TL;DR We're a small ML lab building alpha models for a handful of partners. Our market simulation loop — the part that keeps you honest about look-ahead bias — was 900–1300 ms per window in Python/Pandas, which made every experiment a 6–20 hour run. We went pandas → numpy → hand-written Rust + C++ models and landed at 1–5 ms per window on a cheap cloud box (4–40 µs on a high-clock CPU). This is the honest engineering story, and an actual question at the end for anyone who does HFT/MM. ...