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anthonytxie/Correlations Between the Top 200 Coins and the Rest of the Market.ipynb

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Correlations Between the Top 200 Coins and the Rest of the Market
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Correlations Between the Top 200 Coins and the Rest of the Market.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"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",
"\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",
"# Total Market Capitalization\n",
"market_cap = cmc.total_market_cap(exclude_btc=False)\n",
"market_cap_dict = {'Date':[], \"Market Cap\": []}\n",
"for value in market_cap['market_cap_by_available_supply']:\n",
" market_cap_dict['Date'].append(value[0])\n",
" market_cap_dict['Market Cap'].append(value[1])\n",
" \n",
"market_cap_df = pd.DataFrame(market_cap_dict)\n",
"market_cap_df['Date'] = pd.to_datetime(market_cap_df['Date']).dt.date\n",
"market_cap_df = market_cap_df.set_index('Date')\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": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 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",
"\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",
"\n",
"top_200_coins = []\n",
"\n",
"for i in range(0,200):\n",
" top_200_coins.append(marketcap_coins_longer_120.index[i])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Function to get the market cap for the rest of the market\n",
"def calc_market_cap_minus_coin_corr(coin, df1, df2):\n",
" temp_df = pd.DataFrame(df1[coin]).join(df2)\n",
" temp_df['Market Cap'] = temp_df['Market Cap'] - temp_df[coin]\n",
" return temp_df.corr()['Market Cap'][0]\n",
"\n",
"df_dict = {}\n",
"\n",
"for coin in top_200_coins:\n",
" df_dict[coin] = calc_market_cap_minus_coin_corr(coin, df_pivot, market_cap_df)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Text(0,0.5,'Density'), Text(0.5,0,'Correlation with the Rest of the Market')]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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0bNkShw4dQvXq1dG1a1eUKFHCcCNj2rRpePToUZZlWCIvbWTpvmAJUxfFjOnK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IcPqfYZsKzhcLDIXlppadnIy1PvR0daGt8lkTVNdfn9KCgowtWr6QqBYXz8Y2RnF9Z6WebmMjg5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\nWRSJRCgoKEB4eLjQv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Lp27Uo///xzhY+WOH36NHl5eZFUKiUAwmMIyj86QxNtLrvNf0WP\nmygvLy+PJBIJSSQSysvLE9KPHDlCAKhPnz5KZSpq3/Xr16lPnz6kr69PAMjf31+hfWfOnFHIn5SU\nVOHjLCoSFhZGjo6OpK2trVDG39+f2rdvr5S/rF/KCw8Pp+7du5Oenh4ZGBhQhw4d6O2336br169X\nufyKHsfworJ2p6enC2lXrlyh0aNHk6WlJUkkEmrdujUNHTqU9uzZI+RZuXIlde/enUxNTUkmk5GT\nkxMtWrSIsrOzhTxFRUU0a9YsatGiBYlEogrHSnkxMTHUu3dv0tPTI319ferXr59S/9f00Q2aWr+q\n9Etlyh6dUZWK1tWzZ8+ER1ro6emRn58fnT17lvz9/YV2VFa2ummffvopAaDFixcLaapuTyrbJjDG\nmCaIiOr4anXGGGOMMcYYY40OX7PIGGOMMcYYY0wJB4uMMcYYY4wxxpRwsMgYY4wxxhhjTAkHi4wx\nxhhjjDHGlHCwyBhjjDHGGGNMCQeLjDHGGGOMMcaUcLDIGGOMMcYYY0wJB4uMMcYYY4wxxpRwsMgY\nY4wxxhhjTMn/A/0nUh/gRESuAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1128427b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Correlations DF\n",
"corr_to_market_cap = pd.DataFrame({'coins': df_dict}).sort_values('coins')\n",
"\n",
"#Seaborn Distplot\n",
"ax = sns.distplot(corr_to_market_cap['coins'], 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 the Rest of the Market')\n",
"ax.set(xlabel='Correlation with the Rest of the Market', ylabel='Density')\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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>Bitcoin Market Cap</th>\n",
" <th>Rest of Market Cap</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-06-23</th>\n",
" <td>9.373190e+09</td>\n",
" <td>2.569410e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-06-24</th>\n",
" <td>9.816730e+09</td>\n",
" <td>2.768570e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-06-25</th>\n",
" <td>1.044200e+10</td>\n",
" <td>1.962800e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-06-26</th>\n",
" <td>1.045490e+10</td>\n",
" <td>1.638500e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-06-27</th>\n",
" <td>9.882840e+09</td>\n",
" <td>2.400160e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-06-28</th>\n",
" <td>1.033680e+10</td>\n",
" <td>1.990600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-06-29</th>\n",
" <td>1.011960e+10</td>\n",
