30_sma',\n",
       " 'close_60_sma']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDICATORS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stock Dimension: 100, State Space: 1001\n"
     ]
    }
   ],
   "source": [
    "stock_dimension = len(train.tic.unique())\n",
    "state_space = 1 + 2*stock_dimension + len(INDICATORS)*stock_dimension\n",
    "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "buy_cost_list = sell_cost_list = [0.001] * stock_dimension\n",
    "num_stock_shares = [0] * stock_dimension\n",
    "\n",
    "env_kwargs = {\n",
    "    \"hmax\": 100,\n",
    "    \"initial_amount\": 1000000,\n",
    "    \"num_stock_shares\": num_stock_shares,\n",
    "    \"buy_cost_pct\": buy_cost_list,\n",
    "    \"sell_cost_pct\": sell_cost_list,\n",
    "    \"state_space\": state_space,\n",
    "    \"stock_dim\": stock_dimension,\n",
    "    \"tech_indicator_list\": INDICATORS,\n",
    "    \"action_space\": stock_dimension,\n",
    "    \"reward_scaling\": 1e-4\n",
    "}\n",
    "\n",
    "\n",
    "e_train_gym = StockTradingEnv(df = train, **env_kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Environment for Training and Train for DRL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "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": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent = DRLAgent(env = env_train)\n",
    "\n",
    "if_using_a2c = False\n",
    "if_using_ddpg = False\n",
    "if_using_ppo = False\n",
    "if_using_td3 = False\n",
    "if_using_sac = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agent Training: 5 algorithms (A2C, DDPG, PPO, TD3, SAC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agent 1: A2C\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent = DRLAgent(env = env_train)\n",
    "model_ddpg = agent.get_model(\"ddpg\")\n",
    "\n",
    "if if_using_ddpg:\n",
    "  # set up logger\n",
    "  tmp_path = RESULTS_DIR + '/ddpg'\n",
    "  new_logger_ddpg = configure(tmp_path, [\"stdout\", \"csv\", \"tensorboard\"])\n",
    "  # Set new logger\n",
    "  model_ddpg.set_logger(new_logger_ddpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trained_ddpg = agent.train_model(model=model_ddpg, \n",
    "                             tb_log_name='ddpg',\n",
    "                             total_timesteps=50000) if if_using_ddpg else None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agent 3: PPO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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)\n",
    "\n",
    "if if_using_ppo:\n",
    "  # set up logger\n",
    "  tmp_path = RESULTS_DIR + '/ppo'\n",
    "  new_logger_ppo = configure(tmp_path, [\"stdout\", \"csv\", \"tensorboard\"])\n",
    "  # Set new logger\n",
    "  model_ppo.set_logger(new_logger_ppo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trained_ppo = agent.train_model(model=model_ppo, \n",
    "                             tb_log_name='ppo',\n",
    "                             total_timesteps=50000) if if_using_ppo else None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agent 4: TD3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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)\n",
    "\n",
    "if if_using_td3:\n",
    "  # set up logger\n",
    "  tmp_path = RESULTS_DIR + '/td3'\n",
    "  new_logger_td3 = configure(tmp_path, [\"stdout\", \"csv\", \"tensorboard\"])\n",
    "  # Set new logger\n",
    "  model_td3.set_logger(new_logger_td3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trained_td3 = agent.train_model(model=model_td3, \n",
    "                             tb_log_name='td3',\n",
    "                             total_timesteps=30000) if if_using_td3 else None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agent 5: SAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'batch_size': 128, 'buffer_size': 100000, 'learning_rate': 0.0001, 'learning_starts': 100, 'ent_coef': 'auto_0.1'}\n",
      "Using cpu device\n",
      "Logging to results/sac\n"
     ]
    }
   ],
   "source": [
    "agent = DRLAgent(env = env_train)\n",
    "SAC_PARAMS = {\n",
    "    \"batch_size\": 128,\n",
    "    \"buffer_size\": 100000,\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)\n",
    "\n",
    "if if_using_sac:\n",
    "  # set up logger\n",
    "  tmp_path = RESULTS_DIR + '/sac'\n",
    "  new_logger_sac = configure(tmp_path, [\"stdout\", \"csv\", \"tensorboard\"])\n",
    "  # Set new logger\n",
    "  model_sac.set_logger(new_logger_sac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/env_stock_trading/env_stocktrading.py:178: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  available_amount = self.state[0] // (\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/env_stock_trading/env_stocktrading.py:178: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  available_amount = self.state[0] // (\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/env_stock_trading/env_stocktrading.py:178: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  available_amount = self.state[0] // (\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/env_stock_trading/env_stocktrading.py:178: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  available_amount = self.state[0] // (\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------\n",
      "| time/              |           |\n",
      "|    episodes        | 4         |\n",
      "|    fps             | 6         |\n",
      "|    time_elapsed    | 1709      |\n",
      "|    total_timesteps | 11572     |\n",
      "| train/             |           |\n",
      "|    actor_loss      | 81.1      |\n",
      "|    critic_loss     | 165       |\n",
      "|    ent_coef        | 0.0545    |\n",
      "|    ent_coef_loss   | -481      |\n",
      "|    learning_rate   | 0.0001    |\n",
      "|    n_updates       | 11471     |\n",
      "|    reward          | 26.251299 |\n",
      "----------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/env_stock_trading/env_stocktrading.py:178: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  available_amount = self.state[0] // (\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/env_stock_trading/env_stocktrading.py:178: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  available_amount = self.state[0] // (\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/env_stock_trading/env_stocktrading.py:178: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  available_amount = self.state[0] // (\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/env_stock_trading/env_stocktrading.py:178: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  available_amount = self.state[0] // (\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "day: 2892, episode: 10\n",
      "begin_total_asset: 1000000.00\n",
      "end_total_asset: 35264843.68\n",
      "total_reward: 34264843.68\n",
      "total_cost: 34957.13\n",
      "total_trades: 169977\n",
      "Sharpe: 0.932\n",
      "=================================\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 8        |\n",
      "|    fps             | 9        |\n",
      "|    time_elapsed    | 2396     |\n",
      "|    total_timesteps | 23144    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -145     |\n",
