CODEWITHSUNDEEP
python
Panto
Panto

Reputation: 104

Function for Simple Moving Average (SMA)

I have the following function for calculating SMA in python:

import numpy as np

def calcSma(data, smaPeriod):
    sma = []
    count = 0
    for i in xrange(data.size):
        if data[i] is None:
            sma.append(None)
        else:
            count += 1
            if count < smaPeriod:
                sma.append(None)
            else:
                sma.append(np.mean(data[i-smaPeriod+1:i+1]))

    return np.array(sma)

This function works, but I find it very little pythonic. I don't like the indexing and counting I'm doing, nor the way I have to append to the list and then turn it into a numpy array before I return it.

The reason I have to deal with all these None, is because I want to return an array at the same size as the input array. This makes it easier to plot and deal with on a general level later. I can easily do stuff such as this:

sma = calcSma(data=data, smaPeriod=20)
sma2 = calcSma(data=sma, smaPeriod=10)
plt.plot(data)
plt.plot(sma)
plt.plot(sma2)
plt.show()

So, any ideas on how this can be done prettier and more pythonic?

Upvotes: 2

Views: 13415

Answers (2)

Vlad Bezden
Vlad Bezden

Reputation: 89527

Here is another implementation of moving average just using standard Python library:

from collections import deque
import itertools

def moving_average(iterable, n=3):
    # http://en.wikipedia.org/wiki/Moving_average
    it = iter(iterable) 
    # create an iterable object from input argument
    d = deque(itertools.islice(it, n-1))  
    # create deque object by slicing iterable
    d.appendleft(0)
    s = sum(d)
    for elem in it:
        s += elem - d.popleft()
        d.append(elem)
        yield s / n

# example on how to use it
for i in  moving_average([40, 30, 50, 46, 39, 44]):
    print(i)

# 40.0
# 42.0
# 45.0
# 43.0

Upvotes: -2

Elmex80s
Elmex80s

Reputation: 3504

Pythonic enough I hope

import numpy as np


def calcSma(data, smaPeriod):
    j = next(i for i, x in enumerate(data) if x is not None)
    our_range = range(len(data))[j + smaPeriod - 1:]
    empty_list = [None] * (j + smaPeriod - 1)
    sub_result = [np.mean(data[i - smaPeriod + 1: i + 1]) for i in our_range]

    return np.array(empty_list + sub_result)

Upvotes: 5

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