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

TL;DR

This study develops and validates a regime classification system for MNQ futures based on pre-market conditions, revealing distinct intraday behaviors but limited success in profitable trading strategies after costs.

Contribution

The paper validates the VVG classifier as a descriptive framework for regime identification and documents challenges in turning these patterns into profitable trading strategies.

Findings

Classifier-positive days show systematic intraday reversal patterns.

All tested trading strategies fail institutional validation after costs.

Highest strategy achieves T=1.46 and net +7.80 points but lacks year-stability.

Abstract

This paper constructs and validates a composite day-classification system for Micro E-Mini Nasdaq 100 futures (MNQ) using three pre-market observable conditions: first-30-minute return magnitude, overnight gap magnitude, and abnormal opening-bar volume relative to a rolling baseline. Using 947 regular trading days of five-minute data from 2021-2025, we find that classifier-positive days exhibit statistically distinct intraday behavior, including directional morning drift followed by systematic late-session reversal. Despite these descriptive characteristics, all tested directional trading strategies fail institutional validation standards after transaction costs and multi-year consistency requirements are applied. The highest-performing configuration achieves T = 1.46 and mean net +7.80 points but fails year-stability criteria. The primary contribution is the validation of the Volatility-Volume-Gap (VVG) classifier as a descriptive regime-identification framework and the documentation of failed attempts to convert these statistical patterns into deployable trading signals under realistic execution constraints.