Articles on data analysis and statistics in MQL5

This article walks through creating an MT5 indicator that ingests option chains from native symbols or CSV, inverts prices to implied volatility via a hybrid Newton–Raphson/bisection method, and assembles a clean strike–expiry grid. It then renders a shaded, rotatable 3D surface with the platform's DirectX layer, enabling clear, in-terminal analysis of skew and term structure using live or file-based data.

We extend the stateful supply and demand framework for MetaTrader 5 with a quantitative admission model and a dedicated interaction engine. Candidate zones are scored by structural symmetry, volume participation, and ATR‑normalized displacement, then classified into objective tiers. Admitted zones follow a deterministic lifecycle that tracks first touch, validates bounces, or confirms breakouts, with full telemetry for analysis and reproducibility.

This article turns the verified TDA pipeline into a live MQL5 indicator. It reduces each price window to two persistence-entropy lines (H0 and H1), computes a normalized loop-strength metric with an adaptive percentile band, and places fade marks only when loop strength is high and price hits a window extreme. You can attach the indicator, read six buffers from an Expert Advisor, and tune key window, ranking, and performance parameters.

This article extends single-candlestick analysis to ordered double-candlestick patterns using an MQL5 script. The script encodes candles into symbols, extracts every consecutive two-symbol sequence (treating Aa and aA as different), counts occurrences and percentages, and writes sorted frequency tables to a text file. Readers can quickly identify the most recurrent transitions by symbol, timeframe, and lookback for further statistical testing.

The article presents the revolutionary architecture of PatchTST, a tailored transformer for financial time series analysis that breaks market data into 16-bar patches for efficient processing. We will discuss the full implementation of a trading robot in MQL5 covering everything from mathematical fundamentals and data structures to a ready-made EA with risk management and continuous learning systems.

In Part 2, we introduce a reusable CCovarianceMatrix class that computes and stores a covariance matrix from raw return series using MQL5's native Cov() method. We verify symmetry, print a labeled matrix grid, and call Eig() to obtain eigenvalues and eigenvectors. Readers see how symbols co-move and which factors drive variance, enabling clearer portfolio diagnostics and reuse in scripts or EAs.

In previous articles, we mentioned that sometimes we need to set a value for the ZOrder property. But why? The reason is that many pieces of code that add objects to a chart simply do not use, or more precisely do not define, a value for this property. The point is that I am not here to say what every programmer should or should not do, or how they should or should not write their code. I am here to show you, dear reader, and everyone who truly wants to understand how these processes work internally, what actually happens behind the scenes.

In this article, I will show how to use an indicator to track open positions on the trading server in the simplest and most practical way possible. I am doing this step by step to show that you do not necessarily have to move all of this into an Expert Advisor. Many of you have probably become used to doing that for one reason or another. In fact, that is not really justified, because as this implementation evolves, it will become clear that you can create or implement different types of indicators for this purpose.

Standard MQL5 risk tools read risk from recent history and miss how heavy the downside tail can be. We implement Extreme Value Theory in MetaTrader 5: a Peaks‑Over‑Threshold fit of the Generalized Pareto Distribution via ALGLIB, a live indicator that reports EVT VaR/ES and tail shape, and an EA that sizes positions from the tail estimate. A controlled backtest illustrates reduced drawdown for unchanged entries.

The article builds a reusable validation layer for Expert Advisors in MQL5. It implements lot-size rules and normalization, SL/TP and freeze-level guards, price digit normalization, margin sufficiency checks, unchanged-level filtering on modifications, account order-limit control, new-bar detection, symbol tradability checks, economic-calendar news windows, and session detectors. The result is cleaner code and fewer terminal errors in live trading.

We implement a symbol resolution framework that abstracts broker naming differences in MetaTrader 5. Using a persistent mapping store, layered resolution with validation, a hash-indexed registry, and a cache, it returns selectable symbols with live market data and logs unresolved cases. Practically, you can deploy the same EA across brokers and keep symbol access consistent at low runtime cost.

We complete persistent homology for MQL5 by reducing the Vietoris–Rips boundary matrix to a persistence diagram. The article implements Z/2 column reduction (CTDAReduction), a diagram container with analytics (CTDADiagram), and a facade that runs the six-stage pipeline in one call (CTDA). Outputs are cross-checked against Ripser to numerical agreement, enabling reliable diagram-based metrics.

Abnormal bars inflate mean and standard deviation estimates, distorting ATR, Bollinger Bands, and moving averages. We implement a native MQL5 indicator that detects such bars with the Modified Z-Score applied to four features: body, upper wick, lower wick, and tick volume. The indicator marks flagged bars on the chart and plots a composite score in a separate subwindow, helping you diagnose contamination in rolling-window indicators.

This article presents a multi‑broker CSV normalization framework. An MQL5 include file enriches exports with broker metadata. A Python module resolves schema divergences — pip conventions, symbol aliases, time offsets, commission models, and currency denomination — producing a unified canonical dataset. Comparative visualizations of slippage distributions and net‑of‑cost performance enable reliable cross‑platform strategy analysis without silent data corruption.

