Clemence Benjamin / Profile

This part extends the series with a modular, event-driven MQL5 pipeline: swing detection feeds an object placer for trendlines, SR, Fibonacci, channels, and pitchforks; evaluators monitor interactions and generate signals; adaptive logic executes trades with valid stops per instrument. The topology manager synchronizes placement, scanning, and processing. The code is structured into reusable components for easy reuse and scaling.

We demonstrate a practical customization path for a built-in MetaTrader 5 EA using BullishBearish MeetingLines Stoch. The workflow covers baseline testing in the Strategy Tester, parameter optimization, and code-level changes. Two modifications are implemented: exposing Stochastic thresholds as inputs and adding an optional Moving Average filter to limit counter‑trend signals. The article includes the full modified code for replication.

This article introduces ap.mqh, the ALGLIB port for MQL5, and demonstrates its use in multi‑asset workflows that require robust linear algebra. It covers why built-in indicators fall short, then implements polynomial regression, a rolling correlation matrix indicator, and an adaptive hedge ratio estimator using ridge regression with Cholesky. Practical code shows how to compute spread z‑scores and execute coordinated pairs trades entirely within MetaTrader 5.

This article demonstrates a working prototype integrating Brain-Computer Interface technology with MetaTrader 5, proving thought-based trading is feasible at the software level. A Python Flask server simulates neural command generation, communicating with an MQL5 Expert Advisor via JSON-over-HTTP. The complete pipeline—from signal generation to trade execution—is validated through WebRequest and CTrade. While BCI hardware remains clinically restricted, this simulation establishes a reference architecture for future accessibility options, enabling direct intention-based trading that expands how traders can interact with financial markets.

We present a complete workflow for adaptive filtering in MQL5 using the CNlEq Levenberg–Marquardt–like solver. The EA fits a VAMAC model—two EWMAs with an ATR‑based scaling—by supplying residuals and a Jacobian through CNlEq's reverse‑communication loop, with optional numerical or analytical derivatives. Code, setup instructions, and GBPUSD H1 tests show how to replace static thresholds with on‑bar re‑estimation.