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": []
  }
 ],
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