The third tearsheet challenge is here and this time we're going international -- all the way to Japan! Because of various technical reasons this challenge will be a bit different. The main difference is that global equities are currently only supported in pipeline, not the backtester. As such, you will have to code your factor in pipeline in the notebook. This creates the challenge that for your submission your code would be in the notebook you post. The solution is to delete the code cell with your factor and save the notebook before you submit.
This challenge is a bit more experimental for us. We mainly want to scope out how much alpha we are likely to find in Japan and whether it makes sense for us to start trading there. In addition, we want to motivate you to work with non-US market data. As we currently do not trade the Japanese market, we will not license any factors submitted here in the short-term.
There is also a questionnaire for all entrants that we would appreciate if you filled it out, but you do not have to.
Here are the rules:
- 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. Clone the attached template and add your own pipeline factor code.
- The deadline to submit a factor is November 15, 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 factors 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:
- Your factor must be run on the Japanese equities domain. It can however, use any dataset(s) you want.
- Use the universe provided by the template, or a subset of it.
When selecting a winner, we will primarily look at:
- Total Sharpe Ratio (IR) over the first 5 days in the alpha decay analysis (higher is better). As the risk model is US-only, there is no specific return or risk exposure analysis.
- Turnover (lower is better).
- Universe size (larger is better).
- For more examples on what we look for, check out our last live tearsheet review.
Good luck and happy coding!