Goldma Sachs.ipynb

{
 "nbformat": 4,
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 "metadata": {
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3 (ipykernel)"
  },
  "colab": {
   "name": "Finam of SBER-CNN_Coder_Decoder-Multi.ipynb",
   "provenance": [
    {
     "file_id": "1u14juAdYey4S5vL5kYmpixo9mjjqNzBJ",
     "timestamp": 1584103025516
    },
    {
     "file_id": "1E5Zc5QIeUtwzjNC4s2pOE1URZ5k3Rpr9",
     "timestamp": 1584075321729
    },
    {
     "file_id": "1NXjPSAXS501exqlzAkrUJOv1pmUH2RKs",
     "timestamp": 1582895755061
    },
    {
     "file_id": "1iOm8wU4QyvTYOXZrAPFkQu2iKd8bakXL",
     "timestamp": 1582790559148
    }
   ],
   "collapsed_sections": []
  },
  "accelerator": "GPU"
 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 116,
   "outputs": [],
   "source": [
    "# !pip install utils mxnet seaborn xgboost"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "outputs": [
    {
     "ename": "XGBoostError",
     "evalue": "\nXGBoost Library (libxgboost.dylib) could not be loaded.\nLikely causes:\n  * OpenMP runtime is not installed\n    - vcomp140.dll or libgomp-1.dll for Windows\n    - libomp.dylib for Mac OSX\n    - libgomp.so for Linux and other UNIX-like OSes\n    Mac OSX users: Run `brew install libomp` to install OpenMP runtime.\n\n  * You are running 32-bit Python on a 64-bit OS\n\nError message(s): [\"dlopen(/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/xgboost/lib/libxgboost.dylib, 0x0006): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\\n  Referenced from: /Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/xgboost/lib/libxgboost.dylib\\n  Reason: tried: '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/lib/libomp.dylib' (no such file), '/usr/lib/libomp.dylib' (no such file)\"]\n",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mXGBoostError\u001B[0m                              Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/92/nwcr41r54dl8w3bkn9lrs8100000gn/T/ipykernel_35496/2698665331.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m     20\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0msklearn\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mpreprocessing\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mStandardScaler\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     21\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 22\u001B[0;31m \u001B[0;32mimport\u001B[0m \u001B[0mxgboost\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0mxgb\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     23\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0msklearn\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmetrics\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0maccuracy_score\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     24\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/xgboost/__init__.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      7\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mos\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      8\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 9\u001B[0;31m \u001B[0;32mfrom\u001B[0m \u001B[0;34m.\u001B[0m\u001B[0mcore\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mDMatrix\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mDeviceQuantileDMatrix\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mBooster\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mDataIter\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     10\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0;34m.\u001B[0m\u001B[0mtraining\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mtrain\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcv\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     11\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0;34m.\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mrabit\u001B[0m  \u001B[0;31m# noqa\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/xgboost/core.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m    201\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    202\u001B[0m \u001B[0;31m# load the XGBoost library globally\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 203\u001B[0;31m \u001B[0m_LIB\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0m_load_lib\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    204\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    205\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/xgboost/core.py\u001B[0m in \u001B[0;36m_load_lib\u001B[0;34m()\u001B[0m\n\u001B[1;32m    179\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mlib_success\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    180\u001B[0m         \u001B[0mlibname\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mos\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mpath\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mbasename\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlib_paths\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 181\u001B[0;31m         raise XGBoostError(\n\u001B[0m\u001B[1;32m    182\u001B[0m             f\"\"\"\n\u001B[1;32m    183\u001B[0m \u001B[0mXGBoost\u001B[0m \u001B[0mLibrary\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0;34m{\u001B[0m\u001B[0mlibname\u001B[0m\u001B[0;34m}\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0mcould\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mbe\u001B[0m \u001B[0mloaded\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mXGBoostError\u001B[0m: \nXGBoost Library (libxgboost.dylib) could not be loaded.\nLikely causes:\n  * OpenMP runtime is not installed\n    - vcomp140.dll or libgomp-1.dll for Windows\n    - libomp.dylib for Mac OSX\n    - libgomp.so for Linux and other UNIX-like OSes\n    Mac OSX users: Run `brew install libomp` to install OpenMP runtime.\n\n  * You are running 32-bit Python on a 64-bit OS\n\nError message(s): [\"dlopen(/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/xgboost/lib/libxgboost.dylib, 0x0006): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\\n  Referenced from: /Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/xgboost/lib/libxgboost.dylib\\n  Reason: tried: '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/lib/libomp.dylib' (no such file), '/usr/lib/libomp.dylib' (no such file)\"]\n"
     ]
    }
   ],
   "source": [
    "from utils import *\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "from mxnet import nd, autograd, gluon\n",
    "from mxnet.gluon import nn, rnn\n",
    "import mxnet as mx\n",
    "import datetime\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import math\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import accuracy_score"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "ROOT_DIR = os.path.abspath(os.curdir)\n",
    "print(ROOT_DIR)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from miner import *\n",
    "\n",
    "#Для работы локально на виртуальной машине\n",
    "data_path = ROOT_DIR + '/data/'\n",
    "if not os.path.exists(data_path):\n",
    "\tos.mkdir(data_path)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#periods = {'tick': 1, '1m': 2, '5m': 3, '10m': 4, '15m': 5, '30m': 6, 'H': 7, 'D': 8, 'W': 9, 'M': 10}\n",
    "seccode = \"S&P\"\n",
    "period = \"D\"\n",
    "today = datetime.today().strftime('%Y-%m-%d')\n",
    "print(\"today: \" + today)\n",
    "path_best_model = data_path + today + '_' + seccode + '_' + period + '_' + 'best.h5'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "end_date 2021-11-20 21:02:09.955029\n",
      "http://export.finam.ru/S&P.csv?market=0&em=13944&code=S%26P&df=1&mf=0&yf=2000&from=2000-01-01&dt=20&mt=10&yt=2021&to=2021-11-20+21%3A02%3A09.955029&p=8&f=S%26P&e=.csv&cn=S%26P&dtf=1&MSOR=0&mstime=on&mstimever=1&sep=1&sep2=1&datf=1&at=1\n"
     ]
    }
   ],
   "source": [
    "dataset_null = GetCandles (seccode, period, \"20000101\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     <TICKER> <PER>    <DATE>    <TIME>   <OPEN>   <HIGH>    <LOW>  <CLOSE>  \\\n",
      "0         S&P     D  20030505  00:00:00   928.50   933.50   923.50   925.00   \n",
      "1         S&P     D  20030506  00:00:00   925.00   939.50   924.25   934.00   \n",
      "2         S&P     D  20030507  00:00:00   933.75   937.00   925.25   928.50   \n",
      "3         S&P     D  20030508  00:00:00   928.50   930.25   918.25   919.25   \n",
      "4         S&P     D  20030509  00:00:00   919.25   933.50   918.50   932.75   \n",
      "...       ...   ...       ...       ...      ...      ...      ...      ...   \n",
      "5929      S&P     D  20211115  00:00:00  4681.50  4697.50  4667.00  4676.75   \n",
      "5930      S&P     D  20211116  00:00:00  4676.75  4709.75  4670.75  4693.50   \n",
      "5931      S&P     D  20211117  00:00:00  4693.75  4701.00  4679.25  4691.50   \n",
      "5932      S&P     D  20211118  00:00:00  4691.75  4722.00  4668.00  4717.75   \n",
      "5933      S&P     D  20211119  00:00:00  4717.75  4723.50  4684.25  4697.75   \n",
      "\n",
      "        <VOL>         DT  \n",
      "0     1073353 2003-05-05  \n",
      "1      706102 2003-05-06  \n",
      "2      660418 2003-05-07  \n",
      "3      723603 2003-05-08  \n",
      "4      519679 2003-05-09  \n",
      "...       ...        ...  \n",
      "5929  1003360 2021-11-15  \n",
      "5930   986375 2021-11-16  \n",
      "5931  1019370 2021-11-17  \n",
      "5932  1147513 2021-11-18  \n",
      "5933  1221166 2021-11-19  \n",
      "\n",
      "[5934 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "print(dataset_null)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     <TICKER> <PER>    <DATE>    <TIME>   <OPEN>   <HIGH>    <LOW>  <CLOSE>  \\\n",
      "0         S&P     D  20030505  00:00:00   928.50   933.50   923.50   925.00   \n",
      "1         S&P     D  20030506  00:00:00   925.00   939.50   924.25   934.00   \n",
      "2         S&P     D  20030507  00:00:00   933.75   937.00   925.25   928.50   \n",
      "3         S&P     D  20030508  00:00:00   928.50   930.25   918.25   919.25   \n",
      "4         S&P     D  20030509  00:00:00   919.25   933.50   918.50   932.75   \n",
      "...       ...   ...       ...       ...      ...      ...      ...      ...   \n",
      "5929      S&P     D  20211115  00:00:00  4681.50  4697.50  4667.00  4676.75   \n",
      "5930      S&P     D  20211116  00:00:00  4676.75  4709.75  4670.75  4693.50   \n",
      "5931      S&P     D  20211117  00:00:00  4693.75  4701.00  4679.25  4691.50   \n",
      "5932      S&P     D  20211118  00:00:00  4691.75  4722.00  4668.00  4717.75   \n",
      "5933      S&P     D  20211119  00:00:00  4717.75  4723.50  4684.25  4697.75   \n",
      "\n",
      "        <VOL>         DT  \n",
      "0     1073353 2003-05-05  \n",
      "1      706102 2003-05-06  \n",
      "2      660418 2003-05-07  \n",
      "3      723603 2003-05-08  \n",
      "4      519679 2003-05-09  \n",
      "...       ...        ...  \n",
      "5929  1003360 2021-11-15  \n",
      "5930   986375 2021-11-16  \n",
      "5931  1019370 2021-11-17  \n",
      "5932  1147513 2021-11-18  \n",
      "5933  1221166 2021-11-19  \n",
      "\n",
      "[5934 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "dataset_ex_df = dataset_null\n",
    "print(dataset_ex_df)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     <TICKER> <PER>      Date    <TIME>   <OPEN>   <HIGH>    <LOW>       GS  \\\n",
      "0         S&P     D  20030505  00:00:00   928.50   933.50   923.50   925.00   \n",
      "1         S&P     D  20030506  00:00:00   925.00   939.50   924.25   934.00   \n",
      "2         S&P     D  20030507  00:00:00   933.75   937.00   925.25   928.50   \n",
      "3         S&P     D  20030508  00:00:00   928.50   930.25   918.25   919.25   \n",
      "4         S&P     D  20030509  00:00:00   919.25   933.50   918.50   932.75   \n",
      "...       ...   ...       ...       ...      ...      ...      ...      ...   \n",
      "5929      S&P     D  20211115  00:00:00  4681.50  4697.50  4667.00  4676.75   \n",