      "|    critic_loss     | 154      |\n",
      "|    ent_coef        | 0.0174   |\n",
      "|    ent_coef_loss   | -554     |\n",
      "|    learning_rate   | 0.0001   |\n",
      "|    n_updates       | 23043    |\n",
      "|    reward          | 90.76733 |\n",
      "---------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/env_stock_trading/env_stocktrading.py:178: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  available_amount = self.state[0] // (\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/env_stock_trading/env_stocktrading.py:178: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  available_amount = self.state[0] // (\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/env_stock_trading/env_stocktrading.py:178: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  available_amount = self.state[0] // (\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/env_stock_trading/env_stocktrading.py:178: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  available_amount = self.state[0] // (\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------\n",
      "| time/              |           |\n",
      "|    episodes        | 12        |\n",
      "|    fps             | 10        |\n",
      "|    time_elapsed    | 3170      |\n",
      "|    total_timesteps | 34716     |\n",
      "| train/             |           |\n",
      "|    actor_loss      | -160      |\n",
      "|    critic_loss     | 434       |\n",
      "|    ent_coef        | 0.00586   |\n",
      "|    ent_coef_loss   | -330      |\n",
      "|    learning_rate   | 0.0001    |\n",
      "|    n_updates       | 34615     |\n",
      "|    reward          | 144.39502 |\n",
      "----------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/env_stock_trading/env_stocktrading.py:178: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  available_amount = self.state[0] // (\n",
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/env_stock_trading/env_stocktrading.py:178: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  available_amount = self.state[0] // (\n"
     ]
    }
   ],
   "source": [
    "trained_sac = agent.train_model(model=model_sac, \n",
    "                             tb_log_name='sac',\n",
    "                             total_timesteps=40000) if if_using_sac else None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SAVE trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the trained model\n",
    "path_to_sacmodel_dir=\"trained_models/trained_sac_100_full/\"\n",
    "trained_sac.save(path_to_sacmodel_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Trading "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "outputs": [],
   "source": [
    "data_risk_indicator = processed_full[(processed_full.date<TRAIN_END_DATE) & (processed_full.date>=TRAIN_START_DATE)]\n",
    "insample_risk_indicator = data_risk_indicator.drop_duplicates(subset=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "insample_risk_indicator.vix.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "insample_risk_indicator.vix.quantile(0.996)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "insample_risk_indicator.turbulence.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "insample_risk_indicator.turbulence.quantile(0.996)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "e_trade_gym = StockTradingEnv(df = trade, turbulence_threshold = 70,risk_indicator_col='vix', **env_kwargs)\n",
    "# env_trade, obs_trade = e_trade_gym.get_sb_env()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>ADI</td>\n",
       "      <td>122.290001</td>\n",
       "      <td>122.419998</td>\n",
       "      <td>120.300003</td>\n",
       "      <td>115.601295</td>\n",
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       "      <td>150.449997</td>\n",
       "      <td>148.630005</td>\n",
       "      <td>142.569382</td>\n",
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       "      <td>134.683512</td>\n",
       "      <td>51.721587</td>\n",
       "      <td>27.969733</td>\n",
       "      <td>1.842731</td>\n",
       "      <td>140.816518</td>\n",
       "      <td>137.127408</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   tic        open        high         low       close  \\\n",
       "0  2020-07-01   AAL   13.480000   14.290000   12.630000   12.810000   \n",
       "0  2020-07-01  AAPL   91.279999   91.839996   90.977501   89.779900   \n",
       "0  2020-07-01  ADBE  434.799988  442.049988  433.619995  439.809998   \n",
       "0  2020-07-01   ADI  122.290001  122.419998  120.300003  115.601295   \n",
       "0  2020-07-01   ADP  149.220001  150.449997  148.630005  142.569382   \n",
       "\n",
       "        volume  day       macd     boll_ub     boll_lb     rsi_30      cci_30  \\\n",
       "0  116661700.0  2.0  -0.016407   20.135254   11.086746  47.624796  -20.614394   \n",
       "0  110737200.0  2.0   3.010423   92.570735   80.068732  62.807150  107.494096   \n",
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       "0    1381300.0  2.0   0.791761  152.470034  134.683512  51.721587   27.969733   \n",
       "\n",
       "       dx_30  close_30_sma  close_60_sma        vix  turbulence  \n",
       "0   5.717277     14.015333     12.287167  28.620001  216.488637  \n",
       "0  29.730532     83.817341     77.609739  28.620001  216.488637  \n",
       "0  29.868037    405.434336    376.088668  28.620001  216.488637  \n",
       "0  20.817781    113.869423    106.687574  28.620001  216.488637  \n",
       "0   1.842731    140.816518    137.127408  28.620001  216.488637  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hit end!\n"
     ]
    }
   ],
   "source": [
    "trained_moedl = trained_sac\n",
    "df_account_value, df_actions = DRLAgent.DRL_prediction(\n",
    "    model=trained_moedl, \n",
    "    environment = e_trade_gym)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(336, 2)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_account_value.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "           date  account_value\n",
       "331  2021-10-22   1.408525e+06\n",
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       "335  2021-10-28   1.422670e+06"
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     "metadata": {},
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   "source": [
    "df_account_value.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-06</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-07</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-08</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AAL  AAPL  ADBE  ADI  ADP  ADSK  ALGN  AMAT  AMD  AMGN  ...  UAL  \\\n",
       "date                                                                ...        \n",
       "2020-07-01    0     0    91    0   77    61     0     0   39    87  ...   88   \n",
       "2020-07-02    0     0     0    0    0     0     0     0    0     0  ...    0   \n",
       "2020-07-06    0     0     0    0    0     0     0     0    0     0  ...    0   \n",
       "2020-07-07    0     0     0    0    0     0     0     0    0     0  ...    0   \n",
       "2020-07-08    0     0     0    0    0     0     0     0    0     0  ...    0   \n",
