Articles on machine learning in trading

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.

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.

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.

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.

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.

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.

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.

In this article, we explore the inner workings of the RAFT framework — one of the most precise and elegant approaches to analyzing dynamic processes. Step by step, we adapt its idea of iterative refinement to financial time series, laying a solid foundation for a future model. Readers can look forward to an engaging exploration of the architecture, where every component has its own purpose and function.

The article describes an architecture in which the MQL5 Expert Advisor (EA) handles only data collection and execution, while the logic is moved to a Python server with three LangChain agents: a signal agent, a news agent, and a risk manager. Agents process the WebSocket request sequentially; if any agent rejects it, "hold" is returned. Decisions and actual PnL are stored in SQLite, forming memory and statistics. The reader will receive an interaction diagram, a command protocol, and a feedback approach.