Articles on machine learning in trading
Creating AI-based trading robots: native integration with Python, matrices and vectors, math and statistics libraries and much more.
Find out how to use machine learning in trading. Neurons, perceptrons, convolutional and recurrent networks, predictive models — start with the basics and work your way up to developing your own AI. You will learn how to train and apply neural networks for algorithmic trading in financial markets.
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Weak Supervision for Traders: Combining Several Noisy Labeling Heuristics into One Probabilistic Label (Snorkel-Style)
The article builds an MQL5‑native weak‑labeling engine that infers per‑indicator trust from vote agreement, then fuses five noisy signals into P(Buy) using Expectation‑Maximization. We describe the EA architecture, rolling‑window refits, abstain handling, diagnostics, and a majority‑vote baseline used for comparison. Readers can reuse the EM library, swap in their own labeling functions, and obtain a transparent, probability‑based entry filter.
Local Outlier Factor: Density-Based Anomaly Detection in Native MQL5
This article builds a native MQL5 LOF engine without DLLs, ALGLIB or ONNX. Bars are mapped to five standardized features; k‑NN reachability and density produce a scale‑free LOF score, and the alarm level is a moving quantile of the reference cloud. The score gates only new entries in an EMA‑cross EA, with an indicator for charting and a script for independent checks on XAUUSD, EURUSD and GBPUSD.
Neural Networks in Trading: A Spiking Architecture for Spatio-Temporal Market Analysis (SDformerFlow)
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.
Neural Networks in Practice: ReLU Function Graph
We will create an indicator written entirely in MQL5 to display the activation function and its derivative on the chart, using ReLU as an example. We will explain how derivatives affect the choice of activation function and what precautions are needed at nondifferentiable points. Readers will be able to interactively visualize the curves and gain a practical foundation for deciding when to use certain activation functions and when not to.
Integrating MQL5 with Data Processing Packages (Part 11): Multi-Timeframe Causality Detection using Granger Analysis
This article implements a multi‑timeframe Granger causality monitor for MetaTrader 5. A Jupyter notebook tests predefined timeframe pairs, selects lags, applies multiple‑testing control, and exports a calibration table. An MQL5 Expert Advisor reloads the setup, recomputes the F‑test live on rolling windows, and marks pairs VALID/INVALID to gate its vote‑based BUY/SELL generator. The workflow shows alignment rules, live/offline parity, and a filtered vs unfiltered comparison on EURUSD.
Beluga Whale Optimization (BWO) Algorithm
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.
TradeMux as a Quant Backbone: Connecting Institutional Python Pipelines to Different Terminals and Brokers
The article describes TradeMux as a bridge between a Python pipeline and the MetaTrader 5 terminal for the seamless transfer of trading decisions without duplicating logic. The article examines a four-layer production architecture and a complete Python execution service: connection setup, reading account and position data, signal generation (including CatBoost), pre-trade risk control, kill_switch, and a supervisor. Practical benefits include cross-broker normalization (RoboForex, IC Markets, Alpari, OANDA) and scaling from a single account to multi-account broadcasting without changing the trading logic.