MQL5 Programs Market

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.

Session filters can run an hour off when a broker and a market change their clocks on different dates. This article presents an MQL5 include file, SessionClock.mqh, that converts London, New York, and Tokyo sessions to broker hours for a stated daylight saving time (DST) schedule (EU, US, or fixed) and winter offset. A second script estimates the schedule from server history. It uses calendar arithmetic, so it also works in the Strategy Tester.

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.

Metaprompting is an approach in which the LLM itself optimizes trading instructions based on actual P&L and signal quality metrics. The article demonstrates a practical implementation in Python and MQL5: a prompt version registry, an execution agent, an evaluator for directional accuracy and profit factor, and a meta-LLM that generates improvements in a loop. The solution is integrated into the Expert Advisor without interrupting trading.