       "\n",
       "            ULTA  VRSK  VRSN  VRTX  WBA  WDAY  WDC  WYNN  XEL  \n",
       "date                                                           \n",
       "2020-07-01    96    96     0     0   89     0    0     0    0  \n",
       "2020-07-02    96    96     0     0    0     0    0     0    0  \n",
       "2020-07-06     0     0     0     0    0     0    0     0    0  \n",
       "2020-07-07     0     0     0     0    0     0    0     0    0  \n",
       "2020-07-08     0     0     0     0    0     0    0     0    0  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Backtesting Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BackTestStats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Get Backtest Results===========\n",
      "Annual return          0.302651\n",
      "Cumulative returns     0.422670\n",
      "Annual volatility      0.185075\n",
      "Sharpe ratio           1.526168\n",
      "Calmar ratio           2.605989\n",
      "Stability              0.920151\n",
      "Max drawdown          -0.116137\n",
      "Omega ratio            1.284871\n",
      "Sortino ratio          2.193640\n",
      "Skew                        NaN\n",
      "Kurtosis                    NaN\n",
      "Tail ratio             1.091760\n",
      "Daily value at risk   -0.022196\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/opt/anaconda3/lib/python3.9/site-packages/pyfolio/timeseries.py:724: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  stats = pd.Series()\n"
     ]
    }
   ],
   "source": [
    "print(\"==============Get Backtest Results===========\")\n",
    "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
    "\n",
    "perf_stats_all = backtest_stats(account_value=df_account_value)\n",
    "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
    "perf_stats_all.to_csv(\"./\"+RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Get Baseline Stats===========\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished ^DJI\n",
      "Shape of DataFrame:  (335, 8)\n",
      "Annual return          0.273520\n",
      "Cumulative returns     0.379084\n",
      "Annual volatility      0.139248\n",
      "Sharpe ratio           1.811893\n",
      "Calmar ratio           3.062662\n",
      "Stability              0.918651\n",
      "Max drawdown          -0.089308\n",
      "Omega ratio            1.351851\n",
      "Sortino ratio          2.684720\n",
      "Skew                        NaN\n",
      "Kurtosis                    NaN\n",
      "Tail ratio             1.051856\n",
      "Daily value at risk   -0.016542\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n",
      "/Users/yangwenkai/opt/anaconda3/lib/python3.9/site-packages/pyfolio/timeseries.py:724: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  stats = pd.Series()\n"
     ]
    }
   ],
   "source": [
    "#baseline stats\n",
    "print(\"==============Get Baseline Stats===========\")\n",
    "baseline_df = get_baseline(\n",
    "        ticker=\"^DJI\", \n",
    "        start = df_account_value.loc[0,'date'],\n",
    "        end = df_account_value.loc[len(df_account_value)-1,'date'])\n",
    "\n",
    "stats = backtest_stats(baseline_df, value_col_name = 'close')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-07-01'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_account_value.loc[0,'date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2021-10-28'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_account_value.loc[len(df_account_value)-1,'date']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BackTestPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Compare to NDX===========\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "finished ^DJI\n",
      "Shape of DataFrame:  (335, 8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/project1/FinRL-master/finrl/meta/preprocessor/yahoodownloader.py:55: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n",
      "/Users/yangwenkai/opt/anaconda3/lib/python3.9/site-packages/pyfolio/timeseries.py:724: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  stats = pd.Series()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2020-07-01</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2021-10-28</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>16</td></tr>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Backtest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Annual return</th>\n",
       "      <td>30.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>42.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>18.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>2.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-11.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-2.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha</th>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Worst drawdown periods</th>\n",
       "      <th>Net drawdown in %</th>\n",
       "      <th>Peak date</th>\n",
       "      <th>Valley date</th>\n",
       "      <th>Recovery date</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.61</td>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>2020-09-23</td>\n",
       "      <td>2020-11-16</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.79</td>\n",
       "      <td>2021-02-17</td>\n",
       "      <td>2021-03-04</td>\n",
       "      <td>2021-04-05</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.53</td>\n",
       "      <td>2021-04-16</td>\n",
       "      <td>2021-05-12</td>\n",
       "      <td>2021-06-14</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.20</td>\n",
       "      <td>2021-01-20</td>\n",
       "      <td>2021-01-29</td>\n",
       "      <td>2021-02-08</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.37</td>\n",
       "      <td>2021-09-03</td>\n",
       "      <td>2021-10-04</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/opt/anaconda3/lib/python3.9/site-packages/pyfolio/plotting.py:805: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  oos_cum_returns = pd.Series([])\n",
      "/Users/yangwenkai/opt/anaconda3/lib/python3.9/site-packages/pyfolio/plotting.py:805: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  oos_cum_returns = pd.Series([])\n",
      "/Users/yangwenkai/opt/anaconda3/lib/python3.9/site-packages/pyfolio/plotting.py:805: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  oos_cum_returns = pd.Series([])\n",
      "/Users/yangwenkai/opt/anaconda3/lib/python3.9/site-packages/pyfolio/timeseries.py:541: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  out = pd.Series(index=returns.index)\n",
      "/Users/yangwenkai/opt/anaconda3/lib/python3.9/site-packages/pyfolio/timeseries.py:541: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  out = pd.Series(index=returns.index)\n",
      "/Users/yangwenkai/opt/anaconda3/lib/python3.9/site-packages/pyfolio/timeseries.py:1227: FutureWarning: Indexing a timezone-aware DatetimeIndex with a timezone-naive datetime is deprecated and will raise KeyError in a future version. Use a timezone-aware object instead.\n",
      "  period = returns_dupe.loc[start:end]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Stress Events</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New Normal</th>\n",
