Rolling 30 day correlation of BTC with gold

btc_gold_correlation.py

import os
import pandas as pd
import datetime as dt
import seaborn as sns
import matplotlib.pyplot as plt
import ccxt

sns.set(style = 'ticks', context = 'talk')
plt.style.use("dark_background")

pd.options.mode.chained_assignment = None
  
#Millisecond UTC timestamp to datetime
def ms2dt(x):
    return dt.datetime.utcfromtimestamp(x//1000).replace(microsecond=x%1000*1000)

#Datetime to millisecond
def dt2ms(x):
    return int(float(pd.to_datetime(x).to_datetime64()) / 1e6)
    
if __name__ == '__main__':
    
    symbol = 'BTC/USDT'
    btcStr = 'Bitcoin'
    goldStr = 'Gold'
    
    startDate = dt.datetime(year = 2010, month = 1, day = 1)
    today = dt.datetime.now()
    today = dt.datetime(year = today.year, month = today.month, day = today.day)
    lastYr = today - pd.Timedelta(days = 365)
    
    startDate = startDate.strftime('%Y-%m-%d')
    endDate = today.strftime('%Y-%m-%d')
    
    polo = ccxt.poloniex()
    
    startDate = dt.datetime(year = 2010, month = 1, day = 3)
    startDateMS = dt2ms(startDate)

    btc = polo.fetch_ohlcv(symbol, timeframe = '1d', since = startDateMS)
    btc = pd.DataFrame(btc, columns = ['Date','Open','High','Low','Close','Volume'])
    btc['Date'] = btc['Date'].apply( lambda x: ms2dt(x))
    btc.set_index('Date', inplace = True)
    btc = btc[ btc.index < today ]
    btc = btc[['Close']]
    btc.rename( columns = {'Close':btcStr}, inplace = True)
    
    #load gold data
    gold = pd.read_csv('gold.csv')
    gold.rename( columns = {'Close':goldStr}, inplace = True) #spot price
    gold['Date'] = pd.to_datetime(gold['Date'], errors = 'coerce')
    gold.set_index('Date', inplace = True)
    
    prices = btc.join(gold, how = 'left')
    prices.dropna(how = 'any', axis = 'rows', inplace = True)
    returns = prices.pct_change()
    returns.dropna(how = 'any', axis = 'rows', inplace = True)
    
    rollingCorr = returns.rolling(30).corr().reset_index()
    rollingCorr = rollingCorr[['Date',btcStr]][ rollingCorr['level_1'] == goldStr ]
    rollingCorr.rename( columns = {btcStr:'Correlation'}, inplace = True) #spot price
    rollingCorr = rollingCorr[ rollingCorr['Date'] >= lastYr ]
    
    g = sns.lineplot(x = 'Date', y = 'Correlation', data = rollingCorr)
    plt.xticks(rotation=45)