A MARKET-NEUTRAL cross-sectional SHORT-TERM REVERSAL strategy across a dispersed basket of ~12 liquid Binance USD-M perpetuals, hardened against the specific failure mode of raw reversal. Instead of ranking by raw trailing return (where a single high-volatility name mechanically tops/bottoms the rank and dominates the book), it ranks by VOL-NORMALIZED return — each name's trailing return divided by its own realized volatility (a per-name z-score / 'residual') — and sizes the legs EQUAL-RISK (inverse-volatility), so every position contributes the same risk and the signal reflects genuine relative OVER-EXTENSION rather than raw beta. Long the most-negative-z names (oversold relative to their own vol), short the most-positive-z names (overbought relative to their own vol), dollar/beta-neutral. CRITICAL DESIGN CHOICES grounded in this session's accumulated failures: (1) PURE OHLCV — uses NO supplementary data, deliberately and completely avoiding the data-coverage wall that just killed the OI-surge strategy (open_interest has only ~30 days of catalog history), the taker-flow strategy (3.5-year hole), and threatens every OI/taker-gated idea; OHLCV has full multi-year coverage for every basket member, so walk-forward OOS windows are never data-starved. (2) NON-momentum, NON-breakout — directional momentum/breakout is confirmed regime-dead across 14+ abandons; this is REVERSAL, the analyst's repeatedly-recommended chop-regime mechanism. (3) NOT a pairs-RATIO trade — the closed cross-asset ratio-MR family fails because a 2-asset ratio is non-stationary; a broad cross-sectional rank across 12 dispersed names is a fundamentally different, diversified statistical structure (no single non-stationary spread to blow up). (4) Genuine refinement over a raw-return reversal basket — the vol-normalized ranking + equal-risk weighting is the documented fix for raw reversal's high-vol-name domination. (5) NOT cross-venue/HL, NOT COIN-M, NOT drawdown-accumulation, NOT single-symbol RSI. Fires every 2 days across 12 names → dense OOS, immune to event-sparsity. Intentionally minimal: 3 tunables (zscore_lookback, vol_lookback, rebalance_days).