Continuous Cash-Overlay Filters for a Static Growth–Defensive Risk Sleeve: Direct Slow-Tail Compensation, Fixed V-Shape Crash Brakes, Walk-Forward Validation, and Max-Cash Combination

Battle Count: 80

Directly addresses practical portfolio management questions: when to reduce equity exposure to cash, how to combine slow-regime and fast-crash signals, and how to validate timing rules out-of-sample. The modular design with max-cash combination is immediately implementable. The paper provides concrete parameter grids, walk-forward protocols, and event diagnostics useful for practitioners building cash-overlay strategies. High turnover of V-shape module is a practical concern. The 10bp transaction cost assumption is realistic for ETF trading.

Moderate complexity. No ML model training required; the system uses deterministic parametric rules with logistic mappings. However, implementation requires: (1) constructing 11+ continuous state variables with expanding standardization, (2) forming interaction products and re-standardizing, (3) running 216-configuration grid searches per module, (4) implementing walk-forward parameter re-selection in 63-day blocks, (5) applying the 30% material-trade gate and exit smoothing, and (6) computing the max-cash combination. The code repository reduces implementation burden significantly.

Code repository is publicly available on GitHub. Parameter grids (216 configurations per module), state variable definitions, and walk-forward protocols are fully documented. However, the methodology involves many discretionary choices in variable selection, interaction construction, and multi-criteria scoring weights. The 30% material-trade gate and exit smoothing (eta=0.25) are fixed. Full-sample screening introduces some data-mining risk not fully mitigated by walk-forward alone.