Articles on trading system automation in MQL5
Read articles on the trading systems with a wide variety of ideas at the core. Learn how to use statistical methods and patterns on candlestick charts, how to filter signals and where to use semaphore indicators.
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MQL5 Trading Tools (Part 30): Class-Based Tool Palette Sidebar
We refactor the Tools Palette from a flat, function-based panel into a modular, class-driven sidebar in MQL5. The design introduces supersampled canvas rendering for anti-aliased shapes, theme control, a category registry, snap alignment, and selective corner rounding. The result is a reusable, scalable sidebar foundation that you can extend with tool selection, dragging, and fly-out menus in future steps.
MetaTrader 5 Machine Learning Blueprint (Part 15): How to Calibrate Profit-Taking and Stop-Loss Targets from Synthetic Data
This article applies the Optimal Trading Rule from AFML Chapter 13 to set profit targets and stop-losses without in-sample calibration. We model post-entry P&L with a discrete Ornstein–Uhlenbeck process, run a 100,000-path search, and implement Python, multiprocessing, and a Numba @njit parallel kernel (242× faster). The result is an optimal (PT, SL) under three forecast specifications, constrained by the prop-firm daily loss limit.
The MQL5 Standard Library Explorer (Part 11): How to Build a Matrix-Based Market Structure Indicator in MQL5
Learn to engineer an MQL5 indicator that converts trend, momentum, and volatility into a single raw score using a matrix.mqh (ALGLIB). The article covers a separate‑window oscillator to validate the core mathematics, then a main‑chart indicator that plots non‑repainting buy/sell arrows when the score crosses user‑defined thresholds. An optional long‑term EMA filter, a minimum‑bar cooldown, and built‑in alerts make the tool practical for live trading.
Exploring Conformal Forecasting of Financial Time Series
In this article, we will consider conformal predictions and the MAPIE library that implements them. This approach is one of the most modern ones in machine learning and allows us to focus on risk management for existing diverse machine learning models. Conformal predictions, by themselves, are not a way to find patterns in data. They only determine the degree of confidence of existing models in predicting specific examples and allow filtering for reliable predictions.
Feature Engineering for ML (Part 3): Session-Aware Time Features for Forex Machine Learning
The article addresses the loss of temporal information in ML pipelines by encoding periodic time variables with Fourier harmonics and adding forex session structure. It implements session and overlap flags, lagged session volatility, and calendar effects, then prunes features by timeframe. The get time features function returns an index‑aligned, ML‑ready set of time features suitable for integration with price‑based signals.