We present a timer-based MQL5 EA for Opening Range Breakout aligned to NYSE hours. It screens “Stocks in Play” via opening-range relative volume, enforces price/volume/ATR minimums, sizes positions by risk, and exits at 16:00 ET. A Sharpe-ranked optimization across 30 liquid Nasdaq stocks and a single-symbol test are provided, together with backtest settings and an Excel report for verification.

The article presents an innovative hybrid system for forecasting exchange rates that combines a linear autoregressive model with a U-Transformer architecture for residual analysis. The system automatically switches between signal sources depending on their quality and includes complete trading logic with averaging/pyramiding strategies. The key advantage of this approach is that the neural network is trained on the residuals of the linear model, which simplifies the task and reduces the risk of overfitting. The implementation is done entirely in MQL5 and is ready for use in real trading with automatic adaptation to changing market conditions.

Maximum drawdown is one number that hides what really matters: how often an equity curve declines, how long it stays below a previous peak, and how quickly it recovers. This article builds a native MQL5 tool that reconstructs the underwater curve, breaks it into individual drawdown episodes (depth, duration, recovery time), computes the Ulcer Index, Pain Index, and Recovery Factor, and combines them into a single resilience grade with practical recommendations. No external libraries, no Python, no AI.

The article delivers MQL5 implementations of FIGARCH and HARCH and updates the volatility library for long‑memory processes. It provides code for Hurst and GPH testing, parameter setup (truncation and horizons), and scripts for fitting, forecasting, and simulations. Readers learn how to apply and compare the models on market data to select an appropriate specification.

CTrailingSlidingMedianBiLSTM is a custom MQL5 Wizard trailing module that combines robust median/MAD outlier filtering with a BiLSTM context score in the range [-1, 1]. Four algorithm modes (standard, bands, RSI, adaptive) target noise, mean-reverting bursts and liquidity spikes, reducing premature stop adjustments. This module is intended for side-by-side evaluation with diverse entry signals and money management settings.

Implement a session-focused volume profile in MQL5: acquire ticks with CopyTicksRange(), bin prices, and compute POC, VAH, and VAL by the 70% approach. The indicator renders directly on the chart as native objects, supports fixed-width scaling for consistent geometry across timeframes, and refreshes on each new session. This provides objective reference levels without external dependencies.

What if your trading strategies could learn from each other, like real fighters? Duelist Algorithm is a new optimization method where trading system parameters literally duel for the right to be called the best.

A revolutionary approach to machine learning in trading through quantum computing. The article demonstrates a practical implementation of an adaptive QRC system with continuous retraining for predicting market movements in real time.

We integrate eleven one-minute microstructure measurements from Parts 2–6 into a composite regime label with confidence and direction. A rule-based RegimeClassifier() assigns one of six regimes—Normal, Stressed, Noisy, Informed, Trending, Mean-Reverting—using empirically derived thresholds from 514 NQ M1 sessions (May 2024–May 2026). The deliverable includes MARKET_REGIME, RegimeAnalysis, and PopulateRegimeAnalysis(), enabling position sizing, stop placement, and signal filtering from a single call.

The article explores the revolutionary Mamba/SSM neural network architecture for financial time series forecasting. We will consider a complete MQL5 implementation of a modern alternative to Transformer with linear complexity O(N) instead of quadratic O(N²). Selective State Space Models, hardware-aware optimizations, patching techniques, and advanced AdamW training methods are covered in detail. Practical test results showing an increase in accuracy from 62% to 71% while reducing training time from 45 to 8 minutes are included. A ready-made trading EA with auto learning and adaptive risk management for MetaTrader 5 is presented.

The article establishes a reproducible method to measure portfolio risk for multiple symbols using MQL5 matrices and OpenBLAS. It covers computing log returns, building a covariance matrix, and evaluating wᵀΣw instead of summing individual variances. A complete script prints naive versus true volatility and the cross‑term contribution, enabling you to detect when correlated instruments inflate exposure beyond single‑asset estimates.

A lightweight persistence design lets EAs retain counters, flags, and timestamps between terminal restarts. Using only MQL5, CPersistentStore writes a human-readable key=value file in MQL5/Files and serves reads from a CHashMap write-through cache via a typed API. The article analyzes O(1)/O(n) operations, partial‑write risks, and lack of locking, compares with GlobalVariables/SQLite, and provides a demo that reloads state deterministically.

Trading is characterized by high demands on risk management discipline. The article presents an analysis of the main reasons for traders' failures and proposes a technical solution in the form of the CEnhancedRiskManager class for the MQL5 platform. It includes practical testing on an aggressive grid EA.

This article lays out 'CMoneyKDTreeESN' custom money management class usable with the MQL5 Wizard, that combines the KD-Tree algorithm and the Echo State Network. We use the KD-Tree on log returns and ATR to give us a risk score, while the ESN tracks recent flow to give us a bounded lot size multiplier. Our class is usable in a variety of Wizard assembled Expert Advisors as shown here with the Envelopes and RSI signals, with a broad objective of modulating exposure in high-volatility and tail-risk environments.

In this section, we will look at the implementation of the key interfaces of the library of Gaussian processes in MQL5: IKernel, ILikelihood, and IInference. We will also demonstrate its operation on synthetic data and implement indicators for classification and regression, demonstrating its operation in online mode - with retraining of the model on each new bar.