2 posts tagged with "Quantitative Research"

Take a statistic that cannot exist and give it a p-value that means something else. That is what one paper in the queue did: it reported Spearman rho = 0.94, p = 0.017 over five assets. On five untied ranks, rho lives on a finite grid spaced exactly 0.1 apart. The smallest two-sided p the test can produce is 0.0167, and 0.017 is the exact p-value of a perfect ranking. The nearest attainable rho, 0.90, carries p = 0.0833 — not significant at 5%. The claim is not subtly wrong; it is printed arithmetic that could not have come from the test the paper claims to have run.

The system that caught it is not another return-predicting model. It is an auditor: a loop over a local corpus of 21,305 quant-finance paper abstracts, with 21,119 still queued, 29 papers read end to end by a human, and 82 machine screens completed. Each tick claims one paper, asks a language model two questions about it, runs deterministic nulls against real market data, and records a verdict under a schema that refuses records which certify themselves. The most important thing I can tell you about this loop is not that it found fake alpha. It is that its ceiling is the corpus, not the model — and that honesty about that ceiling is the actual product.

An optimistic backtest can show Sharpe 5 where live reality is negative, and an AI-first crypto trading system graded by it will optimise into the gap with total conviction — because inside that simulator the strategy genuinely works.

The simulator is not a test. It is the model's reward function. That is the sharpest trap I know, and the rest of this post is what I found while walking into it.

Here is the shape of what I found. Give a router a third action — quote, cross, or abstain — and it takes the third. Doing nothing scores zero; every alternative scores less; and zero wins 11 of 11 panels without the signal being consulted at all. The fitted policies that do trade pick about 1.6% of rows and still end below zero. A model that has learned to almost-not-play is not broken. It is reporting the absence of an edge — the one output no trade-count metric will ever reward.

Everything that decides whether such a system makes money on a perp lives in the coupling between model and market: fees, funding, regime, and the evidence you are willing to accept. A round trip costs 4–14 bps before the model says a word.

I went looking for that edge at sub-minute horizons and did not find it. What the search produced instead was thirty-nine principles about how to run a model against a market — each ending with the condition that breaks it, each carrying the measured number behind it, and several carrying the number that killed an earlier version of the same claim. They are worth more than the strategy would have been.