drift/exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1501 entries, 2017-10-01 to 2021-11-09\n",
      "Data columns (total 42 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   BTC_close      1501 non-null   float64\n",
      " 1   BTC_returns    1501 non-null   float64\n",
      " 2   TRX_close      1501 non-null   float64\n",
      " 3   TRX_returns    1501 non-null   float64\n",
      " 4   XRP_close      1501 non-null   float64\n",
      " 5   XRP_returns    1501 non-null   float64\n",
      " 6   IEF_close      1005 non-null   float64\n",
      " 7   IEF_adj_close  1005 non-null   float64\n",
      " 8   IEF_volume     1005 non-null   float64\n",
      " 9   IEF_returns    1005 non-null   float64\n",
      " 10  QQQ_close      1005 non-null   float64\n",
      " 11  QQQ_adj_close  1005 non-null   float64\n",
      " 12  QQQ_volume     1005 non-null   float64\n",
      " 13  QQQ_returns    1005 non-null   float64\n",
      " 14  FIL_close      1501 non-null   float64\n",
      " 15  FIL_returns    1501 non-null   float64\n",
      " 16  GLD_close      1005 non-null   float64\n",
      " 17  GLD_adj_close  1005 non-null   float64\n",
      " 18  GLD_volume     1005 non-null   float64\n",
      " 19  GLD_returns    1005 non-null   float64\n",
      " 20  TLT_close      1005 non-null   float64\n",
      " 21  TLT_adj_close  1005 non-null   float64\n",
      " 22  TLT_volume     1005 non-null   float64\n",
      " 23  TLT_returns    1005 non-null   float64\n",
      " 24  SPY_close      1005 non-null   float64\n",
      " 25  SPY_adj_close  1005 non-null   float64\n",
      " 26  SPY_volume     1005 non-null   float64\n",
      " 27  SPY_returns    1005 non-null   float64\n",
      " 28  ETH_close      1501 non-null   float64\n",
      " 29  ETH_returns    1501 non-null   float64\n",
      " 30  UNI_close      1501 non-null   float64\n",
      " 31  UNI_returns    1501 non-null   float64\n",
      " 32  DOT_close      1501 non-null   float64\n",
      " 33  DOT_returns    1501 non-null   float64\n",
      " 34  ADA_close      1501 non-null   float64\n",
      " 35  ADA_returns    1501 non-null   float64\n",
      " 36  BNB_close      1501 non-null   float64\n",
      " 37  BNB_returns    1501 non-null   float64\n",
      " 38  LTC_close      1501 non-null   float64\n",
      " 39  LTC_returns    1501 non-null   float64\n",
      " 40  SOL_close      1501 non-null   float64\n",
      " 41  SOL_returns    1501 non-null   float64\n",
      "dtypes: float64(42)\n",
      "memory usage: 504.2+ KB\n"
     ]
    }
   ],
   "source": [
    "from load_data import load_files\n",
    "import pandas as pd\n",
    "from pandas.plotting import lag_plot\n",
    "\n",
    "data = load_files('data')\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='y(t)', ylabel='y(t + 1)'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lag_plot(data['BTC_close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "51432b8e5767c06330d9b51dfad63f9db0ea39868e37d921b9c2e277373f8d11"
  },
  "kernelspec": {
   "display_name": "Python 3.9.2 64-bit ('deeplearning': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}