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.
The MQL5 Wizard will help you create robots without programming to quickly check your trading ideas. Use the Wizard to learn about genetic algorithms.
Add a new article
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
MQL5 Trading Tools (Part 24): Depth-Perception Upgrades with 3D Curves, Pan Mode, and ViewCube Navigation
In this article, we enhance the 3D binomial distribution graphing tool in MQL5 by adding a segmented 3D curve for improved depth perception of the probability mass function, integrating pan mode for view target shifting, and implementing an interactive view cube with hover zones and animations for quick orientation changes. We incorporate clickable sub-zones on the view cube for faces, edges, and corners to animate camera transitions to standard views, while maintaining switchable 2D/3D modes, real-time updates, and customizable parameters for immersive probabilistic analysis in trading.
Neuro-Structural Trading Engine — NSTE (Part I): How to Build a Prop-Firm-Safe Multi-Account System
This article lays the system architecture for a multi‑account algorithmic trading setup that operates cryptocurrency CFDs on MetaTrader 5 while respecting prop‑firm constraints. It defines three core principles—fixed dollar risk, one script per account, and centralized configuration—then details the Python–MQL5 split, the 60‑second processing loop, and JSON-based signaling. Readers get practical lot‑size computation, safety checks, and position management patterns for reliable deployment.
The MQL5 Standard Library Explorer (Part 10): Polynomial Regression Channel
Today, we explore another component of ALGLIB, leveraging its mathematical capabilities to develop a Polynomial Regression Channel indicator. By the end of this discussion, you will gain practical insights into indicator development using the MQL5 Standard Library, along with a fully functional, mathematically driven indicator source code.
Neural Networks in Trading: Dual Clustering of Multivariate Time Series (Final Part)
We continue to implement approaches proposed vy the authors of the DUET framework, which offers an innovative approach to time series analysis, combining temporal and channel clustering to uncover hidden patterns in the analyzed data.