Order Book Filtration and Directional Signal Extraction at High Frequency

With the advent of electronic capital markets and algorithmic trading agents, the number of events in tick-by-tick market data has exploded. A large fraction of these orders is transient. Their ephemeral character degrades the informativeness of directional alphas derived from the limit order book (LOB) state. We investigate whether directional signals such as order book imbalance (OBI) can be improved by structurally filtering high-frequency LOB data. Three real-time, observable filtration schemes: based on order lifetime, update count, and inter-update delay. These are used to recompute OBI on structurally filtered event streams. To assess the effect of filtration, we implement a three-layer diagnostic framework: contemporaneous correlation with returns, explanatory power under discretized regime counts, and causal coherence via Hawkes excitation norms. Empirical results show that structural filtration improves directional signal clarity in correlation and regime-based metrics, but leads to only limited gains in causal excitation strength. In contrast, OBI computed using trade events exhibits stronger causal alignment with future price movements. These findings highlight the importance of differentiating between associative and causal diagnostics when designing high-frequency directional signals.