Page 63 — Algorithmic Trading Articles — Guides & Examples

MQL5 Trading Tools (Part 23): Camera-Controlled, DirectX-Enabled 3D Graphs for Distribution Insights

In this article, we advance the binomial distribution graphing tool in MQL5 by integrating DirectX for 3D visualization, enabling switchable 2D/3D modes with camera-controlled rotation, zoom, and auto-fitting for immersive analysis. We render 3D histogram bars, ground planes, and axes alongside the theoretical probability mass function curve, while preserving 2D elements like statistics panels, legends, and customizable themes, gradients, and labels

Graph Theory: Traversal Depth-First Search (DFS) Applied in Trading

This article applies Depth-First Search to market structure by modeling swing highs and lows as graph nodes and tracking one structural path as deeply as conditions remain valid. When a key swing is broken, the algorithm backtracks and explores an alternative branch. Readers gain a practical framework to formalize structural bias and test whether the current path aligns with targets like liquidity pools or supply and demand zones.

Engineering Trading Discipline into Code (Part 3): Enforcing Symbol-Level Trading Boundaries with a Whitelist System in MQL5

This article details an MQL5 framework that restricts trading to an approved set of symbols. The solution combines a shared library, a configuration dashboard, and an enforcement Expert Advisor that validates each trade against a whitelist and logs blocked attempts. It includes fully functional code examples, a clear explanation of the structural design decisions, and validation tests that confirm reliable symbol filtering, controlled market exposure, and transparent monitoring of rule enforcement.

Low-Frequency Quantitative Strategies in Metatrader 5: (Part 1) Setting Up An OLAP-Friendly Data Store

The article outlines a practical data pipeline for quantitative analysis based on Parquet storage, Hive-style partitions, and DuckDB. It details migrating selected SQLite tables to Parquet, structuring market data by source, symbol, timeframe, and date, and querying it with SQL window functions. A Golden Cross example illustrates cross‑symbol evaluation of forward returns. Accompanying Python scripts handle data download, conversion, and execution.

Statistical Arbitrage Through Cointegrated Stocks (Part 7): Scoring System 2

This article describes two additional scoring criteria used for selection of baskets of stocks to be traded in mean-reversion strategies, more specifically, in cointegration based statistical arbitrage. It complements a previous article where liquidity and strength of the cointegration vectors were presented, along with the strategic criteria of timeframe and lookback period, by including the stability of the cointegration vectors and the time to mean reversion (half-time). The article includes the commented results of a backtest with the new filters applied and the files required for its reproduction are also provided.

Markets Positioning Codex in MQL5 (Part 2): Bitwise Learning, with Multi-Patterns for Nvidia

We continue our new series on Market-Positioning, where we study particular assets, with specific trade directions over manageable test windows. We started this by considering Nvidia Corp stock in the last article, where we covered 5 signal patterns from the complimentary pairing of the RSI and DeMarker oscillators. For this article, we cover the remaining 5 patterns and also delve into multi-pattern options that not only feature untethered combinations of all ten, but also specialized combinations of just a pair.

Markets Positioning Codex in MQL5 (Part 1): Bitwise Learning for Nvidia

We commence a new article series that builds upon our earlier efforts laid out in the MQL5 Wizard series, by taking them further as we step up our approach to systematic trading and strategy testing. Within these new series, we’ll concentrate our focus on Expert Advisors that are coded to hold only a single type of position - primarily longs. Focusing on just one market trend can simplify analysis, lessen strategy complexity and expose some key insights, especially when dealing in assets beyond forex. Our series, therefore, will investigate if this is effective in equities and other non-forex assets, where long only systems usually correlate well with smart money or institution strategies.

Reimagining Classic Strategies (Part 17): Modelling Technical Indicators

In this discussion, we focus on how we can break the glass ceiling imposed by classical machine learning techniques in finance. It appears that the greatest limitation to the value we can extract from statistical models does not lie in the models themselves — neither in the data nor in the complexity of the algorithms — but rather in the methodology we use to apply them. In other words, the true bottleneck may be how we employ the model, not the model’s intrinsic capability.

Risk Management (Part 3): Building the Main Class for Risk Management

In this article, we will begin creating a core risk management class that will be key to controlling risks in the system. We will focus on building the foundations, defining the basic structures, variables and functions. In addition, we will implement the necessary methods for setting maximum profit and loss values, thereby laying the foundation for risk management.

Developing a multi-currency Expert Advisor (Part 24): Adding a new strategy (II)

In this article, we will continue to connect the new strategy to the created auto optimization system. Let's look at what changes need to be made to the optimization project creation EA, as well as the second and third stage EAs.

