Alphalens with a single security?

Hello all,

I'd like to use Alphalens to evaluate a factor on just one security. However, I always end up with an error: ValueError: Bin edges must be unique I've tried to work around it using ranking instead of raw numbers (guaranteed no duplicate values), trying bins instead of quantiles, using defined bins instead of equally-sized, etc, but I cannot seem to get it going.

This Pull Request says that this error happens when you have a "single value for factor per group (only one value over a date, or sector)" - pretty sure this is what I'm hitting.

Big picture question: Can Alphalens be used to check out signal alpha on a single equity?

More direct question: If so, what do I need to do to get it working? (Please see attached notebook)

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5 responses

You are getting that error due to bugs in panda.qcut and pandas.cut that were fixed in version v0.20.0 (one, two and three). But don't expect to see pandas v0.20.0 on Quantopian too soon (this is my opinion but v0.20.0 hasn't been released yet). You can use a workaround described here though

Hi Peter,

I don't think Alphalens will give sensible results for one security. It is set up to consider a factor as one value per day per security, which it then 'bins' into say 3 or 5 or 10 groups of securities and shows you the returns to each of those sub-portfolios.

If you pass it a timing signal for a single stock it can only create 1 'bin' on any given day and doesn't have anything to compare amongst. Alphalens is a tool designed to evaluate a cross-sectional signal which can be used to rank many securities each day versus what sounds like a timing signal that tells you what days to be long or short a single stock.

Have you tried out using just the pyfolio tearsheet analysis or a backtest report on your signal? I'd think those should work ok.

Best, Jess

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While I agree with Jessica, there is still a subset of Alphalens features that can be used to quickly verify the ability of a signal to generate alpha: the returns analysis. I used Alphalens to analyse signals generated by technical patterns in here. No assumptions on the number of stocks are required.

Obviously the goal of Alphalens is not to analyse those kind of scenarios, but it works nevertheless.

Here is Peter's NB fixed.

7
Tried cloning and running Luca's version of Peter's NB, and I when I run  create_returns_tear_sheet(factor_data, long_short=False) , I get: