Rolling n Year Max DD for BTC

btc_max_dd.py

import os
import pandas as pd
import datetime as dt
import seaborn as sns
import matplotlib.pyplot as plt
import ccxt
from dateutil import relativedelta
import numpy as np

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)
    
def get_max_dd(nvs =  pd.Series(), window=None):
        n = len(nvs)
        if window is None:
            window = n
        
        peak_series = nvs.rolling(window=window, min_periods=1).max()
        return (nvs / peak_series - 1.0).min()

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 ]
    prices = btc[['Close']]
    prices.rename( columns = {'Close':btcStr}, inplace = True)
    
    returns = prices.pct_change()
    returns.dropna(how = 'any', axis = 'rows', inplace = True)
    
    #Drawdowns
    maxDate = prices.index.max()
    rolling = []
    years = list(range(1,4))
    for year in years:
        strYear = str(year) + ' Year'
        for i in range(prices.shape[0]):
            t0 = prices.index[i]
            t1 = t0 + relativedelta.relativedelta(years=year)
            if t1 <= maxDate:
                p0 = prices[btcStr][ (prices.index >= t0) & (prices.index <= t1) ]
                rolling.append([ strYear, t1, get_max_dd(p0)*-1 ])
    rolling = pd.DataFrame.from_records(rolling, columns = ['Horizon','Date','Max Drawdown'])
    
    dd1Y = rolling[['Date','Max Drawdown']][rolling['Horizon'] == '1 Year']
    dd1Y.set_index('Date', inplace = True)
    dd1Y.rename( columns = {'Max Drawdown':'Historical 1Y Max DD'}, inplace = True)
    g = dd1Y.plot()
    vals = g.get_yticks()
    g.set_yticklabels(['{:,.0%}'.format(x) for x in vals])