35 alphas across two markets
TODO: Open with the headline finding — which signal class generalized across regions and which didn't. Then in one sentence: what surprised you. Then in one sentence: the bet a fund could actually make from this.
Context
TODO: What is an alpha, what is a contest like AlphaVerse, and what does "Sharpe 2.91" mean to a reader who has never traded? Two paragraphs max. Anchor the rest in why out-of-sample survival is the only metric that matters.
The setup
TODO: Universes (US large-cap, China A-shares), data vendors (WorldQuant BRAIN), evaluation regime (in-sample vs out-of-sample, holding periods, turnover caps, the "fitness" function that judges your work).
What I tried
TODO: Group the 35+ signals into 3–4 thematic buckets — e.g., momentum, mean-reversion, microstructure, cross-sectional anomalies. For each bucket: one example signal you can describe without revealing competitive IP, what it tried to capture, what it actually captured.
What generalized
TODO: Which 5–10 signals survived US → China and why. Hypothesis: structural anomalies (e.g., cross-listing arbitrage, calendar effects) travel; behavioural anomalies (e.g., retail momentum chasing) don't.
What broke out-of-sample
TODO: The signals that looked great in-sample and died on holdout. The honest reason — overfitting to specific volatility regimes, look-ahead via data alignment, survivor bias in the universe definition.
Numbers
| Metric | Best | Median (top 10) | |---|---|---| | Sharpe (in-sample) | TODO | TODO | | Sharpe (out-of-sample) | 2.91 | TODO | | Turnover | TODO | TODO | | Cross-correlation (with portfolio) | < 0.7 | TODO |
TODO: One paragraph on which row matters. Out-of-sample Sharpe is the only one a real fund would trust.
What I'd revisit
TODO: Two honest critiques. Candidates: the universe was static so survivor bias is structural; turnover caps were too generous; I didn't separately track signal decay (an alpha that works for 6 weeks is not the same as one that works for 6 quarters).
What this taught me about systems
TODO: Why this informs how you think about backend correctness too — "looks great in dev, dies in prod" has the same shape as "looks great in-sample, dies out-of-sample." The discipline transfers.