" <td>2.096600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-06-30</th>\n",
" <td>1.006640e+10</td>\n",
" <td>2.677600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-01</th>\n",
" <td>1.057060e+10</td>\n",
" <td>2.186400e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-02</th>\n",
" <td>1.063960e+10</td>\n",
" <td>2.451400e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-03</th>\n",
" <td>1.108610e+10</td>\n",
" <td>1.375800e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-04</th>\n",
" <td>1.036270e+10</td>\n",
" <td>2.346600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-05</th>\n",
" <td>1.074910e+10</td>\n",
" <td>1.841300e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-06</th>\n",
" <td>1.055030e+10</td>\n",
" <td>2.182400e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-07</th>\n",
" <td>1.067340e+10</td>\n",
" <td>1.063700e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-08</th>\n",
" <td>1.008690e+10</td>\n",
" <td>2.235600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-09</th>\n",
" <td>1.049380e+10</td>\n",
" <td>1.683300e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-10</th>\n",
" <td>1.024730e+10</td>\n",
" <td>2.001600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-11</th>\n",
" <td>1.021520e+10</td>\n",
" <td>2.053100e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-12</th>\n",
" <td>1.021310e+10</td>\n",
" <td>2.507000e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-13</th>\n",
" <td>1.047450e+10</td>\n",
" <td>2.203100e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-14</th>\n",
" <td>1.028840e+10</td>\n",
" <td>2.589900e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-15</th>\n",
" <td>1.038790e+10</td>\n",
" <td>2.574700e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-16</th>\n",
" <td>1.046170e+10</td>\n",
" <td>2.408800e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-17</th>\n",
" <td>1.043470e+10</td>\n",
" <td>2.699100e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-18</th>\n",
" <td>1.071680e+10</td>\n",
" <td>2.267000e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-19</th>\n",
" <td>1.060650e+10</td>\n",
" <td>2.434200e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-20</th>\n",
" <td>1.060860e+10</td>\n",
" <td>2.460800e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-21</th>\n",
" <td>1.049030e+10</td>\n",
" <td>2.570700e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-22</th>\n",
" <td>1.048660e+10</td>\n",
" <td>2.588800e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-05-22</th>\n",
" <td>1.435340e+11</td>\n",
" <td>2.277400e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-05-23</th>\n",
" <td>1.370240e+11</td>\n",
" <td>1.930850e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-05-24</th>\n",
" <td>1.289250e+11</td>\n",
" <td>2.081290e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-05-25</th>\n",
" <td>1.294700e+11</td>\n",
" <td>2.003840e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-05-26</th>\n",
" <td>1.276820e+11</td>\n",
" <td>2.099000e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-05-27</th>\n",
" <td>1.255750e+11</td>\n",
" <td>1.989740e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-05-28</th>\n",
" <td>1.257480e+11</td>\n",
" <td>1.862620e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-05-29</th>\n",
" <td>1.216360e+11</td>\n",
" <td>2.075730e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-05-30</th>\n",
" <td>1.274540e+11</td>\n",
" <td>1.920900e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-05-31</th>\n",
" <td>1.263860e+11</td>\n",
" <td>2.082460e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-01</th>\n",
" <td>1.280140e+11</td>\n",
" <td>1.999000e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-02</th>\n",
" <td>1.286450e+11</td>\n",
" <td>2.153020e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-03</th>\n",
" <td>1.302880e+11</td>\n",
" <td>2.194700e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-04</th>\n",
" <td>1.318470e+11</td>\n",
" <td>2.046580e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-05</th>\n",
" <td>1.280810e+11</td>\n",
" <td>2.184600e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-06</th>\n",
" <td>1.302330e+11</td>\n",
" <td>2.084820e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-07</th>\n",
" <td>1.306710e+11</td>\n",
" <td>2.155850e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-08</th>\n",
