A Simple Conditional Exceedance Framework For Interpretable Trading Decisions

This paper presents a probability-driven trading framework based on conditional exceedance statistics rather than price or return forecasting. Using daily SPY data, conditional probabilities of large future price moves given large past moves are estimated over fixed horizons using data up to the end of 2024. These probabilities are organized into transparent lookup tables and translated directly into systematic, event-driven buy-hold-sell trading decisions with fixed holding periods. All parameters are frozen prior to evaluation, and performance is assessed strictly out-of-sample on 2025 data. Across multiple parameter configurations, the resulting strategies exhibit strong risk-adjusted performance, with Sharpe ratios exceeding 4 for selected regimes. The results suggest that long-horizon conditional structure in price dynamics can be exploited using simple, interpretable probability tables without reliance on complex predictive models.