History of MQL5.community development

In this part of the Price Action Analysis Toolkit Development series, we develop an MQL5 indicator that automatically detects rising and falling wedge patterns in real time. The system confirms pivot structures, validates boundary convergence mathematically, prevents overlapping formations, and monitors breakout and failure conditions with precise visual feedback. Built using a clean object-oriented architecture, this implementation converts subjective wedge recognition into a structured, state-aware analytical component designed to strengthen disciplined price action analysis.

Let's discuss how we can make our Expert Advisors speech‑capable using text‑to‑speech technology, partnering Python and MQL5. After reading this article, you will walk away with a working example of an EA that speaks dynamic market information. You will master the application of TTS, the WebRequest function, and learn how Python libraries integrate with the MQL5 language to create a truly voice‑aware trading tool.

In this article, we enhance the regression graphing tool in MQL5 by adding a cyberpunk theme mode with neon glows, animations, and holographic effects for immersive visualization. We integrate theme toggling, dynamic backgrounds with stars, glowing borders, and neon points/lines, while maintaining standard mode compatibility. This dual-theme system elevates pair analysis with futuristic aesthetics, supporting real-time updates and interactions for engaging trading insights.

This article presents an adaptive parallel channel detection and breakout system in MQL5. It explains how swing points are identified, channels are constructed and dynamically recalculated, and breakouts are confirmed and visualized with persistent signals. The framework integrates trendline geometry, ATR-based filtering, and retest validation to provide reliable, real-time price action analysis for professional charting and trading decisions.

The article shows how to pair SQLite (OLTP) with DuckDB (OLAP) for statistical arbitrage data processing. DuckDB’s columnar engine, ASOF JOIN, and array functions accelerate core tasks such as quote–trade alignment and RWEC, with measured speedups from 2x to 23x versus SQLite on larger inputs. You get simpler queries and faster analytics while keeping trade execution in SQLite.

This article shows how to build an MQL5 indicator that reads a CSV trading history, extracts Profit($) values and total trades, and computes a cumulative balance progression. We plot the curve in a separate indicator window, auto-scale the Y-axis, and draw horizontal and vertical axes for alignment. The indicator updates on a timer and redraws only when new trades appear. Optional labels display per-trade profit and loss to help assess performance and drawdowns directly on the chart.

Built on lower-timeframe market structure, and then orchestrated on the higher-timeframe, this indicator detects swing extremes where price becomes statistically vulnerable to reversal. It visualizes overextension and pullback zones, offering early insight into mean-reversion behavior.

This article discusses the application of a breakeven mechanism in automated strategies using the MQL5 language. We will start with a simple explanation of what the breakeven mode is, how it is implemented, and its possible variations. Next, this functionality will be integrated into the Order Blocks expert advisor, which we created in our last article on risk management. To evaluate its effectiveness, we will run two backtests under specific conditions: one using the breakeven mechanism and the other without it.

This article shows how to automate Larry Williams Smash Day reversal patterns in MQL5 within a structured context. We implement an Expert Advisor that validates setups over a limited window, aligns entries with Supertrend-based trend direction and day-of-week filters, and supports entry on level cross or bar close. The code enforces one position at a time and risk-based or fixed sizing. Step-by-step development, backtesting procedure, and reproducible settings are provided.

The article extends a liquidity-based strategy with a simple trend constraint: trade liquidity zones only in the direction of the EMA(50). It explains filtering rules, presents a reusable TrendFilter.mqh class and EA integration in MQL5, and compares baseline versus filtered tests. Readers gain a clear directional bias, reduced overtrading in countertrend phases, and ready-to-use source files.

In this discussion we will Automate Swing Extremes and the Pullback Indicator, which transforms raw lower-timeframe (LTF) price action into a structured map of market intent, precisely identifying swing highs, swing lows, and corrective phases in real time. By programmatically tracking microstructure shifts, it anticipates potential reversals before they fully unfold—turning noise into actionable insight.