Our recent estimates data challenge was a huge success, so we are excited to run the next one with very similar parameters. This round we are looking for your best guidance factors. We think that this challenge will be more difficult than the last ones and more creativity will be required in finding alpha, but we are looking forward to seeing what you come up with.
Here are the rules, pretty much the same as before:
- There is no submission or live-updated leaderboard like for the contest. To enter this challenge, simply post an alpha tearsheet as a reply to this thread. For this, you would run a backtest on your factor and run the alpha notebook which loads in your backtest results.
- The deadline to submit a factor is October 21, 2019.
- There is no hold-out testing, just post your best factor starting on June 1, 2015 until Oct 1, 2018.
- We will look at all tearsheet submissions and manually determine 5 best algorithms according to our discretion. Each winner gets a $100 prize.
- There is no limit on the number of submissions.
Algorithm requirements to enter the challenge:
- Use the guidance data set as your primary signal source. It is OK to combine guidance signals with other sources if there are predictive interactions between them.
- Use TargetWeights in the optimizer and do not put any constraints on common risk exposures.
- Use the QTU.
When selecting a winner, we will primarily look at:
- Specific Sharpe Ratio (IR) over the first 5 days in the alpha decay analysis (higher is better).
- Turnover (lower is better).
- Not driven mainly by common risk (but no reason to try and artificially reduce your exposures, ideally your idea is dissimilar enough from common factors that it will be naturally uncorrelated).
- Universe size (larger is better)
- For more examples on what we look for, check out our last live tearsheet review.
These rules are mostly derived from our updated guidelines on getting an allocation and are based on many community members' feedback. Thank you for all your input and creative suggestions. If this challenge is well-received, we will continue to offer more experimental challenges.
Ultimately, we want to keep improving our ability to find your best ideas and fund them!
Good luck and happy coding!