MQL5 Programs Market

We analyze why ratings may be inflated due to the alignment of algorithm trajectories with benchmark diagonals, and we supplement the testing methodology with a requirement to keep the global extremum away from the diagonals. We update Forest and Megacity, and perform RAW verification and calibration using VerifyExtremes.mq5. A drop in HHO and DOAm performance serves as a practical indicator of false leaders.

A complete Hyperdimensional Computing library for MQL5 implements bind, bundle and permute over 4096‑bit hypervectors, graded level ladders for quantities, an encoder for bars and sequences, and a classifier trained by integer addition. It includes a purged, ordered validation split, online updates without refitting, a market scan with two nulls, and a Python reference that reproduces the MQL5 model bit for bit, so you can validate results and deploy a reproducible indicator and test suite.

The article shows how to compare two trading forecasters in MQL5 using the Diebold–Mariano test with HAC‑corrected variance and the Harvey–Leybourne–Newbold adjustment. It explains implementation details, including loss choices, horizon‑dependent lag windows, rolling computation, verdict handling, and a manifest‑driven configuration, plus a Student‑t implementation without external libraries. An EA and an indicator provide a live dashboard and significance bands for practical, defensible model selection.

The STE-FlowNet framework offers a new perspective on financial data analysis by responding to actual market events rather than fixed timeframes. Its architecture preserves local and temporal dependencies, making it possible to track even small impulses in price dynamics.

The article rebuilds a Gaussian Process forecaster in native MQL5 around market-state inputs and direct horizon log-return targets. It employs a Matern 3/2 kernel, symmetric Cholesky with adaptive jitter, standardized targets, and a log‑space grid search that avoids state side effects. The EA trades only when az‑score over observed variance clears a threshold and the edge beats the live spread. Calibration is verified in Strategy Tester.

This article implements the scalar Kalman Filter for price smoothing in MQL5 using the standard local-level model and the exact prediction–update equations. It explains how process and measurement noise (Q, R) shape the Kalman gain, builds two independently configured filters into a fast/slow crossover, and provides a complete indicator and Expert Advisor that trade a stability-confirmed signal via iCustom, giving a reproducible baseline for testing and tuning.

This update adds manual position adoption and multi-symbol lifecycle management. The manager detects and tracks positions regardless of the chart, applies initial protection, transitions to break-even under fixed or ATR triggers, and validates broker constraints per symbol. Using the MACD sample for context, it demonstrates reusable management across instruments and manually opened trades.

A practical introduction to time series regression models: regression on a constant and simple regression with deterministic, exogenous, and endogenous regressors. It describes the key steps in the diagnostic process, including residual analysis and hypothesis testing, that are necessary for making informed trading decisions. MQL5 scripts for MetaTrader 5 are included; they implement tests and plots using real data.