AI-First Crypto Trading Principles
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.