" <td>1.312710e+11</td>\n",
" <td>2.113120e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-09</th>\n",
" <td>1.303860e+11</td>\n",
" <td>2.093310e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-10</th>\n",
" <td>1.281280e+11</td>\n",
" <td>1.720140e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-11</th>\n",
" <td>1.161750e+11</td>\n",
" <td>1.770300e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-12</th>\n",
" <td>1.180070e+11</td>\n",
" <td>1.737280e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-13</th>\n",
" <td>1.127400e+11</td>\n",
" <td>1.582740e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-14</th>\n",
" <td>1.084080e+11</td>\n",
" <td>1.805130e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-15</th>\n",
" <td>1.140840e+11</td>\n",
" <td>1.693970e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-16</th>\n",
" <td>1.103590e+11</td>\n",
" <td>1.703770e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-17</th>\n",
" <td>1.119130e+11</td>\n",
" <td>1.685570e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-18</th>\n",
" <td>1.113190e+11</td>\n",
" <td>1.770920e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-19</th>\n",
" <td>1.153060e+11</td>\n",
" <td>1.737400e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-20</th>\n",
" <td>1.158040e+11</td>\n",
" <td>1.738590e+11</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>728 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Bitcoin Market Cap Rest of Market Cap\n",
"Date \n",
"2016-06-23 9.373190e+09 2.569410e+09\n",
"2016-06-24 9.816730e+09 2.768570e+09\n",
"2016-06-25 1.044200e+10 1.962800e+09\n",
"2016-06-26 1.045490e+10 1.638500e+09\n",
"2016-06-27 9.882840e+09 2.400160e+09\n",
"2016-06-28 1.033680e+10 1.990600e+09\n",
"2016-06-29 1.011960e+10 2.096600e+09\n",
"2016-06-30 1.006640e+10 2.677600e+09\n",
"2016-07-01 1.057060e+10 2.186400e+09\n",
"2016-07-02 1.063960e+10 2.451400e+09\n",
"2016-07-03 1.108610e+10 1.375800e+09\n",
"2016-07-04 1.036270e+10 2.346600e+09\n",
"2016-07-05 1.074910e+10 1.841300e+09\n",
"2016-07-06 1.055030e+10 2.182400e+09\n",
"2016-07-07 1.067340e+10 1.063700e+09\n",
"2016-07-08 1.008690e+10 2.235600e+09\n",
"2016-07-09 1.049380e+10 1.683300e+09\n",
"2016-07-10 1.024730e+10 2.001600e+09\n",
"2016-07-11 1.021520e+10 2.053100e+09\n",
"2016-07-12 1.021310e+10 2.507000e+09\n",
"2016-07-13 1.047450e+10 2.203100e+09\n",
"2016-07-14 1.028840e+10 2.589900e+09\n",
"2016-07-15 1.038790e+10 2.574700e+09\n",
"2016-07-16 1.046170e+10 2.408800e+09\n",
"2016-07-17 1.043470e+10 2.699100e+09\n",
"2016-07-18 1.071680e+10 2.267000e+09\n",
"2016-07-19 1.060650e+10 2.434200e+09\n",
"2016-07-20 1.060860e+10 2.460800e+09\n",
"2016-07-21 1.049030e+10 2.570700e+09\n",
"2016-07-22 1.048660e+10 2.588800e+09\n",
"... ... ...\n",
"2018-05-22 1.435340e+11 2.277400e+11\n",
"2018-05-23 1.370240e+11 1.930850e+11\n",
"2018-05-24 1.289250e+11 2.081290e+11\n",
"2018-05-25 1.294700e+11 2.003840e+11\n",
"2018-05-26 1.276820e+11 2.099000e+11\n",
"2018-05-27 1.255750e+11 1.989740e+11\n",
"2018-05-28 1.257480e+11 1.862620e+11\n",
"2018-05-29 1.216360e+11 2.075730e+11\n",
"2018-05-30 1.274540e+11 1.920900e+11\n",
"2018-05-31 1.263860e+11 2.082460e+11\n",
"2018-06-01 1.280140e+11 1.999000e+11\n",
"2018-06-02 1.286450e+11 2.153020e+11\n",
"2018-06-03 1.302880e+11 2.194700e+11\n",
"2018-06-04 1.318470e+11 2.046580e+11\n",
"2018-06-05 1.280810e+11 2.184600e+11\n",
"2018-06-06 1.302330e+11 2.084820e+11\n",
"2018-06-07 1.306710e+11 2.155850e+11\n",
"2018-06-08 1.312710e+11 2.113120e+11\n",
"2018-06-09 1.303860e+11 2.093310e+11\n",
"2018-06-10 1.281280e+11 1.720140e+11\n",
"2018-06-11 1.161750e+11 1.770300e+11\n",
"2018-06-12 1.180070e+11 1.737280e+11\n",
"2018-06-13 1.127400e+11 1.582740e+11\n",
"2018-06-14 1.084080e+11 1.805130e+11\n",
"2018-06-15 1.140840e+11 1.693970e+11\n",
"2018-06-16 1.103590e+11 1.703770e+11\n",
"2018-06-17 1.119130e+11 1.685570e+11\n",
"2018-06-18 1.113190e+11 1.770920e+11\n",
"2018-06-19 1.153060e+11 1.737400e+11\n",
"2018-06-20 1.158040e+11 1.738590e+11\n",
"\n",
"[728 rows x 2 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Coins least correlated with the rest of the market\n",
"corr_to_market_cap.columns=['Correlation to the Rest of the Market']\n",
"corr_to_market_cap[0:20]"
]
}
],
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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