Hour-Aware Adaptive Risk Management for Autonomous Memecoin Trading: A Multi-Layer Intelligence Framework

This paper introduces a multi-layer intelligence framework for autonomous memecoin trading that incorporates hour-aware adaptive risk thresholds. By analyzing historical trade outcomes against UTC hour-of-day, the framework identifies high-variance time windows and dynamically adjusts position entry criteria, stop-loss thresholds, and filter strictness. Backtests across Solana DEX trading data demonstrate that time-aware filtering substantially reduces drawdown relative to static-threshold baselines while preserving upside capture during favorable regimes. The approach generalizes to any algorithmic DEX trading system where signal quality varies predictably by time of day.