Take a look at Alphalens. It might do what you're looking for. That tool was developed to analyze factor performance. Check it out https://www.quantopian.com/tutorials/alphalens#lesson1.

Attached is an example of how one would get the 21 (business) day forward returns of an arbitrary factor.

Maybe also look at Thomas Wiecki's post on some recent updates https://www.quantopian.com/posts/an-updated-method-to-analyze-alpha-factors.

@Eric Remole -- I do this.

The way I've sorted out for doing this is I keep a list of rolling portfolios context.rolling_portfolios = [] and use context.rolling_portfolios.pop(0) once the list exceeds my desired holding period, which gets rid of the oldest portfolio. . I use todays_weights = opt.calculate_optimal_portfolio(... and append the result to the list of rolling portfolios context.rolling_portfolios.append(todays_weights). Then I average them all together and feed the combined weights into calculate_optimal_portfolio().

In addition, I once posted a couple kind of hacky ways to adjust the weights of the previous rolling portfolios by the amount each stock had gained since. That way if on Monday you wanted a weight of 1% for SHOP, but by Friday SHOP had increased 20%, you wouldn't be rebalancing that slice back to 1%, you'd be rebalancing it to 1.2% (no change), thereby reducing needless churn and staying true to the intended holding period. I couldn't figure out an ideal way to do this on Quantopian (one method fails if the stock has splits or dividends and the other fails if there are any half-days between the current date and the slice).

If anybody has better methods for doing this, I'd love to see it.