Articles on data analysis and statistics in MQL5

A population-based optimization algorithm inspired by a controversial and little-studied phenomenon - the mechanism of human dreams. Agent groups with different "memory", cosine-wave modulation of motion, and an unusual 99/1 phase distribution — learn how these features affect the optimization efficiency of your trading strategies.

We implement the CMonteCarlo module that turns the fitted MMAR parameters into a volatility forecast via Monte Carlo. It runs N independent simulations over a chosen horizon and reports mean, median, standard deviation, and a percentile-based 95% confidence interval, with access to per-run values if needed. Adaptive cascade depth selects the minimal k such that b^k covers the horizon, keeping the run fast and consistent.

MetaTrader 5 provides no native tool for visualizing intraday return patterns across time dimensions simultaneously. This article implements a custom indicator that aggregates historical bar returns into a 5×24 matrix indexed by weekday and hour of day, then renders the result as a color-interpolated heatmap inside an indicator subwindow using CCanvas. Green cells represent positive average returns, red cells negative, with color intensity encoding return magnitude.

This article demonstrates how to build a reusable prop‑firm evaluation module for MQL5 Expert Advisors and export results to an HTML dashboard. The module monitors balance and equity during backtests, simulates single or rolling challenges, checks profit target, daily and overall drawdown, and minimum trading days, then outputs both a terminal summary and a browser‑readable report.

The article describes implementation of the A3 algorithm - a metaheuristic optimization method inspired by chemical processes - in MQL5. Only two adjustable parameters, compactness and a small population, ensure high operating speed with sufficient quality of solutions.

This article introduces a frequency-analysis framework for encoded candlestick patterns in MQL5. By transforming candlesticks into alphabetic symbols, historical price action can be analyzed as a statistical sequence rather than a visual chart. Using GBPUSD and Gold across multiple timeframes, the study examines the occurrence frequency of individual candlestick types, identifies dominant market structures, and reveals the symmetry between bullish and bearish price movements. The results establish a quantitative foundation for pattern discovery and prepare the way for analyzing multi-candlestick sequences and their predictive potential in algorithmic trading systems.

This article describes a live data export framework for MetaTrader 5 built around a decoupled, three‑layer design. The MQL5 component batches bar and tick records via a write buffer and rotates CSV files daily; a Python daemon tails the stream, renders a live dashboard, and flags anomaly thresholds. The demo indicator illustrates integration points, enabling real‑time monitoring and auditability during trading sessions.

In the previous article, I showed how to proceed in order to add a query mechanism. This was needed so that, inside MQL5 code, you could fully use SQL and retrieve results using an SQL SELECT query. But there is still one last function we need to implement. This is the DatabaseReadBind function. Since understanding it properly requires a slightly more detailed explanation, it was decided to cover it not in the previous article, but in today's article. So, since the topic will be fairly extensive, let us proceed directly to the next section.

This article adds six order-flow functions and a new OrderFlowAnalysis struct to MicroStructureFoundation.mqh: VPINOHLC, signed flow imbalance, trade intensity versus a 20-session baseline, a late-minus-early smart-money index, flow momentum, and a wrapper that outputs a confidence weight. Flow confidence is gated by noise and jump intensity from Parts 5 and 4. Calibrated on 602 NQ M1 NY sessions, it provides ready-to-use intraday flow signals with documented thresholds.

This article presents a custom MetaTrader 5 indicator that computes a rolling annualized Sharpe ratio and plots configurable z-score significance bands based on Lo's asymptotic standard error. It uses a circular return buffer with incremental variance to keep O(1) updates. We explain the n^(-1/2) uncertainty scaling, the inflation of intervals at high Sharpe values, and how to set per-instrument annualization for correct deployment.

The article presents an innovative quantum neural network architecture for algorithmic trading that combines the principles of quantum mechanics with modern machine learning methods. The system includes quantum effects (resonance, interference, decoherence), multi-level memory of different time scales, Markov chains with the ALGLIB library, and adaptive parameter control. The full implementation is done in MQL5 using the built-in matrix/vector types, which removes implementation barriers in MetaTrader 5.

Many people tend to underestimate SQL, or even not use it at all, because they do not fully understand how it actually works. When running queries against an SQL database, we are not always looking for a universal answer; in some cases, we need a very specific and practical answer. If a database is created with a proper structure and data model, almost any type of information can be integrated into it.