From Novice to Expert: Time Filtered Trading

Just because ticks are constantly flowing in doesn’t mean every moment is an opportunity to trade. Today, we take an in-depth study into the art of timing—focusing on developing a time isolation algorithm to help traders identify and trade within their most favorable market windows. Cultivating this discipline allows retail traders to synchronize more closely with institutional timing, where precision and patience often define success. Join this discussion as we explore the science of timing and selective trading through the analytical capabilities of MQL5.

Unified Validation Pipeline Against Backtest Overfitting

This article explains why standard walkforward and k-fold CV inflate results on financial data, then shows how to fix it. V-in-V enforces strict data partitions and anchored walkforward across windows, CPCV purges and embargoes leakage while aggregating path-wise performance, and CSCV measures the Probability of Backtest Overfitting. Practitioners gain a coherent framework to assess regime robustness and selection reliability.

Tips for an Effective Product Presentation on the Market

Selling programs to traders effectively does not only require writing an efficient and useful product and then publishing it on the Market. It is vital to provide a comprehensive, detailed description and good illustrations. A quality logo and correct screenshots are equally as important as the "true coding". Bear in mind a simple formula: no downloads = no sales.

Larry Williams Market Secrets (Part 14): Detecting Hidden Smash Day Reversals with a Custom Indicator

This article develops a practical MQL5 indicator that identifies Hidden Smash Day bars by strict numeric criteria and optional confirmation on the following session. We cover detection routines, buffer registration, and plot configuration to place arrows at valid bars. The approach delivers stable, non-repainting signals for historical testing and real-time monitoring.

Using the MQL5 Economic Calendar for News Filtering (Part 2): Stop Management Positions During News Releases

In part 2, we extend the news filter to protect existing positions during news events. Instead of closing trades, we temporarily remove stop-loss and take-profit levels, storing them safely in memory. When the news window ends, stops are deterministically restored, adjusted if price has already crossed the original levels, while respecting broker minimum distance rules. The result is a mechanism that preserves trade integrity without interfering with entry logic, keeping the EA in control through volatility.

Price Action Analysis Toolkit Development (Part 64): Synchronizing Manually Drawn Trendlines with Automated Monitoring

Monitoring manually drawn trendlines requires constant chart observation, which can cause important price interactions to be missed. This article develops a trendline monitoring Expert Advisor that synchronizes manually drawn trendlines with automated monitoring logic in MQL5, generating alerts when price approaches, touches, or breaks a monitored line.

Larry Williams Market Secrets (Part 15): Trading Hidden Smash Day Reversals with Market Context

Build an MQL5 Expert Advisor that automates Larry Williams Hidden Smash Day reversals. It reads confirmed signals from a custom indicator, applies context filters (Supertrend alignment and optional trading‑day rules), and manages risk with stop‑loss models based on smash‑bar structure or ATR and a fixed or risk‑based position size. The result is a reproducible framework ready for testing and extension.

From Novice to Expert: Enhancing Liquidity Strategies with Multi-Timeframe Structural Confirmation in MQL5

The alignment of higher-timeframe liquidity structures with lower-timeframe reversal patterns can greatly influence both the likelihood and direction of the next price movement. By integrating structural liquidity zones from higher timeframes with precise reversal confirmations on lower timeframes, traders can improve entry timing and overall trade quality. This article demonstrates how to reinforce liquidity-based trading strategies through higher-timeframe structural confirmation—and how to implement this approach effectively using MQL5.

Integrating MQL5 with Data Processing Packages (Part 8): Using Graph Neural Networks for Liquidity Zone Recognition

This article shows how to represent market structure as a graph in MQL5, turning swing highs/lows into nodes with features and linking them by edges. It trains a Graph Neural Network to score potential liquidity zones, exports the model to ONNX, and runs real-time inference in an Expert Advisor. Readers learn how to build the data pipeline, integrate the model, visualize zones on the chart, and use the signals for rule-based execution.

MQL5 Trading Tools (Part 24): Depth-Perception Upgrades with 3D Curves, Pan Mode, and ViewCube Navigation

In this article, we enhance the 3D binomial distribution graphing tool in MQL5 by adding a segmented 3D curve for improved depth perception of the probability mass function, integrating pan mode for view target shifting, and implementing an interactive view cube with hover zones and animations for quick orientation changes. We incorporate clickable sub-zones on the view cube for faces, edges, and corners to animate camera transitions to standard views, while maintaining switchable 2D/3D modes, real-time updates, and customizable parameters for immersive probabilistic analysis in trading.

