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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Machine Learning Under Constraint (Part 3): De-Risking Near the Profit Target and the News-Window Rule
Position sizing that ignores proximity to a profit target or the timing of high-impact news exposes banked progress to reversal risk and misstates simulated profit near funded-stage news releases. We implement derisking_factor, a piecewise-linear size reduction keyed to phase progress with a floor set by minimum-trading-days status, and news_window_factor, a phase-gated step function applying FundedNext's news reward-share haircut in a configurable window around correlated events. Both return bounded multipliers combined multiplicatively with the sizer's other modifiers.
Foundation Models for Trading (Part III): Running a Generative AI Forecaster Inside MetaTrader 5
We demonstrate live forecast charting with Kronos in MQL5 and an evaluation pipeline designed to be look‑ahead‑safe. Deterministic dumps are scored per horizon against a random‑walk baseline (15,113 predictions), then fed to a simple Strategy Tester rule on EURUSD H1. The findings show no edge, and illustrate how a high threshold can yield a persuasive but low‑information backtest with very few trades.
Neural Networks in Trading: Training Deep Spiking Models (SEW-ResNet)
We invite you to explore the SEW-ResNet framework, which allows you to build deep spiking models without degradation issues and with effective gradient management. In this article, we demonstrate how to implement a basic spiking neuron and its algorithms using MQL5.
Ollivier-Ricci Curvature: Reading the Shape of a Correlation Network, Not Its Level
The article builds an Ollivier–Ricci curvature library in MQL5 for multi‑symbol analysis. A basket becomes a correlation network, and the reading captures network geometry rather than correlation level. It includes an exact optimal transport solver with an internal certificate, a rolling fragility metric with two informative nulls, an indicator that plots the reading against its null band, and a demonstration expert that uses it as a risk gate.
Harris Hawks Optimization (HHO)
We implement the Harris Hawks Optimization algorithm in MQL5 and analyze five movement modes of agents controlled by a single parameter — the decaying escape energy E. The C_AO_HHO class is introduced; it is compatible with the unified test bench and features a reproducible implementation of Lévy flight. The algorithm was tested on the Hilly, Forest, and Megacity functions with 5, 25, and 500 coordinates — the results indicate anomalous behavior.