RankAlpha

RankAlpha ranks the S&P 500 every month, trades the top and bottom of that ranking as a market-neutral book, and turns the result into a risk-managed, self-explaining portfolio. The key word is ranking: it doesn't predict prices, it predicts relative order — which stocks will outperform their peers — because that's the only thing the strategy actually uses.

The first version did the obvious thing: predict a single stock's direction from its own technical features. It posted a Sharpe of 0.72 — until I audited it. Most of that edge was look-ahead leakage: training labels that quietly peeked into the test window. After purging the leakage and charging realistic costs, accuracy fell to ~0.50 — a coin flip.

That dead end was the most useful result in the project. Single-stock direction is dominated by market-wide noise nobody can predict. The honest failure is what forced the real design.

Instead of "will this stock go up?" the question became "will it outperform the other 499?". You rank all the stocks each month, go long the top decile and short the bottom, and size the two legs equally so the market's move cancels out. What's left is the part that's actually about the individual stock — relative strength — which is where systematic equity edge tends to live.

The ranker is LightGBM with a learning-to-rank objective (LambdaMART) — it optimises the ordering of stocks directly, not each stock's return. Seven price-based factors (momentum, volatility, size, reversal, liquidity), normalised cross-sectionally within each day. It's validated with walk-forward retraining plus a 21-day embargo, so no training label can overlap the test window — the same leakage discipline the first version failed.

The bar it had to clear was a dumb baseline: plain 12-month momentum, no learning. It cleared it — modestly and honestly.

Feature importance said volatility dominated, and I first wrote that up as "the model discovered the low-volatility anomaly." That was wrong, and checking the SHAP direction caught it: the long picks have high volatility with positive contribution — it tilts toward higher-vol names paired with momentum, the opposite of low-vol.

The lesson is worth more than the result: importance tells you which feature matters, never which direction. You confirm the sign before you name the effect.

The real test isn't the headline Sharpe — it's whether stocks the model ranks high actually outperform. Sort by the model's score and look at what happened next month: the returns climb cleanly through the top deciles. The top decile is the strongest; the bottom (short) decile is the weak spot, so the edge leans long.

The score becomes an actual portfolio: long-only top picks, weighted inverse to volatility, capped per name, and scaled to a target volatility with the remainder held as cash. Every holding carries the factor reasons that put it there, and a local LLM turns those reasons into plain English — fed only the model's real numbers, so it explains rather than invents. It's framed throughout as an educational simulation, never investment advice, and it quotes no return probabilities.

To test it honestly, I froze the model and traded it forward — no refitting with hindsight — for 23 months. It returned +19% annualised (Sharpe 1.25 vs 1.21 for an equal-weight benchmark), with realised volatility landing right on the 14% target, so the risk control is well-calibrated.