Clemence Benjamin / 个人资料

This article develops an A-B-C-D candlestick constraint model for MetaTrader 5. Candles A and B establish a completed D1 relationship, Candle C supplies a historical M1 setup, and closed M1 candles inside the developing Candle D either confirm or invalidate the sequence. We implement the design as a visual Expert Advisor with optional trading disabled by default, sequence-specific chart objects, restart-safe history reconstruction, spread and position gates, normalized trade parameters, and a reproducible testing plan.

The article introduces a centralized cache that serves indicators to multiple strategy nodes. It encodes full indicator identity in keys, stores Standard Library objects in CArrayObj entries, reuses matching instances, verifies readiness, refreshes data, and deletes each indicator on final release. Readers gain a predictable resource lifecycle and reduced duplication in multi‑symbol Expert Advisors.

We present a multi‑symbol EA structure that moves symbol‑specific state into independently initialized CStrategyNode objects and manages them with CPortfolioManager. Using CArrayObj with FreeMode, Add(), At(), Delete(), Total(), and Clear(), the manager ensures safe ownership and isolation, while timer‑driven routing and completed‑bar gates keep processing deterministic. This lets you scale portfolios, control risk per node, and avoid duplicated event handling.

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.

Envelopes define percentage boundaries around a moving average, but a boundary touch alone does not identify whether the market is trending or ranging. We build Envelope_Alligator_Strategy.mq5, a custom indicator that reads the built-in Envelopes and Alligator buffers. Ordered Alligator lines classify directional states; interwoven lines identify a neutral state. This context changes how envelope crossings are interpreted and produces testable, non-trading chart signals.

This article presents a practical debugging workflow with MetaEditor's integrated AI Assistant and a comparison to the previous external approach. We fix a controlled set of syntax and API errors in a D1 PriceMarker EA, inspect modifications, and recompile. The result is validated in the Strategy Tester, with clear boundaries between compilation success and required runtime checks.

Many traders know what they want to analyze or automate but cannot navigate MetaTrader 5, use MetaEditor, or translate an idea into working MQL5 code. This article shows how the AI Assistant and MCP turn one natural-language prompt into a complete workflow: strategy development, editing, debugging, chart interaction, testing, and permission-controlled trading. We apply the process by building MCP_Accessibility_Assistant.mq5, leaving you with a working, accessible EA and a reusable prompt-driven development method.

The article develops a multi-symbol basket EA that standardizes prices, derives PCA weights with native MQL5 matrices, forms a synthetic spread, and trades z-score deviations from a rolling mean. It validates symbols and synchronized history, stabilizes component orientation, maps signed weights to leg directions, and applies broker-aware volumes, stops, and netting rules. Basket entries run with rollback protection and chart status, with a reproducible testing procedure.

We convert the Part 15 decision‑forest classifier into a regime‑adaptive Expert Advisor that decouples statistical inference from trading authority. The EA trains on completed bars, scores each new completed bar, and confirms stable bullish, neutral, or bearish regimes before acting. It then applies spread, ownership, risk, and execution checks to authorize opening, holding, closing, or blocking a position.

This part focuses on practical data analysis in MQL5 with dataanalysis.mqh. We prepare a labeled dataset from bars, apply normalization, explore redundancy with PCA, and train a decision forest to classify future bar regimes. The article shows how to obtain out-of-bag estimates and permutation importance, helping you validate the model and understand which inputs matter most.