       "      <td>0.11%</td>\n",
       "      <td>-4.41%</td>\n",
       "      <td>3.46%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangwenkai/opt/anaconda3/lib/python3.9/site-packages/pyfolio/timeseries.py:1227: FutureWarning: Indexing a timezone-aware DatetimeIndex with a timezone-naive datetime is deprecated and will raise KeyError in a future version. Use a timezone-aware object instead.\n",
      "  period = returns_dupe.loc[start:end]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x5184 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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M+nUUBDBL5gKEvWGaK5qnfZwVQ7eW/1vhmmmFXDJi73aagv7B8z7IkqolvPX0t87Q+plTEkHXWkeBu4AvKqXCSqkNmBOiPyowvAlT7K0fgDcA/zXnhpYB//Ef/8Hdd99Ne3s7HR0dfOYzn6Gzs5PXvva1fOITn6C3t5fbbruNN7/5zXR1ddnH/fu//zvf/va36e/vZ+nSpbZoj4yMcPnll3PZZZdx6NAh9u/fz/XXX28f99xzz7Fo0SK6u7v53ve+x/ve9z76+vrs/XfeeSef+9zn6OvrIxwOc8EFF3Daaadx9OhRbrrpJj74wQ/aY5cvX84jjzzC0NAQn/rUp7jxxhvp7u7OuVZLSwtHjhzJ6bKkteaDH/wgDz74IA888IDUbReKQjwVJ5aM4XQ4+afX/pMtsNOhNlCbk5VildudzqSoFUO3wj0bmjbwmcs+MyHPfS4o5dL/DwD/CnRhxtM/p7V+QCm1FHgZWKO17tBaH8k+KPOi9mqtx4pt0Pbt2xkaGir2aXOorKxk7dq1Uw/M8P73v9/uNvSpT32Kd7zjHdTX13PllVdy7bXXAnDZZZdx/vnn8+tf/5r3vOc9ALzjHe9g3Tpzjvmv/uqvuPnmmwG4++67qamp4WMf+5h9jfPOO89+3NzcbIvy9ddfTygUYseOHVx4odmV5XWvex1nnXUWAG94wxv46le/yrveZfZiveGGG7j11ltJp9M4HA7+/M//3D7vjTfeyK233sqzzz7LNddcA0BjYyMf/vCHzWwBVyYWmUrx1re+lcHBQX73u9/h9xe/RrRwcmJ51yFPaMYpgx6nhypflb1AKH9S9Fhpi/ak6AxuIMWiZIKutR7ETF3M396BOWk62XEnVcUpqwQuQGtrK0eOHGH//v386le/oqqqyt6XTCZtoQXsLkcAwWDQLrnb0dFxzCqM2cflHwvktKzz+/0TnieTSRKJBD6fjzvuuINvfvObHDhwAIBIJJLTSamlpWXCB2vfvn289NJLPPLIIyLmQlEZTZrhyuONXdcH621BtyY47ZDLMRYWWVUUsydks4nFYjidTtzu4gv+SV2cayaec6nIbi3X0dHBokWLWLp0KW95y1v4t3/7txmfb8mSJezbt6+YJhbkwIEDvPvd7+ZPf/oT5513Hk6nk3Xr1uUskS7kJZ122ml85CMf4brrruO+++5j/fr1c26rcHJgC7rn+AS9Llhnt5zLj6Efq5Z5fsglmyNHjrBlyxYaGxvZvHnzcdl1LKSWS5nxve99j46ODgYGBvjSl77EDTfcwFvf+lbuuece7rnnHgzDIB6P8/DDD9ue8LG49tpr6enp4Wtf+xqxWIzR0VGeeOKJotsdjUZRStnx79tvv51XXpleh/Q3vvGNfPOb3+SKK65g+/ZjLj0QhGljCXrQHTyu463URZgYQz+mh55JW8zOgddas3v3bp555hlSqZSd7FBsRNDLjJtuuomrr76aZcuW0dLSwhe+8AWWLFnCr3/9a7761a9SX19PS0sLX/nKVzCMyd9UFuFwmPvuu4/f//73NDU1sWzZMn77299OedxMWbNmDbfccgvnnnsuixYt4pVXXuGcc6bu6mLxlre8ha997Wu85jWvYceOHUW3Tzg5yP5GaAn68fbubAg22I9nEkMvFHIZGBjglVdeobm5maamJuLx+GSHzwo1VdWwE43MatH29vZ22tracvYdPnyYxYsXFzpMOAGQ/59wLHbu3ElHRweXX345Sinu33M/v9j2Cy5dfik3brxxxudrH2jn1gfMbKzXr3k916y6htufuZ2nDj7FO898J+ctHU8u2Nu3l4ZQA2FvmP/e/t/cu/NeXrfmdVy76lrGxsa4//77UUpx1VVXsXPnTg4cOMBrX/vaGdu0f/9+li1bBrAsk6adg3jogiCc8AwMDLB7925isRjJpBnDnm3Ipa2qzX5c5a8CxhcWZeehtw+085WHvsKn7/s0MDHL5cgRM3GvubkZl8uF1+vFMIw5WVwngi4IwgnFjqM76BsdXyuRSqV44YUX7HCLFc6YbchFKcU/XvWPvHH9Gzmrxcwoc6mJeej7B/YD42mS+ZOikUgEt9vNpk2bALOJe7adxUQEXRCEE4aOwQ6+8eg3+PjvPg6YYvnAAw8QiUQ49dRTAUgkzBi2VfXQakxxPNQEarhyxZW2OFseenYtl+xYedJI2mJvbR8ZGSEUGs+FtwQ9e5V1sRBBFwThhOHISM66Q7q6uojFYlxwwQW0tJiFVi3Pt3+sH4BKTyWPPPIIL730Eul0msHBQXbu3Dll16FCFJoUtRpgAPSN9k2o5RKJRAiFxpfa1NTUsGTJkpw1HcXipMtD11qXpNC8UFwW2uS9cHxkV0xMGAnGxsbwer1UV1fbQm797on2oAzFoZcPkRxNMjg4yMDAAJFIhFQqxeLFiwmHZ9bX0xJpK5MFxkMtkBF0Y7zaopVmHAyOf0twuVx2+KXYnFQeusPhmFaqn1B+GIaBw3FSvV2FAmQv6Okf7WdsbAyfzxR5j8dsQpFIJEilUwyMDhDuC5MYNQW2tbWVoaEhezIyuxbSdKnwmUVjd/Xu4tH9j6K1zhH03X277ZZ3YU+YsTGzYkmpVkGfVB56IBBgeHiY6upq8dJPILTWDA8PEwjMfflRoTxIGAke3f8otYFaNjZttLfHjfGJxP6xfmKxmC2WSik8Hg/xeJz+0X5coy782m+XzW1tbUUpxf79+wHo7OxkxYoVM9KCan81YAr6rt5d+Fy+HEF/+tDTdlPpKl8Vo9HMatUSvXdPKkEPh8P09/cf151ZmF+8Xu+Mvx4LJyZ7+/Zy+7O30xvtJegJctu1t9n7rPg0wJMdT1I7UsuLQy/yn3f/J2/f/HbcbjfJZJKeaA/eUS9+37hn7PP5WLFiBT09PVRVVdHZ2cnQ0FBOjaSpsATdYk//nhxB74mYpZ9dSRePP/A4wYAZahEPfQ5QSlFbWzvfZgiCcAx++sJP6Y2aRd2iiSgJI2FnjNgeuoYn9j9BVVcVYxVjxMNxvv3Et3lr3VtJpVJ0D3XjiruoWlyF1ePCCslcdtllJJNJurq6OHTo0IwEvcZfk/M8looRTZqC3lbdZqcwVjoqQcPY2Bgul8sOC801EpQUBKGs6ImaXq5VP2U4Nt69MmEkcMVdNPQ24ImZIp92pe39B0cOkkgkOHjYLHK3qGmRXao5O7TidrtpbGzk8OHDpNPjx09FfineseQYXSPmN/6rTrtqfJzbzGq5+OKLedWrXlWyEK8IuiAIZYPVlMLldNHoasSRdPDZ+z9r530nUqagNwebWY3ZAM1wjSc6vNT7ErFEjL7uPtLONIvrFnPZZZdxySWXTLhWS0sL8Xg8p8TzVCilcppNv3jkRWLJGNX+ajYv3mx3RVpWuQwwY+elnPs5qUIugiCUN1bLuOp0NY7DDgJGgEh9hMPDh2mpaKFnbw/uMTeuKhdNoSZaK1thOdSGavnDrj/QP9DPkf4jRIYiJPwJ6kP1eL1eezFPNg0NDXg8Hg4dOkRDQ8OE/ZNR4atgLGlmr1hZN8trlqOU4paLbmEkPsJQ5xD79u0reWaWCLogCGXDwNgAzqSTwEiAeCqOK+miqrOK3e27Ga4aZm/HXpw4qaipYOmipQwPD3PRmosA2N69nT5Hn+3lp0KpnBK4+TgcDpqamjh06BCpVMoOzUxF2BOmm+6cbTUBM7Ye9oYJe8P07u/F7XaXPJtOQi6CIMwpu3t325OcUzEUG8Ib8eJxeQguGV+Ms3f3Xu568S77eSAQYMOGDXarRDDTBLVDM5YcI5lO4nQ7qfBWHPN6LS0tGIZhF9CaDhcvu3jCtpAnt+laKpWak45EUyGCLgjCnNE/2s/XHvkaX3zgixwePmxvT6VSPP744xw6dIjt27fbi32s1Z3BYJB3XPQOe/yunl0cHT5qP/f7/SilcjzgCl8F2qHthT014ZopPeTq6mrcbjf9/f3T/pvOXXIut1x0C6fWnmpvy++KlEwmp+3xFxMRdEEQ5oze0V601owmRrntsdvoHzWF85Wdr7CtfRuPPfUY+/btswW1Y6gDh+GgrqKOhooGNizaYJ5IgyM9Llce98T2bpXeSrTSdhy+Llw3pX1KKcLhcE4f3ekcs6p+FUuqxvv/5hcASyaT4qELgrCwGImP2I8Hxgb41uPfYnB4kPufu5+9/XvZ078HwK5h3jHYgSPtYFGV2by8tbGV9Y3rWVe/jisbr7TPpdwTPe9KXyUpbwqdSTyvC04t6AChUGhGgm6RHZ/PD7mIoAuCsOCIJEyh3Ny8mdpALYeHD/NfD/0Xh4YPEQvFGI4Pk9Zpu/5Kb6QXR9pBY6VZifCCCy7gL17/F6xoWQEJWF2/msZQI+ubJzYTr/RVop0aw22Q8qbsphRTEQqFiMfjxONxnn32WQYGBgqOMwyDrVu30tHRAUBdYPyGEXDnhlxmMslaTETQBUGYMywPvTHUyIq6FQC8cugVUt4UicoEA40DRBIREokEiVQCR8qBw+Gwc7edTicej4fNmzfj8XioD9azsm4lQf/EGueWlzxSP0KkNjJtQbeW5Xd1ddHV1cXLL7/MyMhIzhjDMHjmmWc4ePAg3d1mhsuxPHQRdEEQik53dzft7e2zanc2k5WU+YzER/AP+mEEGkKZXG9tLsO/cNmFaKdmJDVCIpEglorhH/bjdDgnlOjw+/2cf/759vNC4Qx7YlKZP9W+6gljCmHlqB88aK4u7e/v58EHH7T3a63ZsmULvb29uFwuu2JrvodulXjWWmMYBk6nc1rXLyaShy4IC5hXXnmF4eFhdu3axTnnnDOjuiUAvb29PP3005x//vkzOvb5w89z78576Yv04Y16Ge4cZlXTKgBUWuH1eFlZt5KH2x9mMDFohjyMOM6UE2fImVM/3CIcDvPqV7+a4eHhgtkrLocLn8tnN5yo9FVOy1aPx5xgHRwczNk+OjpKIBAgEonQ1dXFqlWr6O3ttQXd5/ZxYduFGGmDJx59Aq01F198MUoptNbioQuCUFxisRj19fUkk8kZ5VpbDA4OYhgG27dvn3aTke3d2/nek99j/8B+u3ysw3CghzWOlAOlFT63j+W1y81rJAdtD12lFS735EIYCARYtGjR5Puz0gfzKyNORqFVpADDw2b6o9XSrrq6GqfTmdNT4W2b38bbTn8bkUiEaDRKT0+PvX8+PHQRdEFYoBiGQSKRoLa2FpfLdVxhl9FRU5D7+/s5fPjwFKPNvPPvPfU9+7kz5aTGX0NDVQP9h/up6DYX+vi8Pmr8NXicHmLpGNGxKPFkHJVWuD3Hnx2S3etzus2h3W53wSX6VuaL9bq5XK6Cr6Ml+ABHjx7NGV9qRNAFYYESi5mhB5/Pd1yCnkwmOXz4MFVVVVRWVvLyyy9P2al++9HtxFNxVjes5gdv+AHvPf29bGjawPq16zES456t1+1FKUV9qJ60M81AZIDomFmG1gqBHA+R+MzTD63GGF6vNyc2H42a9livm9PpnOChw3jKpVIqR9DFQxcEoWgcr6Brrenp6eHRRx+186k3btxIPB6nvb39mMceGDgAwNrGtSilGIuOEQqFWLx4MaFgViZIRnkaQ43mxOjYCJFRU4y9nsIhkOmQ1uYErts5My+/rq6OJUuW2CIcCoVsQbcE3OVyFRR0y0NvaWkhFovZi6Tmw0OXSVFBWKBkC3ohISpEJBJhy5YtDA4O4vF4qKioYPny5VRWVhIKhSak8+VzYNAU9LaqNgBGRkbslo/r16/n3u33AmAo05b6YD1pR5pYMmbHrD3e4/fQG8ONdI90s7p+9YyOO/300wFobm6ms7MzR5inCrlYHnpzczMHDx60Q1OS5SIIQtGwPEev1zttD3337t1EIhE2bNjAkiVLcmLLoVCIoaEhurq6aGxsLBh3HhgzF+XUB+tJpVKMjo6ydOlSc1u9GV5xGA4MTEEPe8KknWlSyRSREdND93uPv13bB879AA/se4DrVl13XMdXVFRQUVHBK6+8wtjYmJ2CCOMhF6016XTa/vut1zkUClFVVUVfXx8gMXRBEIpIIpFAKYXb7Z6WoBuGQVdXF83NzbS2tk4Q7FAoxOjoKM8++yxbt26dkPWitSaSiOBIOvC7/HbH++wUxLDP7At7WsNpgJmVknamSaVTjI2a4yfLOpkOTeEmbtx4I2Hv7PrP+v1+tNbEYjFSqRQOh7ngyfK6s7/tWB66x+Ohrm48N11i6IIgFI1EImHX5J5K0KPRqJ1jnS1K2SxatIja2lqWLl1KZ2cnu3fvzr2ekcAwDKp6q9j9ym5b0LP7aV6x7gpW16/mgrYLgMyCHKcmlU4RG82EiNyl6b95LKzVo2NjYzmrPi2RtuLrYL7OlveenasvMXRBEIpGIpGwM0YsQX/llVfo6upi06ZNVFebedpaa/70pz/Zx4XDhb3bqqoqzj//fDvksGvXLlpbW22POpKI4DAcuJWbgwcP2tfO7nh/9uazWbZkGdWV5rUD7gBaaVI6RSJuhi4C3tK1bJuMyQTd+v3II49w3XVmWGdoaIhQyJzwzRZ08dAFQSgalocO44I+NDREJBLhsccesxca9fT05