Chacha Ian Maroa / プロファイル

This article develops a visual position planning tool in MQL5 for evaluating trade setups before execution. The tool utilizes interactive Entry, Stop-Loss, and Take-Profit lines to calculate the stop distance, risk amount, estimated position size, potential reward, and risk-to-reward ratio directly on the chart. It supports market, limit, and stop order scenarios while keeping the focus strictly on planning and analysis rather than trade execution.

This article implements an MQL5 custom indicator that detects Larry Williams Oops gap reversals and marks bullish and bearish arrows on the chart. It details configurable gap and validity thresholds, same-bar or later confirmation, first-fill-only logic, historical backfilling, and incremental updates so signals remain consistent on both history and newly completed bars.

Learn how to build an MQL5 Expert Advisor that detects and trades Larry Williams’ Oops Gap Reversal pattern using objective gap rules and later-bar confirmation. The EA tracks setup expiration, prepares stop-loss and take-profit levels, supports manual or risk-based position sizing, executes market orders, and is evaluated through historical testing.

This article shows how to build an MQL5 Expert Advisor around the UT Bot Alerts indicator. The EA reads custom indicator signals via iCustom() and CopyBuffer(), evaluates entries only on new bars, using the last closed candle at index 1, and enforces a one-direction-at-a-time model by closing opposite positions before taking new entries. It also adds optional ATR-based stop-losses, reward-to-risk take-profits, dedicated buy/sell execution functions, magic-number tracking, and basic backtesting for repeatable evaluation.

This article demonstrates how to build the UT Bot Alerts indicator in MQL5 using a clear, step-by-step approach. The tutorial explains how to implement an ATR-based trailing stop system, compute a custom EMA for signal detection, and generate buy and sell signals without repainting. The final indicator provides well-structured buffers that enable easy integration with Expert Advisors, automated trading systems, and other algorithmic tools within the MetaTrader 5 platform.

This article implements a real-time monitoring dashboard for a self-healing MetaTrader 5 Expert Advisor. The dashboard displays the current EA state, virtual stop-loss and take-profit levels, breakeven and trailing status, recovery state, synchronization status, and heartbeat information directly on the chart. By exposing the internal recovery state visually, the Expert Advisor becomes easier to monitor, verify, and troubleshoot while managing active trades.

This article adds trade-state reconciliation and Safe Mode recovery to a MetaTrader 5 Expert Advisor. The EA continuously validates recovery integrity by comparing the live broker position with the persisted SQLite state and the in-memory runtime state. Detected inconsistencies trigger an automatic transition to Safe Mode, suspending virtual protection, breakeven, and trailing management until the recovery state can be trusted again.

Building on Part 2, the implementation introduces restart-aware breakeven and trailing-stop systems for MetaTrader 5. The EA persists the state, such as breakeven activation, last trailing price, and virtual SL in SQLite, then restores them on startup. This preserves dynamic protection flow and prevents lost progress after terminal interruptions.

Build a restart-aware virtual protection layer on top of the SQLite persistence from Part 1. The EA reconstructs hidden stop-loss and take-profit after restart, verifies current price against recovered exits, and closes or continues positions accordingly. The result is a consistent recovery path that detects managed positions and sustains safe runtime management.

This article demonstrates how to build the persistence foundation of a self-healing Expert Advisor in MQL5 using SQLite. Readers will learn how to create a permanent trade-state storage layer capable of surviving terminal restarts, shutdowns, and unexpected interruptions. The article covers SQLite integration in MetaTrader 5, database lifecycle management, persistent trade-state structures, and runtime state recovery using practical MQL5 implementations.

This article extends the existing Flask backend to compute performance analytics from stored MetaTrader 5 closed trades and deliver them as both JSON and a simple web view. It calculates total trades, total profit, win rate, average profit, and trade duration metrics, returning JSON at /api/v1/analytics/summary and rendering a dashboard at /api/v1. The result provides a quick, consistent way to review trading performance from persisted SQLite records.

This article extends a Flask backend to reliably receive, validate, and store closed trade data from MetaTrader 5 using SQLite and Flask‑SQLAlchemy. It implements required‑field checks, timestamp conversion, transaction‑safe persistence, and working retrieval endpoints for all trades and single records, plus a basic summary. The result is a complete data pipeline with local testing that records trades and exposes them through a structured API for further analysis.

We build a lightweight bridge that captures closed trades in MetaTrader 5 and sends them to an external backend over HTTP as JSON. It uses OnTradeTransaction for event detection, reads details from deal history, assembles a JSON payload, and posts it via WebRequest. A local Flask API is used to test the flow, delivering a working path to move trade data outside the terminal.

We design a simple external trade analytics pipeline for MetaTrader 5 and implement its backend in Python with Flask and SQLite. The article defines the architecture, data model, and versioned API, and shows how to configure the environment, initialize the database, and run the server locally. As a result, you get a clean base to capture closed-trade records from MetaTrader 5 and store them for later analysis.

ラリー・ウィリアムズの「隠れスマッシュデー」の反転を自動化するMQL5エキスパートアドバイザー(EA)を構築します。カスタムインジケータから確定済みのシグナルを読み取り、コンテキストフィルタ（Supertrendの方向一致および任意の取引日ルール）を適用します。また、スマッシュバーの構造またはATRを基準としたストップロスモデル、固定またはリスクベースのポジションサイズによってリスクを管理します。その結果、テストおよび拡張に対応した、再現可能なフレームワークを構築できます。

厳密な数値条件と、オプションとして次のセッションによる確認を利用して、隠れスマッシュデー反転バーを検出する実用的なMQL5インジケータを開発します。検出ルーチン、バッファ登録、そして有効なバー上に矢印を表示するためのプロット設定について説明します。このアプローチにより、過去データのテストおよびリアルタイム監視において、安定した非リペイントシグナルを提供します。

ラリー・ウィリアムズの隠れスマッシュデー反転を対象とした、透明性の高いMQL5エキスパートアドバイザー(EA)を構築します。シグナルは新しいバーの開始時のみ生成されます。まずセットアップバーを判定し、その次のセッションがその極値の外側でクローズしたことを確認してからシグナルを確定します。リスク管理では、ATRベースまたはストラクチャーベースのストップロスを選択でき、あらかじめ定義したリスクリワード比に基づいて利益確定を設定します。ポジションサイズは固定ロットまたは口座残高に対する割合から算出でき、さらに売買方向フィルタと「同時保有は1ポジションのみ」というルールを組み合わせることで、再現性の高いバックテストを実現します。

体系化されたコンテキストの中で、ラリー・ウィリアムズのスマッシュデー反転パターンをMQL5で自動化する方法を解説します。限定された有効期間内でセットアップを検証し、Supertrendを用いた曜日フィルタを備え、エントリーはレベル突破時またはバー確定時のいずれにも対応するエキスパートアドバイザー(EA)を実装します。エントリー方式としては、レベル突破時の即時エントリー、またはローソク足確定後のエントリーに対応しています。また、同時保有ポジション数を1つに制限し、リスクベースまたは固定ロットによるポジションサイジングをサポートします。さらに、段階的な開発手順、バックテスト方法、再現可能な設定も併せて提示します。