Clemence Benjamin / プロファイル

A reusable MQL5 component measures floating P/L with swap, gross loss, two stop‑loss quantities, equity gap, margin level, and stop coverage, then classifies results for selected positions and the whole account. The snapshot records validity and readable reasons, supporting a quote‑age policy and missing‑stop handling. Integrate it into an Expert Advisor and block new exposure when thresholds or data checks fail.

The EA now computes an equal-weight score at M1 confirmation from normalized body fraction, breakout relative to C's range, and elapsed time, while excursions are measured only afterward. We refine processing of closed minutes and export a typed CSV that distinguishes completed observations from snapshots. The result is a clearer, reproducible basis for analyzing confirmed-signal behavior.

Oscillator thresholds are often treated as direct reversal instructions, causing traders to sell strong bullish markets or buy persistent bearish declines too early. We develop an analytical Expert Advisor that interprets Relative Strength Index events through confirmed swing structure. The model distinguishes continuation, reversal, pending, and expired conditions by combining closed-bar RSI crossings with higher highs, higher lows, lower highs, lower lows, and breaks of structure. It displays each validated interpretation without placing trades.

This article extends the A‑B‑C‑D constraint model with a measurement layer. We capture confirmation timing, breakout beyond Candle C, and post‑signal MFE/MAE; aggregate results by A‑B relationship; and write one CSV row per completed D window while showing concise CCanvas comments. The separation of detection, replay, visualization, measurement, and optional execution provides clean, analyzable data for further research.

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.