MQL5 Programs Market

Permutation tests that assume exchangeable observations miscalibrate feature screens for overlapping financial labels, yielding a tenfold false‑positive inflation on EURUSD. This article implements a calibrated three‑gate pipeline: ADF+KPSS for stationarity, uniform‑grid entropy with modal occupancy, and mutual information corrected via a joint permutation null and Westfall–Young max‑T. The ScreeningReport summarizes gate outcomes and effective test count so you can filter features reliably before fitting models.

This article demonstrates how natural-language prompts generate MQL5 producer programs that place native chart objects, and how a deterministic Expert Advisor (AIObjectTrader) reads them through a structured naming and geometry contract. We compare indicator and script routes, using three cases: horizontal resistance, a persistent Andrews' Pitchfork, and Fibonacci Time Zones. Readers gain a reproducible workflow to connect AI-built objects to TOUCH/BREAK/ZONE evaluation and optional execution.

A practical route from the option chain to probability levels inside MetaTrader 5. For GLD, SLV, SPY, and USO, the risk-neutral density implied by each ETF's options is recovered with the Breeden-Litzenberger identity and checked for static arbitrage, after the method itself is validated against an analytic lognormal. A Python feed publishes the percentiles; an MQL5 service rescales them onto a related symbol, draws levels, and alerts when price leaves the risk-neutral 5 to 95 percent band. Every reading carries its own diagnostics, and full source is included.

An LLM agent with SQL memory in MetaTrader 5 — an architecture that eliminates the main limitation of traditional LLM Expert Advisors (EAs): the loss of context between queries. Each agent decision is recorded in SQLite along with the market conditions at the time of the signal — RSI, MA alignment, volatility — and after the position is closed, the outcome is written to the database. When the next request comes in, the agent reviews its own history — similar past RSI conditions and the last three agent decisions — and only then analyzes the current market. The result is a system that remembers its errors between restarts and adapts its behavior based on accumulated statistics, rather than starting from scratch with each new bar.

The article introduces the concepts and tools for working with random processes in trading: definitions, characteristics, autocorrelation functions, and a practical classification. The article covers white noise, random walks, Wiener and Poisson processes, as well as Markov chains and martingales. MQL5 scripts demonstrate the generation of sample paths and allow you to simulate equity, highlighting the mathematical limitations of strategies.