Articles on machine learning in trading

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.

This is, without a doubt, the article I've enjoyed writing the most so far. In it, I showed that we don't really need anything special to achieve a certain goal. We can achieve this in various ways, provided we have the necessary knowledge and are willing to learn and dedicate ourselves to the task. I would like to express my heartfelt thanks to everyone who helped me choose these functions. The material presented here is intended solely for educational purposes. Under no circumstances should this be viewed as intended for anything other than teaching and studying the concepts presented.

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.

A candidate for our ranking table is Beluga Whale Optimization, a metaheuristic based on three behavioral patterns of the beluga whale: pair swimming, hunting using Lévy flight, and population renewal through whale fall. During implementation, we discovered that the algorithm does not so much optimize as read the geometry of the test bench; we examine how this works and develop an honest, promising modification called BWOm.

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.

We introduce the permutation process for backtest validation: permute bars, rerun the complete development process, and compare the observed Sharpe to the null distribution it produces. Outputs include one-sided p-values, robust z, and quantile confidence intervals, plus a spurious performance curve that quantifies selection versus trial budget. A cache-isolation check prevents false passes. The EURUSD H1 case shows how to set a tolerance and assess whether performance is distinguishable from selection.

The article explores the practical application of the L1 Trend Filter for noise reduction and the construction of structural features suitable for live trading. The full workflow is presented: H1 data for 29 instruments from MetaTrader 5, causal filtering, CatBoost with a horizon of three L1 bars, rigorous walk-forward testing, and VaR-based allocation of lots. The reader is provided with a reproducible code pipeline and a portfolio evaluation methodology.

The article shows why a fixed posterior threshold, checked trade by trade, lacks error control and retires healthy strategies far too often on a real EURUSD case. It introduces WinRateSPRT (with Page's CUSUM for continuous use) and a permutation-tested profit factor trend to cover distinct failure modes. LiveStrategyMonitor integrates both and reports a normalized retirement-progress measure for operational 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.

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.

Implements the Lo–MacKinlay Variance Ratio Test in MQL5 to assess serial dependence via random‑walk variance scaling. Provides a reusable CVarianceRatio class with classical (homoskedasticity) and heteroskedasticity‑robust statistics, plus a script that scans multiple horizons and logs VR and Z‑values. Shows how to embed the test in an Expert Advisor as a statistical gate for mean‑reversion logic.

The article implements the Andean Condor Algorithm (ACA) for MQL5 — a compact optimizer with a multiscale intensification operator. A significant improvement in solution quality was observed with a small population: a single adjustment to the settings propels it into the top 45 — and behind this lies a distinctive feature of the algorithm worth knowing about. This resource provides ready-to-use code and practical guidelines for its application.

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.

This article uses practical examples to explain how to calculate and apply gradients of the weights and bias (intercept) in a linear neuron implemented in MQL5, and also discusses a stochastic variant of gradient descent. We analyze stopping criteria, iteration limits, and the effects of training on data subsets. The results are displayed in the MetaTrader 5 terminal, where a simple chart is also plotted. Readers are invited to modify the training set and analyze the model's behavior.

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.

The article focuses on label-auditing for meta-labeling, not on a new entry signal. It presents a native MQL5 implementation of confident learning with out-of-fold scoring and calibrated pruning, plus an EA switch that trains the meta-filter on raw or cleaned datasets. A controlled Strategy Tester comparison helps you quantify noise and choose a pruning level that preserves generalization.

The article continues the series on AI Hedge Fund and addresses three limitations of v4: analysts' reputation scores are now stored persistently in SQLite, the EA has been removed from the critical execution path, and signals from the council of fifteen are distributed to multiple brokers via the TradeMux REST API. The logic behind the council and risk management has remained the same: Python retrieves data via the MetaTrader 5 SDK and executes orders directly. The result is resilience to restarts and scalability across multiple terminals.

Build and deploy a Neural Additive Model natively in MQL5: one small network per feature, additive output, and no post-hoc explainability. The article provides a PyTorch trainer, two‑output ONNX export (signal and contributions), an include wrapper using OnnxCreateFromBuffer/OnnxRun, and an EA that prints real per‑feature contributions each bar. It covers identical feature math for training/export, threshold measurement, Strategy Tester results, and edge cases such as NaNs and out‑of‑distribution inputs.

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.

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.