BxXaJVmNkopHFUO9vXvo6d3/NhoIooyFC6ni3Q6ze7du80enFkeusvloqmpyX4e8ARAQUqlMNJmGMNqDj2fZAt69jL+7DDUjh07SKfTDAwMUFNTYx+3YcMG1q9fL9UWBUGYnLRO8+C+B+2a4lOR76Gn02ni8TjV1dV4PB47G8OqHghm6t1UfTC11ty56046hjt4Yd8L9vZoImp66A637al6PJ5jNpkIus2bR0IlMNIGWulpLwiaS1wuFx6PZ4KHns2ePXvsMFW2Z97a2kpbW1vpjM1CBF0QThB+v+v3/HTrT/nGY98ouD+eiud0BUomkzmCDqbH6ff7CQQCdsnYI0eOsHz5cl772tfa6XvHon2gnaPRo+aCoJHxUrP/u+N/caQduBwu1q9fT11dHatXHzt90Co7myCBoQ1Q4HEef9piMfH7/YyOjuYI+qJFi+zwCmB/y8n+FjKfSAxdEE4Qnj/8PP4hPz3J3BDJ9u7t/MuT/2J3oP/kpZ9kacVSYrGYLTRWyMAKw6TTaaLRKIcOHUJrzdKlS6cd8338wOMA5oKgUTMvPZaMsbdvL37Dj0ZTWVnJeeedN+W5/G6zlVycOKl0Cq10WYRcwBT0aDSK1toOwSilaG5uZufOnQB2Kd3ZrG4tJuKhC8IJQjQaxRvxEhwMMjQ2ZGet/PDZH9piDvBy98sMDAygtbZju9lhFJfLhdfrJR6P09/fTygUyvE6j0XSSPL0oacBSDvSdA50srNnJ4OxQQCUoWiuaZ52t3ulFGFvGO3UjCZH0Y7yEvSxsbEJ3Yeyb3zW4i0RdEEQpo3WmqFhs1emM+Hk8S2Pc++993Lo0CESRiJnbNATZM+ePSilbEHPFiG3243X6yWZTDIwMDCjsrgvHHmBseQYbqcb7dTEY3G+/vDXORoxGzg3+Btoq2ub0d/WVtVG2pG2PXSfqzzCF36/n1QqRTwezxHsQvF0EXRBEKZNPBVHJU2v13Ab7N27F4ADnQeIpzIFszQ4kg76+/rp6elh5cqVE/KnYVzQtdbE43EqK6dXNxzggb0PAHDJKZeQdqTt63aNdAHgc/hmnN3RVtNG2pk5lyqPLBfITbfMFuz80JTL5ZpyIrlUlIcVgiAck0gigjPlRDs0sXDMbu2258AefMM+6oP1BAYCVBytoP9oP06nk2XLltnHZ4uQy+XKmcSbbrglmoiyq3cXbqeb1658LdpprhR1GA4Oj5iTsV7lnbG32lY1LuhaabzO8hB0qw0eUDDkYm0rF+8cZFJUEE4IrEU7Do+DtDNtC/pIfATfiI+llUtRAcWesT2M9I5Qt7ouJzSQ7UG63e4cscp+fCyiCXN1ZKWvkpAnZHvojrTD9tDduGcscMtqltk3h3KbFLXIFnTrdfV4PHbhs3JBBF0QTgB6o72otKK6spqkL0kynSSWijEcGyblTeFNeNEOUxSTqSSNjY05x+eHXLJFPFu4jkU0aQq6lWqYdmUE3XBwYPAArpgLr3PmHnrIE6I2VEuKFE6XE6ejPAQy+++YLOSydu3akto0FRJyEYQyZyw5xv97+v/hMBz4vX5aa1sBs5nDcGKYsfAYG1ZvwOUw/bNUOnVMQbfqehfaN5oYZV//vkntgPE2b1aYxJFykDbShPvCVPurj2uF5LKaZUSro+jK6bW5KwVKKfvGV8hDV0pRX19PfX39vNhXCBF0QShTRhOj3P3K3bzU/RJos7myoQwaK0yxHogNYKQNKoIVLK5fbAu64TEmLHTJ99DBXAyTL76/ePEXfPnBL/NExxMF7YFxD/36tdejHRqH4cAVd1Htr8ahHNOqu57PspplJANJvP7yCLdYXHjhhaxatSpnnmG6KZnzgYRcBKFMuX/v/fxmx2/M7jsZx3U0PWqn9Y0mTYFtrGokGAzaXXoGg4Ok0ilb4GFiDB3gsssum3BNKxZ+10t3cXrT6TmVD/NDLteuvJZdW3ZxaOSQ3Tu0ubmZJUuWzPhvXVZtTuCWw7L/bLxeLytWrMjZZt0cJytiNp+Ihy4IZcrOrp1UHKnAOeDEkTY/qpevvByvy0vSl7TTFQPeAMFgEL/bT6AlwLBjmKcOPpVzLkuElFL2Y6vkazbWZOtwbJjf7vxtzr58D10pRU24BpVWOFIOGioa2Lx583HVM19es5zr11zPn6/78xkfW2oCgQDnnnsumzZtmm9TJiAeuiCUIUba4ODRg3gNL/4RP96ol5V1Kzml/hReirxEtDZKVJses9/tx+l0cu2111LbUcuPnv0R9+66l/OWnodDmTcCy0N3uVzHDBlYgg5w/577qfRV8tj+x/iLjX9hfyOwBB0yzZ7Timp3NTUVNcf99yqljrvL0HxQTnHzbMRDF4QyZCQ+gpEycDvcbF61mQpXBTX+Gnw+3/hKSmX++F3jYYqzWs6iNlBL90g3Ww5vsbdbnvmxJizjqTjxVByX08X5redjpA3u3HYnncOdfOvxb/G7Xb8DxidFAZorm/E4PCyvWD7t9Edh7hBBF4QyZCQ+gkorPE4Pr7/o9Zy++HTcTjcVFRUTFt5kx51dDheXLr8UgG1HtuWMczgcxxT0Q0OHALPP5xvXvTHnvCljvNtRtb/aflwXruPi1oup99RPO/1RmDsk5CIIZchIYgRlKNwuc5n+BRdcYNcW97hy87zzJxJrA7XAeJqhhdPpnLQtmtaarzz0FcBcxBT2hllRu8K+KVT5qxiKDXH5qZezvnF9zjmtImHioc8/JRN0pVQV8APgamAY+L9a6+8WGPdq4DZgCWAADwN/q7XuLJWtgjDfjMRHcKQdeNyenCJbwEQP3ZUr6NZ+u8ZLhmOFXDqHxz9eG5s2Armhlfec/R5aq1rtTBqL7BuEeOjzTylDLt/BvIEsBq4BPq+UurTAuO3AlVrrqszY3cC/lspIQZhvhiJD3PHYHai0Ktg4Ib+Jcr6Hbi2djxu5gr569WqWL19e8JqPd5g1zutD9dy48cYJ5w24AxPEHHLz28VDn39K4qErpYLAm4DTtdYjwFal1I+AdwIPZI/VWh/JO9wATi2FnYJQDvzwf39IqD+E4TLweScKer6Hnr9U3po0zffQs3t5ZpMwEjy470EA3n3Wuwl7zfzq7GyWyUraiodeXpQq5HIaoLTWL2dt2wpcUWiwUmopsA2owBT0904yrgqoytvcMjtTBWH+SKfT9AyaHYmcKSfOwMS6Jvnimj1hCeMt3OJGHCNt4FCOY6Yq9kR6SBpJGkINtFW32duzPfTJFvxYHrrL5ZqXpshCLqUS9BBm3DybQaDgUiutdQdQpZSqAd6FGYYpxIeBzxbHREGYfw70HCCVzkwyugNcecaVE8bkVyNc1bCq4P54Ks4XH/giLoeLT136qUmv2R0126g1hnLrv2TH5qfy0AOBQFkviT9ZKJWgRzC97WwqgZECY2201v1KqR8DLyilmrXWqbwhtwF35G1rAR45flMFYf54+YD5JXbxqYt5z6veUzAunS3o37n+OxME3grJDMeGGY6ZflTCSEzafNnuNhRqyNmeHcqZTKwtD13CLeVBqQR9F6CVUqu11jsy2zYBL03jWBfQgHlD6M/eobUexPT0bcRLEE5kOg53kHamWdG2YtJJRody8J6z3zNpd59C24Zjw9QF6wqerzuS8dCDjQX3H4tsD12Yf0qS5aK1jgJ3AV9USoWVUhswJ0R/lD9WKfXnSqkVyqQB+CawRWvdnz9WEBYSWmt6e3tJeVOcUnvKMcee2XImZzafWXCf0+HE5cz11Ybj+RHPcSbz0K3J0WMhHnp5Ucq0xQ9g1ozrAn4HfE5r/YBSaqlSKpKZCAUz//wPmGGaFzAnRd9QQjsFYV4YGBxgZGyElDdFW1XbrM6VnwlzLEG3PfS8GPr6xvVcfurlvPecgjkJgOmZL168eEL9dWF+KNnCokx45E0FtndgTppaz2/DjI0LwknFjo4daDQ1tTU5i3qOB4/TQ5So/XwoNjRhzJGRI/zzE//MUGwIp8NJTSC3uJZSihs23HDM6zgcDs4444xZ2SoUD1n6LwhTsLVrK08ffJq6YB3nLz2fReFFRT3/nj17SCQStB9qJ+1Ks6x+2dQHTUF+VkohQb/rpbvoiZgpkg3BBrsyo3DiIoIuCMegfaCdf3niX+zn9++5n+++bkLFiuPm6NGj7NixA4fDQXdvNylPym72MBvyJ0azy+JaZCcQ5MfPhRMTuSULwiSk0il+/NyPc7YljSSpdIpfbf8V7f3tszp/LBZjy5YtuN1u0uk0w2PDGG6Dtpq2WZ0XoNJXmfO8kIde4R3PJM6PnwsnJiLogjAJ9+y8h87hTupDuc0M/mf7/3DPznu49cFbZ3X+rq4uEokE55xzDoY2zAYSbmipmP1i5+wSt1B4UjR79WeVv2rW1xTmHxF0QSjA0chR7t55NwBvO/1tvG7N6+x9z3c9P+PzPfnkkzzwQE7ZIpLJJABVVVV4Auain+qK6oJFsGZKvqAX8tCtFakAIU9own7hxENi6IJQgN19u0mn02xavImV9StZWb+SnmgPjx943J5InC6HDx+mp8c8Rmttx65TqRROpxOlFOGaMFpp3L7i1EPJF/RCMfREKmE/PqNZMlUWAuKhC0IBLAGsD46HWxaFZp7dEovFeP75cY8+kRgX0VQqZa+0rGuuY7hheELziuMlX9Ct9nJ7+vawtWsrAMm0+Q3hnWe+c9KyAMKJhQi6IBTAEvScicPwzCcODxw4gNbafh6JROzHqVTKrlBoYKBdumjCuqx6Gctrl3PtqmvtDkZDsSFuf+Z2vvvkdxkcGyRhmDeXYoR4hPJAQi6CUABrEjF7+ftMPfR0Ok1HR0fOtkgkQm2tKbDJZNL20JOG6S0XS1y9Li8ff9XHAdh+dDt9o30Mx4eJJCJordk/uN8OuYh3vnAQD10QCmBNImZ76PXB+hkVfzty5AixWIwVK1bY22KxmP3YMIwJgu5xFF9crb9hKDZkh1kODBywPXQR9IWDCLogFMDy0Ct844LudronrVhYiCNHjuD1elm5ciXXXXcdXq+XWCxGMpkklUrleOi2uBYphp6NlZM+GBsknU4DsH9gvwj6AkRCLoJQACuGHvbkVhxcFFo07SyXeDxOMBi0vXqfz0c0GuWRRx6hoqIiZ1LUDrk4ih/Ptjz0/tHxgqUHBg/Y4SQR9IWDeOiCkIfWmmjCLGwV8ubmZ89kRWU8HsfjGRdLn89HX18f0WiU0dHRHEFPpOdugtL6ltE32mdvG4mP2FUWRdAXDiLogpBHwkhgpA1cTtcEsWupnP4qzkQigdc7XlPF5zMLZjmdThKJREEPfS7E1fLQDw0fytlupI05u6YwP4igC0Ieo8lRILfrvcWK2hU5z1PpFI/ufzQnnAGml59IJHI89KamJpYsWUJraytjY2Ok02k7bbHYWS7ZtFa1msW/RroL7pe0xYWDCLog5DGaMAU96A5O2Je90Ajgvt338ePnf8zXH/16zvZkMonW2hb0eCpOsDLIpk2bcrx2q3XbXOaE1wXrWNewbtL94qEvHETQBSGPaNKMnxdqMqGU4u8v/HvALG617cg2gAkTpdaKUEu8P/SbD3Hzb28mlU7leO1W67a5DLlA7t+SXZQr6AmKh76AEEEXhDyskEshDx3glBqz32dapxlLjRUcE4/HAfB4PBhpg7Q20wXjqdyJUstDt/LD50rQnQ6n/Th7gdR0+oYKJw4i6IKQh5XhMlkbOCsN0dAGsWSs4JjR0UwcPhCwbxBg3gSyBd16bK3anIu0xfzzZue6WzcaYWEgeeiCkMexJkUBnMr0dtPpNLHU5IKulMLv93M0etTenjSSVFRUsGjRIgKBgH1zsNIW52JhEYDLMf5RzxZ3EfSFhXjogpCHtew/Pwfdwuq9mdZpxpKFQy6jo6P4fD4cDoft8YOZFeNyuTjrrLNYu3atvX0uFxZBnqA73fzFxr8A4C0b3zIn1xPmB/HQBSGP/QP7AVhSuaTgfqUUDoeDdDpte7j5DZbHxsbsCc98QS+EdWPInrAsJtmC7nK4ePXyV3PekvMmDSsJJyYi6MKCR2uNRk+rq/1IfISdPTsBaKtqm3ScUzlJkxuuyG5eEY/HqagwF/S83POyPSZf0HujvWzp2mLfROaqc1C+hw6TzxEIJy4i6MKC59+3/Dtbu7by3rPfy8r6lccce9dLdwHQFG46Zp/N/JtDWqdJppN2loq1CnRgbIA/7vmjPS5b0P+494/85wv/mXOeoKdwZs1scTkLx9CFhYXE0IUFz6P7HyUSj3D7s7dPOXZf/z4A3rjujcccV8jbj6fi9mNL0A8MHsgZY8XKAV4++jL5TDYRO1uyRVzyzhcuIujCScNkKYYWSSPJ0chRlFKsalh1zLHZIQwLS9C11ragdw515owxtJFzvXxmUm99JuTH0IWFybQFXSlVqZTyZx4rpdTblFJvnTvTBGH2ZLd/S6aTOc/zORo5SlqnqQvWTbnA51geumGYou1yuegczhX0lDEecrEEfa5EPJuckIt46AuWmXjovwU2ZB5/GvhH4CtKqS8W3SpBKBLZaYVG2pg0zRDg8MhhABaHF0953kKCbuWkp1KmaLtcLg4Pm+e0mkyk9LigW/H0uQqzZONS44I+V5k0wvwzE0FfDTyXeXwTcAVwEfCXxTZKEIpF9ipNgKH40KRjLfFdXDG1oGcvpbewlu8nk0nSOk37YDudw50opWitagUKe+hLq5ZOeb3Zku2h+10i6AuVmQTTnFrrlFJqMVChtd4GoJSqnRvTBGH2WIW2LIZjwzSFmwqOnYmH7hp24dAO0q40PpePWCpmV0w0DIOOwQ5ejrwMPrPaoZUiaIk+jK8OfcPaN1Dpq+TC1gtn/gdOk+y4uc/tm7PrCPPLTDz0PUqptwHvBf4EoJSqA6LHPEoQSsCWw1u4Z+c9E2Lk+SGWh/c/PGkcfboeejKZxNHnINRr5ozXh8ySupbHnUwmORI5ApnQ+OLwYtujt5pKZI+v9Fby12f+9ZQplbMhO8ulFCEeYX6YiYf+f4CfAHHg+sy2a4Fni22UIMyUX2z7BX2jfWxYtCGnq5C1SrMmUEM0EeXpg09T6avkzevfnHN8dobLovAijkUqlUIphdKmYtcF6jg4eNAW6FQqhcfpQSvzxtFS2WLbkUqn7AVIc9nUIp8cD90lHvpCZdoeutb6Aa11i9Z6udZ6e2bzT4E3zI1pgjB9huPDwPiyfQsrhr6qfhXvP/f9OB1O7tt9n12vxaI70j3tDJdUKoXKuN9BT3BCOMUWdIcp6M0VzbagJowE//fB/8vXH/m6PSlaigYTOTF0mRRdsMw4D10pVa2UWqqUWgo0ZX4EYd5IGAnb281fyGM1Qq4N1LKmYY0dTukfy20ZZ4dbphE/TyaTdpZLla/KDmckDTMt8sCBAzidTtIOszRAQ6jBFvTdfbs5MHCAnT077TTHUuSFZ4dcZFJ04TKTPPTzlFJ7gF6gPfOzP/NbEOaNSDxiPz4wkCvoh4bMxshWGKbSa6YPjsRGcsY9degpAJZVL5vyelZaolaaSl+lHTJJppOMjo4yMDAAtYADltUsY2nlUlu0XzzyYs65HMpRMGOm2GRfQzz0hctMXIPvAfcA/w+ITDFWEEpGdjXDg8MHSaVT9I/2Uxuo5eDQQWC8cmKFzyyYlZ2+mEqnePHIiyiluHjZxVNeL8dD94976AkjQX+/6fknvUlIwZvXvxmllC3o+ROypVrkY4WIQHqILmRmIujLgc1aS0V8obzITk1MGSnu3XUvv37516xrXMdwbBify0ddoA6ACq8p6MOxYfuYeCqO1hq/2z+tlmyWhw6ZkEtGlFNGir6+PjweDwmHmZJoTUBmx7CzKZWgZy+EKsXKVGF+mImgbwOWYoZZBKFsyA65APz65V8D8FL3SwA0VzbbImat2Mz20K388WN5rrFYDK/Xi1KKVCqVE0O38sktDz1QEeBwlxmTtwV9kjh5qSofNoQauPzUy6kP1pfkesL8MBNB/w/gLqXU14Cu7B1a64eLapUgzIBIwhR0pVTBHPPsRhWWh54dQ7cFfZL2byMjIzz44INs2LCB1tZWksmk3YauNlBL32ifeZ54gmg0yvbh7faxXpcXmFiP3JrEncxznwtu2HBDya4lzA8zyXL5F2Az8HPgwayfB4ptlCDMBCs1sa26reD+7Lx0y2OOG+OVEf+w+w/A5B56T08PAEePmr1B4/E4rdWtrKxdydrGtaTGUqBhdNi0Y39s/4TrZed+n9l8pv1Y4tlCMZmJexDWWsuqUKHssIpira5fTd9oX058HHI9dCvbw8oB39m7k4fbzS+YhcR1aGiI/fv3A3DkyBGee+45Dh8+TMAdIOwLk0wkOfjSQTxJDzFnDKfHieEeXw1qxcg3LtrIdauvY0XtCgZiAzzR8YS5X5pNCEVkWh66UsoJ9CmlxJ0Qyg5L0Ct8Fdxy4S05+5RSNFc0288tQbd6gVrhEsgVdK01O3fu5JFHHiGVSlFdXQ1Ab28vp5xyCq2trRiGQTQaNbNYEi4SYwkqKytpqRr/RmDhc/u4fvX1rG5YTdgzPvEqpWyFYjItD11rbSilDgIBIDG3JgnCzLAaV/hcPhZXLOZrV3+Nj977UcCcDLTi2IAd+7Y89GwP2es0x42MjPDQQw+htaalpYW1a9ficrmIRqMEg0EcDgd79+61xzqUA2fSiZEycLvdeJLmjeHdZ7+7oL1W6iRIXRWhuMwkhv4p4AdKqbY5skUQjgvLQ7eEu8pfZacfLqlYkjPW9tDTpofucIx/BDxOjz0BqrVmxYoVbNq0CY/Hg8PhIBwO2+NdLtMXyhF0w8DpdNo3i7pgXUF7rYlZQLJOhKIyE0H/OfBGYK9Sysj+mSPbBKEgWmsGxwbt55agZ088WvVVmiubc461Y+iZRhOWsANoNPv2mT1Fly1bxqpVqybN2XY6zfOMjIzYXn86nsbpdNoZLB5H4Qhldq67CLpQTGYyKXrpnFkhCDPggX0P8PMXfs7fnve3bGzaWFDQg+4gkDshCuMhF0vIrZRFMGuxdHd3s3jxYtatW3dMGywPPRKJ2I8NDFwul32zmCwlMTuFUUIuQjGZtqBrrR+aS0MEYbrs7N0JwJauLaagJycK+tUrr2Zr11bWNKzJOTbfQ89u1JwaTRGPx1m06Njlc2Fc0OPxOLW1tXDQLEHw8IGHSToyZXGnkcHSWt065RhBmC7TFnSl1KRFLmRhkVBKeqJmXvi+PjM8UshD39S0iU1NmyYca3noVqMJa5UnQGI4gfIoGhoaprTBCrkABINBtEOj0oodPTtIVk1d5/zWK2+lf7R/0u5JgnA8zCTk8mCBbdayvLkvFycImPHznogp6EciRxhNjI4L+iSt1To6Oti3bx+bN2/G6coT9KyQS2okRe1ptbjdU3vWlocOEAqG0EqjUGiHts99rLK49cF6iZ8LRWcmDS4c2T9AC2Y5gD+bM+sEIY9IImILuNaan2z9id1mbrJOPD09PYyMjNDR0THuoeuMoKdMQXekHKTjaRobG6dlR7aHHg6G7XZzVpcikBxzofTMuMGFhdb6MPAh4KvFM0cQjo0VbrF49pDZAdE95mbXK7vQWk+o55JImKIdjUbtGPpwbJjnOp+zuwy54i6awk3TCrdAroceDobR1pfVrKQY6+YhCKVitpWBNNKxSCghlqBnF7hCw6n6VPbu3Us0GqW7u5u2tjY7UyUeN+u2jI6O5ojs95/6vl3/fF14Ha3BVoLB4LTsyA+55HvobqdbytQKJWcmk6J/lbcpCNwIPF5UiwThGFiCvnHRRp7tNL1zZ9JJS6CFiooKjhw5AkB7ezsrVqzA6/USi5khmtHR0Zy64DAeQw85QlRWVk5bhJVSKKVwOp243W5byK3fpWgrJwj5zCTk8vm8n/cBu4F3TudgpVSVUupOpdSIUqpTKfX+Sca9TSn1nFJqODPuG1JDRrCwBH1Vwyp7gY4j5cDtdLNp0yZaWlpoa2sDoLu7m3Q6TTKZpKKignQ6zeOPPo57bDy2bXn5ylB4PNN/mymlcLlc+P1+8yaQdx+Q+LkwH8wkD33qZovH5juZ6y3G7H50n1Jqh9Y6v/xuAPgw8DRQA/wa+CTwuVleX1gAWILeEGygpbKFHUd34DAcuBwugsEgp59+OlprOjo6iEQijI5mSuu2teF2u9m5cyfB/iCR2ggpX8r20NOp9IwEHbAFHcY9c6VNZRcPXZgPZhJy+YXWekKFfKXUz7TWN05xbBB4E3C61noE2KqU+hGmd58j6Frr72U97VJK/QS4bpLzVgFVeZsnlroTFgyWoNcH6+22cg7DgdvttuPaSikCgQCRSIQXXngBp9NJbW0toVCI+vp67nnpHpxJ57iga7Pn5nTSFbNZvnw5gYC50jMWjuGKu0h5M0W/xEMX5oGZuBFXT7L9ymkcexqgtNYvZ23bClwxjWMvBrZPsu/DwGencQ5hAZAwEgyODeJwOKj2V7OmYQ2P7H8Eh+HA58tNWQwGg3R3dwNwxhlnEAqFgPHJTMuTjqViqLTCoRwz9tCXLRv/0mp4DIYWj7e1Ew9dmA+mfNdlrRB1KqUuIjdauBKITDxqAiFgOG/bIHDMjryZidgLgU2TDLkNuCNvWwvwyDRsEk4weqO9gNn2zelwckbzGfzl6X/J0O4hO/RhYWWrrFy5ksWLF9vbrXi3Jeh9o3040qaYz1TQj4U0rhDmg+m4EQ9mfmsgu56Lxuwt+olpnCMCVORtqwRGCowFQCl1PfB14Aqt9ZFCY7TWg5g3huzjpmGOcCLSMdgBQH3AXGGplOLiZRfzp/Y/TQiXtLa2EggE7AnSbLTS9hrnSDyCK+3C4/TMOORyLLIrKgpCqZhS0DOrQlFKvaS1PnYJusnZBWil1Gqt9Y7Mtk3AS4UGK6WuAn4EXKu13nqc1xROUEYTozzT+QzLa5bb/UCNtMHdO+8GYNPiTTnjU6lUTl44QCgUssMs+WilbQ8dIOQMHVfIpRBN4SbesPYNE6o8CkIpmEmWy/GKOVrrqFLqLuCLSql3AMswJ0QLTbJeBvwU+DOt9ZPHe03hxOWHz/6QbUe2Ue2v5itXfQWHcvDYgcc4MnKE+lA9F7VdlDO+kKAfk6yQC0B4LIy3ymtPcM6W0xefXpTzCMJMmXYeulLKoZT6hFJqt1JqKLPtSqXUu6Z5ig8wHqb5HfA5rfUDSqmlSqmIUmppZtynMcMxd2e2R5RSk02KCguQwyOHARgYG2DH0R3EU3F+vePXALxhzRtyJhzT6TSGYcwoXJIdckGD1/CyZMmSmd0UJkFCfsJ8MpN38OeAa4F/AH6Q2bYH+DLwr1MdnIl3v6nA9g7MSVPruTTSOMkZiY9PrTy6/1E6BjsYig3RWt3Kmc1nEolE2LJlC2effbYtoDMRY6007pgbz6iHhD+B1+ktipgLwnwzk5Wifwm8Tmt9J2D17WoH2optlHDyEk/FiafM2itKKbZ0beGBfeZShetXXY9Sir6+PgYHBzl69CipVKY70AxDLgCBgQAqrfC6Zi/ofreZZdNc0TzFSEGYO2Yi6GHgUN42J5AqnjnCyc5QzMzlrg3UsrZhLUbaYGBsALfTzaqGVQD26s/e3t7jEnS7MiJmLL0YHvonLvkEr1nxGm7cdMw1doIwp8xE0F8E3pC37TpgS/HMEU52huPmcoUKXwVnLznb3n5q7al4nGYWytiYWf+8r6+PZDLTHWgGMfTsCdFieehN4SbevP7NhDyFM2sEoRTM5F38ccz6K68DfEqp7wNvZnorRQWhIFrrnIlEy0Ov8FZwWt1p9vZV9avsx5aHPjY2xtCQOX4mgpwj6EXy0AWhHJhJx6KngDMxF/I8CLiB12NOlArCjPn1jl/z4bs/zKGh8UietRq0JlBDbaDW3r6qfhXxeJz77ruPgYEBampqAOxyuTMS5Kz+Fw4cRfHQBaEcmJagK6UuVEr9PXCq1vpmzFDLC8BdmF66IMyI5zqf4zc7fsNoYpTHDjxmb+8c7gTGJxc/eekneffZ7+aUmlNob2+3a5u3tbXh9Xrp7+8HwOv1Tvva2R56hdtcwCyCLiwEphR0pdTfYC75/wTwG6XUxzDzyD8EfBRYO6cWCguO3mgvdzx/h/18e/f4MoPO4U4cSQeeqBkvX1a9jLNaziKZTNLe3m6Pa2xspK6uDq01Hs/Mlu2f0XSG/TjsNpfoi6ALC4HpeOg3A3+hta7HTF38Ema64hqt9Y+11uljHi0IeWzt2kosGbNj5L2jZphFa03XSBfh3jBH9h6xJzwB9u/fTyqV4uKLL+bqq6/G5XJRW2uGZKbbNs7i0nMvpSnchFZaBF1YUExH0Jdorf8r8/gXmd9/p7VOzJFNwgJncGwQb8TLypqVOB1OkkaShJEgkoiQTCVx48blcNHb20tHR4ftnTc0NFBZWWmLb12dWQ99poLe1NREqMHMRgm5c8vqCsKJzHTexbboa60NpdSI1jo6hzYJC5yBoQH8Q36SvUmC6SCRZIRYMsZgbJDAYACv04yHP/us2TO0s7OTeDzOqaeemnOeQCDAkiVLaGqaeZ/y+lA9u/Vuaj21uA1p6CwsDKYj6F6l1GeynvvynqO1/kJxzRIWKgkjwdaDWwkQYLR3lGBPEEfKwWhylMGxQTyjHrx+U9AXL17M4cOH6e3tJRwO25ktFkopNm3adFx2rF+8HgbAM+ahoaFhtn+WIJQF0xH0J4Ds+ipP5T3XgAi6MC1+se0X9tpil3LZhbYi8Qh9Y32kXWk8Tg9XXXUVbrebrq4utNY0NTUV1Yt2Op24HC6SyeRxefiCUI5Mpx76JSWwQ1igDAwMEA6H7Rj1w+0P4zPMdnEti1to7zczV/a376fH1YNKK+qa6uysFa3NpHFrArRYOJ1O+3d9fX1Rzy0I88VMlv4LwowwDINHH32UZ555BoD+UTNnXKUVWmk2b9pMcJE5oXlgzwG2bduGSitaqif2+a6srCyqbZag19fXy4SosGAQQRfmDKtwVm+vmZY4GBsEwGE4OKvtLIL+INXN1aQ8KV7pfYXUcAq/209rbat9js2bN7N06dKitoeDcUFftGhRUc8rCPOJuCZC0Xj88cftXHEYF3QwQyexlLnKs9HTyCmNpwAQTUaJ1EbwxDwEBgI0hZtyVn02NzfT3Fz8krS1tbUsX75c4ufCgkI8dKFo9PX12cWygJyFQffeey/9g/04Ug4cKYcdtz6l+hRwQMKfIO1M0xhqLLo3Xgi3282aNWsk3CIsKETQhaJgGIb92PLMsz10wzDoPdqLZ9SDy+GyUwUvOeUSLjnlElBw6rJTcTvcRWnWLAgnI+KeCEXBKmkLEI1GqaystAV906ZNtLe3s69/H55RD4FFAbshs9fl5aZNN3H1aVcTcAU41HGIqqqq+fgTBOGERzx0oShEIhH7cTRqLiS2BL2mpob6+nqiw1EchoNQ5cQmEDWBGnweH6eeeioOh7wtBeF4kE+OUBSyBT2RMMv8ZLeHq6urI2WYzy3vXBCE4iKCLhSFSCRiZ6cUEnR30E1Km88rQhXzY6QgLHAkhi4UhUgkQkVFBQMDA3Z2SyqVQinFYx2P8ZMtP6EyWYlCEQpK301BmAvEQxdmjdaaSCRCKBTC4/HkeOgul4uXul8CYMw/RsqTIhwIz6e5grBgEUEXZk08HieVShEKhXC73baHHovF8Hg8tA+Y9VoaFzdiLDZoqZy4tF8QhNkjIRdh1lgTotmCrrVmYGAAf4WfwcFBfG4fn77006R1GqfDOc8WC8LCRDx0YdZYgh4MBnG73fT393Pw4EFisRjdqW4A2qrbUEqJmAvCHCIeunDcaK0ZGRkhFouhlMLn89kLjF544QVGk6M8cPAB8EBbVdv8GisIJwHioQvHzeDgIA899BC7d+/G7TbbuK1atcreP5Yaw3CbJQFW1K6YLzMF4aRBBF04brKX+1tFrhoaGuyStM6AExT43D7WL1o/LzYKwsmEhFyE48ZKTwSzemEqbS4c8vnMjkQOnwMS8Kplr5ImzIJQAkTQheMmHo/bjx0OB7c+eCspI8VbW98KQNqbBiDoDs6LfYJwsiGCLhw32YI+ODbIwZGDAKR9afx+P71es1NRwCO1WwShFEgMXThusgW9e6Tbfhx1Rrn88suJabNDkXjoglAaRNCF4yYej9ulbo9GjtrbO4c7AYgmzDK64qELQmkQQS9j2g+30364fb7NmJREIkE4HCat0/SP9tvbLXEfTZpZMEGPeOiCUApE0MsUrTXf/eV3+e4vv4vWer7NmYDWmng8TjAYZCg2RNI53j90OD5M0kjSGzVj6GGPFOMShFIggl6mJNPjAmlo4xgj5wfDMDAMw2wXtxii1VHWNa4DTEHfdmQbY8kxWipbqAnUzK+xgnCSIIJepsRT4xOORrr8BN2aEPV6vRzVR8EBZzSfAUDnUCfff+r7AJy79Nx5s1EQTjZE0MuUWCpmP8721ssFS9A9Hg+JlLnAqDHUOGHcOS3nlNQuQTiZEUEvU8YSY/ZjqxdnOZHtoScMU9D9bn/OmLA3TJW/qtSmCcJJiwh6mZIj6Om5EfTsPPKZYi3793q9xI2Mt+705Iy5adNNx2+cIAgzRlaKlinReNR+PBeTon19fTz++OO86lWvoqJi5k2bc0IuGQ/d4/TwgfM+wMtHX+bitoulM5EglBgR9DJlLD63IZfDhw8DMDQ0dNyC7nQ5SZO2Y+hel5dNTZvY1LSpmKYKgjBNRNDLlFhy7iZFtdYcPWou/rG6Dc2U6FiUP+7/I8888Iztobud7qLZKAjCzBFBL1NyPPRUcT30SCRi1zI/XkHvGe4h7UhzaOgQYFZbdDnk7SQI84l8AsuMzs5OxsbGcgQ9njz+yctCWN55ZWUl0Wh0itGFGY2Nop3jK1jT6XRRbBME4fiRLJcyY//+/ezcuTOnG1Cxs1yOHj1KOBymrq6OaDRKR0cHQ0NDMzpHdDRK2iEiLgjlhAh6mRGJREin0wwdHRfYRDJxjCNmhmEY9PX10dDQQCgUIp1O88ILL/Dwww/P6Bxj8TG0o/xqzAjCyYyEXMqIRCJh53ePRcdDLslU8SZFo9EoWmsqKyvx+/1TH1CARCJBwkiQdoqHLgjlhHjoZUQ0GmUkPkJUR+3MEYCkUVxBB3B4HGzt25pTJ2a6cfB4PE7ciIuHLghlhgh6GRGJRHj56Ms8MvQIkcR49kkx89CtrJanup/i5y/+nC3OLdTX1wMwMjIyrXPE43Hiqbh46IJQZpRM0JVSVUqpO5VSI0qpTqXU+ycZt04p9XulVJ9S6qRyAUciI8SNOIlAgpQjRcpjCnmxBd3n87GzbycAhxOHaTutzbz+NARda82hQ4dIGAnx0AWhzCilh/4dzJj9YuAa4PNKqUsLjEsCdwLvLKFtZcHA8ABpVxoUDDcME6swFxclk8ULuQwNDVFZWZmzWEm5FQ6Hg+HhYUZGRtiyZYsdy89neHiYzsOdJI1kTpbLmze8uWg2CoJwfJRkUlQpFQTeBJyutR4BtiqlfoQp2g9kj9Va7wR2KqVOncZ5q4CqvM0nZAGRnp4eent7MVwGDoeDNGl05gtKsdIWU6kUkUiEpqYmjnaP9wAdS40RDofp6elhcHCQvr4+AE4//fQJ5zAMg3gqjkbnuAOvOfU1RbFREITjp1Qe+mmA0lq/nLVtK7Buluf9MNCe9/PILM9ZchKJBE8++STRWJSUJ0VzRTMfuegjbGjaABTPQ49EImitcfvdjCXHs2hiqRjhcJjh4WFbzCdbQZpKpUgYCZK+cZu8Lm9R7BMEYXaUStBDwHDetkFgts0mbwOW5f1cNMtzlhxrUY+RNkj6kgTcAVbWr6SppgmYXZnbbKwSAiOp3Fj5aHJ0QoGuWCxGIQzDIJFKMFYxRtgbZlF4ER86/0NFsU8QhNlRqjz0CJBf0q8SmF5axSRorQcxbww2SqnZnHJesAR9MDBIOpYm6AkC4Ha70Q5NfLQ4gm4YZoriUDx3VehYcoymQFPOtng8TjqdxuHIveenUimz/rkbzmw5kxs33lgU2wRBmD2l8tB3AVoptTpr2ybgpRJdv6wZGhrC5XWxLb4NFPhcPgBcDheGyyAxNvuVoolEgj179gAwEB/I2TeaHKW+vp5FixaxYcMGVq1ahda64DcDO4auNNW+6lnbJQhC8SiJoGuto8BdwBeVUmGl1AbMCdEf5Y9VJj7Ak3nuyzxfsAwPD+P2j5eePTh0EMgIutsgEUug9exSBA8dOkR/fz8AfTEzTh7yhgDTQ3e5XJx11lm0trZSWVlpbh8bm3AeK4aulZb2coJQZpQybfEDgAa6gN8Bn9NaP6CUWqqUiiillmbGtQJjwPbM87HMz4LEyjzxBMbbt1254krAFPS0K42RMiZNI5wu2ZOclqAvqVwCkDNBCtglAQrF0Q3DMEMuCqr94qELQjlRsloumXj3mwps78CcNLWe7wdOvED4cTI8bM4VO/1OAGoDtZzdcjZgNl02XAZGzCASieD15maTHD16lKNHj7Ju3dTJQjmCPmYKemtVKzuO7mA0OZoz1uczvxAV8tBjiRiRZAQU1AXqpvtnCoJQAmTpfwnp6emZ0KzCmhB1eM1/RWt1qz2x63f5SbvSpNKpCWmE+/bt46mnnqK9vX1aWTDW8RpNf6wfpRStVa3mvnjuud1uNy6Xq6CHvq9vHymdYlnNMuqCIuiCUE6IoJcIK9f8wQcfzMkrHxoawuv1knKYQm9NiILpoaedaVI6RTQaZXh4mJ6eHgAOHDhgj7O8/MlIJpO26MdSMbTW1PhrqA3WAjAQG5hwjN/vL+ihj4yNoJXmtNrTpvunC4JQIkTQS4QlqGNjYzz00EO2qI+MjFBRUUHMML3hgDtgH+N3+0FByml66A899BBPPvkkiUSCSCTC8uXLgakFPdu7t3qV1gXr7CyVwbHBCcf4fL6Cgp5MJdFK43F5JuwTBGF+EUEvEdlhkbGxMZ577jm01kSjUYLBoD0xme2hW4+TzmSOKA8MmB51Y2MjXq93yjZy2cdapXgrvBVU+CpwKAcj8ZEJJXr9fn/BkEsylQQHeJ2yOlQQyg0R9BJhZalccsklbNy4kZ6eHvbt20cymSQQCIwLuntc0C1vPa7iOS3p+vvNGHhlZSVer3fSVZ0Avb29tLe32wuEEmnTjrA3jEM5qPSZKYpDsdzFRj6fz15clE0qlRIPXRDKFBH0EjA6Ospzzz0HgMfjYcmSJYTDYXbt2gVgCnrKFHS/a7yLkCXuMUcsJw+9p6eHiooKXC6XLbyFGBsb44knnmBoaIhg0Fx9anniYa9ZdcHKJe8b7cs51u/3o7WecLNIJpNoh8bjFEEXhHJDBL0EdHR02I89Hg9KKZqbm+2MF7/fTzxpinJ2DN2hHPhcPgyXkVNxcWhoiOpqM/7t8/km9dCthUTWNaqqqlAhM4PGEvSmsLnk//DI4ZxjrdTF/HOnkqaHLiEXQSg/RNBLQLYHbaUkNjQ02NtCoRDDcXNi06rjYmHloueX0LUE3ev1Eo/HC64kzV6MpLXmoosuQjWZ1w95zNT/lkqz2vChoUO5180sLsqeGNVak0qlSDvS4qELQhkiTaJLgJVrnt2UuaKigtraWpqbm3G5XHbIozZQm3NswB1gwDGAw+0w19lmqKmpAUxP2qq7YnnVFtnpkU1Npic+EjfroVV4zVpp1mpRq9wAwPOHn8erTA8820M3DIN0Oo12aSmZKwhliAj6HGMYBsPDw5x66qmsWrXK3t453Ilu1ixdupS0Ttu54Pn1USp9lXQOd4IbyFr9b90csj3pfEFPJBK43W4uvfRSPB7Tox5JmIJuhVwsD71zqBMjbdA32sf3nvweAH/m/7McDz2ZTGKkDYmhC0KZIoI+h2zv3s7uzt04tdOMX2eV9v38Hz8PwKLQImoCNaTTaSp8FROEsjpghlYMl5Ej6Na5rMnOaDRqh2EAurq6aG9vJxAI5JQMsFaFWoW5Qp4QtYFa+kb7ODJyhFd6X7HHOj3OHA89mUxiaFPQxUMXhPJDBH0Oue2x2/BGvFwQvICqqip7e3Y8fDg+bLZzA2r8NRPOYS3+STgS+DG9cafTae8PBAIopSbkoltZNdlx9FQ6xVhyDIdyEHSPx+rbqtvoG+1j/+B+dvXusrdrp57goae12RpPPHRBKD9kUnSOiKfMiVBn0onD5cgJhxwZOWI/NtIGO3t2AtBc2TzhPFZFwyimYPv9fi644AJ7v8PhwO/3T7q4KLt2TLZ3nv1twarpcmDwAD3RHnt70pGcNOQiHroglB/ioc8R3ZFuAJwJJ+6AO0dADwyO12GJJqM8f/h5AE5vmtiU2RL0iCvCmjVraGtry/HQwQy7ZAv6ZLXTrUyasCe3819rtSno+wf25wh6XMUhgd25KJVKmR66xNAFoSwRD32O6BrpwpF04Ew57dK4Fk8dfMp+fGjoEB2DHXhdXlY3rM4/jS3oQ7Ehli9fPkHMYVzQLSG3HgeDQc455xx7XCSRGz+3WFpllqJv72+3a72AebPRWturRe0YuoRcBKEsEUGfI46MHMEbNcMSnlpT/CKJCE8ffJpXesYnHh878BgA6xvXFxRJK+ulUEVEi2AwSDKZtNMUrWJdZ5xxRk6+u5WymO+hhzyhgqVwrRuA1YvUWvzkdDlPyN6tgrDQEUGfI7pGunCmnGYLOW22kPvGo9/gX5/515yQiLUU//TFE8MtAEF3ELfTTSwZm9BZyB6TlekCZgVHpRShUK4nbgm0lbKYTWOo0X58ZsuZ5nmS5g3AsjeRNCdYXU6J1AlCOSKCPkd0jXThSDlIu9KMJkfZ2buTg4Pji3fOW3qe/djldLF+0fqC51FK2WGXgbHCXrol6E8++ST9/f0MDw8TDAZxOp0Y6fFVplZnIr/bP+Ec1gIjt9PNdauuA8Zz1u2QSypp2ysIQvkhn8w5IK3TdI90EzbCJJwJRpOjOcWvHA4Hm5s380THEwDcuPHGgiJrUe2v5mjkKIOxQRZXLJ6wPxAw67+kUimee+45HA6HnSb5mfs/QzwV52tXf41EyvSwC2WoXH3a1aR1mouXXUyl16zAOJwYBs+4oCeSCbRD43LI20YQyhH5ZM4BI/ER0klTBNOuNHv69tjedVO4ifef+34Sxnh++IWtFx7zfJaH/ofdf2Bv/17bg7awSuOCmaMejUZZunQpqXSKo5GjgNmpyEqlLCToAU+AN60fb/nqd/tJjaVIppM5HrpWGrfDPb0XQhCEkiKCPgcMxgZxpB04lROn20lvtJfeaC9gxsoXhRcB8L5z30dzRfOUE4xVvirAXHm6vXs7Zyw+o6CnDuPedDgcJpYaz1jZ2rWVB/Y9AEyvOUWNv4bukW7iqXiOh45CBF0QyhSJoc8BQ2NDqLSi0lfJ31/895y79Fx7X3ZoZfPizTmTkZORv4I0norTG+3lKw99hRe6XgCgpcWsyWKtDPX5fDkpiD969kf24+yuSJNhfSvIFvSkYXroTsfE1ElBEOYfEfQ5YDA2iEorPE4PteFa1jastfcdK1Y+GVY9FwuN5pfbf8nevr1854nvALBp0yYqKyvtFEOPx2NPguYznRzyan81qFxBt7oViYcuCOWJCPpxoLWedDUmZARdm4Ludrtz0gSzOxJNFyvkYhFPxYkmcpf6K6VwucYjaG63e9I0x+ks268OVKOVJm7EcxYWaYeWLBdBKFPkk3kc3PH8Hbx84GWuqLyCyy65bMLqzb7RPhxpBx53AUE/Dg99QsjFiONUE8Melh2WuGfH0LOZTj9Q65o5IZdUEhSS5SIIZYp46DNEa83jBx4nNhxjW8c2oqPRCftf6n4JlVZU+atwOp2zFvT8hUDxVByHmvivszx0l8uFUmrSkMt0Yuh1gTq00sRSMVvQDcNc9i+CLgjliQj6DLGKVzlTTo5EjvBsx7M5+/cP7Gc4NkzIHaI6aMa+rXZvMD0xzSc/CyaWipFMj3cjstIRLUF3u932uEJMJ8ulMdQICsaSY2aXIq3ttEURdEEoT+STOU2Ghobo7u5mJGiunnQkzXthe297zritXVsBWBJaYncJcjvHJxGLIYYJI2Ev4wezcFdDqMEOuVjXnU0MvdJXicflIZlOEolHSCQSZrVFV1oEXRDKFPlkTpOnnnqKeDxOYmkCNDgNUzxDrtx6KVYaYVOwyfaUAf5s7Z/RHe2eVpriVMRSMbu2OZhlcRtCDRM89NkIulKK+lA9I4zQF+mjbqQOrTWGy8i5QQmCUD6IoE+TdDpNWqc52H0QlVZ2w+ZYfDys8e0nvk3ncCdel5ewM5zT+u3qlVfP6vpKKTuzZjQxatc2B3K8dTA99KHYEM925oaDLKbrYVf5qxhhxLze8DBpncZwG+KhC0KZIjH0abKzfyePHniUPUf24Ew6CbjN+inxhBm/HooNsa1rGwCbmjYRG4vZRbOKwfvOeZ/9uH2gHSNt2M+tFEYr1r506VL+6ZF/slengllQ64K2C7hyxZXTvqbl8afSKbOCo1OhHbKwSBDKFRH0adIx2AGAK+bCp312dULLQ7fayAG8fsXrSafTE8rXzobTF5/OTZtuAmBv396cfZaHvnz5cs4//3zq6uroGunKGdMQbODtm9/OG9e/cdrXtPLNk6kkIyMjuHwuWfovCGWMfHeeBlprHIZ573PH3VRQgbfWS9qZJp6I8/vdv+euF+8C4HVrXkc6Yab5FdNDh9xsGTALao0mRu14usvlora2NqcJtYVVP2YmWLHy7o5unBWZzksRyUMXhHJFPPRjsO3INr775HcZigwBEAvHiIfiNLY1smbTGrRDE4/HbTEHc8l8b28vDoeDcHhiI4nZEPAEcp6vaVgDMGHVaP9of87z0+pO46K2i2Z8PcsTT+u02VfUa75dZKWoIJQn8sk8Bt9+/NsABDE97bQrTSKQYO3KtTRWNZJ2pYnH4rgcLjyjHkarR6nwVtC9r5va2tqcLJdikL9idE3DGp499CzRZK6gZzd6/uSln2RZ9bLjup4l3NZkrMOTEXQlbxtBKEfkkzkN2vvNXHOtND6Xj7NbzmYoNoThNIhFYgTjQVRaodIKR8JBJBJh2bLjE9FjYVVABKgP1VMfrAfISWGEcUG/oO2C4xZzyPXQAcisiRIPXRDKEwm5TIO+iNltSCvNrVfeitvpJuAOkHaZQqfSZnaJO+7mwCsHAGhsnH2+eT7Z+ePVvmqC7kwv0byQi5XdUh+on9X1rFi5Jehp0jnbBUEoL0TQp0EqZU4yttW02XVV/G6/HYJIeccnIZOxJFVVVfj9M6/ZMhMqvBUEPaag5+eh94yaHrrlwR8vLoeLoUVDVC6tZOPGjXa5AclyEYTyRAR9Eqz6KABKmx540DeetaKUIlwVZrR6lGj1uIfsUI458c4tLmi7AIDXrHgNIa+Z9RJJRHLK+Vohl9kKutvpRjs1nmoPS5cutXuSTqeeuiAIpUe+O09CdhjDFnRvbhpiha+CnsD4BKQVAqmtrZ0zu27aeBOvX/16qvxVgCm6SSNJwkjgdXnRWtuCXhesm9W1LE/cSoO0+qBOp3SAIAilRzz0ScgJY2Sc33xBz65m+DfX/A2nLz4dgMrKyjmzy+1022IO47npI/ERnuh4gvaBdmLJGD63b0Le+kyxFxZlQi1xw/zWIh66IJQn4qEX4MW9L7JvcJ/93PLQQ/5cgRyOjddTWXnKSg7uO8jY2FhO56C5JuAJMDA2wMP7H+benffa2+uD9VM2n54Ka/Lzsf2PceWKK+2Qi3joglCeiKDnMZYc447f32FmsGQiFlYWS4WvImfsDRtu4PZnbreX07/qVa86Zmu6ucDywrcd2Zazfbbxc8id/Pzhsz8kaZieunjoglCeiKDnsbt7Nw7DgXaYwnx+6/k8v+15AEK+XA/97JazWVG7ws4PL/ZCoulgCfrg2GDO9rrA7OLnkJtvPhwbtotyiaALQnkiMfQ8dh40i2xZXvkZi89AaYVWekJMWilFTaBm1qGN2WDdTPJz0Su8FYWGz4jsfHOnw2ln/oigC0J5IoKeR0e3WVXRipu3Vreagu6YKOjlwNrGtQW3H0/v0nyyQy5uh1uyXAShzBFBz0JrzdG+o4DpoTsTTjr3deJP+UEVx+stNivrVhYUWKte+2zI7kzkdDhtQRcPXRDKE4mhZ9Ed6cYYM1CY3nm4J8zevXs5o+UMgrXBCdUOywG3083axrU83/l8zvZieOjZIReNRmuzuYU0uBCE8kQEPYsdh3egtMJwGziTpmgppXjja984r3HyqdiwaMOcCLqhx7siSfxcEMofCblksa/TzD1fvGgxTuVkdf1qnE5nWYs5mIKeb2NRQi5ZMXQr517i54JQvoigZ3Ho6CG00lx42oVc0HpBUXK5S0HYG+bM5jNzthVD0Jsrmu3aMRI/F4TyRwQ9QzwVZ2hoiLQnTXNV83ybM2Pedda7cmq3+Ny+WZ9TKcWb1705Z5tVbVIQhPKjZIKulKpSSt2plBpRSnUqpd5/jLF/mxkzopT6hVJqztNL9vbtxZlwUl1dTUtTC0uWLJnrSxYVpVRO2KVYnnT2jcHpcPK61a8rynkFQSg+pfTQv4M5CbsYuAb4vFLq0vxBSqnXAJ/NjGkG3MC359q47Qe2A9DS0IJSira2trm+ZNEZS44V/ZwO5aDaX41Sined9S5WN6wu+jUEQSgOJclyUUoFgTcBp2utR4CtSqkfAe8EHsgb/nbg37TWWzPH/gOwRSn1Pq31aN55q4CqvONbjsfGPYf2ALBu2TpgfpbxzxarFV12/ngx+PsL/56EkWBp1dKinlcQhOJSqrTF0wCltX45a9tW4IoCY9cB91hPtNY7MqGEFcALeWM/jOnNz4p4Kk5vby/ao1m7yFx5eSIKukVTuKmo51sUXlTU8wmCMDeUKuQSAobztg0ChWbYQsBQ3rahScbeBizL+7lopsYppXjdpa/j/DPPtxcPud1uwuEwmzZtmunp5g0rI+X61dfPsyWCIMwHpfLQI0D+xGYlMDLNsRWFxmqtBzFvDDbHkzPucXq4eMXFOduUUlxyySUzPtd88tZNb+WqFVeJRy0IJyml8tB3AVoplT2jtgl4qcDYl4CN1hOl1CpAAbvn0sCFgMvhEjEXhJOYkgi61joK3AV8USkVVkptwJwQ/VGB4XcA71BKbVBKhYEvAb/InxAVBEEQcill2uIHMLtzdgG/Az6ntX5AKbVUKRVRSi0F0FrfB3wxM6YLSAMfLKGdgiAIJyQlK86ViXe/qcD2DsyJ0Oxt36YEueeCIAgLCVn6LwiCsEAQQRcEQVggiKALgiAsEETQBUEQFggi6IIgCAsEEXRBEIQFggi6IAjCAmEhNol2Ahw6dGi+7RAEQSgqWbrmLLRfaa1LZ00JUEpdCDwy33YIgiDMIRdprR/N37gQBd0LnIVZNsCYwaEtmDeCi4BycO/FnmNTTvaILZNTbvZAedk0U1ucQBPwjNY6nr9zwYVcMn/khDvXVGSV3T2ktd5fTJuOB7Hn2JSTPWLL5JSbPVBeNh2nLXsn2yGTooIgCAsEEXRBEIQFggi6IAjCAkEEfZxB4PPktbSbRwYRe47FIOVjzyBiy2QMUl72QHnZNEgRbVlwWS6CIAgnK+KhC4IgLBBE0AVBEBYIJ52gK6UWXO69cHKispKY5xullGe+bRBOIkFXSjUopb4MXDXftgAopQJKKfd822Fh3eiUUvP+nigzWyqtBublgFKqSSn1XgBdBhNgmc/VN4F3z7ctAEqpkFKqcr7tmC/m/QNTCpRSXwH2AB/DXDY7r95Nxp5ngV8ppf5KKRWa6pg5tueTwPeVUpVa6/Q8vzblZMuXga3AD5RSX1RKLZsvWzL2fAXYCWzMPJ9XDz3rc3UzUJPZNm+akrFnG/A/SqmPKqWWZLbP53vIk/ldktdlQQu6UuoGpdQAcDawEvgkcDnMn3ejlPoWcD5wI/AEcAvwKaVUweppc2zLEqXUz4EPA6cAfwHz89qUmS3rlFJPYv6fXg18E7gB2FxqWzL2nKWU2ge8BtiotX4fzOt7+M1KqSHMz9VS4F3AlRmb0vNk0xeACzE/3z8Frga+rpRyzePr9CngbqVUXcY5mXO9XdCCjuk1vEtrfZnWugsIAYZSKlhqQ5RSDqVUE3Au8G6t9Vat9f8F/hdTvG4otU2AD3gOuB54EHi1UupUy94S2+IvI1scwDe11q/SWu8D4kA98/d5aQQSwN9qrduVUmuUUhfP4zcGDfxN5nM1CKSBiFKqudSGKKWcmRDLucDntNb7tNa3A/8OXAZ8IDOuZP+7TBjqduCvMT9jN0NpbnYLStAzcekN1nOt9fe01ndleb8vYpadjJbansw/8wjmh3Nl1rBngCXAm5VSdXNsjzvz25mxaTfwM631k8AfgBRwU5a9c2lLUCl1gfWVVGu9C/jPMrFlG/C/SilXJuzyJ+B+YLlS6k1KqdoS2ePN2PNb4HHgg0qpezO2fBTYopR6y1w7KAXs+S+t9X9lfa46gTWUaKFO9v9La21orYeAFZiVCy1eAcLA25VSi0v8zcEFPI35/r0duFQptSlj+5xq7oIRdKXUxzHfWD9SSv1CKfX6zHaX1toqo/s0MKKUung+7Ml89bsT+IRSynrznQ38J5AEVs2hPX8PvKKUWqm1NqyJR6314czvJzFvLpuUWVN+zt58SqlbgMPAbcBvsyb5DpWBLe/J2BDD/GA+BYS01m/CnPf4M+Bv58KWAvb8Rin1gcyufwY2AAcx3yevA76G+c3u/BLa857MdgemZw7wAOb79zWZfXMWsy7w/3p/Ztd3gX9USm3K2HYF8BPMOYdL5sqejE1WnNxylA4D/621fhx4GNiBGUqcey9da33C/wAXYH5dXwmcCnwWGACWZfZbK2JXYsatLyuxPZ/L2LMUqAYewizxuxf4HdAG7ALOngNbgsCtmG+qx4BfFhjjyHp9fgj8S9a+iiLbswbT29yQeS3+BrNu/cV548rGluzXCfh/wI8A3xz8ryaz55LM/rOAQJ49W4Eb5+h9PN3/VSPwG8zwZtHtmIY9F2X235n5XO3CvMmcmnnPXzWHNt2CeTPbmHnuKjDm9ZnP/Osyz51zZs9c/gPm+idLqP8KeDpv313Aw9njMo9fAP5P5rGjhPb8d5Y9IWA5cGHW/kczH1hVZJtCwJswJ/fOAXYD1032xsL0+P4D+BRwH/DRItvzWuAA4Mna9i+Zv7+xHG3Je//8HPj8HL2fJ7PncaAua5sz6/FDwF+U2J5C/6v7gX+c7H1VJHuuKWDPdzOvTwjzG1U9cFbe6/OaObDFk3lfPovpJD5dYIylB43AV4B7svbVzMVrdEKHXHTmlQGqgP1KqZqs3X8NnK2Uulprra34H/B7YLNSSukif/2Zwp53ZOx5rdY6AuzXWj+qlHIrpf4T6ANeyDpHsWyKAH/QWv8ReB5TkD6T2WdYX4+zviY/B1wKfBrYqrX+WjHtwey4sgUz5mnxYaAVc0LUjvWXiS1OYJFSyp+Z6NoM3FNkO6ayZwnwhix7XJnJwNsxheXBEtuT/fpYn6u7MSeyHXo8xFlsHAXsuRnz9blRa50C+rXWzyilfEqpn2JO4D45B7akM+f9FKYHvlIpdROMr6OwPsta625Mh25MKXWbUupRzChC8ZmLu0Spfhi/A64G+sl8FWQ8hPBV4KG8Y27DnPkuqnd+PPZgfmU8iPl1dU7u2AVsPA0zPn1L5rkra9+ZQAfw22Lbk/XaLMP8lvRWcj3NjwCvlJstmKL2LcwJ7V+R5SnPkz1h4MuYIbz/Bmrn056sbTdjiv2EkMM8vHfeiDkp+pu5eH2yruPPevz3QE++zVnPmzM2xch8k5kTm+bqxEV+4ZYBVQX+wQpwZx7/HHMCIpy170bMGHWt9QYAvGVgT31m20pg7Rzb48jb5sJc1bc36zVZnPndBqycpS2ezG9n3vbs1+YbmF/R12btOwfzG0RbZltLGdhySmbbRmB9Ef5Ps7FnS9ZrcymweZ7tsf9Xme3uMrDHmjNbCqyYrT0zsFthflN6Bfhy/uuB+Y1iJ2Z0YE4dt5L8wbN4oRoxJzdewYxTfZDMxBhZwpx5Qf2Ys99/D7Rktt8C/EjsyZ1MxMzP/wXmPMODwJ+KYMti4GfAtwvsy/amvJk3/2OY31jWZba/A/hFkV6XsrFF7Dnx7Mm63rFuMDmOUubxdZjptt7M86WZ3/Vk3fzm8mfOLzDLF/RHwB2Zx3+H+RXqh3ljvoPpCbuBt2DeBZ/EzP+MAG/Jf+HLwZ4Svz5/JNeb8mPeCFLAt4pgx1mZv3Fr5sN2eWa7o4AtuzG/Mb0Gc0XfXsxFIFEyWRKz+V8V25aF9NqIPdO2abo3mOqsx67M7/8CHsn8LbuL8R6ake2lvuA0X1AHUIG5oOMG64XEXNo7jLm8N4A5w/wgmTthZlwbZmz6VqBV7OFBMt8QMuOqMGf+nwGai2TPeZhe0mbg65g5uNY+hVk/557MdVuz9oUw87r/TxFfm7KxRew5Ie2ZyQ1mB7A6a5sT+B/MVMpvFMumGdk/Hxed5IVcli0wQAOwHbgib9zXgGczj1dlbS/qZMwCtMeKlzvJxMxnawvjXzv9mItvwEyPvB9zabg13k3uNwQXRUrPLCdbxJ4Tz54C9s3kBrMka58js30rRXKUjsv++bpw1gtRi5k9sBMzn/SfrRcK82vPH60XM/N7BeYS/tdmvZBFy3sVe2Zsy+K861cDn8D89lCbt08tRFvEnhPPniy7ZnuDcWbbX2z7Zvozr3nomTondwNDwFrMlMImwFrO+w/AxUqpK3XmFcOMQx/BfOHRWqd1kfJexZ7jsuUDmevozO8BzFosQ2SWO2N+GNEmC8oWsefEsydjU61S6leYWWf/BdyWqfkyhhmTBzNz5o/AjVn1e1Ja6/3KxKnNWjLZ9s8r872wqB7zrv0OrXVKa30nZv0T6wVqx5zN/r5SanlmWxdmHPiI2FNSeyazJX9hEsDLmPVpLlBK3QrsUkpds0BtEXtOMHvK8QZTLErajk0ptQ5Yh7kIYCvmpMMrWmutlHJrrZOY5SbD1jFa639QSm0G/kOZNaovBEYxZ7zFnjmyZ6a2ZH1DQGs9llnReCHmqsKPa63vXgi2iD0nnj0FsG4wX81c+06l1Plk3WCybLJuMO/J3GDerJS6eQ5sKg66BHEdMvWlMcMB/4WZiXEL4zE0K37lxJxsuMZ6nvndgFlA/zbgI2LP3NlzvLZkHaswl6kngU8tFFvEnhPPnqxzr8PsObAp89xHZuKS8QVL3ycv5Tfr+Bsx69HvBd5ULLvm4qc0FzHzOh8gk+KDuTT3PuDTeeOqMOt3ZM8ee8Se0tlTDFswJ5oCC8kWseeEtKcsbzBz+TNnMXSlVJUaL4B/FmYK3Y7MRMJdwK8xi2Rdn3XYGiCitT6olLpeKdUBvFPsmVt7imjLX4MZ29daj57otog9J549eSwCNmFWX3wT5mflqqxrWcX5wph57duyjrVa120FKrXWXyqSTXNK0QVdKbVCKfUHzFom/6uUWoFZUGdEKXWJHp9I+CVwFDNLw+q4cgXgVmZXlu8D/6C1/r7YMzf2zIEt31sItog9J549WXaV8w1mzimqoCul/hozh/R5zFlhP2aZyBrMr2I3WmO12dXjBTJdepRZcnIdZiGkZ7TWi7XWPxF75sYesUXsWSj2ZM5bljeYklPM+A3wJbJqYGAm7I9gxsXejJnzeWPW/nWY8S0rpvVailiNTOwRW8Sek8Kev8YsQ/0VzAqmf8RsjLIZs8PUD/LGvx+zLHMQM9PvLszc8y8Uy6b5+inuycySp1ZpWC9QiRmXWouZKvQ5zJViGzJj3oZZ9W/WJW3FHrFF7Dlp7SmrG8x8/szNSceXwm4EXmJ8BrsSuANzafozmE0g/nzO/0ixR2wRexasPZTZDWY+f+ZkYZHOvGqYhfh3aa0Tme1DwNuVUkuBM7TWv5qL64s9YovYc/LYo7U+BPaCoLhSahXm/OBurXVCKfVNzKqnP1VKxTD7+b5Lax2fS7vmgzkR9MyMsoHZSeR3mW3vBV4FfEZrvRuzvVhJEHvEFrFn4dtTLjeY+WSuPHQjM5tdA9QppR7BbOT6rsw/uaSIPWKL2LPw7Sm3G8y8MFexHGA9ZmfsLoqwPF7sEVvEHrFnGva4MLuEfRKzc9B+4DXzbVepfqxJjaKjlPIAfwt8V2sdm5OLiD1ii9gj9uTasx4z/7wb+Cet9dfn2aSSMmeCLgiCUGrK7QZTakTQBUEQFgjz3eBCEARBKBIi6IIgCAsEEXRBEIQFggi6IAjCAkEEXRAEYYEggi4IgrBAEEEXBEFYIIigC4IgLBD+P2jEjKJ7//4AAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"==============Compare to NDX===========\")\n",
    "%matplotlib inline\n",
    "# S&P 500: ^GSPC\n",
    "# Dow Jones Index: ^DJI\n",
    "# NASDAQ 100: ^NDX\n",
    "backtest_plot(df_account_value, \n",
    "             baseline_ticker = '^DJI',\n",
    "             baseline_start = df_account_value.loc[0,'date'],\n",
    "             baseline_end = df_account_value.loc[len(df_account_value)-1,'date'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "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": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e297c3ef13a14140d1d625af153e80a7c3c985fcd8b0175f764e12897f52a268"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}