Articles with examples of trading robots developed in MQL5
An Expert Advisor is the 'pinnacle' of programming and the desired goal of every automated trading developer. Read the articles in this section to create your own trading robot. By following the described steps you will learn how to create, debug and test automated trading systems.
The articles not only teach MQL5 programming, but also show how to implement trading ideas and techniques. You will learn how to program a trailing stop, how to apply money management, how to get the indicator values, and much more.
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Neural Networks in Trading: Robust Trading Signals in Any Market Regime (Conclusion)
The article provides a detailed examination of the integration of the ST-Expert framework's approaches into the Extralonger architecture, which enables the simultaneous analysis of temporal and spatial representations of data. The results of testing on real historical data are presented, demonstrating the model's effectiveness and its robustness to market anomalies. The article describes the framework's modular structure, which ensures reproducibility, flexibility for research, and the ability to optimize components in stages.
Neural Networks in Trading: A Spiking-Semantic Approach to Spatiotemporal Identification (S3CE-Net)
We invite you to explore the S3CE-Net framework and its SSAM and STFS mechanisms, which accurately process spiking events while taking causality into account. The model is lightweight, parallel, and capable of identifying complex relationships across time and space.
Larry Williams Market Secrets (Part 18): Automating the Greatest Swing Value Breakout Strategy in MQL5
This article converts the Greatest Swing Value breakout rules into a configurable MQL5 Expert Advisor. It implements measurable setup conditions, failure-swing calculations, one-bar setup validity, and M1 breakout confirmation. The EA also supports broker-compatible risk management, manual or percentage-risk sizing, position control, and trade-server validation, providing a reproducible framework for independent Strategy Tester research across different markets and historical periods.
Neural Networks in Trading: Generalizing Time Series Without Data-Specific Dependence (Core Model Modules)
We continue our acquaintance with the Mamba4Cast framework. Today, we will delve into the practical implementation of the proposed approaches. Mamba4Cast was designed not for lengthy warm-up on every new time series, but for immediate deployment. Thanks to the concept of Zero-Shot Forecasting, the model can produce high-quality forecasts on real-world data without additional training or hyperparameter tuning.
How to Obtain Synchronized Arrays for Use in Portfolio Trading Algorithms
The article describes a practical approach to synchronizing bars between instruments in a portfolio in MQL5. Classes are provided for loading, storing, and aligning OHLCV data, with options to use an empty bar or carry over values from the previous bar, select a synchronization symbol, and process new bars asynchronously. Examples of use in multi-chart and basket indicators are shown. Readers receive a ready-to-use API for reliable portfolio calculations.
Neural Networks in Trading: A Unified View of Space and Time (Conclusion)
The Extralonger framework demonstrates a unique ability to integrate spatial and temporal factors into a single model, ensuring high forecast accuracy. Its architecture allows it to adapt to different planning horizons and financial instruments while maintaining the system's transparency and manageability.
Neural Networks in Trading: From Transformers to Spiking Neurons (SpikingBrain)
The SpikingBrain framework demonstrates a unique approach to data processing: neurons respond only to significant events, effectively filtering out noise. Its event-driven architecture reduces computational costs while preserving key information about price movements. Adaptive thresholds and the ability to use pre-trained modules ensure the model's flexibility and scalability.
How to Create and Adapt an RL Agent with an LLM and Quantum Encoding for Algorithmic Trading in MQL5
The article proposes a hybrid approach to algorithmic trading based on quantum encoding of market states, Double DQN with a prioritized experience replay buffer, and an LLM acting as a contextual EA. The SEAL methodology enables asynchronous continued training of the agent without halting trading. A lightweight Q-learning filter (USE/SKIP/REDUCE) controls signal execution at the meta-level. Practical details are provided on integrating the system with the MetaTrader 5 trading platform, along with a scheme for adapting it to market regime shifts.
Implementing an LLM Agent with SQL Memory in MetaTrader 5
An LLM agent with SQL memory in MetaTrader 5 — an architecture that eliminates the main limitation of traditional LLM Expert Advisors (EAs): the loss of context between queries. Each agent decision is recorded in SQLite along with the market conditions at the time of the signal — RSI, MA alignment, volatility — and after the position is closed, the outcome is written to the database. When the next request comes in, the agent reviews its own history — similar past RSI conditions and the last three agent decisions — and only then analyzes the current market. The result is a system that remembers its errors between restarts and adapts its behavior based on accumulated statistics, rather than starting from scratch with each new bar.
