To have the best shot at winning the Quantopian Open, or having your algo selected for the Quantopian hedge fund, you need to focus on low beta strategies with consistent, low volatility returns.
Statistical arbitrage strategies are a great example of an algo type that achieves this purpose. Instead of investing in a few securities, investing in a large basket of securities will help decrease beta and increase consistent returns. The most simple form of a stat arb strategy is a pairs trade where you compare two individual stocks within the same industry (for example Coke and Pepsi or Shell and Exxon). But more interesting and complex strategies can be created by investing in larger baskets of stocks that you believe should behave similarly but for some reason don't.
A typical workflow for these strategies is to filter down to a universe of a couple thousand securities. Rank those securities based on some factor, and then long the top decile and short the bottom decile.
Unfortunately, writing a stat arb algo of this type is difficult on Quantopian today. You are limited to only fundamental data within before_trading_starts() where you can filter your universe and many of these strategies reply on pricing data as well. Your universe is restricted to 200 securities, which makes filtering to the best universe difficult. Many of our members have resorted to doing their analysis elsewhere and then importing a buy list via fetcher.
We understand this is far from ideal.
We've spent the last few weeks hard at work designing an API to make implementing stat arb strategies easier. Thursday May 14th, at 1PM EST, I am going to host a webinar to walk through the API design and a couple of psuedo coded examples. We haven't built this yet, and I am hoping to get feedback from the community on our design and on what functionality you want.
If you can't make it, please feel free to share your thoughts below. At this stage basic examples of what you are trying to do (like this one from Bo Dong) are incredibly helpful and any you are willing to contribute to the discussion are appreciated.