Pinescript correlation(source_a, source_b, length) -> to python

Question

I need help with translating pine correlation function to python, I've already translated stdev and swma functions, but this one is a bit confusing for me.

I've also found this explanation but didn't quite understand how to implement it:

in python try using pandas with .rolling(window).corr where window is the correlation coefficient period, pandas allow you to compute any rolling statistic by using rolling(). The correlation coefficient from pine is calculated with : sma(y*x,length) - sma(y,length)*sma(x,length) divided by stdev(length*y,length)*stdev(length*x,length) where stdev is based on the naïve algorithm.

Pine documentation for this func:

> Correlation coefficient. Describes the degree to which two series tend
> to deviate from their sma values. correlation(source_a, source_b,
> length) → series[float] RETURNS Correlation coefficient.

ARGUMENTS

source_a (series) Source series.

source_b (series) Target series.

length (integer) Length (number of bars back).

Answer 1

Using pandas is indeed the best option, TA-Lib also has a CORREL function. In order for you to get a better idea of how the correlation function in pine is implemented here is a python code making use of numpy, note that this is not an efficient solution.

import numpy as np
from matplotlib import pyplot as plt

def sma(src,m):
    coef = np.ones(m)/m
    return np.convolve(src,coef,mode="valid")

def stdev(src,m):
    a = sma(src*src,m)
    b = np.power(sma(src,m),2)
    return np.sqrt(a-b)

def correlation(x,y,m):
    cov = sma(x*y,m) - sma(x,m)*sma(y,m)
    den = stdev(x,m)*stdev(y,m)
    return cov/den

ts = np.random.normal(size=500).cumsum()
n = np.linspace(0,1,len(ts))
cor = correlation(ts,n,14)

plt.subplot(2,1,1)
plt.plot(ts)
plt.subplot(2,1,2)
plt.plot(cor)
plt.show()