Articles on trading system automation in MQL5

For our next Exploration on notions that are testable with the MQL5 Wizard we examine if Skip Lists and the Hopfield Network can give us a profit-guarding trailing strategy. Trailing Stop Management, as already argued, can be overlooked in most trading systems at the expense of Entry Signals or even Money Management. Trailing stops can make all the difference in certain situations such as trending markets, and thus we test this out with GBPJPY.

Net profit and win rate do not tell you if a strategy's edge is statistically real. This MQL5 toolkit analyzes return series built from price data or deal history and reports t‑statistics, p‑values, and confidence intervals using one-sample and Welch t‑tests, the Mann–Whitney U test, and volatility‑regime analysis to support evidence‑based trading decisions.

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.

This article builds a constant-memory EW covariance engine and a chart heatmap for monitoring cross-symbol correlations in MQL5. CEWCovariance updates in O(N²) time per bar and exposes covariance/correlation accessors; CHeatmapRenderer shows a five‑symbol matrix with values and colors. You will learn λ-to‑window mapping, how to set a meaningful min_obs warm‑up, and how to size the variance guard epsilon for real FX M1 data.

This article builds the Spectrum Fitter: from tau(q) we compute f(alpha) with a discrete Legendre transform, then fit Normal, Binomial, Poisson, and Gamma spectra under box constraints using BLEIC. The best model by SSE is selected, and its parameters (eg, alpha min, alpha max or alpha_0, gamma) become the cascade inputs for multifractal simulation.

Financing drives multi‑day index‑CFD results: in one full‑history test, swap consumed 44% of gross profit and all profit on one symbol. We convert swaps to annualized rates, contrast four brokers and two financing models with a read‑only script, and quantify a Strategy Tester issue where a single current swap is used for all history, inflating implied rates by up to seven times. The piece provides a repeatable cost‑audit method.

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.

The article shows how to build an MQL5 EA that writes every deal to an SQLite database the moment it appears, using the built-in Database API as the SQLite bridge. It implements an event data model, a prepared INSERT workflow reused across calls, session-safe recovery after restarts, and deal detection via OnTrade(). You can open the resulting file with any SQLite client to run queries for analysis and reporting.

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.