      "5930      S&P     D  20211116  00:00:00  4676.75  4709.75  4670.75  4693.50   \n",
      "5931      S&P     D  20211117  00:00:00  4693.75  4701.00  4679.25  4691.50   \n",
      "5932      S&P     D  20211118  00:00:00  4691.75  4722.00  4668.00  4717.75   \n",
      "5933      S&P     D  20211119  00:00:00  4717.75  4723.50  4684.25  4697.75   \n",
      "\n",
      "        <VOL>         DT  \n",
      "0     1073353 2003-05-05  \n",
      "1      706102 2003-05-06  \n",
      "2      660418 2003-05-07  \n",
      "3      723603 2003-05-08  \n",
      "4      519679 2003-05-09  \n",
      "...       ...        ...  \n",
      "5929  1003360 2021-11-15  \n",
      "5930   986375 2021-11-16  \n",
      "5931  1019370 2021-11-17  \n",
      "5932  1147513 2021-11-18  \n",
      "5933  1221166 2021-11-19  \n",
      "\n",
      "[5934 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "dataset_ex_df.rename(columns={ '<DATE>':'Date','<CLOSE>':'GS'}, inplace=True)\n",
    "print(dataset_ex_df)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Date      GS\n",
      "0  20030505  925.00\n",
      "1  20030506  934.00\n",
      "2  20030507  928.50\n",
      "3  20030508  919.25\n",
      "4  20030509  932.75\n"
     ]
    }
   ],
   "source": [
    "dataset_ex_df = dataset_ex_df[['Date','GS']]\n",
    "print (dataset_ex_df.head())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Date       GS\n",
      "0    2003-05-05   925.00\n",
      "1    2003-05-06   934.00\n",
      "2    2003-05-07   928.50\n",
      "3    2003-05-08   919.25\n",
      "4    2003-05-09   932.75\n",
      "...         ...      ...\n",
      "5929 2021-11-15  4676.75\n",
      "5930 2021-11-16  4693.50\n",
      "5931 2021-11-17  4691.50\n",
      "5932 2021-11-18  4717.75\n",
      "5933 2021-11-19  4697.75\n",
      "\n",
      "[5934 rows x 2 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/92/nwcr41r54dl8w3bkn9lrs8100000gn/T/ipykernel_35496/1748069043.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset_ex_df['Date']  = pd.to_datetime(dataset_ex_df['Date'],format='%Y%m%d')#dataset['Date']#.apply(lambda _: datetime.strptime(_,\"%m/%d/%Y\"))#pd.to_datetime(dataset['Date'], format='%Y-%m-%d')#datetime.datetime.strptime(str(dataset['Date']), '%Y%m%d').date()#dataset['Date'] #datetime.strptime(str(dataset['Date']), '%Y%m%d')#datetime.strptime(str(dataset['Date']))\n"
     ]
    }
   ],
   "source": [
    "dataset_ex_df['Date']  = pd.to_datetime(dataset_ex_df['Date'],format='%Y%m%d')#dataset['Date']#.apply(lambda _: datetime.strptime(_,\"%m/%d/%Y\"))#pd.to_datetime(dataset['Date'], format='%Y-%m-%d')#datetime.datetime.strptime(str(dataset['Date']), '%Y%m%d').date()#dataset['Date'] #datetime.strptime(str(dataset['Date']), '%Y%m%d')#datetime.strptime(str(dataset['Date']))\n",
    "\n",
    "print(dataset_ex_df)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 5934 number of days in the dataset.\n"
     ]
    }
   ],
   "source": [
    "# dataset_ex_df = dataset_ex_df\n",
    "print('There are {} number of days in the dataset.'.format(dataset_ex_df.shape[0]))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1400x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 5), dpi=100)\n",
    "plt.plot(dataset_ex_df['Date'], dataset_ex_df['GS'], label='Goldman Sachs stock')\n",
    "# plt.vlines(datetime.date(2016,4, 20), 0, 270, linestyles='--', colors='gray', label='Train/Test data cut-off')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('USD')\n",
    "plt.title('Figure 2: Goldman Sachs stock price')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training days: 4153. Number of test days: 1781.\n"
     ]
    }
   ],
   "source": [
    "num_training_days = int(dataset_ex_df.shape[0]*.7)\n",
    "print('Number of training days: {}. Number of test days: {}.'.format(num_training_days, dataset_ex_df.shape[0]-num_training_days))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "outputs": [],
   "source": [
    "def get_technical_indicators(dataset):\n",
    "    # Create 7 and 21 days Moving Average\n",
    "    dataset['ma7'] = dataset['GS'].rolling(window=7).mean()\n",
    "    dataset['ma21'] = dataset['GS'].rolling(window=21).mean()\n",
    "\n",
    "    # Create MACD\n",
    "\t# candles['ema20'] = pd.Series.ewm(candles['<CLOSE>'], span=20).mean()\n",
    "    dataset['26ema'] = pd.Series.ewm(dataset['GS'], span=26).mean()\n",
    "    dataset['12ema'] = pd.Series.ewm(dataset['GS'], span=12).mean()\n",
    "    dataset['MACD'] = (dataset['12ema']-dataset['26ema'])\n",
    "\n",
    "    # Create Bollinger Bands\n",
    "    # dataset['20sd'] = pd.stats.moments.rolling_std(dataset['GS'],20)\n",
    "    # dataset['upper_band'] = dataset['ma21'] + (dataset['20sd']*2)\n",
    "    # dataset['lower_band'] = dataset['ma21'] - (dataset['20sd']*2)\n",
    "\n",
    "    # Create Exponential moving average\n",
    "    dataset['ema'] = dataset['GS'].ewm(com=0.5).mean()\n",
    "\n",
    "    # Create Momentum\n",
    "    dataset['momentum'] = dataset['GS']-1\n",
    "\n",
    "    return dataset"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "outputs": [
    {
     "data": {
      "text/plain": "       GS  ma7  ma21       26ema       12ema      MACD        ema  momentum\n0  925.00  NaN   NaN  925.000000  925.000000  0.000000  925.00000    924.00\n1  934.00  NaN   NaN  929.673077  929.875000  0.201923  931.75000    933.00\n2  928.50  NaN   NaN  929.251602  929.338337  0.086735  929.50000    927.50\n3  919.25  NaN   NaN  926.455591  926.153843 -0.301748  922.58125    918.25\n4  932.75  NaN   NaN  927.915290  927.945996  0.030705  929.38843    931.75",
      "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>GS</th>\n      <th>ma7</th>\n      <th>ma21</th>\n      <th>26ema</th>\n      <th>12ema</th>\n      <th>MACD</th>\n      <th>ema</th>\n      <th>momentum</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>925.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>925.000000</td>\n      <td>925.000000</td>\n      <td>0.000000</td>\n      <td>925.00000</td>\n      <td>924.00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>934.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>929.673077</td>\n      <td>929.875000</td>\n      <td>0.201923</td>\n      <td>931.75000</td>\n      <td>933.00</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>928.50</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>929.251602</td>\n      <td>929.338337</td>\n      <td>0.086735</td>\n      <td>929.50000</td>\n      <td>927.50</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>919.25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>926.455591</td>\n      <td>926.153843</td>\n      <td>-0.301748</td>\n      <td>922.58125</td>\n      <td>918.25</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>932.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>927.915290</td>\n      <td>927.945996</td>\n      <td>0.030705</td>\n      <td>929.38843</td>\n      <td>931.75</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_TI_df = get_technical_indicators(dataset_ex_df[['GS']])\n",
    "dataset_TI_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x1000 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_technical_indicators(dataset, last_days):\n",
    "    plt.figure(figsize=(16, 10), dpi=100)\n",
    "    shape_0 = dataset.shape[0]\n",
    "    xmacd_ = shape_0-last_days\n",
    "\n",
    "    dataset = dataset.iloc[-last_days:, :]\n",
    "    x_ = range(3, dataset.shape[0])\n",
    "    x_ =list(dataset.index)\n",
    "\n",
    "    # Plot first subplot\n",
    "    plt.subplot(2, 1, 1)\n",
    "    plt.plot(dataset['ma7'],label='MA 7', color='g',linestyle='--')\n",
    "    plt.plot(dataset['GS'],label='Closing Price', color='b')\n",
    "    plt.plot(dataset['ma21'],label='MA 21', color='r',linestyle='--')\n",
    "    # plt.plot(dataset['upper_band'],label='Upper Band', color='c')\n",
    "    # plt.plot(dataset['lower_band'],label='Lower Band', color='c')\n",
    "    # plt.fill_between(x_, dataset['lower_band'], dataset['upper_band'], alpha=0.35)\n",
    "    plt.title('Technical indicators for Goldman Sachs - last {} days.'.format(last_days))\n",
    "    plt.ylabel('USD')\n",
    "    plt.legend()\n",
    "\n",
    "    # Plot second subplot\n",
    "    plt.subplot(2, 1, 2)\n",
    "    plt.title('MACD')\n",
    "    plt.plot(dataset['MACD'],label='MACD', linestyle='-.')\n",
    "    plt.hlines(15, xmacd_, shape_0, colors='g', linestyles='--')\n",
    "    plt.hlines(-15, xmacd_, shape_0, colors='g', linestyles='--')\n",
    "    # plt.plot(dataset['log_momentum'],label='Momentum', color='b',linestyle='-')\n",
    "\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "plot_technical_indicators(dataset_TI_df, 400)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "outputs": [],
   "source": [
    "# just import bert\n",
    "import bert\n",
    "data_FT = dataset_ex_df[['Date', 'GS']]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1298: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return np.asarray(x, float)\n",
      "/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1298: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return np.asarray(x, float)\n",
      "/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1298: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return np.asarray(x, float)\n",
      "/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1298: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return np.asarray(x, float)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1400x700 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "close_fft = np.fft.fft(np.asarray(data_FT['GS'].tolist()))\n",
    "fft_df = pd.DataFrame({'fft':close_fft})\n",
    "fft_df['absolute'] = fft_df['fft'].apply(lambda x: np.abs(x))\n",
    "fft_df['angle'] = fft_df['fft'].apply(lambda x: np.angle(x))\n",
    "plt.figure(figsize=(14, 7), dpi=100)\n",
    "fft_list = np.asarray(fft_df['fft'].tolist())\n",
    "for num_ in [3, 6, 9, 100]:\n",
    "    fft_list_m10= np.copy(fft_list); fft_list_m10[num_:-num_]=0\n",
    "    plt.plot(np.fft.ifft(fft_list_m10), label='Fourier transform with {} components'.format(num_))\n",
    "plt.plot(data_FT['GS'],  label='Real')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('USD')\n",
    "plt.title('Figure 3: Goldman Sachs (close) stock prices & Fourier transforms')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x560 with 1 Axes>",
