History of MQL5.community development

With the multifractal parameters from Part 2 in hand, this article builds the full MMAR process. We construct the multiplicative cascade for trading time, generate Fractional Brownian Motion via Davies-Harte FFT, and combine both into X(t) = B_H[theta(t)]. A 100-path Monte Carlo simulation produces the volatility forecast, which we then pit against GARCH on the same EURUSD M5 data. Does Mandelbrot's fractal architecture outforecast Engle's conditional variance framework? Part 3 of a eight-part series leading to a native MQL5 library and Expert Advisor.

This article shows how to convert subjective flag recognition into reproducible MQL5 logic for live charts. It combines ATR-normalized pole strength, retracement limits, consolidation structure checks, breakout confirmation, and overlap control. Readers gain a workable approach that renders adaptive channels and zones, updates active setups efficiently, and provides optional alerts for newly confirmed patterns.

The article presents a V-in-V nested cross-validation pipeline for financial data that breaks leakage at three decision points: hyperparameter search, calibration, and final evaluation. A temporal three‑zone split isolates an inner walk‑forward search with the 1‑SE rule from an outer walk‑forward or CPCV evaluation, while OOF isotonic calibration is fitted independently. The resulting UnifiedValidationCalibrator delivers unbiased out‑of‑sample scores and well‑calibrated probabilities for deployment.

This article implements a Fenwick Tree (Binary Indexed Tree) for volume-aware money management inside an MQL5 Wizard Expert Advisor. We structure cumulative volume in O(log n) and apply four scaling modes—linear, conservative, aggressive, and mean-reversion—optionally gated by a lightweight 1D CNN. Practical tests compare the algorithm alone versus the CNN‑filtered approach to illustrate adaptive lot sizing and risk control under varying volume topologies.

Eagle Strategy is an algorithm that mimics the eagle's two-phase hunting strategy: global search via Levy flights using Mantegna method, alternating with intense local exploitation using the firefly algorithm, a mathematically sound approach to balancing exploration and exploitation, and a bioinspired concept that combines two natural phenomena into a single computational method.

This article examines a comprehensive approach to developing trading algorithms: from project setup and logic debugging to protecting the finished product. We will explore MetaEditor's built-in tools, including step-by-step debugging using real ticks, performance profiling, and direct integration with C++ DLLs to speed up calculations. The article also explains how to protect intellectual property using MQL5 Cloud Protector. The application of the described techniques will transform Expert Advisor development from a chaotic search for solutions into a systematic process, significantly reducing the time required to develop a strategy.

Building on the partition function analysis from Part 1, this article deepens the theoretical foundation before completing the analytical pipeline. We first give a full treatment of the Hurst exponent: what it measures, what it implies about market memory, and why it matters for the MMAR. This is followed by an intuitive exploration of multifractal spectra and what f(α) reveals about volatility heterogeneity. We then move to implementation: extracting the scaling function τ(q), estimating H via R/S analysis, and fitting the multifractal spectrum across four candidate distributions. By the end, we have the complete parameter set needed to construct the MMAR process in Part 3. Part 2 of an eight-part series.

Building a purchasing power parity (PPP)-based exchange rate analysis system using Python. The author developed an algorithm with 5 methods for calculating fair exchange rates using IMF data. A practical guide to fundamental currency analysis, economic data processing, and integration with trading systems. Full code in open source.

Many MetaTrader 5 setups run several EAs on one account, so risk gets fragmented and correlated exposure slips through. The article introduces RiskGate, a centralized Service that evaluates EA intents account‑wide: EAs send a JSON signal, the Service returns approved, lot and reason. You will see the client/server wiring, example rules (daily loss, exposure and correlation caps), unit‑tested handler design, and an EA example. The result is consistent portfolio‑level risk with simpler EAs.

This article shows how to run Python-trained models natively in MetaTrader 5 via the terminal's ONNX functions. We build an MQL5 class that encapsulates session creation, fixes input/output tensor shapes, applies min-max feature normalization to mirror training, and executes OnnxRun once per bar to protect the CPU, the result is a reliable, maintainable inference path for live charts and the Strategy Tester without sockets or DLLs.

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.

This work presents an end-to-end pipeline: collect MetaTrader 5 data, engineer entropy/volatility/trend features, train a PyTorch classifier, and expose predictions through a Flask API. An MQL5 EA posts rolling prices each tick, receives probability and regime, and applies adaptive position sizing and stop distances. The result is a clear recipe for integrating ML inference with MetaTrader 5.

This article presents a custom MQL5 signal class, CSignalBitwisePerceptron, for ultra-lightweight entry logic. It packs 64 bars into a single uint64 via bitwise vectorization and evaluates them with a perceptron that sums weights only for active bits. A two-gate flow (algorithmic hash map plus neural threshold) minimizes array iteration and heavy math. Readers get a practical template to cut latency and refine entry validation.

We turn the MQL5 AI trading assistant into a dispatch-driven system that routes seven trading actions through a single central dispatcher. A line-based key-value protocol constrains AI output, while each action maps to market or pending orders and instrument-aware stop levels. A canvas-based UI with a custom prompt editor and pixel-accurate text fitting makes signals consistent, auditable, and ready to render on the chart

This article starts the MMAR pipeline on EURUSD M5 data. We load market data via the MetaTrader5 Python API and run partition-function analysis with non-overlapping intervals to test for multifractal scaling. The result is an evidence-based decision on fractality, a prerequisite for building MMAR and for choosing whether to proceed beyond GARCH.

This article presents CPyramidEngine, a reusable MQL5 class that adds disciplined pyramiding to any Expert Advisor with about six lines of integration. The engine enforces three constraints: strictly decreasing lot sizes, a single unified stop that advances after each add-on, and broker-level validation of every modification. It explains common failure modes in naive implementations and shows how to keep total account risk quantifiable and controlled as positions are added.

Downloading international monetary fund data in Python: Mining IMF data for use in macroeconomic currency strategies. How can macroeconomics help an ordinary and an algorithmic trader?