spreads.py

import pandas as pd
import numpy as np
from tabulate import tabulate
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib as mpl
import matplotlib.dates as mdates


ytdETH = 'pythonDev/ETHYTD.csv'
ytdNMR = 'pythonDev/NMRYTD.csv'
ytdBTC = 'pythonDev/BTCYTD.csv'


# Notes: You need to copy paste from your browser the historical data from CMC into Excel and save as csv. 
#        This is part of a bigger script, but also works in standalone mode, disregard the bottom chart.
#        Prettify the resulting chart in photoshop.



def main(verbose = 1):
    # BTC
    df_BTC = pd.read_csv(
        ytdBTC, header=0, parse_dates=[0], index_col=0, squeeze=True, thousands=',')
    # REMOVE COLUMNS
    df_BTC.drop(
        columns=['Volume', 'Market Cap', 'Open*', 'High', 'Low'],
        axis=1,
        inplace=True)
    df_BTC = df_BTC.rename(columns={'Close**': 'Close'})
    # REVERSE SORT
    df_BTC = df_BTC[::-1]
    # Calculate Index
    firstVal_BTC = df_BTC['Close'].iloc[0]
    df_BTC['index_BTC'] = (((df_BTC['Close']/firstVal_BTC)-1)*100)


    # ETH
    df_ETH = pd.read_csv(
        ytdETH, header=0, parse_dates=[0], index_col=0, squeeze=True, thousands=',')
    # REMOVE COLUMNS
    df_ETH.drop(
        columns=['Volume', 'Market Cap', 'Open*', 'High', 'Low'],
        axis=1,
        inplace=True)
    df_ETH = df_ETH.rename(columns={'Close**': 'Close'})
    # REVERSE SORT
    df_ETH = df_ETH[::-1]
    # Calculate Index
    firstVal_ETH = df_ETH['Close'].iloc[0]
    df_ETH['index_ETH'] = (((df_ETH['Close']/firstVal_ETH)-1)*100)


    # NMR
    df_NMR = pd.read_csv(
        ytdNMR, header=0, parse_dates=[0], index_col=0, squeeze=True, thousands=',')
    # REMOVE COLUMNS
    df_NMR.drop(
        columns=['Volume', 'Market Cap', 'Open*', 'High', 'Low'],
        axis=1,
        inplace=True)
    df_NMR = df_NMR.rename(columns={'Close**': 'Close'})
    # REVERSE SORT
    df_NMR = df_NMR[::-1]
    # Calculate Index
    firstVal_NMR = df_NMR['Close'].iloc[0]
    df_NMR['index_NMR'] = (((df_NMR['Close']/firstVal_NMR)-1)*100)


    # new Dataframe with indexes
    df_Spreads = pd.concat([df_BTC['index_BTC'], df_ETH['index_ETH'], df_NMR['index_NMR']], axis=1, keys=['index_BTC', 'index_ETH','index_NMR'])
    # Calculate spread

    df_Spreads['Spread BTC ETH'] = df_Spreads['index_BTC'] - df_Spreads['index_ETH']

    sns.set(rc={'figure.figsize':(11, 6)})
    plt.style.use('fivethirtyeight')
    plt.subplot(211)
    df_Spreads['index_BTC'].plot(linewidth=2)
    df_Spreads['index_ETH'].plot(linewidth=2)
    df_Spreads['index_NMR'].plot(linewidth=2)
    # print(df_Spreads.head())
    plt.title('Index BTC ETH NMR')
    frame = plt.gca()
    frame.set_xlabel('')
    plt.legend()
    plt.subplot(212)
    ax = df_Spreads['Spread BTC ETH'].plot(kind='bar',width=1)
    ax.axhline(y=df_Spreads['Spread BTC ETH'].mean(), color='dimgray', lw = 1)
    ax.xaxis.set_major_locator(plt.MaxNLocator(6))

    plt.title('Spread BTC ETH')
    frame = plt.gca()
    frame.set_xlabel('')
    plt.legend()
    plt.tight_layout()
    plt.savefig('pythonDev/img/spreadBTCETHNMR_2.png')
    if verbose != 0:
        print(df_Spreads['Spread BTC ETH'].mean())
        plt.show()


if __name__ == "__main__":
    main()