      "image/png": 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qaz6Q+3btzh8/+1vdpxI4YFTV9a219XNNc59KgAmrqqx90Oqszeo8fuOx0y4HYFk4/A0AQDehEgCAbkIlAADdhEoAALoJlQAAdBMqAQDoJlQCANBNqAQAoJtQCQBAN6ESAIBuQiUAAN2ESgAAugmVAAB0EyoBAOgmVAIA0E2oBACgm1AJAEA3oRIAgG5CJQAA3YRKAAC6CZUAAHQTKgEA6CZUAgDQTagEAKCbUAkAQLdFhcqq+sOq2lRVraoet0C7F1XVF6vqS1X1hqo6ZGKVAgCwYi12T+VfJnlKkmvna1BVpyZ5dZKnJjk9yUOSvKS3QAAAVr5FhcrW2odaa1v20OyZSd7dWruxtdaSvC7Js3oLBABg5ZvkOZUbcv89mZuGcQAAHOCmdqFOVZ1bVVtmhu3bt0+rFAAAOk0yVG5OcsrY843DuDm11s5rra2fGdatWzfBUgAAWE6TDJXvTPKMqjqpqirJzyR52wTnDwDACrXYWwqdX1VbkqxP8g9VdfUw/oKqekaStNa+nORVST6a5OokNyc5f0mqBgBgRVmzmEattXPmGf/iWc/fkOQNE6gLAID9iP+oAwBAN6ESAIBuQiUAAN2ESgAAugmVAAB0EyoBAOgmVAIA0E2oBACgm1AJAEA3oRIAgG5CJQAA3YRKAAC6CZUAAHQTKgEA6CZUAgDQTagEAKCbUAkAQDehEgCAbkIlAADdhEoAALoJlQAAdBMqAQDoJlQCANBNqAQAoJtQCQBAN6ESAIBuQiUAAN2ESgAAugmVAAB0EyoBAOgmVAIA0E2oBACgm1AJAEA3oRIAgG5CJQAA3YRKAAC6CZUAAHQTKgEA6CZUAgDQTagEAKCbUAkAQDehEgCAbkIlAADdhEoAALoJlQAAdBMqAQDoJlQCANBNqAQAoJtQCQBAN6ESAIBuQiUAAN2ESgAAugmVAAB0EyoBAOgmVAIA0E2oBACgm1AJAEA3oRIAgG5CJQAA3YRKAAC6CZUAAHRbdKisqjOq6tKquqqqPlFVj56jzaqqOq+qvlBVn62qD1TV6ZMtGQCAlWZv9lSen+T1rbVHJHlNkgvnaPOMJN+R5LGttW9O8v4kv9VbJAAAK9uiQmVVnZjkzCQXD6PemeTkOfZCtiSHJjmsqirJkUm2TKhWAABWqDWLbHdykhtaazuTpLXWqmpzkg1Jrh5r954k353kxiR3Jrk+yXfONcOqOjfJuTPPjzrqqL0uHgCAlWHSF+qcmeSbkjw8ycMyOvz9urkattbOa62tnxnWrVs34VIAAFguiw2V1yV5aFWtSZLh0PaGJJtntXtukktaa7e11nYneXNGey4BADiALSpUttZuSvKpJM8ZRp2dZEtr7epZTb+c5Huq6kHD8/+Q5F8nUSgAACvXYs+pTJJzklxYVa9IckeSFyRJVV2Q5N2ttXcn+Z9JHpXkM1V1X0bnVv7MZEsGAGClWXSobK1dmeTJc4x/8djje5P89GRKAwBgf+E/6gAA0E2oBACgm1AJAEA3oRIAgG5CJQAA3YRKAAC6CZUAAHQTKgEA6CZUAgDQTagEAKCbUAkAQDehEgCAbkIlAADdhEoAALoJlQAAdBMqAQDoJlQCANBNqAQAoJtQCQBAN6ESAIBuQiUAAN2ESgAAugmVAAB0EyoBAOgmVAIA0E2oBACgm1AJAEA3oRIAgG5CJQAA3YRKAAC6CZUAAHQTKgEA6CZUAgDQTagEAKCbUAkAQDehEgCAbkIlAADdhEoAALoJlQAAdBMqAQDoJlQCANBNqAQAoJtQCQBAN6ESAIBuQiUAAN2ESgAAugmVAAB0EyoBAOgmVAIA0E2oBACgm1AJAEA3oRIAgG5CJQAA3YRKAAC6CZUAAHQTKgEA6CZUAgDQTagEAKCbUAkAQDehEgCAbkIlAADdFh0qq+qMqrq0qq6qqk9U1aPnafeYqvpgVf3vYfiRyZULAMBKtGYv2p6f5PWttQur6plJLkzy+PEGVXV4kncleW5r7SNVtTrJsZMqFgCAlWlReyqr6sQkZya5eBj1ziQnV9Xps5r+ZJKPtdY+kiSttV2ttZsnVSwAACvTYg9/n5zkhtbaziRprbUkm5NsmNXuG5PcW1V/W1VXVNVFVXXCXDOsqnOrasvMsH379n1dBwAApmzSF+qsSfK0JOck+ZYk1yf5k7kattbOa62tnxnWrVs34VIAAFguiw2V1yV5aFWtSZKqqoz2Um6e1W5zkg+01q4f9mZenORJkyoWAICVaVGhsrV2U5JPJXnOMOrsJFtaa1fPavqOJI+vqiOH5z+Q5DOTKBQAgJVrb67+PifJhVX1iiR3JHlBklTVBUne3Vp7d2ttc1X9VpJLq2p3Roe/XzLpogEAWFkWHSpba1cmefIc41886/lbkrylvzQAAPYX/qMOAADdhEoAALoJlQAAdBMqAQDoJlQCANBNqAQAoJtQCQBAN6ESAIBuQiUAAN2ESgAAugmVAAB0EyoBAOgmVAIA0E2oBACgm1AJAEA3oRIAgG5CJQAA3YRKAAC6CZUAAHQTKgEA6CZUAgDQTagEAKCbUAkAQDehEgCAbkIlAADdhEoAALoJlQAAdBMqAQDoJlQCANBNqAQAoJtQCQBAN6ESAIBuQiUAAN2ESgAAugmVAAB0EyoBAOgmVAIA0E2oBACgm1AJAEA3oRIAgG5CJQAA3YRKAAC6CZUAAHQTKgEA6CZUAgDQTagEAKCbUAkAQDehEgCAbkIlAADdhEoAALoJlQAAdBMqAQDoJlQCANBNqAQAoJtQCQBAN6ESAIBuQiUAAN2ESgAAugmVAAB0EyoBAOi26FBZVWdU1aVVdVVVfaKqHr1A26qqS6rqtolUCQDAirY3eyrPT/L61tojkrwmyYULtH1Zki911AUAwH5kUaGyqk5McmaSi4dR70xyclWdPkfbRyf5oSS/M6EaAQBY4Ra7p/LkJDe01nYmSWutJdmcZMN4o6o6JMkbkpyTZNcE6wQAYAWb9IU6r0ryV621/72nhlV1blVtmRm2b98+4VIAAFguiw2V1yV5aFWtSUYX4mS0l3LzrHbfmeTnqmpTko8kObKqNlXVCbNn2Fo7r7W2fmZYt27dPq8EAADTtahQ2Vq7KcmnkjxnGHV2ki2ttatntXtqa+2U1trGJE9JckdrbWNr7eYJ1gwAwAqzN4e/z0lyTlVdleTlSV6QJFV1QVU9YymKAwBg/7BmsQ1ba1cmefIc4188T/tNSY7e18IAANh/+I86AAB0EyoBAOgmVAIA0E2oBACgm1AJAEA3oRIAgG5CJQAA3YRKAAC6CZUAAHQTKgEA6CZUAgDQTagEAKCbUAkAQDehEgCAbkIlAADdhEoAALoJlQAAdBMqAQDoJlQCANBNqAQAoJtQCQBAN6ESAIBuQiUAAN2ESgAAugmVAAB0EyoBAOgmVAIA0E2oBACgm1AJAEA3oRIAgG5CJQAA3YRKAAC6CZUAAHQTKgEA6CZUAgDQTagEAKCbUAkAQDehEgCAbkIlwAS11nLZNbdk6533Zuud9+aya25Ja23aZQEsuVopH3br169vW7ZsmXYZAPtsy7YdedYbPpbrbr37fuM3HHt4/uynn5j1xxw+pcoAJqOqrm+trZ9rmj2VABPQWstPvfHjDwiUSbL51h153psus8cSOKAJlQATcPm12+YMlDOuvWVHLr922zJWBLC8hEqACdi09a6sqvmnr6rKpq13LV9BAMtMqASYgI3HH5FdCxze3t1aNh5/xDJWBLC8hEqACTjzlGOy4dj5L8Q55bjDc+YpxyxjRQDLS6gEmICqylte9MScfOzaB0w75bjDc9GLnpiqBY6PA+zn3FIIYIJaa3nK71yS62+/J6sqedtLnpTHbzxWoAQOCAvdUmjNchcDcCCrqhx+6JpUkjWrVuUJpx437ZIAloXD3wAAdBMqAQDoJlQCANBNqAQAoJtQCQBAN6ESAIBuQiUAAN2ESgAAugmVAAB0EyoBAOgmVAIA0E2oBACgm1AJAEC3RYfKqjqjqi6tqquq6hNV9eg52nxPVV1WVV+oqs9X1e9WleAKAHCA25vAd36S17fWHpHkNUkunKPNtiQ/0Vr7xiTfluTbkzy3t0gAAFa2RYXKqjoxyZlJLh5GvTPJyVV1+ni71tqnW2tfHh7fk+SKJBsnVSwAACvTYvdUnpzkhtbaziRprbUkm5NsmO8FVXVSkmcm+dveIgEAWNmW5HzHqjoyyXuS/G5r7fJ52pxbVVtmhu3bty9FKQAALIPFhsrrkjy0qtYkSVVVRnspN89uWFUPTvLeJO9qrZ033wxba+e11tbPDOvWrdv76gEAWBEWFSpbazcl+VSS5wyjzk6ypbV29Xi7qlqXUaB8b2vtNyZZKAAAK9feHP4+J8k5VXVVkpcneUGSVNUFVfWMoc1LkzwhyY9U1RXD8CsTrRgAgBVnzWIbttauTPLkOca/eOzxbyb5zcmUBgDA/sKNyQEA6CZUAgDQTagEAKCbUAkAQDehEgCAbkIlAADdhEoAALoJlQAAdBMqAQDoJlQCANBNqAQAoJtQCQBAN6ESAIBuQiUAAN2ESgAAugmVAAB0EyoBAOgmVAIA0E2oBACgm1AJAEA3oRIAgG5CJQAA3YRKAAC6CZUAAHQTKgEA6CZUAkxQay077t2ZlmTn7t257Jpb0lqbdlkAS06oBJiQLdt25KzXfiDX335PkmR3S37s/I/lO1/7wWzZtmPK1QEsLaESYAJaa/mpN34819169wOmbb51R573psvssQQOaEIlwARcfu22bL5l/r2Rm27Zkcuv3baMFQEsL6ESYAKuuXl7di2wI3L37pZNW+9avoIAlplQCTAB9+zcveD0luTu+3YtTzEAUyBUAkzA2kNW77HNYWt85AIHLp9wABOw8fgjsnqBT9TVlZx6wrrlKwhgmQmVABNw5inHZMOxh887fePxR+TMU45ZxooAlpdQCTABVZXXPvOxWV0PnLZmVeV3n/nNqZpjIsABQqgEmIDWWn7pLz8z5xXgO3e3/PJfftZ9KoEDmlAJMAGXX7ttzhufz7jWfSqBA5xQCTABm7belVULHN1eVeU+lcABTagEmICNxx+RXQsc3t61e3c2Hn/EMlYEsLyESoAJ+LYNR4/ucD6PVatq1AbgACVUAkzAJzfftuD03W3PbQD2Z0IlwARs2npXVi9wUuVq51QCBzihEmACnFMJHOyESoAJcE4lcLATKgEm4JObb1swVDqnEjjQCZUAE3DNzduza4Hpu3Y351QCBzShEmAC7rlvoUg5suOrO5ehEoDpECoBJuDGO+7Zc5vb99wGYH8lVAJMxAL/o3Gw/V57KoEDl1AJMAFnnXHcHtv84xe+krbAbYcA9mdCJcAEfOzLt+yxzVfuuDef2HTrMlQDsPyESoBOrbWc/6FrFtX2A/920xJXAzAdQiVAp8uv3Za779u9qLYu1gEOVEIlQKe9uf/kSUcduoSVAEyPUAnQ6e69uP/kYYesXsJKAKZHqATodOWNdyy67QUfusYV4MABSagE6NBayzs+ef2i29/51V35u8/dsIQVAUyHUAnQ4T1XbMl9u/Zuz+MvvP3T9lYCBxyhEmAf7N69O7/0jk/n59/+2b1+7T27kmedf2l2717cFeMA+4NaKd+W169f37Zs2TLtMgDup7WWy665JRdfek0+fNVNue2rk19GJTlq7ep85yNOyE8+8ZQ84dTjUrXnf/sIsNyq6vrW2vo5pwmVwP6qtZbLr92Wa27ennt27s7aQ1Zn4/FH5MxTjkmSXHbNLXnrv2zKJ6+9LbfvuDdf3ZV8dWV85E3U6koOWZ0cddiarFmzJmtWVb73UQ/Jr/zAv8uqVavm7SPBFdhbEwmVVXVGkjcnOT7J7Ume31r7/BztXpTk5RkdWr8kyX9qrd23p/kvV6i8++678y2/dkncfhgA2N8ds3ZNLv3ls7J27dplWd5CoXJvzqk8P8nrW2uPSPKaJBfOsaBTk7w6yVOTnJ7kIUlesrcFL5UX/elleZRACQAcILbdvTOP+rVL8qI/vWzapSwuVFbViUnOTHLxMOqdSU6uqtNnNX1mkne31m5so12gr0vyrEkV2+Puu+/O+6+8edplAABM3PuvvDl33333VGtYs8h2Jye5obW2M0laa62qNifZkOTqsXYbklw79nzTMG7qvuO1H/ra41d97E156F23TLEaAIA+NxxxXH7tSS/82vPveO2H8qlXfv/U6pnaLYWq6tyq2jIzbN++fUmXd9uOxf8bNQCA/c20s85i91Rel+ShVbWmtbazRpcMbkiyeVa7zUlOG3u+cY42SZLW2nlJzpt5vn79+iW9JvPow9fk1qGzx1M9wFI4Zu0h2d1abr/HF1pgeRx9+GJj3dJY1J7K1tpNST6V5DnDqLOTbGmtXT2r6TuTPKOqThqC588kedukiu3x0V86a9olAAeBhx11aN724ifkU698eq541fflz1/8+Dz8yAdNuyzgIDDtrLM3kfacJBdW1SuS3JHkBUlSVRdkdHHOu1trX66qVyX56PCaD2Z01fjUrV27Nt/7yBNcrANM1INWVw47ZFWecvoJ+cOfeGzWrLn/x+qTTz8xH33F07Nr1668+m8/n7/97A25c8fO3HsA3i8TmJ7ve9SJy3ZbofkcdDc/d59KYLZVSdY+qLLh2CNy0lGH5ai1h+Sx64/KZ7fckdvvvi/f+Yjj8+8eemQ233r3xG8cPnMD901b78qGY9fm3264Ix+8amuOWrvmazXccufd2brjvtx46/ZsuzdZGZ/awEqwku5TedCFSgAA9s2kbn4OAABzEioBAOgmVAIA0E2oBACgm1AJAEA3oRIAgG5CJQAA3YRKAAC6CZUAAHQTKgEA6CZUAgDQTagEAKCbUAkAQLdqrU27hiRJVd2b5OZlXOS6JNuXcXl8nb6fHn0/Pfp+evT99Oj76Vmqvj+htXboXBNWTKhcblW1pbW2ftp1HIz0/fTo++nR99Oj76dH30/PNPre4W8AALoJlQAAdDuYQ+V50y7gIKbvp0ffT4++nx59Pz36fnqWve8P2nMqAQCYnIN5TyUAABMiVAIA0O2gC5VVdUZVXVpVV1XVJ6rq0dOuaX9WVX9YVZuqqlXV48bGz9vP+zqN+6uqw6rqb4a++kxV/VNVnT5MO7Gq3ltVX6yqf62qs8Zet0/TuL+q+seq+mxVXVFVH66qbxnG2/aXQVW9YPjc+aHhuW1+GQyf91cO2/0VVfXjw3jb/RKqqkOr6o+H7fRzVXXxMH5l9Xtr7aAaklyS5PnD42cm+cS0a9qfhyRnJVmfZFOSxy2mn/d1muEBfX9Ykh/I18+N/s9JPjg8flOSXx0ePz7JliSH9EwzPKD/jx57/MNJPjM8tu0vfd9vTHJpkn9J8kPDONv88vT9/T7rx8bb7pe23/97kj8a+7w/aSX2+9Q7apnflBOT3JFkzfC8ktyY5PRp17a/D+MfNAv1875Om/b67Q9DkjOTbBoeb5/50BmeX5bkaT3TDAv2/fOTXGHbX5a+XpXkfUm+LckH8/VQaZtfnv7/2mf92Djb/dL2+RFDPx250vv9YDv8fXKSG1prO5OkjXpzc5INU63qwLNQP+/rNPbspUneVVXHZbSn5caxaZuSbNjXaUta9X6sqi6qquuSvDrJT8W2vxzOTfLR1tonZ0bY5pfdRcMh2DdW1Qmx3S+105LcmuQVVXX5cLrN92YF9vvBFirhgFRVr8joG+h/nXYtB5PW2nNbaycn+W9JXjPteg50VfVNSc5O8hvTruUgdlZr7ZuTfGuSrUnePOV6DgZrkpyS5AuttTOT/HyStw/jV5SDLVRel+ShVbUmSaqqMkrmm6da1YFnoX7e12nMo6p+McmPJPm/Wms7Wmu3JNlZVSeNNduYZPO+TlvK+g8ErbU3J/nujM7Hs+0vnadmtE1+sao2JXlSktcn+bHY5pdFa23z8PO+JH+Q0XviM39pbU6yO8lbk6S19ukk12QUNFdUvx9UobK1dlOSTyV5zjDq7CRbWmtXT6+qA89C/byv05av+v1LVZ2b5FlJnt5au21s0l8k+ZmhzeOTPDzJP3dOY1BVR1fVw8ae/1CSW5LY9pdQa+1PWmsPba1tbK1tTPKxJC9prf1JbPNLrqqOqKqjx0Y9K8mnfeYvrdba1iTvT/L9SVJVpyY5NclHs9L6vfekzP1tSPLIjK4YvCrJ5UkeM+2a9uchyfkZ7Z3ZmeQrSa7eUz/v6zTDA/p+fZKW5EsZXSRyRZKPD9MekuQfk3wxyeeTfPfY6/ZpmuF+fX9KRhd0fC7JZzK6cORxwzTb/vK9Dx/M1y/Usc0vfX9/Q5JPJ/nssO2/K8nGYZrtfun7/gNjnzlnr8R+928aAQDodlAd/gYAYGkIlQAAdBMqAQDoJlQCANBNqAQAoJtQCQBAN6ESAIBuQiUAAN2ESgAAuv3/nZ8L6byZJqAAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import deque\n",
    "items = deque(np.asarray(fft_df['absolute'].tolist()))\n",
    "items.rotate(int(np.floor(len(fft_df)/2)))\n",
    "plt.figure(figsize=(10, 7), dpi=80)\n",
    "plt.stem(items)\n",
    "plt.title('Figure 4: Components of Fourier transforms')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "\nstatsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\nbeen removed in favor of statsmodels.tsa.arima.model.ARIMA (note the .\nbetween arima and model) and statsmodels.tsa.SARIMAX.\n\nstatsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\nis both well tested and maintained. It also offers alternative specialized\nparameter estimators.\n",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNotImplementedError\u001B[0m                       Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/92/nwcr41r54dl8w3bkn9lrs8100000gn/T/ipykernel_35496/1720051735.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      4\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[0mseries\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdata_FT\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'GS'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 6\u001B[0;31m \u001B[0mmodel\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mARIMA\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mseries\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0morder\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m5\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      7\u001B[0m \u001B[0mmodel_fit\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmodel\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdisp\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      8\u001B[0m \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel_fit\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msummary\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/statsmodels/tsa/arima_model.py\u001B[0m in \u001B[0;36m__init__\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m     43\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     44\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m__init__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 45\u001B[0;31m         \u001B[0msuper\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m__init__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     46\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     47\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/statsmodels/tsa/arima_model.py\u001B[0m in \u001B[0;36m__init__\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m     27\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     28\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m__init__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 29\u001B[0;31m         \u001B[0;32mraise\u001B[0m \u001B[0mNotImplementedError\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mARIMA_DEPRECATION_ERROR\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     30\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     31\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mNotImplementedError\u001B[0m: \nstatsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\nbeen removed in favor of statsmodels.tsa.arima.model.ARIMA (note the .\nbetween arima and model) and statsmodels.tsa.SARIMAX.\n\nstatsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\nis both well tested and maintained. It also offers alternative specialized\nparameter estimators.\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from pandas import DataFrame\n",
    "from datetime import datetime\n",
    "\n",
    "series = data_FT['GS']\n",
    "model = ARIMA(series, order=(5, 1, 0))\n",
    "model_fit = model.fit(disp=0)\n",
    "print(model_fit.summary())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pandas.tools'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/92/nwcr41r54dl8w3bkn9lrs8100000gn/T/ipykernel_35496/3961318771.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0;32mfrom\u001B[0m \u001B[0mpandas\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtools\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mplotting\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mautocorrelation_plot\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      2\u001B[0m \u001B[0mautocorrelation_plot\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mseries\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      3\u001B[0m \u001B[0mplt\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfigure\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mfigsize\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m10\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m7\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdpi\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m80\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      4\u001B[0m \u001B[0mplt\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mshow\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'pandas.tools'"
     ]
    }
   ],
   "source": [
    "from pandas.tools.plotting import autocorrelation_plot\n",
    "autocorrelation_plot(series)\n",
