比特币下跌与加密货币的联动效应（附代码）

import ccrypto as cc
import numpy as np
import pandas as pd
import pickle
import matplotlib.pyplot as plt
 
grey = (.8,.8,.8)
 
# 下载 BTC OHLC 
b = cc.getCryptoSeries('BTC', 'USD', freq='h', ohlc=True, exch='Coinbase', \
                            start_date='2021-03-13 00:00')
 
fig, ax1 = plt.subplots(1,1, figsize=(12,7))
ax1.plot(b.BTCUSD_H, 'b:')
ax1.plot(b.BTCUSD_C, 'k')
ax1.plot(b.BTCUSD_L, 'r:')
ax1.grid()
plt.title('BTC Price: Max %.2f, Min %.2f' % (b.BTCUSD_H.max(), b.BTCUSD_L.min()))

ccys = ['BTC', 'ETH', 'LTC', 'LINK', 'BCH', 'XLM', 'WBTC', 'AAVE', 'EOS', 'ATOM',
        'XTZ', 'DASH', 'GRT', 'MKR', 'SNX', 'ALGO', 'COMP', 'ZEC', 'ETC', 'YFI',
        'CGLD', 'OMG', 'UNI', 'UMA']

timeseries = {}
fi = True
 
plt.figure(figsize=(12,7))
 
for ccy in ccys:
    timeseries[ccy] = cc.getCryptoSeries(ccy, 'USD', freq='m', exch='Coinbase', \
                            start_date='2021-03-15 00:00')
    
    if fi:
        df = timeseries[ccy]
        fi = False
    else:
        df = pd.merge(left=df, right=timeseries[ccy], how='left', 
                      left_index=True, right_index=True)
        
    plt.plot((timeseries[ccy]-timeseries[ccy].mean())/timeseries[ccy].std(), color=grey)
    
plt.plot((timeseries['BTC']-timeseries['BTC'].mean())/timeseries['BTC'].std(), 
         color='r', label='Bitcoin')
plt.legend(loc=3)
plt.grid()
plt.ylabel('Standardised Price')
plt.xlabel('Date/Time')
plt.savefig("/Users/pawel/Desktop/trig2.png", bbox_inches='tight')

df_ret = df.pct_change(periods=1)
 
mask = df_ret.isna()
df_ret[mask] = np.nan 
 
df_ret.dropna(how='all', axis=0, inplace=True)
 
del df_ret['GRTUSD']
del df_ret['ALGOUSD']
del df_ret['CGLDUSD']
del df_ret['UMAUSD']

df_ret_dir = df_ret.copy()

display(df_ret_dir.head(10))

for i in range(0, df_ret.shape[0]):
    row = df_ret.iloc[i]
    btc_sign = -1 if df_ret.iloc[i][0] < 0.0 else 1
    df_ret_dir.iloc[i] = np.sign(row) == btc_sign

display(df_ret_dir.head(10))

fraction = df_ret_dir.astype(int).sum(axis=1) / df_ret_dir.shape[1]

display(pd.DataFrame(fraction, columns=['Fraction']))

fig, ax1 = plt.subplots(1,1, figsize=(12,9))
 
for ccy in ret.columns.tolist():
    ax1.plot((df[ccy]-df[ccy].mean())/df[ccy].std(), color=grey)
    
ax1.plot((df['BTCUSD']-df['BTCUSD'].mean())/df['BTCUSD'].std(), 'r-', label='1-min BTCUSD')
ax1.grid()
ax1.set_ylabel('Normalised Price')
ax1.set_xlabel('Date and Time')
ax2 = ax1.twinx()
ax2.plot(ratio.iloc[1:], '-', color='#f09547', alpha=0.35, label='1-min Fraction')
ax2.plot(fraction.rolling(5).mean(), color='#f09547', label='5-min rolling average fraction')
ax2.set_ylabel('Fraction')
ax2.legend(loc=4)
ax1.legend(loc=3)
ax1.set_xlim([pd.to_datetime('2021-03-15 00:00'), pd.to_datetime('2021-03-15 13:00')])
plt.savefig("/Users/pawel/Desktop/trig6.png", bbox_inches='tight')

fig, ax1 = plt.subplots(1,1, figsize=(12,7))
 
for ccy in ret.columns.tolist():
    ax1.plot((df[ccy]-df[ccy].mean())/df[ccy].std(), color=grey)
    
ax1.plot((df['BTCUSD']-df['BTCUSD'].mean())/df['BTCUSD'].std(), 'r-', label='1-min BTCUSD')
ax1.grid()
ax1.set_ylabel('Normalised Price')
ax1.set_xlabel('Date and Time')
ax2 = ax1.twinx()
ax2.plot(fraction.rolling(3).mean(), color='#f09547', label='3-min rolling average ratio')
ax2.set_ylabel('Fraction')
ax2.legend(loc=1)
ax1.legend(loc=3)
ax1.set_ylim([-2.1, 0])
ax1.set_xlim([pd.to_datetime('2021-03-15 08:30'), pd.to_datetime('2021-03-15 13:00')])
plt.title('Max Fraction = %.1f%% at %s' % 
          (100*(fraction[fraction.index > '2021-03-15 08:30']).max(), 
           fraction[fraction.index > '2021-03-15 08:30'].index[fraction[fraction.index > 
                                                               '2021-03-15 08:30'].argmax()]))