We resume the topic of optimization by the scientific community. CoSO should not be viewed as a ready-made solution, but as a promising research platform. With proper development, CoSO can find its niche in tasks where adaptability and resilience to change are important, and computation time is not critical.
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 extends the chart‑object detector into a modular monitoring and execution layer. It defines objective interaction rules (touch, cross, breakout) for trendlines, Fibonacci levels, channels, rectangles, and pitchforks, then routes events through an interaction detector, alert manager, and optional trade executor. Orders use object geometry for stop‑loss and take‑profit. The result is a reproducible pipeline that converts static drawings into actionable alerts and, if enabled, trades.
This article implements the MMAR Simulation Engine that turns fitted parameters (H, distribution, coefficients, sample volatility) into synthetic price paths. It builds multifractal trading time via a multiplicative cascade, synthesizes fractional Brownian motion with Davies–Harte or Cholesky, scales it to target volatility, and composes the process by time deformation. Readers get a reusable MQL5 class, method choices by path length, and validation steps for scenario testing and Monte Carlo use in the next part.
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.
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.
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.
Secrets of effective optimization of trading strategies in metaheuristic approaches. Community of Scientists Optimization is a new population-based algorithm inspired by the mechanisms of the scientific community. Unlike traditional nature-inspired metaphors, CoSO models unique aspects of human scientific activity: publishing results in journals, competing for grants, and forming research teams.
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.
We add a descriptor-driven property stack and a floating ribbon that binds to the current selection on the drawing layer. The article covers the descriptor list for each tool, the engine get/set API with snapshot-and-restore live preview, and widget renderers for color, opacity, line width, line style, fonts, and level visibility. You get in-place, real-time editing of object appearance via a compact, draggable panel.
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.
In this article we consider a custom MQL5 Wizard class that processes Money Management. Our custom class is labelled ‘CMoneyWaveletLSTM’, and is developed by combining the Wavelet Thresholding algorithm with an LSTM network. As has been the case throughout these series, the developed model is testable with MQL5 Wizard-Assembled Expert Advisors that can be tuned with different trailing stops and entry Signals classes. We maintain our entry Signal, as in past articles as the built-in 'Envelopes' class and the RSI class.
Direct calls to the MQL5 History API inside analytics components create hidden terminal dependencies that make isolated testing structurally impossible. This article constructs an ITradeRepository abstraction layer with CLiveTradeRepository and CMockTradeRepository implementations, enabling the same analytics engine and equity curve panel to operate identically against live account data or a deterministic in-memory dataset. Repository injection eliminates direct API coupling, supports offline validation, and confines data source changes to a single implementation class.
The article presents the Competitive Learning Algorithm (CLA), a new metaheuristic optimization method based on simulating the educational process. The algorithm organizes the population of solutions into classes with students and teachers, where agents learn through three mechanisms: following the best in the class, using personal experience, and sharing knowledge between classes.
The article considers the classification model of Gaussian processes. We will start by studying its theoretical principles moving on to the practical development of the GP library in MQL5.
The article presents a reproducible MetaTrader 5 to Python pipeline for large-scale indicator research. An MQL5 export schema captures fixed columns, including custom lag and whipsaw counters. A baseline module performs parameter-matched comparisons across symbols and timeframes, while a walk-forward module locks the InSample optimum and evaluates it on unseen data. Readers gain unbiased robustness measurements and automation that removes manual selection bias.
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.
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.
MetaTrader 5 provides rich performance data but limited structural analysis. This article shows how to export results to CSV from MQL5 and build five Python visualizations that expose cross-asset parameter consistency, the lag‑versus‑noise trade-off, walk‑forward decay, drawdown depth and duration, and intraday hour‑by‑day clusters. A unified automation module runs the full pipeline on any new export to deliver repeatable diagnostics.
For this article we switch to a custom MQL5 Wizard class that examines entry Signals. Our custom class is ‘CSignalDSUDBN’ this time around, and is coded by combining the Disjoint Set Union algorithm with a Deep Belief network. As has been the case throughout these series, our model is testable with MQL5 Wizard-Assembled Expert Advisors that can be tuned with different trailing stops and money management classes.
The article discusses the Extremal Optimization (EO) algorithm, an optimization method inspired by the Bak-Sneppen self-organized criticality model, where evolution occurs through the elimination of the worst-case components of the system. The modified population version of the algorithm demonstrates a shift away from theoretical principles in favor of practical efficiency, leading to the creation of powerful computational tools.
The article presents a new approach to creating trading systems based on quantum principles and artificial intelligence. The author describes the development of a unique neural network that goes beyond classical machine learning by combining quantum mechanics with modern AI architectures.
For this article we rotate to a custom MQL5 Wizard class implementation that explores Trailing Stops. Our custom class is ‘CTrailingReservoirLinReg’ that we derive by combining the Reservoir Sampling algorithm with a Linear Regression network. As has been the case throughout these series, this formulation is testable with MQL5 Wizard Assembled Expert Advisors that can be tuned with various entry signals and money management classes.
