anthonytxie/Correlations Between the Top 200 Coins and Bitcoin.ipynb

## Correlations Between the Top 200 Coins and Bitcoin.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2698: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "import coinmarketcappy as cmc\n",
    "from cryptocmd import CmcScraper\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "import seaborn as sns\n",
    "% matplotlib inline\n",
    "\n",
    "# Plot styles\n",
    "plt.style.use('seaborn-poster')\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.rcParams['axes.edgecolor'] = '#ffffff'\n",
    "plt.rcParams['axes.facecolor'] = '#ffffff'\n",
    "plt.rcParams['figure.facecolor'] = '#ffffff'\n",
    "plt.rcParams['patch.edgecolor'] = '#ffffff'\n",
    "plt.rcParams['patch.facecolor'] = '#ffffff'\n",
    "plt.rcParams['savefig.edgecolor'] = '#ffffff'\n",
    "plt.rcParams['savefig.facecolor'] = '#ffffff'\n",
    "plt.rcParams['xtick.labelsize'] = 16\n",
    "plt.rcParams['ytick.labelsize'] = 16\n",
    "\n",
    "\n",
    "# Read in historical data for every coin on CMC. CSV is generated by cryptomarketcap-historical-prices scraper\n",
    "df = pd.read_csv('/Users/axie/Desktop/coindata.csv')\n",
    "df['Close'] = pd.to_numeric(df['Close'], errors='coerce')\n",
    "df['Market Cap'] = pd.to_numeric(df['Market Cap'], errors='coerce')\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "\n",
    "#Pivot the table and take only rows after June 22, 2016\n",
    "df_pivot = df[df['Date'] > '2016-06-22'].pivot(index='Date', columns='Coin', values='Market Cap').sort_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Bitcoin Market Cap\n",
    "bitcoin__market_cap = pd.DataFrame(df_pivot['bitcoin'])\n",
    "\n",
    "# Take the top 200 coins by market cap with at least 120 days of historical data\n",
    "marketcap_coins = pd.DataFrame(df[df['Date']=='2018-06-20'].set_index('Coin')['Market Cap']).sort_values(by='Market Cap', ascending=False)\n",
    "longer_than_120_coins = []\n",
    "\n",
    "for coin in df_pivot.count().index:\n",
    "    if df_pivot.count()[coin] > 120:\n",
    "        longer_than_120_coins.append(coin)\n",
    "\n",
    "marketcap_coins_longer_120 = marketcap_coins[marketcap_coins.index.isin(longer_than_120_coins)]\n",
    "top_200_coins = []\n",
    "\n",
    "for i in range(0,200):\n",
    "    top_200_coins.append(marketcap_coins_longer_120.index[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Density'), Text(0.5,0,'Correlation with BTC')]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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YyzN67H/33Xfq0aOHgoKCtHr1artBaG5Xrlw5SUrx89HaAlm2bNm7qpMSV1xX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\nMj4+PiZv3rymefPmTh+9k9b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6Zlc2cWJjeXt7uigi5+4cVOTYsSuKjv7TRdEA9w6JGgAA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\n6u5j3xAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116902d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlations DF\n",
    "corr_with_bitcoin = pd.DataFrame(df_pivot[top_100_coins].corr()['bitcoin'])\n",
    "corr_with_bitcoin.columns = ['corr with bitcoin']\n",
    "corr_with_bitcoin = corr_with_bitcoin.sort_values(by='corr with bitcoin', ascending=True)\n",
    "\n",
    "#Seaborn Distplot\n",
    "ax = sns.distplot(corr_with_bitcoin['corr with bitcoin'], hist=True, kde=True, \n",
    "             bins=int(180/5), color = 'darkblue', \n",
    "             hist_kws={'edgecolor':'black'},\n",
    "             kde_kws={'linewidth': 4})\n",
    "\n",
    "#Graph titles and axes labels\n",
    "plt.title('Density Plot of the Correlation Between the Top 200 Coins and Bitcoin')\n",
    "ax.set(xlabel='Correlation with BTC', ylabel='Density')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation to Bitcoin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coin</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dai</th>\n",
       "      <td>-0.686623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rchain</th>\n",
       "      <td>-0.256544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paypex</th>\n",
       "      <td>-0.211133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nuls</th>\n",
       "      <td>-0.178693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genesis-vision</th>\n",
       "      <td>-0.111648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zilliqa</th>\n",
       "      <td>-0.102094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all-sports</th>\n",
       "      <td>-0.087901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game</th>\n",
       "      <td>-0.074591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kickico</th>\n",
       "      <td>-0.060137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metal</th>\n",
       "      <td>-0.015950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gnosis-gno</th>\n",
       "      <td>0.022232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aion</th>\n",
       "      <td>0.027940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vechain</th>\n",
       "      <td>0.044680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hydro-protocol</th>\n",
       "      <td>0.055090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gifto</th>\n",
       "      <td>0.071484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revain</th>\n",
       "      <td>0.103725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bottos</th>\n",
       "      <td>0.126053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cybermiles</th>\n",
       "      <td>0.129340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>theta-token</th>\n",
       "      <td>0.177880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cryptonex</th>\n",
       "      <td>0.207123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Correlation to Bitcoin\n",
       "Coin                                  \n",
       "dai                          -0.686623\n",
       "rchain                       -0.256544\n",
       "paypex                       -0.211133\n",
       "nuls                         -0.178693\n",
       "genesis-vision               -0.111648\n",
       "zilliqa                      -0.102094\n",
       "all-sports                   -0.087901\n",
       "game                         -0.074591\n",
       "kickico                      -0.060137\n",
       "metal                        -0.015950\n",
       "gnosis-gno                    0.022232\n",
       "aion                          0.027940\n",
       "vechain                       0.044680\n",
       "hydro-protocol                0.055090\n",
       "gifto                         0.071484\n",
       "revain                        0.103725\n",
       "bottos                        0.126053\n",
       "cybermiles                    0.129340\n",
       "theta-token                   0.177880\n",
       "cryptonex                     0.207123"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coins least correlated with BTC\n",
    "corr_with_bitcoin.columns=['Correlation to Bitcoin']\n",
    "corr_with_bitcoin[0:20]"
   ]
  }
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