Rolling 30 Day Correlations between BTC & BCH

Rolling 30 Day Correlations.ipynb

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   "source": [
    "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",
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    "plt.rcParams['xtick.labelsize'] = 16\n",
    "plt.rcParams['ytick.labelsize'] = 16\n",
    "\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"
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   "source": [
    "def calc_rolling_corr(df, days, fork_coin, main_coin):\n",
    "    return df.rolling(window=days).corr().xs(fork_coin, level=1, drop_level=False)[main_coin].dropna().reset_index().drop(['Coin'], axis=1)\n",
    "\n",
    "\n",
    "bcash_bitcoin_rolling_corr = calc_rolling_corr(df_pivot, 30, 'bitcoin-cash', 'bitcoin')\n",
    "bcash_bitcoin_rolling_corr.columns = ['Date', 'Correlation Coefficient']\n",
    "bcash_bitcoin_rolling_corr.plot(x='Date', y='Correlation Coefficient', title='BCash Correlation with Bitcoin on a Rolling 30 Day Window')"
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