Daily Cross-Sectional Volume Attention Reversal: Long Lowest-Volume-Z + Short Highest-Volume-Z Across 6 Major Binance Perps

A dollar-neutral, cross-sectional mean-reversion strategy on 6 major Binance USD-M perpetuals (BTC, ETH, SOL, BNB, AVAX, LINK — all confirmed clean OHLCV data) that captures the ATTENTION ANOMALY documented in equity factor research and observable in every crypto cycle. Every 24 hours, the strategy computes each asset's volume z-score relative to its 30-day baseline (today's volume - mean_30d) / std_30d, ranks the 6 assets by this z-score, goes LONG the 2 assets with the LOWEST volume-z (most-neglected, under-attended) and SHORT the 2 assets with the HIGHEST volume-z (FOMO/attention-spiked, typically near local tops or panic peaks). Equal-weighted within each leg, dollar-neutral, hold 24h, re-rank. Middle 2 assets excluded. This is the FIFTH in the cross-sectional series on the same universe — covering five orthogonal signal axes: DailyCrossSectional7DReturnReversal (DIRECTION), DailyCrossSectionalLowVolAnomaly (MAGNITUDE), DailyCrossSectional60DayDrawdownReversal (POSITION VS PEAK), DailyCrossSectionalBTCCorrelationReversal (RELATIONSHIP), and this strategy (ATTENTION). Together they form a complete mean-reversion factor stack on the major-basket. The attention-reversal mechanism: low-volume assets have been forgotten by retail and arbitrageur capital, often trading at depressed levels due to neglect; high-volume assets are crowded with FOMO buyers (at tops) or panic sellers (at bottoms), creating overshoots in both directions. The cross-sectional ranking captures the EXTREMES regardless of direction. Hong-Lim-Stein (2000), Da-Engelberg-Gao (2011) documented this in equities; well-observed in crypto FOMO bull-runs and post-FTX-style attention shocks. Uses ONLY BINANCE USD-M perp DAILY bars — no supplementary data, no spot, no options. Avoids ALL confirmed failure modes. Fills FIVE under-represented buckets: long-short direction (14.5% → toward 45%), multi-instrument scope (8.2% → grow), cross-sectional mechanism class (confirmed winner), portfolio diversifier via 5th orthogonal axis, and OHLCV-only data dependency. Position sizing: 12% per leg × 4 legs = 48% gross exposure, dollar-neutral.