how do I compute a weighted moving average using pandas

Question

Using pandas I can compute

• simple moving average SMA using pandas.stats.moments.rolling_mean
• exponential moving average EMA using pandas.stats.moments.ewma

But how do I compute a weighted moving average (WMA) as described in wikipedia http://en.wikipedia.org/wiki/Exponential_smoothing ... using pandas?

Is there a pandas function to compute a WMA?

Answer 1

Using pandas you can calculate a weighted moving average (wma) using:
.rolling() combined with .apply()

Here's an example with 3 weights and window=3:

data = {'colA': random.randint(1, 6, 10)}
df = pd.DataFrame(data)

weights = np.array([0.5, 0.25, 0.25])
sum_weights = np.sum(weights)

df['weighted_ma'] = (df['colA']
    .rolling(window=3, center=True)
    .apply(lambda x: np.sum(weights*x) / sum_weights, raw=False)
)

Please note that in .rolling() I have used argument center=True.
You should check if this applies with your usecase or whether you need center=False.

Answer 2

No, there is no implementation of that exact algorithm. Created a GitHub issue about it here:

https://github.com/pydata/pandas/issues/886

I'd be happy to take a pull request for this-- implementation should be straightforward Cython coding and can be integrated into pandas.stats.moments