Godwin EA

Act as a professional Quantitative Developer and Risk Manager. I want to build a systematic trading strategy rulebook that prioritizes capital preservation and statistical edge over raw performance. Please generate a structured trading strategy using the following framework: 1. ASSET CLASS & TIMEFRAME: - Asset: [e.g., Apple (AAPL), Bitcoin (BTC), or EUR/USD] - Timeframe: [e.g., 5-minute, 1-hour, Daily] 2. CORE

JOB TITLE: XAUUSD ONLY MQL5 Strategy Developer Needed To Improve Existing MT5 EA Winrate JOB DESCRIPTION: I already have a working MetaTrader 5 Expert Advisor with a fixed set file and existing trading logic. The strategy is based on Volume Profile / Delta bias, ATR-based TP/SL, ATR deviation filtering, ATR trade spacing, break-even after TP1, and TP lock management. I am looking for an experienced MQL5 / trading

Part 1: Project setup Input settings (risk, stop loss, take profit, EMA periods) Indicator initialization Trade management framework Part 2: Trading logic EMA crossover detection Buy/Sell entry rules One-trade-per-symbol check Part 3: Risk management Automatic lot size calculation Stop-loss and take-profit placement Trade execution and error handling Part 4: Final touches On-screen information Optimization

Hello, I am reopening this project with a fully updated and clarified specification. I am looking for a high‑level MQL5 developer who can deliver a clean, stable, and professional Phase 1 version of my: Institutional‑Grade Multi‑Currency MT5 EA (A2SR + SMC + Smart Recovery + Smart Grid + Liquidity + Volatility + Safety Filters) This EA is not a simple indicator conversion or a basic strategy. It is a structured

Title Professional AI Automation Trading Bot for Forex & Crypto Solution Language Python (preferred) or MQL5 depending on integration requirements. Categories Expert Advisor (EA) for MetaTrader 5 Automated trading strategies AI/ML-based signal generation Risk management automation Required Skills Strong knowledge of MQL5/Python Experience with MetaTrader API integration Machine learning model deployment