To calculate a mean you can use SimpleMovingAverage() and for the daily returns you can use DailyReturns()

from quantopian.pipeline.factors import SimpleMovingAverage, DailyReturns

 mean_daily_return = SimpleMovingAverage(inputs=[DailyReturns()], window_length=20)  

But it would be much simpler to do:

from quantopian.pipeline.factors import Returns

monthly_return = Returns(window_length=20)  

Hello,

Here

Great input here! Shows there are a number of ways to calculate returns.

@Viridian Hawk you're correct that probably the simplest approach is to use the built in Returns factor. Good example of how to get the average of a factor over a given window by using it as an input to SimpleMovingAverage. Be careful however with averaging arithmetic returns. One can't really do that. Consider the case averaging +10% and -10%. It's not 0%. Need to use log returns in this situation something like this.

 mean_daily_log_return = SimpleMovingAverage(inputs=[DailyReturns().log1p()], window_length=20)  

@Mathieu Meynier good observation that one needs to use log returns. As a side note, one can easily convert an arithmetic return factor to log returns by using the log1p() method. One can just as easily do the opposite and turn a log return factor into arithmetic returns by using the expm1() method.

Be a little careful with the window length in whichever method one chooses. Because returns are based upon daily differences, there will be n-1 returns over a period of n days. To calculate n daily returns one needs n+1 days.

@ Rishesh Garg hope this gives you some direction. If we maybe all misunderstood your original question please reply. There may have been something else you were trying to accomplish?

Attached is a notebook which summarizes the three approaches (built in Returns, summing average daily returns, and a custom factor). It shows they have identical results for both log and arithmetic returns.

Different ways to compute returns in pipeline