Articles on machine learning in trading

Batch normalization is the pre-processing of data before it is fed into a machine learning algorithm, like a neural network. This is always done while being mindful of the type of Activation to be used by the algorithm. We therefore explore the different approaches that one can take in reaping the benefits of this, with the help of a wizard assembled Expert Advisor.

In this article, we shift from Python research to native MQL5 engineering. We build the first module of the MMAR library: a shared constants header, an SVD-based OLS regression class, a Generalized Hurst Exponent estimator, and the partition analysis engine that computes the partition function, extracts tau(q), estimates H via zero-crossing interpolation, and scores multifractality through three diagnostic tests. Tested on 500,000 bars of EURUSD M10, the engine correctly classifies the data as multifractal in under four seconds. Part 4 of an eight-part series. Part 5 fits the tau(q) curve to four candidate distributions via the Legendre transform.

We port AFML Chapter 17 structural break tests to MQL5 as a single include, CStructuralBreaks, delivering six bar-indexed features for EAs: CSW statistic and critical value, Chow-Type DFC, SADF with a rolling lookback (default 252), SM-Exp, and SM-Power. SADF uses O(L²) rolling windows for real-time viability. A companion StructuralBreaksViewer indicator plots all series with per‑series visibility and optional z‑score normalization. SB_EMPTY marks invalid values for safe integration.

Downloading international monetary fund data in Python: Mining IMF data for use in macroeconomic currency strategies. How can macroeconomics help an ordinary and an algorithmic trader?

For this article we look at a custom MQL5 Wizard class for Trailing Stops. Our implemented custom class ‘CTrailingConvexHullGRU’, is built from merging the Convex Hull algorithm with a GRU network. As always we seek to develop a model that is testable with MQL5 Wizard-Assembled Expert Advisors and can be tuned with various Money Management and entry Signals classes. Our testing is with the 'Envelopes' and the RSI classes for Signal.

The article establishes a reproducible method to measure portfolio risk for multiple symbols using MQL5 matrices and OpenBLAS. It covers computing log returns, building a covariance matrix, and evaluating wᵀΣw instead of summing individual variances. A complete script prints naive versus true volatility and the cross‑term contribution, enabling you to detect when correlated instruments inflate exposure beyond single‑asset estimates.

The article provides a fascinating look at how SwiGLU embedding reveals hidden market patterns, and how a sparse Mixture of Experts within a Decoder-Only Transformer makes forecasts more accurate at reasonable computational cost. We take an in-depth look at the integration of Time‑MoE into MQL5 and OpenCL, and provide a step-by-step guide to configuring and training the model.

Mantis is a versatile tool for in-depth time series analysis that can be flexibly scaled to accommodate any financial scenario. Learn how a combination of patching, local convolutions, and cross-attention enables a highly accurate interpretation of market patterns.

We evaluate blind source separation for market noise control using FastICA applied to SMA-filtered, time-lagged OHLC features. The study compares classical and surrogate targets, measures accuracy across lags, tunes KNN models, and inspects residual structure with clustering. Models are exported to ONNX and integrated into an MQL5 Expert Advisor for testing. The result is a reproducible pipeline from data extraction to deployment.

With the multifractal parameters from Part 2 in hand, this article builds the full MMAR process. We construct the multiplicative cascade for trading time, generate Fractional Brownian Motion via Davies-Harte FFT, and combine both into X(t) = B_H[theta(t)]. A 100-path Monte Carlo simulation produces the volatility forecast, which we then pit against GARCH on the same EURUSD M5 data. Does Mandelbrot's fractal architecture outforecast Engle's conditional variance framework? Part 3 of a eight-part series leading to a native MQL5 library and Expert Advisor.

We test AFML's claim that the sequential bootstrap decorrelates bagged trees on overlapping triple‑barrier labels by isolating two levers: draw count and draw rule. One decision identical tree is bagged under four row‑sampling regimes and evaluated on EURUSD 2022–2023 for draw uniqueness, between‑tree correlation, AUC, and calibration. Decorrelation comes almost entirely from throttling max_samples to average uniqueness; the sequential draw adds little. Out-of-bag inflation is largest under full-count sequential sampling.

CTrailingSlidingMedianBiLSTM is a custom MQL5 Wizard trailing module that combines robust median/MAD outlier filtering with a BiLSTM context score in the range [-1, 1]. Four algorithm modes (standard, bands, RSI, adaptive) target noise, mean-reverting bursts and liquidity spikes, reducing premature stop adjustments. This module is intended for side-by-side evaluation with diverse entry signals and money management settings.

The article examines slice sampling — an adaptive MCMC algorithm that automatically adjusts its sampling parameters. Its effectiveness is demonstrated using Bayesian linear and logistic regression models, and the results are compared with classical frequentist methods.

Price outliers distort indicators based on the mean and standard deviation. This article delivers a robust MQL5 library (RobustStats.mqh) implementing the median, 1.4826-scaled MAD, and Theil–Sen slope, plus three drop‑in indicators that replace Bollinger Bands, the linear regression channel, and the z‑score oscillator. A comparison overlay and a breakdown‑point measurement on EURUSD show how the robust instruments hold their shape when a single spike moves the classical ones.

The article diagnoses two defects that neutralize sequential bootstrap during cross‑validation: type erasure of SequentiallyBootstrappedBaggingClassifier and a fold‑level shape mismatch from cloning full samples info sets. It retains the classifier's identity, adds find seq bagging to re‑inject fold‑sliced t1 in CalibratorCV.fit, and resets state per split. A new bootstrap_comparison module reports OOF and OOB metrics and memory, letting you verify that sequential sampling is applied correctly and quantify its impact.

