Articles with examples of trading robots developed in MQL5

The article explains the internal mechanics of the STSSM block and demonstrates how modern SSM approaches can be adapted to the event-driven logic of spiking models while maintaining high speed and expressive representations. Step by step, we work our way through the architecture, transforming the rigorous theory behind the authors' approach into a practical financial instrument for analyzing financial time series.

The SDformerFlow framework transforms complex events in financial time series into structured representations, enabling the model to identify local fluctuations and global trends simultaneously. The multi-level U-structure ensures consistency between the forward pass and the backward pass, synchronization of gradients, and computational stability. Ultimately, SDformerFlow proves to be a powerful and flexible tool for building modern trading systems.

The classic SuperTrend loses accuracy when the market regime changes because it uses fixed ATR parameters and a fixed multiplier. The article examines the architecture of ML SuperTrend Pro v2.00 written entirely in MQL5: a background test matrix with adaptive parameter updates, a regime grid as a context detector, an accuracy layer consisting of five filters, and Parabolic-style rendering with well-designed buffers. The article presents the principles of L1 regularization, the results of a comparison with the classic SuperTrend, and practical recommendations for deployment and integration via iCustom.

This article turns the Part 1 evidence into a working MQL5 Expert Advisor. It formalizes entry conditions, designs direction-aware stop loss and take profit, and stress-tests the combined logic before coding, with every rule linked to measured features. Readers receive a reproducible implementation with configurable inputs and per-bar execution consistent with the study.

We invite you to explore the E-STMFlow framework, which effectively processes event streams by extracting informative embeddings, filtering out noise, and identifying key movements. Its architecture makes it possible to identify complex relationships between features and provides scalability, accuracy, and high computational efficiency for intelligent analysis and forecasting.

An EA that passes CodeBase validation can still fail under execution delay and a freeze level. Using a tick scalper, the article reproduces four such failures in the Strategy Tester and shows each fix with its limits. An ablation under a fixed delay measures each change on its own. The burst-based stop distance removes most rejected modifications, while the weekend case behind the session buffer did not recur in those runs.

The article presents an adaptation of the SDformerFlow framework that achieves high adaptability by integrating spiking attention with multi-window convolution and weighted summation of query elements. The architecture allows each attention head to learn its own parameters, which improves the model's accuracy and sensitivity to the structure of the data being analyzed.

The article presents an adaptation of the SDformerFlow spiking architecture for the task of dense analysis of price micro-movements. The spatio-temporal structure provides a high level of detail, while the spiking logic ensures computational efficiency and the ability to work with sparse, pulse-like data. As a result, traders gain access to a tool that detects even the slightest shifts in liquidity and provides the foundation for more accurate and consistent real-time decisions.

The implementation of the EV-MGRFlowNet framework demonstrates its key advantages: modularity, resilience to market fluctuations, and the ability to develop strategies independently. These features make the framework a powerful tool for analysis, forecasting, and the development of autonomous trading strategies.

Oscillator thresholds are often treated as direct reversal instructions, causing traders to sell strong bullish markets or buy persistent bearish declines too early. We develop an analytical Expert Advisor that interprets Relative Strength Index events through confirmed swing structure. The model distinguishes continuation, reversal, pending, and expired conditions by combining closed-bar RSI crossings with higher highs, higher lows, lower highs, lower lows, and breaks of structure. It displays each validated interpretation without placing trades.

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.

The article takes readers deep into the heart of the EV-MGRFlowNet framework, showing how its architecture is applied in a practical implementation for financial forecasting tasks. Step by step, we build a carefully designed combination of modules capable of detecting subtle temporal patterns and translating them into meaningful market signals.

The article discusses the adaptation of the EV-MGRFlowNet architecture — originally developed for processing event-based video data — to the field of financial time series. The approach presented here offers a new perspective on the market as a stream of micro-movements, where price, volume, and liquidity form a dynamic structure that lends itself to recurrent analysis without explicit supervision.

Most EAs trade more than one setup, yet the Strategy Tester judges them all by a single equity curve. CSetupLedger is a small MQL5 class that stores each trade's setup in its magic number and rebuilds the per-setup record from the account's deal history, so a restart never wipes it. It compares a Wilson interval of each setup's win rate with the breakeven rate implied by that setup's own average win and loss, and gives no verdict before 30 closed trades. Each setup ends up trusted, rejected or still undecided. In two backtests the per-setup totals match the tester exactly. Full source and two working EAs are attached.

Introducing the TMA framework — an intelligent system capable of forecasting market dynamics with sufficient accuracy. In this article, we have integrated all the components into a single architecture and transformed it into a fully-fledged trading agent that analyzes the market and makes decisions in real time.

In this article, theory meets practice. We implement the key modules of the TMA framework — MPE and MPA. Here, data takes on meaning, and cross-attention becomes a tool for precise analysis of market dynamics. Minimal unnecessary operations, maximum efficiency — a step toward next-generation intelligent trading.

This part extends the news filter with an ordered tier scale that adds a special tier above the broker's High, per‑event time windows, and one classifier shared by live calendar data and historical CSV. A rule table (with optional CSV overrides) promotes or demotes specific releases and can set custom windows. A diagnostic script prints your broker's calendar, event codes, and server time so your rules match what the terminal actually reports.

The TMA framework offers a new perspective on market dynamics, enabling models to capture not only the state of the market but also the passage of time itself. Its ability to identify patterns in a continuous stream of data makes the analysis more in-depth and accurate than with traditional approaches. Recurrent adaptation turns this method into a practical tool for working with real-world quotes.

The article develops a volume-change strategy that transforms tick or real volume with Ln(volume+1), differences it bar to bar, and applies arctan to produce a bounded, scale‑independent signal. A threshold drives entries, with selectable gates for positive, negative, or bi‑directional signals and candle‑based confirmation. An MQL5 Expert Advisor implements the logic and supports fixed TP/SL, N‑candle exits, or both, enabling straightforward testing and deployment.

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.

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.

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.

This part turns stored account references into a financial monitoring engine for MetaTrader 5. It calculates daily drawdown from the confirmed session balance, overall drawdown from the initial challenge balance with a static equity floor, and profit‑target progress from realized balance. The EA derives usage, remaining allowance, and status for each rule, then aggregates them into a single guard state where risk conditions override target completion.

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.

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.

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.

The article introduces a centralized cache that serves indicators to multiple strategy nodes. It encodes full indicator identity in keys, stores Standard Library objects in CArrayObj entries, reuses matching instances, verifies readiness, refreshes data, and deletes each indicator on final release. Readers gain a predictable resource lifecycle and reduced duplication in multi‑symbol Expert Advisors.

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.

This article presents an exception-like model for MQL5 that prevents VM faults through defensive guards and cooperative TRY/CATCH/FINALLY macros. It introduces structured error objects (CException), result propagation (CResult), manual stack tracing, and integrated logging for clear diagnostics. Readers learn how to validate risky operations, propagate failures predictably, and keep EAs stable when facing zero‑divide, null access, invalid handles, and array‑bounds conditions.

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.