Collaboration on a Mean Reversion Strategy

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Collaboration on a Mean Reversion Strategy

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Getting Started Mean Reversion Collaboration Request Research Pipeline

Over the past 18 months I've been working to refine a pretty simple mean reversion trading concept. The basic idea is that stocks comprising the S&P500 that are trading multiple standard deviations and also a certain percentage below (above) their 20 day moving average will rebound toward their moving average and should be bought (sold short) for up to a small percentage of the total portfolio value. Because the moving average declines toward the stock price, I've found that there is about 30 basis points of decay per day on the expected return, which also helps to explain why there needs to be some hard percentage distance from the moving average as a buffer.

If they do not revert back toward the mean, they are stopped out when the negative return is equal to (the percent difference in the moving average and the current price)/3.18; the reason why I chose that is because when you just build a trade log(rather than a portfolio) based on the concept back-tested over 20 years, 1:3.18 recommended trades do not revert back to the mean (so there's not actually a phenomenal mathematical rationale for it, but it has worked well to limit losses in paper trading).

If they do revert back to the mean, they are sold at their 20 day moving average.

Paper trading this idea (by hand) for the past 3 months I've had the following results:

Total return of 11.81% compared to an index return of 4.43%, beta of 1.52, alpha of 5.05, Sharpe ratio of 1.9909. I've never tried to use leverage. In fact, it's only on rare occasions (maybe 5 days in the past 3 months) that I've dipped below 50% cash in the mock portfolio (which creates another question of efficient capital deployment).

I've found the results promising so far, but I definitely need guidance from someone with experience and expertise on Quantopian to take the testing of the idea to the next level so I could hopefully make this more than just a paper trading exercise. If you are interested in discussing the concept further or have the ability and inclination to program something along these lines, I hope to hear from you.

Thanks in advance for reading this long-winded post.

6 responses

bump

Bueller?

You can do a custom factor to get a price history pipeline set up. Use pandas functions to get std, mean, etc on the data. Schedule a function to sell since all open orders are closed daily (including stops). For tracking, you can create a data structure in context.trades and then do bookkeeping in there - set it up in your initialization function, then access it in other scheduled functions.

Anyone have the programming experience to execute something like this

bump

Still interested?

@Devin - absolutely. If you have time to schedule a call or email exchange, I'll share what I have w/ you. Would be happy to get any new input from anyone.

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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.

In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.