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.
Add a new article
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Fundamentals of Bayesian Inference in Discrete and Continuous Settings: From Theory to Practical Model Implementation
The article discusses the fundamentals of Bayesian statistics in both discrete and continuous settings. We will progress from classical Bayes’ theorem and simple examples involving coin flips to conjugate distributions and dynamic Bayesian updating, which enables real-time analysis of market quotes. Using the beta-binomial model as an example, a simple change-point detection indicator has been implemented to help identify shifts in market regimes.
Behavioural Cloning in a Live Trading System: What Live Experience Can and Cannot Teach
This article measures a live-to-train behavioral-cloning loop for an RL trading system across 83 models. Outcome-weighted imitation shifted the policy toward Hold (48% → 82%), with 12 final models not trading at all. Live records were instead used to fit spread and slippage to broker fills, revealing execution costs ~85× higher than in simulation. We describe the loop, the failure mechanism, the calibration, and a protocol to apply.
Beyond Squared Returns: Path-Dependent Volatility
This article implements a path-dependent volatility model in MQL5 that keeps the sign of returns. The engine uses two-timescale EWMA kernels (four accumulators) to form activity and trend features, with closed-form coefficients and a lightweight kernel search. We provide a calibration workflow and an indicator that plots the decomposition, then test out of sample against EWMA and GARCH on multiple symbols. Results show clear gains on equity indicators and minimal gains on FX.
Bull Optimization Algorithm (BOA)
We present an evolutionary algorithm without a selection operator: the best individual becomes the sole mating partner for the entire population, and classical mutation is replaced by multiplicative mutation with a self-adjusting step size. In this article, we examine three key ideas, implement the algorithm in MQL5 using the C_AO framework, and test it on the standard benchmark suite and in an anti-cheat test — where BOA comes very close to the cutoff for the top 45 but does not make the ranking.
Neural Networks in Trading: Iterative Forecast Refinement Models (Conclusion)
Introducing the RAFT framework — a powerful tool for analyzing and forecasting financial time series. Its flexible and optimized architecture ensures accurate forecasts, stable performance, and faster data processing. RAFT reduces the risk of errors and makes it easier to create effective trading strategies.
How to Train an MLP Using Markov Chain Features in MQL5
The article describes the two-level MarkovMLPOscillator indicator: a three-state Markov chain uses historical data to construct a transition matrix and generate 15 probabilistic features for each bar, and an MLP is trained on these features to predict price direction at a specified forecast horizon. The article covers feature generation, the validation procedure using a holdout set, and parameter settings. The result is an interpretable oscillator with a color histogram, a smoothed signal, and a display of the current transition matrix.
Optimal Trade Exit in MQL5: Longstaff-Schwartz Least-Squares Monte Carlo for Continuation-Value Estimation
This article models position exits as an American option and implements the Longstaff–Schwartz least‑squares Monte Carlo in native MQL5. The engine calibrates drift and volatility from recent returns, simulates forward price paths, and uses backward induction to compare immediate exercise to continuation. It integrates into an EA with hard time and price backstops and a live indicator, and is tested on four symbol/timeframe combinations with mixed, reproducible Strategy Tester results.
Neural Networks in Practice: An Example of Implementing an XOR Gate
In this article, I will try to show you, dear reader, that things are not always what they seem. We often tend to think that everything is arranged in a certain way, even though we may end up believing something that isn't necessarily true. Neural networks are undoubtedly one of the most interesting topics, whether viewed from the perspectives of mathematics, electronics, or software. Contrary to what many people believe or claim, neural networks are neither a problem nor a definitive solution. This is just one area of research where we should continue to delve deeper and explore what goes on behind the scenes.
Color Harmony Algorithm (CHA)
We take a closer look at the Color Harmony Algorithm (CHA) — a meta-heuristic optimization method based on Munsell's theory of color harmony. We explore the structure of the hue circle, harmony templates, the alternation between the concentration and dispersion phases, and the role of solution memory. From a theoretical framework to a working implementation in MQL5 and unbiased testing on a standard benchmark.
Neural Networks in Trading: Temporal Motion Aggregation (TMA)
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.
Neural Networks in Practice: Gradient Descent
In this article, I will try to explain as simply and clearly as possible one of the most controversial issues that arises when discussing neural networks: how to find the best possible point, or the minimum of the loss function. I'll show you the difference between linear regression and gradient descent. Both methods are fairly simple and serve as examples of how the path that seems obvious isn't always the best one.
A Grid Expert Advisor Based on a Cellular Automaton with Online Learning in MQL5 (Part I): Continuous Adaptation of the Trading Model on Each Bar
The article examines the architecture of an Expert Advisor based on a cellular automaton with 10,000 adaptive parameters and an independent binary predictor with a 10-bar prediction horizon. The article demonstrates a three-level online learning process, the evolution of strategies, and validation using a ring buffer and a confusion matrix. The input parameters are reduced to Magic Number, while trading settings are calculated from ATR and five genomes. The EURUSD H1 test yielded an approximate hit rate of 58%, compared to roughly 51% for the fixed MLP.
Neural Networks in Trading: Temporal Motion Aggregation (Key Components)
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.
Neural Additive Models for Fully Interpretable Signal Attribution in MQL5
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.
How to Replace a WebSocket EA with TradeMux REST in MetaTrader 5
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.