Interpretable Hypothesis-Driven Trading:A Rigorous Walk-Forward Validation Framework for Market Microstructure Signals

TL;DR

This paper introduces a rigorous, interpretable walk-forward validation framework for algorithmic trading that emphasizes transparency, realistic constraints, and regime-dependent performance, aiming to improve reproducibility and trust in quantitative finance research.

Contribution

It presents a novel validation protocol combining interpretability, strict out-of-sample testing, and regime analysis, extending to hypothesis generators like large language models.

Findings

Modest annualized returns of 0.55% with low drawdowns

Strong performance during high-volatility regimes

Statistically insignificant aggregate results demonstrating validation robustness

Abstract

We develop a rigorous walk-forward validation framework for algorithmic trading designed to mitigate overfitting and lookahead bias. Our methodology combines interpretable hypothesis-driven signal generation with reinforcement learning and strict out-of-sample testing. The framework enforces strict information set discipline, employs rolling window validation across 34 independent test periods, maintains complete interpretability through natural language hypothesis explanations, and incorporates realistic transaction costs and position constraints. Validating five market microstructure patterns across 100 US equities from 2015 to 2024, the system yields modest annualized returns (0.55%, Sharpe ratio 0.33) with exceptional downside protection (maximum drawdown -2.76%) and market-neutral characteristics (beta = 0.058). Performance exhibits strong regime dependence, generating positive returns during high-volatility periods (0.60% quarterly, 2020-2024) while underperforming in stable markets (-0.16%, 2015-2019). We report statistically insignificant aggregate results (p-value 0.34) to demonstrate a reproducible, honest validation protocol that prioritizes interpretability and extends naturally to advanced hypothesis generators, including large language models. The key empirical finding reveals that daily OHLCV-based microstructure signals require elevated information arrival and trading activity to function effectively. The framework provides complete mathematical specifications and open-source implementation, establishing a template for rigorous trading system evaluation that addresses the reproducibility crisis in quantitative finance research. For researchers, practitioners, and regulators, this work demonstrates that interpretable algorithmic trading strategies can be rigorously validated without sacrificing transparency or regulatory compliance.