Articles with examples of indicators developed in MQL5

This article extends the MQL5 RQA library to Cross-Recurrence Quantification Analysis (CRQA) for comparing two time series. We implement dual‑series embedding, cross‑recurrence matrix construction, adapted metrics (CRR, CDET, CLAM, CENTR, and others), and rolling‑window analysis, with optional GPU acceleration via OpenCL. A ready-to-use indicator compares two symbols in real time, supporting timestamp alignment and normalization for practical inter-market analysis.

The article adds a self-adaptive layer to the Malaysian Engulfing indicator by optimizing the retest bar range with a constrained brute-force search scored by MFE and MAE. It details the data model, helper routines, and an MQL5 implementation that gathers historical setups, computes excursions, and selects the best parameter. Readers learn how to remove manual tuning and run the indicator with context-appropriate settings across symbols and timeframes.

Learn how to implement a tick-based chart in MQL5 where each bar is built from a fixed number of ticks instead of time. The article covers creating and configuring a custom symbol, capturing real-time ticks, forming OHLC values, and pushing data with CustomRatesUpdate. This approach produces activity-driven candles that better reflect market intensity and short-term momentum for precise intraday analysis.

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.

This article implements an MQL5 module that analyzes the lower‑timeframe bars inside each liquidity‑zone base candle. It detects swing points and applies objective rules to classify the internal structure as an ascending, descending, or symmetrical triangle; a rectangle; M; W; or undefined. The indicator displays geometry labels on the chart and adds the pattern to alerts, reducing manual lower‑timeframe inspection.

Head and Shoulders patterns are difficult to identify consistently in live market data due to noise and structural ambiguity. This article presents a structured, triangle-based MQL5 indicator that isolates pattern components, constructs the neckline, and validates formations using ATR, symmetry, and slope constraints. The system detects and draws standard and inverse patterns, assigns a quality score, and confirms breakouts with optional alerts, enabling consistent and rule-based chart analysis.

This article explores the practical application of L1 trend filtering in MetaTrader 5, covering both its mathematical foundations and usage in MQL5 programs. The L1 filter enables extraction of piecewise-linear trends that preserve essential market structure while reducing price noise. The study analyzes parameter scaling, trend estimation behavior, and integration of the method into algorithmic trading strategies. Experimental results demonstrate how L1 trend filtering can enhance signal stability, trade timing, and overall robustness of trading systems.

This article builds an order-flow footprint indicator in MQL5 that aggregates tick-by-tick volume into quantized price levels and supports Bid vs Ask and Delta display modes. A canvas overlay renders color-scaled volume text aligned with the candles and updates on every tick. You will learn sorting of price levels, max-value normalization for color mapping, and responsive redraws on zoom, scroll, and resize to read volume distribution and aggressor dominance inside each bar.

This article develops a practical MQL5 indicator that identifies Hidden Smash Day bars by strict numeric criteria and optional confirmation on the following session. We cover detection routines, buffer registration, and plot configuration to place arrows at valid bars. The approach delivers stable, non-repainting signals for historical testing and real-time monitoring.

During sideways price movements, traders face excessive signals from multiple moving average crossovers. Today, we discuss how ALGLIB preprocesses raw price data to produce filtered crossover layers, which can also generate alerts when they occur. Join this discussion to learn how a mathematical library can be leveraged in MQL5 programs.

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.