Blog

We ported a numba backtest kernel line-for-line to Rust and called it across a process boundary four ways, with an equivalence gate confirming identical PnL to the last trade. Shipping the entire 1.2 MB price series through a Unix socket costs ~2 ms — about 0.1% of the job. JSON-encoding the same payload costs 1348x more than raw bytes, chatty per-combo calls re-ship the data 80 times, and a per-bar call pattern would pay 2.1 s of pure IPC on a 2.0 s job. The boundary is cheap; the tax is in how you cross it.

A parameter search is a machine for manufacturing luck. On pure noise — 1,000 strategies with zero true edge — the best annual Sharpe averages 1.63 and the naive significance test flags a discovery 100% of the time. We build controlled ground truth and show that the Deflated Sharpe Ratio, the Harvey-Liu haircut, and White's Reality Check restore honesty: false discoveries drop from 1.000 to 0.001-0.057, genuine edges above the noise ceiling are kept with power ~1 — and one real trap (correlated grids) where the raw DSR over-deflates and the verdict must be read across a whole band of effective-trial estimates, not one.

To search for the 'best' strategy you must first define 'best' — and that scalar silently chooses the winner. On synthetic data with a known edge (600 seeds, T=2000, 80 thresholds), a naive per-trade Sharpe crowns a lottery: it picks a sub-5%-exposure winner in 56% of seeds and degenerates in 57% — on the starkest seed, 8 trades posting an in-sample Sharpe of 21.09 that collapses to 0.13 out of sample. The honest repair is almost dull: measure on the full timeline, which never degenerates (out-of-sample 1.71). A trade-count (conf_k) shrinkage and an exposure floor can retrofit a per-trade metric, but even fully repaired they only match full-timeline Sharpe (1.70 vs 1.71) — never beat it. Goodhart's law, in a backtest, with controlled ground truth.

A controlled study of the subtle look-ahead leaks that quietly inflate backtests. With zero real edge, a same-bar fill manufactures an annualized Sharpe of +14.8 out of pure noise; a one-bar indicator peek, +4.8. The taxonomy, the measured magnitudes, and how to detect each leak before it costs you money.

Five implementations of the same 80-combo parameter sweep, all verified to produce identical PnL: pandas rolling.apply takes 69.9 seconds, numpy 3.1, numba 2.0, parallel numba 0.23 — a measured 298x speedup on an Apple M2 Max with zero hardware changes, and still ~13x over a competent vectorized baseline. What each rung buys, why a GPU is not the missing piece, and where the real bottleneck in mass parameter search lives.

How we built researcher.marketmaker.cc — a unified, full-text-searchable archive of quant research (arXiv papers, GitHub repos, quant blogs, TradingView Pine scripts) that humans browse and AI agents query over MCP.

A deep look at algo-investor-skills — a set of Claude Code skills that take an algotrading strategy from raw measured facts to an audited, honesty-forward investor proposal. Six composable skills, a financial-models engine, an independent-verification proof pack, and a mandatory skeptical-investor audit gate that never fabricates a number.