Notes from our own data: real-fill backtesting, prop-firm rule math, live bot transparency. We publish the numbers including the ones that hurt.
A month of running futures edges through a locked train/test gate. Carry, momentum, calendar, opening-range, relative-value, even our own promising finds — nearly all died. Two uncorrelated survivors (mean-reversion + trend) became a book. And simple beat clever, twice.
June 2026 · series #5 · the whole storyAlmost every raw edge in liquid futures sits at profit factor 1.1. The lever that moves it isn't a better model — it's conditioning. A single "only after a down day" filter took our overnight edge from PF 1.07 to 1.21, nearly tripled the Sharpe, and halved the drawdown. Plus: why stacking correlated edges doesn't compound.
June 2026 · series #4 · the conditioning leverZ-score pairs mean reversion is the most-cloned retail quant strategy. On futures, with a locked train/test protocol, the cleanest pair posted a training profit factor of 0.72 — and the way the others broke (forced rolls, secular trends) is the real lesson.
June 2026 · series #3 · stat-arb autopsyThe edges that survived 18 years of real-fill testing have profit factors of 1.1–1.3 — and the candidate with t = 4.9 in training died out of sample. Why year-count beats magnitude, and why thin edges live or die on prop-firm rule math.
June 2026 · series #2 · edge anatomyWe compared bot-reported P&L against broker statements on our own accounts, found an $800+ gap, and traced it to the fill artifact hiding in most retail backtests. Four strategy families died on the way to two thin, real edges. Full numbers inside.
June 2026 · series #1 · fill realism · 143M data points