2 posts tagged with "scalping"

A fill-at-touch backtest can show Sharpe 5 where live reality is negative. The fills you actually receive are the ones that lose money. An AI graded by that simulator learns to farm phantom maker fills. The simulator is not a test. It is the model's reward function. That is the sharpest trap I know.

Everything that decides whether an AI makes money on a crypto perp lives in the coupling between model and market: fees, fills, latency, funding, regime. A round trip costs 4–14 bps before the model says a word. Here are eleven principles, ordered the way a trader would order them, each ending with the condition that breaks it.

Every feature in a production trading system has an origin story — a paper, a theorem, a decades-old insight from probability theory or market microstructure. This post catalogs 14 ML features implemented in a Rust crypto scalping engine, traces each back to its foundational research, shows the actual formulas, and includes real production code. The engine processes limit order book (LOB) snapshots, trade ticks, and funding rate data in real time to generate scalping signals for crypto perpetual futures.