Discussing the article: "Market Microstructure in MQL5 (Part 4): Volatility That Remembers"
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Check out the new article: Market Microstructure in MQL5 (Part 4): Volatility That Remembers.
This article adds eight volatility functions to MicroStructure_Foundation.mqh, including realized volatility, duration-adjusted volatility, fractional volatility, a FIGARCH-inspired proxy, a volatility clustering index, a GJR-GARCH asymmetry measure (using the Dube library), bipower-variation jump detection, and a wrapper function. The MFDFA implementation is revised to return the conventional Legendre-transform Δα with an R² confidence field, replacing the τ-spread proxy used in the original submission. Thresholds are derived from 514 NY sessions of NQ E-mini Nasdaq 100 futures (May 2024–May 2026); no new include file is created.
Part 1 built a defensive foundation: guarded math, validated price feeds, and stable statistical primitives. Part 2 added confidence-weighted Hurst estimation, establishing that US100 M1 Globex operates near the random-walk boundary (pooled H = 0.511, rolling H ≈ 0.48). Part 3 added the GPH estimator for the fractional differencing parameter d, with pooled d = −0.006 and session-to-session standard deviation of 0.153.
Standard volatility indicators — ATR and standard deviation of returns — treat each bar as independent. They ignore four empirical facts about financial volatility: clustering (large moves follow large moves), persistence (volatility shocks decay slowly), asymmetry (negative returns increase future volatility more than positive returns of the same magnitude), and regime mixing (the series may be a mixture of different scaling laws, making average measures like H and d misleading). On intraday US100 M1 data, these properties directly affect position sizing, stop-loss placement, and signal filtering. A trading system that ignores them is systematically miscalibrated.
This article adds two measurement families to MicroStructure_Foundation.mqh: (1) a revised MFDFA-based multifractal spectrum that returns Δα with an R² confidence field, replacing the raw τ-spread proxy used in the original submission; (2) a volatility suite: RealizedVolatility(), DurationVolatility(), FractionalVolatility(), a FIGARCH-inspired proxy, VolatilityClusteringIndex(), LeverageEffect(), JumpIntensity(), and PopulateVolatilityAnalysis().
Author: Max Brown