    "plt.figure(figsize=(10, 7), dpi=80)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/92/nwcr41r54dl8w3bkn9lrs8100000gn/T/ipykernel_35496/289115997.py:2: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
      "  from pandas import datetime\n"
     ]
    },
    {
     "ename": "NotImplementedError",
     "evalue": "\nstatsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\nbeen removed in favor of statsmodels.tsa.arima.model.ARIMA (note the .\nbetween arima and model) and statsmodels.tsa.SARIMAX.\n\nstatsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\nis both well tested and maintained. It also offers alternative specialized\nparameter estimators.\n",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNotImplementedError\u001B[0m                       Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/92/nwcr41r54dl8w3bkn9lrs8100000gn/T/ipykernel_35496/289115997.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m     10\u001B[0m \u001B[0mpredictions\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mlist\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     11\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mt\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mrange\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtest\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 12\u001B[0;31m     \u001B[0mmodel\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mARIMA\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mhistory\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0morder\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m5\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     13\u001B[0m     \u001B[0mmodel_fit\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmodel\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdisp\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     14\u001B[0m     \u001B[0moutput\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmodel_fit\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mforecast\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/statsmodels/tsa/arima_model.py\u001B[0m in \u001B[0;36m__init__\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m     43\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     44\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m__init__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 45\u001B[0;31m         \u001B[0msuper\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m__init__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     46\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     47\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/statsmodels/tsa/arima_model.py\u001B[0m in \u001B[0;36m__init__\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m     27\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     28\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m__init__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 29\u001B[0;31m         \u001B[0;32mraise\u001B[0m \u001B[0mNotImplementedError\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mARIMA_DEPRECATION_ERROR\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     30\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     31\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mNotImplementedError\u001B[0m: \nstatsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\nbeen removed in favor of statsmodels.tsa.arima.model.ARIMA (note the .\nbetween arima and model) and statsmodels.tsa.SARIMAX.\n\nstatsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\nis both well tested and maintained. It also offers alternative specialized\nparameter estimators.\n"
     ]
    }
   ],
   "source": [
    "from pandas import read_csv\n",
    "from pandas import datetime\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "X = series.values\n",
    "size = int(len(X) * 0.66)\n",
    "train, test = X[0:size], X[size:len(X)]\n",
    "history = [x for x in train]\n",
    "predictions = list()\n",
    "for t in range(len(test)):\n",
    "    model = ARIMA(history, order=(5,1,0))\n",
    "    model_fit = model.fit(disp=0)\n",
    "    output = model_fit.forecast()\n",
    "    yhat = output[0]\n",
    "    predictions.append(yhat)\n",
    "    obs = test[t]\n",
    "    history.append(obs)\n",
    "error = mean_squared_error(test, predictions)\n",
    "print('Test MSE: %.3f' % error)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6), dpi=100)\n",
    "plt.plot(test, label='Real')\n",
    "plt.plot(predictions, color='red', label='Predicted')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('USD')\n",
    "plt.title('Figure 5: ARIMA model on GS stock')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total dataset has 5934 samples, and 8 features.\n"
     ]
    }
   ],
   "source": [
    "dataset_total_df = dataset_TI_df\n",
    "print('Total dataset has {} samples, and {} features.'.format(dataset_total_df.shape[0], dataset_total_df.shape[1]))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "outputs": [
    {
     "ename": "XGBoostError",
     "evalue": "\nXGBoost Library (libxgboost.dylib) could not be loaded.\nLikely causes:\n  * OpenMP runtime is not installed\n    - vcomp140.dll or libgomp-1.dll for Windows\n    - libomp.dylib for Mac OSX\n    - libgomp.so for Linux and other UNIX-like OSes\n    Mac OSX users: Run `brew install libomp` to install OpenMP runtime.\n\n  * You are running 32-bit Python on a 64-bit OS\n\nError message(s): [\"dlopen(/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/xgboost/lib/libxgboost.dylib, 0x0006): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\\n  Referenced from: /Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/xgboost/lib/libxgboost.dylib\\n  Reason: tried: '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/lib/libomp.dylib' (no such file), '/usr/lib/libomp.dylib' (no such file)\"]\n",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mXGBoostError\u001B[0m                              Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/92/nwcr41r54dl8w3bkn9lrs8100000gn/T/ipykernel_35496/1515329492.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0;32mimport\u001B[0m \u001B[0mxgboost\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0mxgb\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      2\u001B[0m \u001B[0;32mdef\u001B[0m \u001B[0mget_feature_importance_data\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdata_income\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      3\u001B[0m     \u001B[0mdata\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdata_income\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcopy\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      4\u001B[0m     \u001B[0my\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdata\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'GS'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      5\u001B[0m     \u001B[0mX\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdata\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0miloc\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/xgboost/__init__.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      7\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mos\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      8\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 9\u001B[0;31m \u001B[0;32mfrom\u001B[0m \u001B[0;34m.\u001B[0m\u001B[0mcore\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mDMatrix\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mDeviceQuantileDMatrix\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mBooster\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mDataIter\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     10\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0;34m.\u001B[0m\u001B[0mtraining\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mtrain\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcv\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     11\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0;34m.\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mrabit\u001B[0m  \u001B[0;31m# noqa\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/xgboost/core.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m    201\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    202\u001B[0m \u001B[0;31m# load the XGBoost library globally\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 203\u001B[0;31m \u001B[0m_LIB\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0m_load_lib\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    204\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    205\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/xgboost/core.py\u001B[0m in \u001B[0;36m_load_lib\u001B[0;34m()\u001B[0m\n\u001B[1;32m    179\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mlib_success\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    180\u001B[0m         \u001B[0mlibname\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mos\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mpath\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mbasename\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlib_paths\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 181\u001B[0;31m         raise XGBoostError(\n\u001B[0m\u001B[1;32m    182\u001B[0m             f\"\"\"\n\u001B[1;32m    183\u001B[0m \u001B[0mXGBoost\u001B[0m \u001B[0mLibrary\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0;34m{\u001B[0m\u001B[0mlibname\u001B[0m\u001B[0;34m}\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0mcould\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mbe\u001B[0m \u001B[0mloaded\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mXGBoostError\u001B[0m: \nXGBoost Library (libxgboost.dylib) could not be loaded.\nLikely causes:\n  * OpenMP runtime is not installed\n    - vcomp140.dll or libgomp-1.dll for Windows\n    - libomp.dylib for Mac OSX\n    - libgomp.so for Linux and other UNIX-like OSes\n    Mac OSX users: Run `brew install libomp` to install OpenMP runtime.\n\n  * You are running 32-bit Python on a 64-bit OS\n\nError message(s): [\"dlopen(/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/xgboost/lib/libxgboost.dylib, 0x0006): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\\n  Referenced from: /Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/xgboost/lib/libxgboost.dylib\\n  Reason: tried: '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/lib/libomp.dylib' (no such file), '/usr/lib/libomp.dylib' (no such file)\"]\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "def get_feature_importance_data(data_income):\n",
    "    data = data_income.copy()\n",
    "    y = data['GS']\n",
    "    X = data.iloc[:, 1:]\n",
    "\n",
    "    train_samples = int(X.shape[0] * 0.65)\n",
    "\n",
    "    X_train = X.iloc[:train_samples]\n",
    "    X_test = X.iloc[train_samples:]\n",
    "\n",
    "    y_train = y.iloc[:train_samples]\n",
    "    y_test = y.iloc[train_samples:]\n",
    "\n",
    "    return (X_train, y_train), (X_test, y_test)\n",
    "# Get training and test data\n",
    "(X_train_FI, y_train_FI), (X_test_FI, y_test_FI) = get_feature_importance_data(dataset_TI_df)\n",
    "regressor = xgb.XGBRegressor(gamma=0.0,n_estimators=150,base_score=0.7,colsample_bytree=1,learning_rate=0.05)\n",
    "xgbModel = regressor.fit(X_train_FI,y_train_FI, eval_set = [(X_train_FI, y_train_FI), (X_test_FI, y_test_FI)], verbose=False)\n",
    "eval_result = regressor.evals_result()\n",
    "training_rounds = range(len(eval_result['validation_0']['rmse']))\n",
    "\n",
    "plt.scatter(x=training_rounds,y=eval_result['validation_0']['rmse'],label='Training Error')\n",
    "plt.scatter(x=training_rounds,y=eval_result['validation_1']['rmse'],label='Validation Error')\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('RMSE')\n",