Neural Networks in Trading: Adaptive Periodic Segmentation (Conclusion)
We invite you to dive into the exciting world of LightGTS — a lightweight yet powerful framework for time-series forecasting, where adaptive convolution and RoPE encoding are combined with innovative attention mechanisms. In our article, you will find a detailed description of all components — from creating patches to the complex mixture of experts in the decoder — ready for integration into MQL5 projects. Discover how LightGTS takes automated trading to a whole new level!
Neural Networks in Trading: The Adaptive Graph Diffusion Model (Attention Module)
In this article, we will take a detailed look at the practical implementation of the key components of the SAGDFN framework. We will show how sparse attention and the selection of significant neighbors are organized for time series forecasting. The approaches presented strike a balance between forecast accuracy and computational efficiency.
Swing Extremes and Pullbacks (Part 5): Filtering Weak Swings Using Candle Imbalance
This article implements an MQL5 Expert Advisor that scores each impulse leg behind a swing extreme with a 0–100 Candle Imbalance Score. The score combines four bounded components—body dominance, directional consistency, leg efficiency, and Fair Value Gap coverage—to filter weak swings before any pullback setup is armed. You will see enforced swing alternation, a state machine for first-clean-retest-only entries, and a three-mode trailing stop, enabling consistent setup selection and exit management.
Path Signatures for Lead-Lag Detection
Build a level-2 path-signature engine in pure MQL5 to read the lead-lag ordering between two data streams without choosing a lag and without a linear model. The article delivers a reusable library, an indicator that plots the Levy‑area oscillator, and a simple rule‑based Expert Advisor. Code is cross‑checked against closed‑form cases, and the components are ready to plug into your projects.
Neural Networks in Trading: A Cross-Domain Time Series Forecasting Framework (Conclusion)
The article focuses on the practical implementation of the TimeFound model for time series forecasting. The key stages of implementing the framework's main approaches using MQL5 are examined.
Building a Dynamic and Customizable Table in MQL5
This article presents a reusable CTable class for building chart-based tables in MQL5. It covers table architecture, creation and destruction of objects, coordinates and sizing, cell properties, horizontal/vertical headers, dynamic row/column edits, object naming, index conversion, and efficient refreshing. You will be able to assemble consistent, aligned on-chart dashboards for market data, indicators, and signals with minimal boilerplate.
Neural Networks in Trading: From Transformers to Spiking Neurons (Key Components)
We present to the reader an implementation of the SpikingBrain framework’s approaches based on recurrent linear attention with gates, discussed in detail in this article. Forward pass, gradient propagation, and weight update algorithms ensure efficient processing of financial time series and enable the practical implementation of the framework’s key concepts.
Neural Networks in Trading: Iterative Forecast Refinement Models (RAFT)
The RAFT framework offers a fundamentally different approach to forecasting market dynamics — not as a one-time snapshot, but as an iterative refinement of the state in real time. It takes both local and global changes into account, maintaining a high degree of accuracy even with complex price structures.
Neural Networks in Trading: Training Deep Spiking Models (SEW-ResNet)
We invite you to explore the SEW-ResNet framework, which allows you to build deep spiking models without degradation issues and with effective gradient management. In this article, we demonstrate how to implement a basic spiking neuron and its algorithms using MQL5.
The MQL5 Standard Library Explorer (Part 15): Building a Market-Regime Classifier with dataanalysis.mqh
This part focuses on practical data analysis in MQL5 with dataanalysis.mqh. We prepare a labeled dataset from bars, apply normalization, explore redundancy with PCA, and train a decision forest to classify future bar regimes. The article shows how to obtain out-of-bag estimates and permutation importance, helping you validate the model and understand which inputs matter most.
Neural Networks in Trading: The Adaptive Graph Diffusion Model (SAGDFN)
In this article, we explore the architecture of SAGDFN — a modern framework capable of transforming the approach to processing spatiotemporal data. It preserves key information even in complex graphs while reducing computational costs.
