Computing a Linear Weighted Moving Average Efficiently in Python

# Calculating a Linear Weighted Moving Average in Python


# Calculating a Linear Weighted Moving Average in Python












Usually called WMA. The weighting is linear (as opposed to exponential) defined here: Moving Average, Weighted. I attempt to implement this in a python function as show below. The result is a list of values. My question is: are the result right? Also it is very slow...

I input a dataframe from pandas with a column called 'close'

```
def wma(df):
    n = 20
    k = (n * (n + 1)) / 2.0
    wmas = []
    for i in range(0, len(df) - n + 1):
        product = [df['close'][i + n_i] * (n_i + 1) for n_i in range(0, n)]
        wma = sum(product) / k
        wmas.append(wma)
    return wmas
```

Any help would be appreciated. Thanks.

## Answer by amdopt (score 3, accepted)

https://quant.stackexchange.com/a/57730

Though your code is already giving you the correct result, I almost feel bad for you that you have to wait 5 seconds for such a small amount of data. Your code is slow because you are kind of reinventing the wheel instead of using some built-in pandas and numpy functionality. For example, `product` and `wma` in your code can be combined and accomplished using numpy's dot product function (`np.dot`) that is applied to the whole column in a rolling fashion with an anonymous function by chaining pandas `.rolling()` and `.apply()` methods. It is always better to look for ready-made solutions becuase the functions are optimized behind the scenes. I ran your code on my machine, and the results take about 2 seconds for 5200 values. Try something like this (I added some basic functionality as an example to get you thinking):

```
import pandas as pd
import numpy as np

def wma(df, column='close', n=20, add_col=False):

    weights = np.arange(1, n + 1)
    wmas = df[column].rolling(n).apply(lambda x: np.dot(x, weights) /
                                       weights.sum(), raw=True).to_list()

    if add_col == True:
        df[f'{column}_WMA_{n}'] = wmas
        return df
    else:
        return wmas
```

The above function will take the same dataframe you were using and return the same list the same way you had it--just call `wma(df)`. In addition can change the column name, the period value, and you can opt to not return a list but add the values as a new column to the dataframe that you originally passed in. It also runs on my machine in about 20 milliseconds--almost 100x faster than your original code...