    "plt.title('Training Vs Validation Error')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'xgbModel' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/92/nwcr41r54dl8w3bkn9lrs8100000gn/T/ipykernel_35496/135009404.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0mfig\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mplt\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfigure\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mfigsize\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m8\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m8\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      2\u001B[0m \u001B[0mplt\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mxticks\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mrotation\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m'vertical'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 3\u001B[0;31m \u001B[0mplt\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mbar\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mi\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mi\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mrange\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mxgbModel\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfeature_importances_\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mxgbModel\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfeature_importances_\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtolist\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtick_label\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mX_test_FI\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcolumns\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      4\u001B[0m \u001B[0mplt\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtitle\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'Figure 6: Feature importance of the technical indicators.'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[0mplt\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mshow\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mNameError\u001B[0m: name 'xgbModel' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 576x576 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.bar([i for i in range(len(xgbModel.feature_importances_))], xgbModel.feature_importances_.tolist(), tick_label=X_test_FI.columns)\n",
    "plt.title('Figure 6: Feature importance of the technical indicators.')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1080x360 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def gelu(x):\n",
    "    return 0.5 * x * (1 + math.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * math.pow(x, 3))))\n",
    "def relu(x):\n",
    "    return max(x, 0)\n",
    "def lrelu(x):\n",
    "    return max(0.01*x, x)\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=.5, hspace=None)\n",
    "\n",
    "ranges_ = (-10, 3, .25)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot([i for i in np.arange(*ranges_)], [relu(i) for i in np.arange(*ranges_)], label='ReLU', marker='.')\n",
    "plt.plot([i for i in np.arange(*ranges_)], [gelu(i) for i in np.arange(*ranges_)], label='GELU')\n",
    "plt.hlines(0, -10, 3, colors='gray', linestyles='--', label='0')\n",
    "plt.title('Figure 7: GELU as an activation function for autoencoders')\n",
    "plt.ylabel('f(x) for GELU and ReLU')\n",
    "plt.xlabel('x')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot([i for i in np.arange(*ranges_)], [lrelu(i) for i in np.arange(*ranges_)], label='Leaky ReLU')\n",
    "plt.hlines(0, -10, 3, colors='gray', linestyles='--', label='0')\n",
    "plt.ylabel('f(x) for Leaky ReLU')\n",
    "plt.xlabel('x')\n",
    "plt.title('Figure 8: LeakyReLU')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "outputs": [],
   "source": [
    "VAE_data = dataset_TI_df\n",
    "batch_size = 64\n",
    "n_batches = VAE_data.shape[0]/batch_size\n",
    "VAE_data = VAE_data.values\n",
    "\n",
    "train_iter = mx.io.NDArrayIter(data={'data': VAE_data[:num_training_days,:-1]}, \\\n",
    "                               label={'label': VAE_data[:num_training_days, -1]}, batch_size = batch_size)\n",
    "test_iter = mx.io.NDArrayIter(data={'data': VAE_data[num_training_days:,:-1]}, \\\n",
    "                              label={'label': VAE_data[num_training_days:,-1]}, batch_size = batch_size)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "outputs": [],
   "source": [
    "model_ctx =  mx.cpu()\n",
    "class VAE(gluon.HybridBlock):\n",
    "    def __init__(self, n_hidden=400, n_latent=2, n_layers=1, n_output=784, \\\n",
    "                 batch_size=100, act_type='relu', **kwargs):\n",
    "        self.soft_zero = 1e-10\n",
    "        self.n_latent = n_latent\n",
    "        self.batch_size = batch_size\n",
    "        self.output = None\n",
    "        self.mu = None\n",
    "        super(VAE, self).__init__(**kwargs)\n",
    "\n",
    "        with self.name_scope():\n",
    "            self.encoder = nn.HybridSequential(prefix='encoder')\n",
    "\n",
    "            for i in range(n_layers):\n",
    "                self.encoder.add(nn.Dense(n_hidden, activation=act_type))\n",
    "            self.encoder.add(nn.Dense(n_latent*2, activation=None))\n",
    "\n",
    "            self.decoder = nn.HybridSequential(prefix='decoder')\n",
    "            for i in range(n_layers):\n",
    "                self.decoder.add(nn.Dense(n_hidden, activation=act_type))\n",
    "            self.decoder.add(nn.Dense(n_output, activation='sigmoid'))\n",
    "\n",
    "    def hybrid_forward(self, F, x):\n",
    "        h = self.encoder(x)\n",
    "        #print(h)\n",
    "        mu_lv = F.split(h, axis=1, num_outputs=2)\n",
    "        mu = mu_lv[0]\n",
    "        lv = mu_lv[1]\n",
    "        self.mu = mu\n",
    "\n",
    "        eps = F.random_normal(loc=0, scale=1, shape=(self.batch_size, self.n_latent), ctx=model_ctx)\n",
    "        z = mu + F.exp(0.5*lv)*eps\n",
    "        y = self.decoder(z)\n",
    "        self.output = y\n",
    "\n",
    "        KL = 0.5*F.sum(1+lv-mu*mu-F.exp(lv),axis=1)\n",
    "        logloss = F.sum(x*F.log(y+self.soft_zero)+ (1-x)*F.log(1-y+self.soft_zero), axis=1)\n",
    "        loss = -logloss-KL\n",
    "\n",
    "        return loss"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "outputs": [],
   "source": [
    "n_hidden=400 # neurons in each layer\n",
    "n_latent=2\n",
    "n_layers=3 # num of dense layers in encoder and decoder respectively\n",
    "n_output=VAE_data.shape[1]-1\n",
    "\n",
    "net = VAE(n_hidden=n_hidden, n_latent=n_latent, n_layers=n_layers, n_output=n_output, batch_size=batch_size, act_type='gelu')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VAE(\n",
      "  (encoder): HybridSequential(\n",
      "    (0): Dense(None -> 400, Activation(gelu))\n",
      "    (1): Dense(None -> 400, Activation(gelu))\n",
      "    (2): Dense(None -> 400, Activation(gelu))\n",
      "    (3): Dense(None -> 4, linear)\n",
      "  )\n",
      "  (decoder): HybridSequential(\n",
      "    (0): Dense(None -> 400, Activation(gelu))\n",
      "    (1): Dense(None -> 400, Activation(gelu))\n",
      "    (2): Dense(None -> 400, Activation(gelu))\n",
      "    (3): Dense(None -> 7, Activation(sigmoid))\n",
      "  )\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/parameter.py:896: UserWarning: Parameter 'vae0_dense0_bias' is already initialized, ignoring. Set force_reinit=True to re-initialize.\n",
      "  v.initialize(None, ctx, init, force_reinit=force_reinit)\n",
      "/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/parameter.py:896: UserWarning: Parameter 'vae0_dense1_bias' is already initialized, ignoring. Set force_reinit=True to re-initialize.\n",
      "  v.initialize(None, ctx, init, force_reinit=force_reinit)\n",
      "/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/parameter.py:896: UserWarning: Parameter 'vae0_dense2_bias' is already initialized, ignoring. Set force_reinit=True to re-initialize.\n",
      "  v.initialize(None, ctx, init, force_reinit=force_reinit)\n",
      "/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/parameter.py:896: UserWarning: Parameter 'vae0_dense3_bias' is already initialized, ignoring. Set force_reinit=True to re-initialize.\n",
      "  v.initialize(None, ctx, init, force_reinit=force_reinit)\n",
      "/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/parameter.py:896: UserWarning: Parameter 'vae0_dense4_bias' is already initialized, ignoring. Set force_reinit=True to re-initialize.\n",
      "  v.initialize(None, ctx, init, force_reinit=force_reinit)\n",
      "/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/parameter.py:896: UserWarning: Parameter 'vae0_dense5_bias' is already initialized, ignoring. Set force_reinit=True to re-initialize.\n",
      "  v.initialize(None, ctx, init, force_reinit=force_reinit)\n",
      "/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/parameter.py:896: UserWarning: Parameter 'vae0_dense6_bias' is already initialized, ignoring. Set force_reinit=True to re-initialize.\n",
      "  v.initialize(None, ctx, init, force_reinit=force_reinit)\n",
      "/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/parameter.py:896: UserWarning: Parameter 'vae0_dense7_bias' is already initialized, ignoring. Set force_reinit=True to re-initialize.\n",
      "  v.initialize(None, ctx, init, force_reinit=force_reinit)\n"
     ]
    }
   ],
   "source": [
    "net.collect_params().initialize(mx.init.Xavier(), ctx=mx.cpu())\n",
    "net.hybridize()\n",
    "trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': .01})\n",
    "\n",
    "print(net)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "outputs": [
    {
     "ename": "MXNetError",
     "evalue": "MXNetError: Invalid Input: 'gelu', valid values are: {'relu', 'sigmoid', 'softrelu', 'softsign', 'tanh'}, in operator Activation(name=\"\", act_type=\"gelu\")",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mMXNetError\u001B[0m                                Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/92/nwcr41r54dl8w3bkn9lrs8100000gn/T/ipykernel_35496/4242799075.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m     18\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     19\u001B[0m         \u001B[0;32mwith\u001B[0m \u001B[0mautograd\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mrecord\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 20\u001B[0;31m             \u001B[0mloss\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnet\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdata\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     21\u001B[0m         \u001B[0mloss\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mbackward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     22\u001B[0m         \u001B[0mtrainer\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mstep\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdata\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mshape\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/block.