Neural Networks in Trading: A Unified View of Space and Time (Extralonger)
The Extralonger framework demonstrates an approach to integrating spatial and temporal factors into a single model, which makes it possible to account for both local patterns and long-term cycles simultaneously. This architecture makes time series forecasting more resilient to market noise and enables data analysis across different time horizons. The article takes a detailed look at how these ideas are put into practice using OpenCL and MQL5.
Neural Networks in Trading: Robust Trading Signals in Any Market Regime (Attention Modules)
In this article, we continue implementing the ST-Expert framework approaches, focusing on the practical aspects of applying them using MQL5. Earlier, we examined the theoretical foundations and key components of the model; now we move on to working directly with graph attention algorithms and local and global attention distribution. The main goal of this work is to demonstrate how ST-Expert's conceptual ideas are transformed into workable solutions for analyzing and forecasting financial time series.
Price Action Analysis Toolkit Development (Part 81): Adding Persistent Historical Bookmarks to an MQL5 Navigator
We introduce a persistent bookmark layer for the MetaTrader 5 History Navigator. Bookmarks capture a chart's symbol, timeframe, and historical position with a name and notes, write them to a CSV file, and reload them later without manual date entry. The implementation integrates bookmark management into the current navigation engine, enabling quick creation, selection, navigation, and deletion for efficient historical study.
Neural Networks in Trading: Adaptive Perception of Market Dynamics (Conclusion)
The article continues the work on implementing the STE-FlowNet framework, which combines multi-stream processing with recurrent structures for accurate analysis of complex data. The tests conducted confirmed its stability and flexibility in various scenarios. This architecture speeds up computations and enables deeper modeling of dependencies in time series. This approach opens up new possibilities for practical application in trading and analytics.
Building Your Personal Expert Advisor (Part 6): Risk Management V — Portfolio and Correlated Risk
This part implements PortfolioRisk.mqh, a shared library that shifts risk management to the account level. It scans positions and pending orders, computes margin and floating results, counts symbols, and decomposes pairs into currencies to detect concentration, then validates each new trade against portfolio limits. The Series EA example illustrates configuring scope (account-wide or magic-filtered), registering magics, and integrating the pre-trade gate.
Neural Networks in Trading: Training Deep Spiking Models (Conclusion)
The article demonstrates a practical implementation of the SEW ResNet framework using MQL5, with a focus on its applied use in trading. The Double Bottleneck makes it possible to simultaneously analyze univariate streams and inter-channel dependencies without losing gradients during training. Spiking activations with adaptive thresholds and gates improve robustness to noise and sensitivity to market novelty. The text provides details on the implementation and test results.
Neural Networks in Trading: Heterogeneity-Informed Meta-Parameter Learning (Conclusion)
The article describes a practical implementation of the HimNet framework based on MQL5, ready for integration into automated trading. We demonstrate how heterogeneity-adapted meta-parameters transform the model into a universal tool capable of handling fluctuating volatility.
Neural Networks in Trading: Adaptive Perception of Market Dynamics (STE-FlowNet)
The STE-FlowNet framework offers a new perspective on financial data analysis by responding to actual market events rather than fixed timeframes. Its architecture preserves local and temporal dependencies, making it possible to track even small impulses in price dynamics.
Where your backtest stops being real: Measuring the depth of your broker's tick history
In the "Every tick based on real ticks" mode, the Strategy Tester's journal reports the minutes for which real ticks were absent or discarded and says that every tick generation was used for them. We show where the journal records this, measure how far back usable tick history goes on two demo servers, and check the ticks from inside an Expert Advisor, so a test range can be chosen by its real-tick share.
Building Your Personal Expert Advisor (Part 7): Risk Management VI (Final)—Consolidating the Risk Framework
The article consolidates trade, basket, and account risk into a modular framework for MQL5 EAs. Risk logic moves into reusable classes and shared types, leaving the EA with its inputs, signal, configuration, and call sequence. The result is a shorter strategy file and a second EA that reuses the basket tier and the shared conversions.
