2. pick pairs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/crypto_1m.csv', index_col='unix')\n",
    "df = df[-60*24*2:-60*24*1] # pick highest cointegrated pairs from t-24 hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "272"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = corr.shape[0]\n",
    "total_pairs = n**2 - n\n",
    "total_pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ada  bnb     0.910302\n",
       "bnb  zec     0.908293\n",
       "btc  ltc     0.908075\n",
       "     dash    0.884487\n",
       "eos  zec     0.873636\n",
       "               ...   \n",
       "btt  xrp     0.358421\n",
       "xmr  xrp     0.351506\n",
       "eth  zec     0.350773\n",
       "btc  trx     0.312370\n",
       "bnb  xmr     0.311738\n",
       "Length: 100, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_top_n_corr(corr, n):\n",
    "    return corr.unstack().sort_values(ascending=False)[corr.shape[0]:corr.shape[0]+n*2][::2]\n",
    "top_corr = get_top_n_corr(corr, 100)\n",
    "top_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_values = []\n",
    "for i, j in list(top_corr.axes[0]):\n",
    "    pair = i + ' ' + j\n",
    "    b1, b0, adf_p = fit_model(df[i], df[j])\n",
    "    p_values.append({'pair':pair, 'p_value':adf_p})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pair</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>eth xrp</td>\n",
       "      <td>0.000253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>btc ltc</td>\n",
       "      <td>0.000446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>dash xlm</td>\n",
       "      <td>0.000615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>eth etc</td>\n",
       "      <td>0.000833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>link etc</td>\n",
       "      <td>0.001035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>btc qtum</td>\n",
       "      <td>0.624239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>qtum dash</td>\n",
       "      <td>0.704077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>xlm eth</td>\n",
       "      <td>0.708785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>xlm btt</td>\n",
       "      <td>0.903599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>qtum btt</td>\n",
       "      <td>0.958025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         pair   p_value\n",
       "46    eth xrp  0.000253\n",
       "2     btc ltc  0.000446\n",
       "27   dash xlm  0.000615\n",
       "54    eth etc  0.000833\n",
       "87   link etc  0.001035\n",
       "..        ...       ...\n",
       "58   btc qtum  0.624239\n",
       "23  qtum dash  0.704077\n",
       "90    xlm eth  0.708785\n",
       "84    xlm btt  0.903599\n",
       "77   qtum btt  0.958025\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(p_values).sort_values(by='p_value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0034806468521588222\n"
     ]
    }
   ],
   "source": [
    "# Note: Orders matter slightly !\n",
    "b1, b0, adf_p = fit_model(df['eth'], df['btc'])\n",
    "print(adf_p)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}