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, *args)\u001B[0m\n\u001B[1;32m    680\u001B[0m             \u001B[0mhook\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    681\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 682\u001B[0;31m         \u001B[0mout\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mforward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    683\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    684\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0mhook\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_forward_hooks\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/block.py\u001B[0m in \u001B[0;36mforward\u001B[0;34m(self, x, *args)\u001B[0m\n\u001B[1;32m   1333\u001B[0m                                      'Find all contexts = {}'.format(ctx_set))\n\u001B[1;32m   1334\u001B[0m                 \u001B[0;32mwith\u001B[0m \u001B[0mctx\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1335\u001B[0;31m                     \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_call_cached_op\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1336\u001B[0m             \u001B[0;32mwith\u001B[0m \u001B[0mctx\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1337\u001B[0m                 \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/block.py\u001B[0m in \u001B[0;36m_call_cached_op\u001B[0;34m(self, *args)\u001B[0m\n\u001B[1;32m   1043\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m_call_cached_op\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1044\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_cached_op\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1045\u001B[0;31m             \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_build_cache\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1046\u001B[0m         \u001B[0;32massert\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_cached_op\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m\"Gluon failed to build the cache. \"\u001B[0m\u001B[0;31m \u001B[0m\u001B[0;31m\\\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1047\u001B[0m                                 \u001B[0;34m\"This should never happen. \"\u001B[0m\u001B[0;31m \u001B[0m\u001B[0;31m\\\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/block.py\u001B[0m in \u001B[0;36m_build_cache\u001B[0;34m(self, *args)\u001B[0m\n\u001B[1;32m    922\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    923\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m_build_cache\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 924\u001B[0;31m         \u001B[0mdata\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mout\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_get_graph\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    925\u001B[0m         \u001B[0mdata_names\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m{\u001B[0m\u001B[0mdata\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mname\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mi\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mi\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdata\u001B[0m \u001B[0;32min\u001B[0m \u001B[0menumerate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdata\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m}\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    926\u001B[0m         \u001B[0mparams\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcollect_params\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/block.py\u001B[0m in \u001B[0;36m_get_graph\u001B[0;34m(self, *args)\u001B[0m\n\u001B[1;32m    914\u001B[0m             \u001B[0mparams\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m{\u001B[0m\u001B[0mi\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mj\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvar\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mi\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mj\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_reg_params\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mitems\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m}\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    915\u001B[0m             \u001B[0;32mwith\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mname_scope\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 916\u001B[0;31m                 \u001B[0mout\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mhybrid_forward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msymbol\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0mgrouped_inputs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mparams\u001B[0m\u001B[0;34m)\u001B[0m  \u001B[0;31m# pylint: disable=no-value-for-parameter\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    917\u001B[0m             \u001B[0mout\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_out_format\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0m_flatten\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mout\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m\"output\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    918\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
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      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/block.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, *args)\u001B[0m\n\u001B[1;32m    680\u001B[0m             \u001B[0mhook\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    681\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 682\u001B[0;31m         \u001B[0mout\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mforward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    683\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    684\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0mhook\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_forward_hooks\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/block.py\u001B[0m in \u001B[0;36mforward\u001B[0;34m(self, x, *args)\u001B[0m\n\u001B[1;32m   1346\u001B[0m         \u001B[0mparams\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m{\u001B[0m\u001B[0mi\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mj\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvar\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mi\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mj\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_reg_params\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mitems\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m}\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1347\u001B[0m         \u001B[0;32mwith\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mname_scope\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1348\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mhybrid_forward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msymbol\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mparams\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1349\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1350\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mhybrid_forward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mF\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/nn/basic_layers.py\u001B[0m in \u001B[0;36mhybrid_forward\u001B[0;34m(self, F, x)\u001B[0m\n\u001B[1;32m    117\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mhybrid_forward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mF\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    118\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0mblock\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_children\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 119\u001B[0;31m             \u001B[0mx\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mblock\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    120\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    121\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/block.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, *args)\u001B[0m\n\u001B[1;32m    680\u001B[0m             \u001B[0mhook\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    681\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 682\u001B[0;31m         \u001B[0mout\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mforward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    683\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    684\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0mhook\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_forward_hooks\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/block.py\u001B[0m in \u001B[0;36mforward\u001B[0;34m(self, x, *args)\u001B[0m\n\u001B[1;32m   1346\u001B[0m         \u001B[0mparams\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m{\u001B[0m\u001B[0mi\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mj\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvar\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mi\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mj\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_reg_params\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mitems\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m}\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1347\u001B[0m         \u001B[0;32mwith\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mname_scope\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1348\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mhybrid_forward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msymbol\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mparams\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1349\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1350\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mhybrid_forward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mF\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/nn/basic_layers.py\u001B[0m in \u001B[0;36mhybrid_forward\u001B[0;34m(self, F, x, weight, bias)\u001B[0m\n\u001B[1;32m    225\u001B[0m                  flatten=self._flatten, name='fwd')\n\u001B[1;32m    226\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mact\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 227\u001B[0;31m             \u001B[0mact\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mact\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mact\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    228\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mact\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    229\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/block.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, *args)\u001B[0m\n\u001B[1;32m    680\u001B[0m             \u001B[0mhook\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    681\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 682\u001B[0;31m         \u001B[0mout\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mforward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    683\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    684\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0mhook\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_forward_hooks\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/block.py\u001B[0m in \u001B[0;36mforward\u001B[0;34m(self, x, *args)\u001B[0m\n\u001B[1;32m   1346\u001B[0m         \u001B[0mparams\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m{\u001B[0m\u001B[0mi\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mj\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvar\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mi\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mj\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_reg_params\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mitems\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m}\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1347\u001B[0m         \u001B[0;32mwith\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mname_scope\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1348\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mhybrid_forward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msymbol\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mparams\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1349\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1350\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mhybrid_forward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mF\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/gluon/nn/activations.py\u001B[0m in \u001B[0;36mhybrid_forward\u001B[0;34m(self, F, x)\u001B[0m\n\u001B[1;32m     51\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mhybrid_forward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mF\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     52\u001B[0m         \u001B[0mact\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mF\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnpx\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mactivation\u001B[0m \u001B[0;32mif\u001B[0m \u001B[0mis_np_array\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32melse\u001B[0m \u001B[0mF\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mActivation\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 53\u001B[0;31m         \u001B[0;32mreturn\u001B[0m \u001B[0mact\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mact_type\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_act_type\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mname\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m'fwd'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     54\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     55\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m__repr__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/symbol/register.py\u001B[0m in \u001B[0;36mActivation\u001B[0;34m(data, act_type, name, attr, out, **kwargs)\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/_ctypes/symbol.py\u001B[0m in \u001B[0;36m_symbol_creator\u001B[0;34m(handle, args, kwargs, keys, vals, name, is_np_op, output_is_list)\u001B[0m\n\u001B[1;32m    125\u001B[0m \u001B[0;32mdef\u001B[0m \u001B[0m_symbol_creator\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mhandle\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mkwargs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mkeys\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mvals\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mname\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mis_np_op\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0moutput_is_list\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mFalse\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    126\u001B[0m     \u001B[0msym_handle\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mSymbolHandle\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 127\u001B[0;31m     check_call(_LIB.MXSymbolCreateAtomicSymbol(\n\u001B[0m\u001B[1;32m    128\u001B[0m         \u001B[0mctypes\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mc_void_p\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mhandle\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    129\u001B[0m         \u001B[0mmx_uint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mkeys\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/mxnet/base.py\u001B[0m in \u001B[0;36mcheck_call\u001B[0;34m(ret)\u001B[0m\n\u001B[1;32m    244\u001B[0m     \"\"\"\n\u001B[1;32m    245\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0mret\u001B[0m \u001B[0;34m!=\u001B[0m \u001B[0;36m0\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 246\u001B[0;31m         \u001B[0;32mraise\u001B[0m \u001B[0mget_last_ffi_error\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    247\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    248\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mMXNetError\u001B[0m: MXNetError: Invalid Input: 'gelu', valid values are: {'relu', 'sigmoid', 'softrelu', 'softsign', 'tanh'}, in operator Activation(name=\"\", act_type=\"gelu\")"
     ]
    }
   ],
   "source": [
    "n_epoch = 150\n",
    "print_period = n_epoch // 10\n",
    "start = time.time()\n",
    "\n",
    "training_loss = []\n",
    "validation_loss = []\n",
    "for epoch in range(n_epoch):\n",
    "    epoch_loss = 0\n",
    "    epoch_val_loss = 0\n",
    "\n",
    "    train_iter.reset()\n",
    "    test_iter.reset()\n",
    "\n",
    "    n_batch_train = 0\n",
    "    for batch in train_iter:\n",
    "        n_batch_train +=1\n",
    "        data = batch.data[0].as_in_context(mx.cpu())\n",
    "\n",
    "        with autograd.record():\n",
    "            loss = net(data)\n",
    "        loss.backward()\n",
    "        trainer.step(data.shape[0])\n",
    "        epoch_loss += nd.mean(loss).asscalar()\n",
    "\n",
    "    n_batch_val = 0\n",
    "    for batch in test_iter:\n",
    "        n_batch_val +=1\n",
    "        data = batch.data[0].as_in_context(mx.cpu())\n",
    "        loss = net(data)\n",
    "        epoch_val_loss += nd.mean(loss).asscalar()\n",
    "\n",
    "    epoch_loss /= n_batch_train\n",
    "    epoch_val_loss /= n_batch_val\n",
    "\n",
    "    training_loss.append(epoch_loss)\n",
    "    validation_loss.append(epoch_val_loss)\n",
    "\n",
    "    \"\"\"if epoch % max(print_period, 1) == 0:\n",
    "        print('Epoch {}, Training loss {:.2f}, Validation loss {:.2f}'.\\\n",
    "              format(epoch, epoch_loss, epoch_val_loss))\"\"\"\n",
    "\n",
    "end = time.time()\n",
    "print('Training completed in {} seconds.'.format(int(end-start)))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "outputs": [],
   "source": [
    "dataset_total_df['Date'] = dataset_ex_df['Date']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "outputs": [],
   "source": [
    "vae_added_df = mx.nd.array(dataset_total_df.iloc[:, :-1].values)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the newly created (from the autoencoder) features is (5934, 8).\n"
     ]
    }
   ],
   "source": [
    "print('The shape of the newly created (from the autoencoder) features is {}.'.format(vae_added_df.shape))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence. The requested array would exceed the maximum number of dimension of 32.",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/92/nwcr41r54dl8w3bkn9lrs8100000gn/T/ipykernel_35496/2279556738.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0;31m# We want the PCA to create the new components to explain 80% of the variance\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      2\u001B[0m \u001B[0mpca\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mPCA\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mn_components\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m.8\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 3\u001B[0;31m \u001B[0mx_pca\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mStandardScaler\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit_transform\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mvae_added_df\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      4\u001B[0m \u001B[0mprincipalComponents\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpca\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit_transform\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx_pca\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[0mprincipalComponents\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mn_components_\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/sklearn/base.py\u001B[0m in \u001B[0;36mfit_transform\u001B[0;34m(self, X, y, **fit_params)\u001B[0m\n\u001B[1;32m    845\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0my\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    846\u001B[0m             \u001B[0;31m# fit method of arity 1 (unsupervised transformation)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 847\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mX\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mfit_params\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtransform\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mX\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    848\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    849\u001B[0m             \u001B[0;31m# fit method of arity 2 (supervised transformation)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\u001B[0m in \u001B[0;36mfit\u001B[0;34m(self, X, y, sample_weight)\u001B[0m\n\u001B[1;32m    804\u001B[0m         \u001B[0;31m# Reset internal state before fitting\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    805\u001B[0m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_reset\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 806\u001B[0;31m         \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mpartial_fit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mX\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0msample_weight\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    807\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    808\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mpartial_fit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mX\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mNone\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0msample_weight\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mNone\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\u001B[0m in \u001B[0;36mpartial_fit\u001B[0;34m(self, X, y, sample_weight)\u001B[0m\n\u001B[1;32m    839\u001B[0m         \"\"\"\n\u001B[1;32m    840\u001B[0m         \u001B[0mfirst_call\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mhasattr\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m\"n_samples_seen_\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 841\u001B[0;31m         X = self._validate_data(\n\u001B[0m\u001B[1;32m    842\u001B[0m             \u001B[0mX\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    843\u001B[0m             \u001B[0maccept_sparse\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"csr\"\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m\"csc\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/sklearn/base.py\u001B[0m in \u001B[0;36m_validate_data\u001B[0;34m(self, X, y, reset, validate_separately, **check_params)\u001B[0m\n\u001B[1;32m    559\u001B[0m             \u001B[0;32mraise\u001B[0m \u001B[0mValueError\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"Validation should be done on X, y or both.\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    560\u001B[0m         \u001B[0;32melif\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mno_val_X\u001B[0m \u001B[0;32mand\u001B[0m \u001B[0mno_val_y\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 561\u001B[0;31m             \u001B[0mX\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mcheck_array\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mX\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mcheck_params\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    562\u001B[0m             \u001B[0mout\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mX\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    563\u001B[0m         \u001B[0;32melif\u001B[0m \u001B[0mno_val_X\u001B[0m \u001B[0;32mand\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mno_val_y\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/sklearn/utils/validation.py\u001B[0m in \u001B[0;36mcheck_array\u001B[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001B[0m\n\u001B[1;32m    736\u001B[0m                     \u001B[0marray\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0marray\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mastype\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdtype\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcasting\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m\"unsafe\"\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcopy\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mFalse\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    737\u001B[0m                 \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 738\u001B[0;31m                     \u001B[0marray\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0masarray\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0marray\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0morder\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0morder\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdtype\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mdtype\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    739\u001B[0m             \u001B[0;32mexcept\u001B[0m \u001B[0mComplexWarning\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0mcomplex_warning\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    740\u001B[0m                 raise ValueError(\n",
      "\u001B[0;31mValueError\u001B[0m: setting an array element with a sequence. The requested array would exceed the maximum number of dimension of 32."
