Articles on the MQL5 programming and use of trading robots

The article implements two swing setups in MQL5: Anti, which enters on the first 3/10 oscillator pullback after a signal line zero confirmation with price confirmation, and Holy Grail, which enters on the first pullback to the 20 EMA in a strong rising ADX trend. A single EA runs two independent state machines with precise entry and exit rules, ATR-based stops, and state recovery. You get runnable code and traceable logging.

Trade authorization controls what a cooperating Expert Advisor is permitted to send, but not every account action passes through that code. A trade may originate from the desktop terminal, a mobile device, the web terminal, or another Expert Advisor. This installment develops an independent auditor that records live transaction stages through OnTradeTransaction(), reconciles available historical orders and deals within a configured recovery window, and writes an account-bound CSV journal. An on-chart panel summarizes the selected period and provides refresh, detail, and export controls without modifying trades.

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.

The article presents the comprehensive architecture of the STE-FlowNet encoder, which combines stack memory, recurrent processing, and a correlation mechanism to extract hidden market dependencies. The article demonstrates how these modules are sequentially integrated into a single computational chain capable of performing comprehensive time-series analysis.

We analyze why ratings may be inflated due to the alignment of algorithm trajectories with benchmark diagonals, and we supplement the testing methodology with a requirement to keep the global extremum away from the diagonals. We update Forest and Megacity, and perform RAW verification and calibration using VerifyExtremes.mq5. A drop in HHO and DOAm performance serves as a practical indicator of false leaders.

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.

This update adds manual position adoption and multi-symbol lifecycle management. The manager detects and tracks positions regardless of the chart, applies initial protection, transitions to break-even under fixed or ATR triggers, and validates broker constraints per symbol. Using the MACD sample for context, it demonstrates reusable management across instruments and manually opened trades.

The article examines the architecture of a committee consisting of 15 AI agents: ten analysts and four risk officers vote in three parallel phases, and the Chair records the final result. Isolated pair contexts with separate reputations are used for eight currency pairs. The dynamic vote threshold depends on the daily PnL targets. The Expert Advisor uses only the signal, SL and TP, which allows for evaluation pf the quality of its decisions without any additional mechanics.

In our interpretation, S3CE-Net skillfully translates the market into the language of events and captures early impulses that traditional indicators simply average out. STFS ensures robust training — the model sees the data from different perspectives and does not overfit to local anomalies. SSAM blocks and the OpenCL implementation deliver practical speed and accuracy, while the separation between training and deployment modes saves resources in production.

A compact MetaTrader 5 library builds a synthetic market from agents: a minority game, a zero‑intelligence double auction, a stylized‑facts analyzer, and a parameter scanner. The result is a testbed with known answers, providing the control real price backtests lack. Each result is cross‑checked by the model's phase transition, a matching Python implementation, and detection of a planted edge.

In this article, we took a detailed look at the integration of the SSAM module into the SEW-ResNeXt block, demonstrating how the S3CE-Net framework enables the effective combination of spiking attention with residual blocks. This architecture ensures accurate processing of temporal and spatial data streams and high training stability. The modularity and flexibility of the components make it easier to extend the model and reuse proven methods.

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.

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.

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.

The article presents a practical implementation of the key components of the SEW-ResNet framework using MQL5. The use of dynamic arrays and spiking mechanisms enables flexible model architecture design and efficient processing of financial time series. The proposed solutions demonstrate how SEW-ResNet can optimize computations and improve the extraction of meaningful features.

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.

We demonstrate live forecast charting with Kronos in MQL5 and an evaluation pipeline designed to be look‑ahead‑safe. Deterministic dumps are scored per horizon against a random‑walk baseline (15,113 predictions), then fed to a simple Strategy Tester rule on EURUSD H1. The findings show no edge, and illustrate how a high threshold can yield a persuasive but low‑information backtest with very few trades.

Neural networks are already changing the way we analyze markets, and new architectures are opening up even more possibilities. In this article, we wrap up our work with the SpikingBrain framework, which opens up new possibilities for us.

This work delivers an MQL5 Expert Advisor that reads open-position symbols, builds return series, and computes a rolling Pearson correlation matrix, displayed as a CCanvas heatmap. Off-diagonal pairs that meet a warning threshold are bordered for quick scanning. The code details return processing, correlation arithmetic, and matrix indexing, with a verification script, helping you track shifting co-movements across your active book.

Learn how to bring advanced plotting and data visualization to the MetaTrader 5 terminal by leveraging Matplotlib, one of Python's most powerful and widely used visualization libraries.

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.

We give our AI chat assistant two new abilities in MQL5: sight and tool access. It can now capture the chart as a screenshot, attach it to a message, and view it in the panel, and it can call tools that read live positions, trade history, indicator values, chart objects, and the economic calendar. The assistant reasons from what it sees and queries rather than text alone.

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.

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.

We use the MetaTrader 5 AI Assistant to execute the full workflow end to end: create an EA from a natural‑language prompt, compile it, break and watch it self‑correct from compiler output, backtest it, and compare a controlled re‑run. The article details the underlying MCP extensions, configuration and safety limits, and how to connect external AI clients. Readers get a repeatable process for building and testing EAs inside the platform.

This article describes an MQL5 Expert Advisor that models the market as a random graph rather than a fixed structure. Bars are encoded into discrete states; bar-to-bar moves form a decaying, Laplace-smoothed transition matrix, and a k-step random walk yields a bounded directional signal gated by an Erdos–Renyi null-model test. A separate trade-outcome graph over R-milestones learns survival probabilities to manage exits, turning position management into evidence-based rules.

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.

The article develops a multi-symbol basket EA that standardizes prices, derives PCA weights with native MQL5 matrices, forms a synthetic spread, and trades z-score deviations from a rolling mean. It validates symbols and synchronized history, stabilizes component orientation, maps signed weights to leg directions, and applies broker-aware volumes, stops, and netting rules. Basket entries run with rollback protection and chart status, with a reproducible testing procedure.

This article applies the Kelly criterion to position sizing in native MQL5. It presents a reusable CKelly class that estimates win rate and payoff from closed deals, derives the Kelly fraction, and sizes lots from a stop distance. A Monte Carlo sweep of the Kelly multiplier shows growth peaking at full Kelly while drawdown and ruin increase, motivating fractional Kelly such as half Kelly that preserves most growth with materially lower drawdown.

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.

Volatility arrives in clusters: one large move makes the next large move more likely, and quiet spells stay quiet. This article builds a Hawkes self-exciting point process in pure MQL5 to measure that effect directly, ending in a single number, the branching ratio, that says how reflexive a market currently is. You get a small, tested library, an indicator that plots the fitted intensity live, and a demonstration Expert Advisor, along with the honest limits of all three.