We invite you to explore the HimNet framework, which combines the flexibility of spatio-temporal adaptation with high computational efficiency, enabling accurate and stable forecasts for financial time series. The article explains in detail how its key components interact with one another, transforming complex algorithms into a manageable architecture.

This article presents 'CSignalUKFCapsNet', as a custom class coded in MQL5. This class is meant to be used with the MQL5 Wizard when assembling an Expert Advisor and when selected in the Wizard it defines the Expert Advisor's entry signals. In building this custom class, we brought together the algorithm Unscented Kalman Filter and the Capsule Neural Network. Our algorithm is showcased with four operation modes, and the coding of this as a custom class for the MQL5 Wizard, allows testing with various Trailing Stop methods and Money Management systems.

The article will show you how Mamba4Cast turns theory into a working trading algorithm and lays the groundwork for your own experiments. Do not miss this opportunity to gain a full range of knowledge and inspiration for developing your own strategy.

The Mantis framework transforms complex time series into informative tokens and serves as a reliable foundation for an intelligent trading agent capable of operating in real time.

SEAL (Self-Evolving Adaptive Learning) is a system for the continuous adaptation of large language models (LLMs) for algorithmic trading, designed to address the problem of rapid model degradation in changing markets. Instead of periodic retraining, which takes hours and erases old patterns, SEAL learns from every closed trade, maintains priority memory for important examples, and automatically initiates incremental fine-tuning when accuracy drops or a market regime change occurs.

Hardcoded prop-firm rules lock the sizer to one program. This article factors those rules into a PropFirmRuleSet and refactors PropFirmAccountState and the sizing modifiers to consume it, including dynamic versus fixed daily limits and the news-window profit-credit haircut. Parity against the original FundedNext behavior is validated on a simulated equity path, so you can retarget sizing by configuration instead of rewriting code.

The article discusses the ECO optimization algorithm, which is based on ecological concepts: populations are grouped into habitats based on territorial proximity, exchange genetic material within habitats, and migrate between them. Despite its wide range of operators and elegant biological metaphor, the algorithm produced a certain result discussed below.

This article presents a compact MQL5 library for market-complexity analysis: LZ76 complexity and Normalized Compression Distance built on a SAX symbolizer, exposed through a simple facade and an efficiency indicator. It explains the discretization choices, normalization, and distance formulation, and validates the code with unit checks and an independent cross-check. You get a ready-to-use library and indicator, plus a disciplined way to interpret readings with a shuffle null and a direction check.

We present a native MQL5 implementation of the catch22 feature set: all 22 canonical time-series characteristics in a reusable class validated against pycatch22. Using a leak-free pipeline (chronological split, purging, embargo), we run a three-arm ablation—classic indicators, catch22, and combined—for volatility-regime classification. Finally, we deploy the combined model as a Strategy Tester regime filter to quantify its impact on a simple baseline strategy.

The Metropolis-Hastings algorithm is a fundamental Markov chain Monte Carlo (MCMC) method that is widely used to approximate posterior distributions in Bayesian inference. This article describes the theoretical foundations of the algorithm, the implementation of the MHSampler class in MQL5, and examples of its application, including an analysis of the resulting samples.

Close-to-close volatility ignores the high, the low, and overnight gaps. We build a reusable MQL5 library implementing four range-based estimators from Parkinson to the gap-robust Yang-Zhang, and put it to work in a comparison indicator and a set of adaptive volatility bands.

The article provides a detailed explanation of the SCNN architecture and one way to implement it using MQL5. We will show how time series decomposition can be combined with neural network methods and attention mechanisms.

We continue to work on implementing the CATCH framework, which combines the Fourier transform and frequency patching mechanisms, ensuring accurate detection of market anomalies. In this article, we complete the implementation of our own vision of the proposed approaches and test the new models on real historical data.

What if lunar cycles and seasonal patterns influence the foreign exchange markets? This article shows how to translate astrological concepts into the language of mathematics and machine learning. I built a Python system with 88 features based on astronomical cycles, trained CatBoost on 15 years of EURUSD data, and obtained some intriguing results. The code is open-source, the methods are verifiable, and the conclusions are unexpected — ancient wisdom meets gradient boosting.

This article presents a reproducible MetaTrader 5 workflow: collect history, engineer nine context features, label simulated EMA crossover trades, train with FLAML, and export to ONNX with fixed opset and plain probabilities. The Expert Advisor loads the model natively, mirrors the Python feature contract, and uses a tunable confidence threshold as a trade filter. Readers can swap signals and features to reuse the same pipeline.

Experimental evaluation on standard benchmark functions reveals the advantages and limitations of directly adapting combinatorial algorithms. The article provides a detailed description of the ECEA algorithm's mechanisms and test results.

The Dendritic Cell Algorithm (DCA) is a metaheuristic inspired by the mechanisms of the innate immune system. Dendritic cells patrol the search space, accumulate signals about the quality of positions, and reach a collective decision: whether to exploit what they have found or to continue exploration. Let's take a look at how a biological model for detecting pathogens is transformed into an optimization algorithm.

In Part 2, we introduce a reusable CCovarianceMatrix class that computes and stores a covariance matrix from raw return series using MQL5's native Cov() method. We verify symmetry, print a labeled matrix grid, and call Eig() to obtain eigenvalues and eigenvectors. Readers see how symbols co-move and which factors drive variance, enabling clearer portfolio diagnostics and reuse in scripts or EAs.