Mastering Log Records (Part 6): Saving logs to database

This article explores the use of databases to store logs in a structured and scalable way. It covers fundamental concepts, essential operations, configuration and implementation of a database handler in MQL5. Finally, it validates the results and highlights the benefits of this approach for optimization and efficient monitoring.

Cluster analysis (Part I): Mastering the slope of indicator lines

Cluster analysis is one of the most important elements of artificial intelligence. In this article, I attempt applying the cluster analysis of the indicator slope to get threshold values for determining whether a market is flat or following a trend.

The MQL5 Standard Library Explorer (Part 10): Polynomial Regression Channel

Today, we explore another component of ALGLIB, leveraging its mathematical capabilities to develop a Polynomial Regression Channel indicator. By the end of this discussion, you will gain practical insights into indicator development using the MQL5 Standard Library, along with a fully functional, mathematically driven indicator source code.

Neural Networks in Trading: Dual Clustering of Multivariate Time Series (DUET)

The DUET framework offers an innovative approach to time series analysis, combining temporal and channel clustering to uncover hidden patterns in the analyzed data. This allows models to adapt to changes over time and improve forecasting quality by eliminating noise.

Neuro-Structural Trading Engine — NSTE (Part I): How to Build a Prop-Firm-Safe Multi-Account System

This article lays the system architecture for a multi‑account algorithmic trading setup that operates cryptocurrency CFDs on MetaTrader 5 while respecting prop‑firm constraints. It defines three core principles—fixed dollar risk, one script per account, and centralized configuration—then details the Python–MQL5 split, the 60‑second processing loop, and JSON-based signaling. Readers get practical lot‑size computation, safety checks, and position management patterns for reliable deployment.

Neural Networks in Trading: Dual Clustering of Multivariate Time Series (Final Part)

We continue to implement approaches proposed vy the authors of the DUET framework, which offers an innovative approach to time series analysis, combining temporal and channel clustering to uncover hidden patterns in the analyzed data.

Feature Engineering With Python And MQL5 (Part IV): Candlestick Pattern Recognition With UMAP Regression

Dimension reduction techniques are widely used to improve the performance of machine learning models. Let us discuss a relatively new technique known as Uniform Manifold Approximation and Projection (UMAP). This new technique has been developed to explicitly overcome the limitations of legacy methods that create artifacts and distortions in the data. UMAP is a powerful dimension reduction technique, and it helps us group similar candle sticks in a novel and effective way that reduces our error rates on out of sample data and improves our trading performance.

MQL5 Trading Tools (Part 25): Expanding to Multiple Distributions with Interactive Switching

In this article, we expand the MQL5 graphing tool to support seventeen statistical distributions with interactive cycling via a header switch icon. We add type-specific data loading, discrete and continuous histogram computation, and theoretical density functions for each model, with dynamic titles, axis labels, and parameter panels that adapt automatically. The result lets you overlay distribution models on the same sample and compare fit across families without reloading the tool.

MetaTrader 5 Machine Learning Blueprint (Part 8): Bayesian Hyperparameter Optimization with Purged Cross-Validation and Trial Pruning

GridSearchCV and RandomizedSearchCV share a fundamental limitation in financial ML: each trial is independent, so search quality does not improve with additional compute. This article integrates Optuna — using the Tree-structured Parzen Estimator — with PurgedKFold cross-validation, HyperbandPruner early stopping, and a dual-weight convention that separates training weights from evaluation weights. The result is a five-component system: an objective function with fold-level pruning, a suggestion layer that optimizes the weighting scheme jointly with model hyperparameters, a financially-calibrated pruner, a resumable SQLite-backed orchestrator, and a converter to scikit-learn cv_results_ format. The article also establishes the boundary — drawn from Timothy Masters — between statistical objectives where directed search is beneficial and financial objectives where it is harmful.

Automating Trading Strategies in MQL5 (Part 40): Fibonacci Retracement Trading with Custom Levels

In this article, we build an MQL5 Expert Advisor for Fibonacci retracement trading, using either daily candle ranges or lookback arrays to calculate custom levels like 50% and 61.8% for entries, determining bullish or bearish setups based on close vs. open. The system triggers buys or sells on price crossings of levels with max trades per level, optional closure on new Fib calcs, points-based trailing stops after a min profit threshold, and SL/TP buffers as percentages of the range.

From Novice to Expert: Forex Market Periods

Every market period has a beginning and an end, each closing with a price that defines its sentiment—much like any candlestick session. Understanding these reference points allows us to gauge the prevailing market mood, revealing whether bullish or bearish forces are in control. In this discussion, we take an important step forward by developing a new feature within the Market Periods Synchronizer—one that visualizes Forex market sessions to support more informed trading decisions. This tool can be especially powerful for identifying, in real time, which side—bulls or bears—dominates the session. Let’s explore this concept and uncover the insights it offers.

