21/06/2026
MMAR research in Python has covered data loading, partition scaling, Hurst extraction, spectrum fitting, cascade construction, fBM generation, Monte Carlo tests, and benchmarking versus GARCH, with MMAR showing stronger results.
Operational use requires a native MQL5 implementation. The current focus is a dependency-free library that reacts per tick and integrates with Strategy Tester without bridge processes or IPC latency.
Work starts with the Partition Analysis engine in MQL5. It computes S_q(dt) across log-spaced time scales and a q-grid, runs OLS on log-log fits to obtain tau(q), estimates H via tau(q)=0 with GHE as fallback, and applies diagnostics to confirm multifractality from scaling quality and curve shape.