为什么 Corcoef 会返回一个矩阵？

corrcoef returns the normalised covariance matrix.

The covariance matrix is the matrix

Cov( X, X )    Cov( X, Y )


Cov( Y, X )    Cov( Y, Y )

Normalised, this will yield the matrix:

Corr( X, X )    Corr( X, Y )


Corr( Y, X )    Corr( Y, Y )

correlation1[0, 0 ] is the correlation between Strategy1Returns and itself, which must be 1. You just want correlation1[ 0, 1 ].

The correlation matrix is the standard way to express correlations between an arbitrary finite number of variables. The correlation matrix of N data vectors is a symmetric N × N matrix with unity diagonal. Only in the case N = 2 does this matrix have one free parameter.

It allows you to compute correlation coefficients of >2 data sets, e.g.

>>> from numpy import *
>>> a = array([1,2,3,4,6,7,8,9])
>>> b = array([2,4,6,8,10,12,13,15])
>>> c = array([-1,-2,-2,-3,-4,-6,-7,-8])
>>> corrcoef([a,b,c])
array([[ 1.        ,  0.99535001, -0.9805214 ],
[ 0.99535001,  1.        , -0.97172394],
[-0.9805214 , -0.97172394,  1.        ]])

Here we can get the correlation coefficient of a,b (0.995), a,c (-0.981) and b,c (-0.972) at once. The two-data-set case is just a special case of N-data-set class. And probably it's better to keep the same return type. Since the "one value" can be obtained simply with

>>> corrcoef(a,b)[1,0]
0.99535001355530017

there's no big reason to create the special case.

Consider using matplotlib.cbook pieces

for example:

import matplotlib.cbook as cbook
segments = cbook.pieces(np.arange(20), 3)
for s in segments:
print s

The function Correlate of numpy works with 2 1D arrays that you want to correlate and returns one correlation value.

You can use the following function to return only the correlation coefficient:

def pearson_r(x, y):
"""Compute Pearson correlation coefficient between two arrays."""


# Compute correlation matrix
corr_mat = np.corrcoef(x, y)


# Return entry [0,1]
return corr_mat[0,1]