Developing Trading Strategy: Pseudo Pearson Correlation Approach

Generating new indicators from existing ones offers a powerful way to enhance trading analysis. By defining a mathematical function that integrates the outputs of existing indicators, traders can create hybrid indicators that consolidate multiple signals into a single, efficient tool. This article introduces a new indicator built from three oscillators using a modified version of the Pearson correlation function, which we call the Pseudo Pearson Correlation (PPC). The PPC indicator aims to quantify the dynamic relationship between oscillators and apply it within a practical trading strategy.

Developing a Trading Strategy: The Triple Sine Mean Reversion Method

This article introduces the Triple Sine Mean Reversion Method, a trading strategy built upon a new mathematical indicator — the Triple Sine Oscillator (TSO). The TSO is derived from the sine cube function, which oscillates between –1 and +1, making it suitable for identifying overbought and oversold market conditions. Overall, the study demonstrates how mathematical functions can be transformed into practical trading tools.

Overcoming The Limitation of Machine Learning (Part 9): Correlation-Based Feature Learning in Self-Supervised Finance

Self-supervised learning is a powerful paradigm of statistical learning that searches for supervisory signals generated from the observations themselves. This approach reframes challenging unsupervised learning problems into more familiar supervised ones. This technology has overlooked applications for our objective as a community of algorithmic traders. Our discussion, therefore, aims to give the reader an approachable bridge into the open research area of self-supervised learning and offers practical applications that provide robust and reliable statistical models of financial markets without overfitting to small datasets.

From Novice to Expert: Detecting Liquidity Zone Flips Using MQL5

This article presents an MQL5 indicator that detects and manages liquidity zone flips. It identifies supply and demand zones from higher timeframes using a base–impulse pattern, applies objective breakout and impulse thresholds, and flips zones automatically when structure changes. The result is a dynamic support‑resistance map that reduces manual redraws and gives you clear, actionable context for signals and retests.

Creating Custom Indicators in MQL5 (Part 9): Order Flow Footprint Chart with Price Level Volume Tracking

This article builds an order-flow footprint indicator in MQL5 that aggregates tick-by-tick volume into quantized price levels and supports Bid vs Ask and Delta display modes. A canvas overlay renders color-scaled volume text aligned with the candles and updates on every tick. You will learn sorting of price levels, max-value normalization for color mapping, and responsive redraws on zoom, scroll, and resize to read volume distribution and aggressor dominance inside each bar.

The article explores the Battle Royale Optimizer algorithm — a metaheuristic in which solutions compete with their nearest neighbors, accumulate “damage,” are replaced when a threshold is exceeded, and periodically shrink the search space around the current best solution. It presents both pseudocode and an MQL5 implementation of the CAOBRO class, including neighbor search, movement toward the best solution, and an adaptive delta interval. Test results on the Hilly, Forest, and Megacity functions highlight the strengths and limitations of the approach. The reader is provided with a ready-to-use foundation for experimentation and tuning key parameters such as popSize and maxDamage.

Building a Research-Grounded Grid EA in MQL5: Why Most Grid EAs Fail and What Taranto Proved

This article implements a regime-adaptive grid trading EA based on the PhD research of Aldo Taranto. It presents a regime‑adaptive grid trading EA that constrains risk through restartable cycles and equity‑based safeguards. We explain why naive grids fail (variance growth and almost‑sure ruin), derive the loss formula for real‑time exposure, and implement regime‑aware gating, ATR‑dynamic spacing, and a live kill switch. Readers get the mathematical tools and production patterns needed to build, test, and operate a constrained grid safely.

Introduction to MQL5 (Part 43): Beginner Guide to File Handling in MQL5 (V)

The article explains how to use MQL5 structures with binary files to persist Expert Advisor parameters. It covers defining structures, accessing members, and distinguishing simple from complex layouts, then writing and reading entire records using FileWriteStruct and FileReadStruct in FILE BIN mode. You will learn safe patterns for fixed-size data and how shared storage (FILE COMMON) enables reuse across sessions and terminals.

Developing Market Entropy Indicator: Trading System Based on Information Theory

This article explores the development of a Market Entropy Indicator based on principles from Information Theory to measure the uncertainty and information content within financial markets. By applying concepts such as Shannon Entropy to price movements, the indicator quantifies whether the market is structured (trending), transitioning, or chaotic.