Alex David Hernandez Maturrano / 新闻源

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.

We introduce a versioned persistence layer that saves and restores the zone engine's logical state across sessions. The snapshot preserves lifecycle status, interaction history, scores, blacklist prices, and the geometry and time anchors of user‑drawn rectangles, and it is reconstructed and reconciled with chart objects on startup. User zones enter with the minimum score so they integrate into the same lifecycle and trading pipeline.

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.

Learn how Smart Trendline Manager separates creation from lifecycle control while handling both manual and auto-generated trendlines. It demonstrates discovery, registration, proximity and touch handling, bounce/break confirmation, post-break resurrection, expiration, and state-driven visualization. This gives you a consistent, configurable way to manage multiple lines through one orchestrated update process.

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.

The article presents a session analysis workflow in MQL5 that standardizes sessions as structured data with timing, OHLC, range, net move, and the sequence of extremes. It measures how range builds at 25%, 50%, 75%, and 100% of session duration. The chart shows session markers, a compact comparison panel, and an interactive inspector to review individual occurrences.

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.

We outline a modular analyzer for MetaTrader 5 that separates detection, interpretation, and visualization. The engine identifies swing highs and lows, assigns structural labels, evaluates impulses and pullbacks, and stores results in a market state object. An on‑chart dashboard and interactive inspection tools make the latest structure and measurements immediately accessible.

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.

Part 9 adds a second measurement layer to Part 8's micro‑trend signal: pullback quality. It maps Fibonacci retracement depth to a six‑level PULLBACK QUALITY label, adds an H1 range position from a 60‑bar rolling proxy, and uses lag‑1 momentum autocorrelation. These inputs form a single composite entry‑quality score in [0,1] for filtering setups and sizing trades within MicroStructure_Foundation.mqh.