Articles with examples of trading robots developed in MQL5

The article provides a critical analysis of an LLM strategy in which forecasting the direction is separated from trading decisions, and demonstrates why this leads to a disconnect between metrics and PnL. We will describe procedures for dataset balancing, feature engineering, prompt and response preparation, fine-tuning configuration in Ollama, and reliable parsing. Backtesting and forward testing reveal systematic degradation. The practical conclusion is that the problem must be formulated as a direct optimization of trading outcomes.

This article explains how to use the Z-Score to quantify price deviations in standard deviations and apply the concept in MQL5. We cover the calculation, interpretation across market regimes, and the implementation of a custom indicator and three trading strategies as Expert Advisors. Readers get complete code examples and a testing workflow, plus practical notes on lookback selection, fat tails, execution timing, and risk controls.

We are pleased to present the final stage of the TQNet framework’s development and testing, where theory meets real-world trading practice. We will move from historical training to a stress test using recent market data, evaluating the model's robustness and accuracy. The final results are not just dry statistics, but also a clear demonstration of the practical value of the proposed approach.

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.

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.

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.

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.

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.

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.

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.