Hour-Aware Adaptive Risk Management for Autonomous Memecoin Trading on Solana DEXs: Evidence, Theory, and Design Lessons from a 15-Day Deployment

Abstract

We report a 15-day paper-traded autonomous memecoin trading deployment on Solana decentralised exchanges (DEXs), designed as a controlled measurement of three microstructure questions on which classical equity theory offers well-defined predictions but on which the AMM Solana venue lacks published measurement: (i) time-of-day return patterns on a 24/7 permissionless venue; (ii) whether decision-time filter stacks are net-positive against counterfactual returns of rejected tokens; (iii) whether small-sample cumulative-return statistics on a heavy-tailed venue are structurally robust or fragile. The 190-trade sample (March 29 to April 12, 2026) shows a 40.5 percent win rate, mean per-trade return +0.62 percent, cumulative +117.7 percent, skewness -1.21, excess kurtosis 6.61. Mann-Whitney U on three exploratorily identified worst entry hours (n=17, mean -11.60 percent) versus all others (n=173, mean +1.82 percent) yields p = 0.5634; directional and non-confirmatory. A parallel counterfactual rejection-tracker collected 4,874 forward-sample observations across 184 rejection events; of 48 events observed for at least six hours, 27 (56.25 percent) reached a 50 percent drawdown from reference (the full-cohort 17.9 percent is a censored lower bound). Removing the top three trades (1.6 percent of sample) flips cumulative return unprofitable. The three findings connect to Kyle (1985) informed-flow, Precup-Sutton-Singh (2000) off-policy evaluation, and Bailey-Lopez de Prado (2014) deflated-Sharpe predictions. Alongside the trade log and rejection-sample corpus (CC-BY-4.0), we deposit audit.py (MIT), an assertion-based reproduction script that exits zero iff every headline number reproduces from the deposited CSVs. Companion dataset: Zenodo concept DOI 10.5281/zenodo.20043301. The paper's principal contribution is measurement infrastructure and three transferable design lessons.