Cointegration

As i already investigate the futures inter-market correlation , lets investigate inter-market cointegration (since 01/01/2017) as per above

Great article about cointegration: https://www.quantopian.com/lectures/integration-cointegration-and-stationarity#notebook

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import Quandl
import pandas_datareader.data as web
import talib as ta
import seaborn; seaborn.set()
from statsmodels.tsa.stattools import coint

def find_cointegrated_pairs(data):
    n = data.shape[1]
    score_matrix = np.zeros((n, n))
    pvalue_matrix = np.ones((n, n))
    keys = data.keys()
    pairs = []
    for i in range(n):
        for j in range( n):
            S1 = data[keys[i]]
            S2 = data[keys[j]]
            result = coint(S1, S2)
            score = result[0]
            pvalue = result[1]
            score_matrix[i, j] = score
            pvalue_matrix[i, j] = pvalue
            if pvalue < 0.05:
                pairs.append((keys[i], keys[j]))
    return score_matrix, pvalue_matrix, pairs

data = pd.read_pickle('data/data_Backtest.pkl')
data = data['20170101':]
scores, pvalues, pairs = find_cointegrated_pairs(data.ix[:,:25])
seaborn.heatmap(pvalues, xticklabels=data.ix[:,:25].columns.values, yticklabels=data.ix[:,:25].columns.values, cmap='RdYlGn_r'
                #, mask = (pvalues >= 0.05)
                , annot=True
                )
plt.show()