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

Battle Count: 73

Directly addresses the core methodological challenges in quantitative trading: overfitting, lookahead bias, and lack of interpretability. Provides a complete, reproducible validation template applicable to any trading strategy. The regime-dependent findings about daily microstructure signals are practically important for strategy deployment. The framework's extensibility to LLM-based hypothesis generation is highly relevant to current industry trends. However, the modest returns (0.55% annualized) and statistical insignificance mean it serves primarily as a validation methodology rather than a profitable strategy.

Moderate complexity: involves multiple components (feature engineering with 54 features, hypothesis generation, RL agent with ε-greedy policy, walk-forward partitioning, transaction cost modeling, position sizing, risk management). However, the framework is well-specified mathematically, open-source code is provided, computational requirements are modest (45 minutes on standard laptop, ~20×10^6 operations). The main complexity lies in understanding the interaction between components and ensuring information set discipline.

Complete mathematical specifications provided, open-source Python implementation on GitHub with Zenodo archival, publicly available data from Yahoo Finance, detailed feature specifications in appendix, full hypothesis specifications with thresholds, computational complexity analysis provided, and honest reporting of non-significant results. The framework is explicitly designed as a reproducible template for other researchers.