Relative Strength Index in python pandas

Question

I am new to pandas. What is the best way to calculate the relative strength part in the RSI indicator in pandas? So far I got the following:

from pylab impor         


        

Answer 1

You can get a massive speed up of Bill's answer by using numba. 100 loops of 20k row series( regular = 113 seconds, numba = 0.28 seconds ). Numba excels with loops and arithmetic.

import numpy as np
import numba as nb

@nb.jit(fastmath=True, nopython=True)   
def calc_rsi( array, deltas, avg_gain, avg_loss, n ):

    # Use Wilder smoothing method
    up   = lambda x:  x if x > 0 else 0
    down = lambda x: -x if x < 0 else 0
    i = n+1
    for d in deltas[n+1:]:
        avg_gain = ((avg_gain * (n-1)) + up(d)) / n
        avg_loss = ((avg_loss * (n-1)) + down(d)) / n
        if avg_loss != 0:
            rs = avg_gain / avg_loss
            array[i] = 100 - (100 / (1 + rs))
        else:
            array[i] = 100
        i += 1

    return array

def get_rsi( array, n = 14 ):   

    deltas = np.append([0],np.diff(array))

    avg_gain =  np.sum(deltas[1:n+1].clip(min=0)) / n
    avg_loss = -np.sum(deltas[1:n+1].clip(max=0)) / n

    array = np.empty(deltas.shape[0])
    array.fill(np.nan)

    array = calc_rsi( array, deltas, avg_gain, avg_loss, n )
    return array

rsi = get_rsi( array or series, 14 )