Codes
Project Template Generator for MetaTrader 5
This script serves as a practical example of how developers can programmatically work with files using MQL5. One of its key objectives is to demonstrate effective project file organization, which is essential for developers working on large-scale systems or aiming to create portable, self-contained
Telegram integration made easy for MetaTrader 5
The goal is to make the function readily available for any Telegram integration task in MQL5 development. By adding this file to your CodeBase, you can simply include it in your Expert Advisors and call the function directly from the included module. This eliminates the need to redevelop the code
Articles
From Novice to Expert: Candlestick Momentum Confirmation for Classic Crossover Strategies for MetaTrader 5
In this article, we refine a moving average crossover strategy with a momentum candle filter and an immediate retracement bar confirmation. When both conditions are met, a pending stop order is placed using a pivot-based stop loss and a 2R take profit. The complete MQL5 Expert Advisor code
How to Detect and Normalize Chart Objects in MQL5 (Part 5): Fibonacci in Focus for MetaTrader 5
The article bridges automated placement with manual analysis for the Fibonacci family in MQL5. It scans charts, identifies user Fibonacci objects, and normalizes their level arrays, interaction flags, and visuals per object type while preserving coordinates. With manual-priority enforcement, Expert
From Novice to Expert: Weekend Gap Size Effect Research Using MQL5 and Python for MetaTrader 5
The article provides a practical research setup for weekend gap analysis: MQL5 extracts precise pip‑based gaps and tracks fills, while Python performs statistical testing and visualization. You will compute fill rates by gap buckets, model fill probability with logistic regression, and assess
From Novice to Expert: Systematic Profit Conservation Using Candle Range Theory for MetaTrader 5
A hybrid exit engine for MQL5 replaces static TPs with CRT-derived structural levels. The CRT_ProfitConserve class secures a partial at the first level and then trails the remaining position by structural anchors rather than fixed pips. The article walks through the class API, essential methods, and
How to Detect and Normalize Chart Objects in MQL5 (Part 4): Fully Automated Analytical Objects System for MetaTrader 5
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
How to Test and Customize Built-in MQL5 Programs: Custom BullishBearish MeetingLines Stoch Expert Advisor for MetaTrader 5
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
The MQL5 Standard Library Explorer (Part 14): Building a Dynamic Hedge EA with the ALGLIB Port (ap.mqh) for MetaTrader 5
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
Overcoming Accessibility Problems in MQL5 Trading Tools (Part VI): Neural Command Integration for 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 MQL5 Standard Library Explorer (Part 13): Implementing the Math Solvers Library in Trading for MetaTrader 5
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
How to Detect and Normalize Chart Objects in MQL5 (Part 3): Alerting and Automated Trading from Manually Drawn Objects for MetaTrader 5
This article extends the chart‑object detector into a modular monitoring and execution layer. It defines objective interaction rules (touch, cross, breakout) for trendlines, Fibonacci levels, channels, rectangles, and pitchforks, then routes events through an interaction detector, alert manager, and
Forum
What could the trading world become if EAs and custom indicators were able to run on mobile platforms?
In my opinion, I believe this could result in higher trading activity and increased EA sales for developers due to the large number of mobile users. According to some research on Google: 95.8% use mobile phones to access the internet. By comparison, almost 62.9% use laptops or desktops (Source