One post tagged with "quantitative-research"

An optimistic backtest can show Sharpe 5 where live reality is negative, and an AI 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. always_abstain scores exactly 0.0 and is the best arm in 11 of 11 panels, without consulting the signal 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 correctly, which is the only honest output available and the one every trade-count metric punishes.

Everything that decides whether an AI makes money on a crypto 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 fifteen 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.