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.
The article provides a detailed guide to creating the innovative 3DBarCustomSymbol.mq5 indicator, which generates custom symbols in MetaTrader 5 that combine price, time, volume, and volatility into a single three-dimensional representation. The mathematical foundations, system architecture, practical aspects of implementation and application in trading strategies are considered.
This article presents CTradeJournal, a self-contained MQL5 class for live tracking of open positions at tick frequency. It maintains MAE, MFE, and initial risk in money, calculates the R-multiple when a position closes, and writes a complete CSV record. The text explains the design choices, provides the implementation, and shows simple EA integration so you can analyze entries, stop placement, and outcome distribution.
Multi-core optimization in MetaTrader 5 can silently drop results when parallel agents contend for the same CSV file. A reusable MQL5 export engine applies an iteration-based spin-lock to acquire the file handle reliably and append rows without loss. It persists custom metrics such as the Sortino Ratio, average trade duration, and signal-quality measures (lag and whipsaws) into a consolidated CSV for downstream analysis.
The article explores the possibility of using regression models in algorithmic trading. Regression models, unlike binary classification, allow for the creation of more flexible trading strategies by quantifying predicted price changes.
The article extends the MQL5 recurrence library to Recurrence Network Analysis (RNA) by treating recurrence matrices as adjacency matrices of undirected graphs. It implements core network metrics—clustering, transitivity, average path length, betweenness, assortativity, and density—and applies them in rolling windows for single-series RNA and Joint RNA (JRNA). A modular metrics engine and two indicators visualize the evolving network structure on MetaTrader 5 charts for practical time-series analysis.
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.
We extend the canvas drawing layer from the previous part with seven new categories of multi-anchor analytical drawing tools, covering three channel variants, three pitchfork variants, three Gann tools, and the six Fibonacci tools. We work through how each tool encodes its geometry on the canvas, how derived handles let users reshape compound shapes coherently, and how shared helpers handle ray clipping, scanline filling, and anti-aliased arc rendering. By the end, we will have a full set of analytical drawing tools that live on the same interactive canvas alongside the basic line tools from the previous part.
This article builds the Spectrum Fitter: from tau(q) we compute f(alpha) with a discrete Legendre transform, then fit Normal, Binomial, Poisson, and Gamma spectra under box constraints using BLEIC. The best model by SSE is selected, and its parameters (eg, alpha min, alpha max or alpha_0, gamma) become the cascade inputs for multifractal simulation.
The article presents a detailed analysis of the Exchange Market Algorithm (EMA) inspired by the behavior of stock market traders. The algorithm simulates stock trading, where market participants with varying levels of success employ different strategies to maximize profits.
When running multiple strategies in parallel, you may want to periodically close all open positions and start the strategies over again. The existing code only allows this behavior to be implemented through manual intervention. Let's try to automate this part.
For this article we switch to a custom MQL5 Wizard class implementation that explores Money Management. We are labelling our custom class ‘CMoneySuffixAE’ that we derive by combining the Suffix Automaton algorithm with an Autoencoder neural network. As always, this formulation is testable with MQL5 Wizard Assembled Expert Advisors that can be tuned with various entry signals and trailing stop approaches.
This article shows how to implement a production Z-Score engine in MQL5 using an object-oriented include file, the library computes a rolling mean and population standard deviation, exposes a shift parameter for historical queries, and avoids redundant tick work by running on bar close. An Expert Advisor executes rule-based entries at positive/negative sigma thresholds and closes on mean reversion; a custom indicator provides visual verification.
In today's article we will begin studying the use of SQL in MQL5 code. We will also look at how to create a database. Or, more precisely, how to create a SQLite database file using the features built into MQL5. We will also see how to create a table, and then how to establish a relationship between tables by using primary and foreign keys. All of this, once again, will be done with MQL5. We will see how easy it is to create code that can later be migrated to other SQL implementations by using a class that helps hide the implementation being created. And, most importantly, we will see that at various points we may face the risk that something will go wrong when using SQL. This happens because, in MQL5 code, SQL code will always be placed inside a string.
This article adds eight volatility functions to MicroStructure_Foundation.mqh, including realized volatility, duration-adjusted volatility, fractional volatility, a FIGARCH-inspired proxy, a volatility clustering index, a GJR-GARCH asymmetry measure (using the Dube library), bipower-variation jump detection, and a wrapper function. The MFDFA implementation is revised to return the conventional Legendre-transform Δα with an R² confidence field, replacing the τ-spread proxy used in the original submission. Thresholds are derived from 514 NY sessions of NQ E-mini Nasdaq 100 futures (May 2024–May 2026); no new include file is created.
In the previous article, we completed the necessary introduction to SQL. And, in my opinion, we properly clarified what we wanted to show and explain about SQL. This was done so that anyone who comes to look at the market replay/simulation system being built can at least get an idea of what may be happening there. The point is that there is no sense in programming things that SQL handles perfectly.
