Distilled from the quant.stackexchange thread 'statistical arbitrage vs factor trading' (Michael, 2017; accepted answer by mperlow) which discusses Avellaneda & Lee (2010) 'Statistical Arbitrage in the US Equities Market'. The one concrete, testable mechanism in the source is the paper's ETF-factor version: regress a stock's daily returns on its sector ETF's returns over a trailing 60-session window, integrate the residual into an auxiliary process X_t, fit an AR(1)/Ornstein-Uhlenbeck model to X, and trade the dimensionless s-score s = (X_t - m)/sigma_eq, holding 1$ of stock against -beta $ of the ETF so the position is factor-neutral (exactly the hedging the accepted answer quotes from p.16). Per L173/L137 we do NOT run the 20-500 name PCA book (the Bybit PCA-residual top-20 timed out; neutral multi-name books die on leg costs). Instead it is ONE stock-vs-its-own-factor pair on the zero-commission USEQ venue, where the second leg costs ~0.02-0.05% rather than a crypto round trip. Instrument choice is a pre-registered rule, not a hand-picked list: the stock is the largest-weight constituent of the sector ETF (XOM has been XLE's top holding for essentially the whole ETF history since 1998). Signal is measured on XOM and XLE and traded on XOM and XLE (no signal-on-A/trade-B). DAILY bars give ~27 years of history. All thresholds are the paper's published values, not tuned: entry |s| > 1.25, exit long when s > -0.50, exit short when s < +0.75, with the paper's mean-reversion-speed filter (only trade when the fitted OU half-life < ~30 sessions, i.e. kappa > 252/30). This is a revise-free, first-principles restatement, not a code port.