Building a Prop Firm Compliance Monitor in MQL5 (Part 2): Daily Sessions and Restart-Safe Starting Balances
Establishes persistent daily-session management for Prop Firm Guard in MetaTrader 5. The EA derives the session identity from the trading-server date, account login, and server, persists state in SQLite, and adds a confirmation step for the daily starting balance. A timer detects trading-day changes and restores the confirmed baseline after EA removal or terminal restarts, ensuring a stable reference for daily risk checks.
How to Test and Customize Built-in MQL5 Programs: Envelopes with Alligator Strategy
Envelopes define percentage boundaries around a moving average, but a boundary touch alone does not identify whether the market is trending or ranging. We build Envelope_Alligator_Strategy.mq5, a custom indicator that reads the built-in Envelopes and Alligator buffers. Ordered Alligator lines classify directional states; interwoven lines identify a neutral state. This context changes how envelope crossings are interpreted and produces testable, non-trading chart signals.
RSI Divergence Grid Expert Advisor with Risk Manager in MQL5
A simple grid trading strategy becomes a resilient system thanks to three components: an entry filter based on RSI divergence, a strict limit on the number of grid levels, and an independent risk manager that monitors equity on every tick and blocks trading when the daily limit or maximum drawdown is reached. The article examines the mathematics behind the classic martingale trap, the implementation of pivot point and RSI divergence detection in MQL5, the complete code for the CRiskManager class with protection against floating losses, and the architecture of the grid itself, with TP based on the weighted average entry price.
Building A Candlestick Trend Constraint Model (Part 12) Measuring Lower-Timeframe Trigger Quality
This article extends the A‑B‑C‑D constraint model with a measurement layer. We capture confirmation timing, breakout beyond Candle C, and post‑signal MFE/MAE; aggregate results by A‑B relationship; and write one CSV row per completed D window while showing concise CCanvas comments. The separation of detection, replay, visualization, measurement, and optional execution provides clean, analyzable data for further research.
Neural Networks in Trading: Heterogeneity-Informed Meta-Parameter Learning (Key Components)
In this article, we take a detailed look at the algorithms used to implement the key components of the HimNet framework. We demonstrate how, with a minimal number of trainable components, a high degree of consistency and controllability can be achieved throughout the entire system. The presented implementation is compact and transparent, which makes it easier to adapt to real-world market tasks.
Integrating News Sentiment to Avoid Strategy Disruption in MQL5
We present a dynamic news‑sentiment filter for MQL5 Expert Advisors. It uses WebRequest to query a REST API, parses JSON sentiment and confidence, and gates entries and exits alongside Moving Average and RSI signals. You get an example EA, integration steps, timer‑based updates, and Strategy Tester workarounds. The outcome is fewer trades against dominant news flow and earlier risk reduction when sentiment flips.
Controller Objects for Everything: Draggable Slider Control
The article details a complete MQL5 implementation of a draggable slider for controlling ranges on the chart. It introduces the CDragHandle class, private state, public APIs for dimensions, colors, range, and value, plus Refresh* and UpdateHandlePosition logic and event processing. A working example changes CHART_SCALE, demonstrating how to connect the control to platform properties.
Neural Networks in Trading: Iterative Forecast Refinement Models (Key Components)
In this article, we explore the inner workings of the RAFT framework — one of the most precise and elegant approaches to analyzing dynamic processes. Step by step, we adapt its idea of iterative refinement to financial time series, laying a solid foundation for a future model. Readers can look forward to an engaging exploration of the architecture, where every component has its own purpose and function.
Interactive Supply and Demand Zone Manager in MQL5 (Part V): Persisting Zones Across Terminal and EA Restarts
We introduce a versioned persistence layer that saves and restores the zone engine's logical state across sessions. The snapshot preserves lifecycle status, interaction history, scores, blacklist prices, and the geometry and time anchors of user‑drawn rectangles, and it is reconstructed and reconciled with chart objects on startup. User zones enter with the minimum score so they integrate into the same lifecycle and trading pipeline.
Neural Networks in Trading: Iterative Forecast Refinement Models (Conclusion)
Introducing the RAFT framework — a powerful tool for analyzing and forecasting financial time series. Its flexible and optimized architecture ensures accurate forecasts, stable performance, and faster data processing. RAFT reduces the risk of errors and makes it easier to create effective trading strategies.