     ]
    }
   ],
   "source": [
    "# We want the PCA to create the new components to explain 80% of the variance\n",
    "pca = PCA(n_components=.8)\n",
    "x_pca = StandardScaler().fit_transform(vae_added_df)\n",
    "principalComponents = pca.fit_transform(x_pca)\n",
    "principalComponents.n_components_"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "outputs": [],
   "source": [
    "gan_num_features = dataset_total_df.shape[1]\n",
    "sequence_length = 17\n",
    "\n",
    "class RNNModel(gluon.Block):\n",
    "    def __init__(self, num_embed, num_hidden, num_layers, bidirectional=False, \\\n",
    "                 sequence_length=sequence_length, **kwargs):\n",
    "        super(RNNModel, self).__init__(**kwargs)\n",
    "        self.num_hidden = num_hidden\n",
    "        with self.name_scope():\n",
    "            self.rnn = rnn.LSTM(num_hidden, num_layers, input_size=num_embed, \\\n",
    "                                bidirectional=bidirectional, layout='TNC')\n",
    "\n",
    "            self.decoder = nn.Dense(1, in_units=num_hidden)\n",
    "\n",
    "    def forward(self, inputs, hidden):\n",
    "        output, hidden = self.rnn(inputs, hidden)\n",
    "        decoded = self.decoder(output.reshape((-1, self.num_hidden)))\n",
    "        return decoded, hidden\n",
    "\n",
    "    def begin_state(self, *args, **kwargs):\n",
    "        return self.rnn.begin_state(*args, **kwargs)\n",
    "\n",
    "lstm_model = RNNModel(num_embed=gan_num_features, num_hidden=500, num_layers=1)\n",
    "lstm_model.collect_params().initialize(mx.init.Xavier(), ctx=mx.cpu())\n",
    "trainer = gluon.Trainer(lstm_model.collect_params(), 'adam', {'learning_rate': .01})\n",
    "loss = gluon.loss.L1Loss()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RNNModel(\n",
      "  (rnn): LSTM(9 -> 500, TNC)\n",
      "  (decoder): Dense(500 -> 1, linear)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(lstm_model)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "outputs": [],
   "source": [
    "class TriangularSchedule():\n",
    "    def __init__(self, min_lr, max_lr, cycle_length, inc_fraction=0.5):\n",
    "        self.min_lr = min_lr\n",
    "        self.max_lr = max_lr\n",
    "        self.cycle_length = cycle_length\n",
    "        self.inc_fraction = inc_fraction\n",
    "\n",
    "    def __call__(self, iteration):\n",
    "        if iteration <= self.cycle_length*self.inc_fraction:\n",
    "            unit_cycle = iteration * 1 / (self.cycle_length * self.inc_fraction)\n",
    "        elif iteration <= self.cycle_length:\n",
    "            unit_cycle = (self.cycle_length - iteration) * 1 / (self.cycle_length * (1 - self.inc_fraction))\n",
    "        else:\n",
    "            unit_cycle = 0\n",
    "        adjusted_cycle = (unit_cycle * (self.max_lr - self.min_lr)) + self.min_lr\n",
    "        return adjusted_cycle\n",
    "\n",
    "class CyclicalSchedule():\n",
    "    def __init__(self, schedule_class, cycle_length, cycle_length_decay=1, cycle_magnitude_decay=1, **kwargs):\n",
    "        self.schedule_class = schedule_class\n",
    "        self.length = cycle_length\n",
    "        self.length_decay = cycle_length_decay\n",
    "        self.magnitude_decay = cycle_magnitude_decay\n",
    "        self.kwargs = kwargs\n",
    "\n",
    "    def __call__(self, iteration):\n",
    "        cycle_idx = 0\n",
    "        cycle_length = self.length\n",
    "        idx = self.length\n",
    "        while idx <= iteration:\n",
    "            cycle_length = math.ceil(cycle_length * self.length_decay)\n",
    "            cycle_idx += 1\n",
    "            idx += cycle_length\n",
    "        cycle_offset = iteration - idx + cycle_length\n",
    "\n",
    "        schedule = self.schedule_class(cycle_length=cycle_length, **self.kwargs)\n",
    "        return schedule(cycle_offset) * self.magnitude_decay**cycle_idx"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "schedule = CyclicalSchedule(TriangularSchedule, min_lr=0.5, max_lr=2, cycle_length=500)\n",
    "iterations=1500\n",
    "\n",
    "plt.plot([i+1 for i in range(iterations)],[schedule(i) for i in range(iterations)])\n",
    "plt.title('Learning rate for each epoch')\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Learning Rate\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "outputs": [],
   "source": [
    "num_fc = 512\n",
    "\n",
    "# ... other parts of the GAN\n",
    "\n",
    "cnn_net = gluon.nn.Sequential()\n",
    "with net.name_scope():\n",
    "\n",
    "    # Add the 1D Convolutional layers\n",
    "    cnn_net.add(gluon.nn.Conv1D(32, kernel_size=5, strides=2))\n",
    "    cnn_net.add(nn.LeakyReLU(0.01))\n",
    "    cnn_net.add(gluon.nn.Conv1D(64, kernel_size=5, strides=2))\n",
    "    cnn_net.add(nn.LeakyReLU(0.01))\n",
    "    cnn_net.add(nn.BatchNorm())\n",
    "    cnn_net.add(gluon.nn.Conv1D(128, kernel_size=5, strides=2))\n",
    "    cnn_net.add(nn.LeakyReLU(0.01))\n",
    "    cnn_net.add(nn.BatchNorm())\n",
    "\n",
    "    # Add the two Fully Connected layers\n",
    "    cnn_net.add(nn.Dense(220, use_bias=False), nn.BatchNorm(), nn.LeakyReLU(0.01))\n",
    "    cnn_net.add(nn.Dense(220, use_bias=False), nn.Activation(activation='relu'))\n",
    "    cnn_net.add(nn.Dense(1))\n",
    "\n",
    "# ... other parts of the GAN"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Conv1D(None -> 32, kernel_size=(5,), stride=(2,))\n",
      "  (1): LeakyReLU(0.01)\n",
      "  (2): Conv1D(None -> 64, kernel_size=(5,), stride=(2,))\n",
      "  (3): LeakyReLU(0.01)\n",
      "  (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
      "  (5): Conv1D(None -> 128, kernel_size=(5,), stride=(2,))\n",
      "  (6): LeakyReLU(0.01)\n",
      "  (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
      "  (8): Dense(None -> 220, linear)\n",
      "  (9): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
      "  (10): LeakyReLU(0.01)\n",
      "  (11): Dense(None -> 220, linear)\n",
      "  (12): Activation(relu)\n",
      "  (13): Dense(None -> 1, linear)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(cnn_net)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "outputs": [],
   "source": [
    "# Initialize the optimizer\n",
    "from bayes_opt import BayesianOptimization\n",
    "from bayes_opt import UtilityFunction\n",
    "\n",
    "utility = UtilityFunction(kind=\"ucb\", kappa=2.5, xi=0.0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'plot_prediction' from 'utils' (/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/utils/__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mImportError\u001B[0m                               Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/92/nwcr41r54dl8w3bkn9lrs8100000gn/T/ipykernel_35496/1842779451.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0;32mfrom\u001B[0m \u001B[0mutils\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mplot_prediction\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      2\u001B[0m \u001B[0mplot_prediction\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'Predicted and Real price - after first epoch.'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mImportError\u001B[0m: cannot import name 'plot_prediction' from 'utils' (/Users/mazeinsv/PycharmProjects/python.Neural.Market/venv/lib/python3.8/site-packages/utils/__init__.py)"
     ]
    }
   ],
   "source": [
    "from utils import plot_prediction\n",
    "plot_prediction('Predicted and Real price - after first epoch.')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ]
}