Articles on data analysis and statistics in MQL5

The article introduces the Mapper pipeline in MQL5 by implementing the two fundamental components: CTDAMapperFilter (lens) and CTDAMapperCover (overlapping intervals). It explains three lens options—eccentricity, density, and coordinate—plus cover parameters (resolution and gain), and demonstrates how a price point cloud is reduced to one value per point and interval memberships. Readers obtain ready inputs for subsequent clustering and graph construction.

MQL5's file system operates within a strict sandbox. Understanding its access flags and path resolution rules is the foundation of any reliable export pipeline. This article builds a CCSVExporter class that handles file creation, safe appending, and error recovery. It also covers CSV parsing, field tokenization, concurrent access conflicts, and write-buffering strategies for high-frequency optimization runs.

For this article we look at a custom MQL5 Wizard class for Trailing Stops. Our implemented custom class ‘CTrailingConvexHullGRU’, is built from merging the Convex Hull algorithm with a GRU network. As always we seek to develop a model that is testable with MQL5 Wizard-Assembled Expert Advisors and can be tuned with various Money Management and entry Signals classes. Our testing is with the 'Envelopes' and the RSI classes for Signal.

A GPH‑based estimator for d, the key ARFIMA parameter, is added to MicroStructure_Foundation.mqh. GPHEstimator() computes d via log‑periodogram regression, while PopulateARFIMAAnalysis() stores d with an R² confidence score and validates the theoretical relationship H = d + 0.5. An empirical study on 72 US100 M1 sessions confirms pooled d = −0.006, consistent with the random walk boundary established in Part 2.

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.

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.

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 time-to-fill via Kaplan–Meier curves. All steps are configurable and reproducible for EURUSD, GBPUSD, USDJPY and beyond.

The article presents a practical foundation for shape analysis of price series in MQL5. It implements Takens time‑delay embedding to build a phase‑space point cloud and computes the full pairwise distance matrix under selectable norms. The CTDAPointCloud and CTDADistance classes are provided with a demo script that embeds chart data and outputs results, preparing inputs for downstream topological tools.

This article builds a white-box classifier in MQL5 using the Tsetlin Machine. It learns human-readable AND-rules instead of weights, trains with integer state updates, and requires no external dependencies. You will assemble the automaton, clause, and multi-class voter, verify on XOR and other boolean tasks, booleanize indicators, label by forward ATR-scaled return, save the model to CSV, and view active rules on a live chart.

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.

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.

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 extends MicroStructure_Foundation.mqh with a MicrostructureAnalysis struct and five functions that decompose M1 price variation into a quoted spread proxy, Roll-implied spread, OHLC-based noise ratio, order imbalance, and an adverse selection component. A wrapper populates these fields and links them to the volatility suite from Part 4. Empirical thresholds come from 602 NQ E-mini NY sessions (Jan 2024–Jun 2026), helping you gate volatility signals, size risk, and recognize spread-driven frictions.

This article presents 'CSignalUKFCapsNet', as a custom class coded in MQL5. This class is meant to be used with the MQL5 Wizard when assembling an Expert Advisor and when selected in the Wizard it defines the Expert Advisor's entry signals. In building this custom class, we brought together the algorithm Unscented Kalman Filter and the Capsule Neural Network. Our algorithm is showcased with four operation modes, and the coding of this as a custom class for the MQL5 Wizard, allows testing with various Trailing Stop methods and Money Management systems.

A statistically significant backtest is not proof of a robust edge. This article presents a three-part validation battery in Python that consumes an MQL5 trade-level CSV export. A sign-randomization permutation test evaluates whether the Sortino reflects real directional skill, bootstrap BCa intervals assess metric stability, and Monte Carlo trade-order shuffling tests sequence dependence of drawdowns. The results feed a five-condition framework for deployment decisions.

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.

We implement a Future Swing Projection indicator in MQL5 that analyzes historical swing structure and estimates the next move from recent price behavior. It locates six alternating swing points, measures five completed legs, and uses their average distance to project a target five bars ahead. The indicator draws swing legs, a projection line, ATR‑based support and resistance zones, and a label with the projected price to keep the process rule‑based and reproducible.

We describe an MQL5 framework that aligns entries with session rhythms and scheduled clock events. A script identifies the broker's time zone and DST by detecting NFP spikes on EURUSD and matching them to EU/US/AU transition dates, producing EA‑ready settings. Session-to-broker time conversion and 15-minute marks constrain execution. A multi‑timeframe AMA signal aggregates trends for strategy selection and optimization.

Manually drawn analytical object tools like Fibonacci tools, and Andrews Pitchforks are invisible to automated trading logic. This article extends a base detector to extract anchor points, level arrays, and geometric offsets from complex objects. You will implement a reusable collector that normalizes the raw chart data into structured memory arrays, ready for strategy decisions.

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.

The article examines slice sampling — an adaptive MCMC algorithm that automatically adjusts its sampling parameters. Its effectiveness is demonstrated using Bayesian linear and logistic regression models, and the results are compared with classical frequentist methods.

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, we will see how to visualize a database and, from that, understand how it is structured. This is done by analyzing the database’s internal structure. Although this may seem unnecessary at first, it is fully justified if we really want to become database administrators. After all, some people make a living maintaining and designing databases.

We add eight shape tools and nine annotation tools to the canvas and implement a full in-place label-editing system. The article walks through geometry, AA rendering, shared word-wrap and supersampled text helpers, and the caret-driven state machine for typing, navigation, and selection. This yields a complete, consistent annotation toolkit with editable labels that plugs into the prior interaction pipeline.

We build a CSV exporter for MQL5 custom indicators that preserves the exact values seen on the chart. The script creates the indicator handle with iCustom, waits for BarsCalculated, aligns buffers to CopyRates, and writes a locale-safe CSV that pandas loads with parsed dates and NaN for warm-up bars. It addresses compile-time argument limits, jagged-array workarounds, and EMPTY_VALUE handling, enabling reliable Python backtests without re-coding the indicator.

Part 8 adds bar-by-bar micro-trend scoring for NQ M1. GetMicroTrendStrength() builds a continuous [-1, +1] composite from EMA alignment, ATR‑normalized price position, slope consistency, and volume, with a contradiction penalty to suppress alignment/price conflicts. Session-adaptive thresholds scale by Part 7 confidence to modulate signal frequency across regimes. Outputs include a seven-state label, a binary signal, and a persistence check, calibrated on 514 New York sessions (May 2024–May 2026).

Net profit and win rate tell you how much a strategy made, not how the result is distributed. This article builds a native MQL5 script that reads your closed trades and measures profit concentration: the top-N trade share, the Gini coefficient of the winners, an outlier-dependence stress test that removes the best few winners, and the largest day against a prop-firm consistency limit. It combines these into one A+ to F score with recommendations, running inside MetaTrader 5.

We turn a price-embedded point cloud into a Vietoris–Rips filtration and its boundary matrix. The article enumerates vertices, edges, and triangles with filtration values, sorts them in entry order, and builds O(1) vertex/edge lookups. You get MQL5 classes CTDARips and CTDABoundary and a sparse Z/2 boundary suitable for the next-step persistence reduction.

This article shows how to build a hands-free trading workflow for MetaTrader 5 by translating webcam-tracked hand gestures into MQL5 trade commands. We cover the architecture (MediaPipe/OpenCV in Python plus an MQL5 EA), gesture-to-action mapping, and interprocess communication via Global Variables or HTTP polling. You will implement the EA, execute BUY/SELL/CLOSE actions, and validate latency and reliability under real‑time conditions.