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Feature Request: Combining multiple algos into a single backtest

I know this has been discussed before but I wanted to expand on the idea.

I'd like to be able to select multiple algorithms, assign each algo a weight (percentage), and then backtest the combined portfolio, a sort of "meta-algo". I'd like to see how combining different strategies can improve performance metrics (reduce volatility, max draw-down, etc). I'd love to see a correlation calculation between the algos, which would help find uncorrelated returns.

I know that this process of identifying the best combination of algos is a core part of what Quantopian is doing, so hopefully providing this functionality to users won't conflict with their goals. If anything, it might help Q in finding great combinations of algos.

2 responses

It's a great feature request, thank you. As you know, it's been requested before. It's good to hear the request from other people, too, because that helps us prioritize.

We don't have any immediate plans to build this feature just because we have so many other great things to do first. I'm sure we will get to it. As you say, it will be helpful to us and to the community.

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I published an algorithm that does this with strong theoretical guarantees to the best combination in hindsight and to any benchmark. Here's the paper: http://papers.nips.cc/paper/5436-exploiting-easy-data-in-online-optimization

I hope to contribute an implementation that works on Quantopian, but I've been really busy transitioning into a postdoc. If anyone's familiar with the Quantopian IDE and wants to collaborate on this, just get in contact.