Season-Structured Validation and Governance Architecture for Autonomous Strategy-Proposal Generation in Algorithmic Trading

Autonomous trading research pipelines can generate large volumes of machine-produced strategy proposals; however, testing them without leakage, overfitting, or silent selection bias remains an open problem. Existing autonomous trading systems primarily optimize strategy generation, whereas considerably less attention has been devoted to the governance of autonomous research pipelines that continuously generate, validate, and discard machine-generated hypotheses under strict temporal-integrity constraints. We introduce a season-structured validation and governance architecture that addresses this gap by combining leakage-free feature constraints, layered temporal validation, Bayesian risk-parameter optimization, and season-structured human oversight into a single reproducible workflow: within each season a local large language model (LLM) generates candidate strategies, and across season boundaries a human reviewer updates objectives and gates, forming a mixed-initiative governance layer. Across twelve adaptive seasons, 34,333 proposals produced 329 accepted strategies. Two case studies—a BNB/USDT candidate and a BTC/USDT replication—passed the predefined validation gates and showed positive selection-holdout performance under conservative calendar-time risk metrics. A feature-catalog sensitivity analysis on the BTC/USDT case study found that 3 of 50 random feature subsets of the same catalog reached the selected candidate’s recorded holdout Sharpe, with holdout Sharpe increasing with subset size and the comparison not budget-matched. A post hoc code audit conducted during the revision of this paper, disclosed here as an author-identified correction, found that the feature list included in every LLM proposal was never applied to training in any season: every reported model was trained on the full leakage-free catalog. As a result, this analysis compares the full catalog against random subsets, not LLM feature selection against chance. Therefore, the contribution is the validation and governance architecture itself, which turns a large stream of machine-generated proposals into a small set of credibly testable candidates, rather than any superior LLM feature intuition. Because the reviewer’s between-season adjustments consume out-of-sample information, the season sequence is a prolonged model-selection procedure. The case studies are therefore descriptive evidence of candidate rejection, not prospective proof of profitability.