A Validated Volatility-Volume-Gap Classifier for Regime Identification in MNQ Intraday Data

This paper asks whether a small set of observable pre-market characteristics can identify trading days with systematically different intraday behavior in Micro E-Mini Nasdaq-100 (MNQ) futures. I construct a simple day-classification framework based on the overnight gap, the first 30-minute return, and first-bar trading volume relative to a rolling 20-day baseline. The framework, referred to as the Volatility-Volume-Gap (VVG) classifier, is evaluated using 947 trading days of five-minute MNQ data from 2021-2025 with all classification thresholds computed on an expanding window to avoid lookahead bias. The classifier identifies a small subset of trading days that exhibit a consistent intraday profile characterized by morning directional continuation followed by late-session reversal. I then test whether these recurring patterns can be converted into deployable trading strategies. None of the evaluated strategies satisfy the same validation criteria used throughout this research program: out-of-sample walk-forward testing, positive net returns after transaction costs, and consistent performance across years. The primary contribution is descriptive rather than predictive. The VVG classifier provides a simple framework for identifying a distinct intraday market regime, but the observed structure does not translate into a robust standalone trading signal under realistic execution assumptions.