MT5 Quantitative Strategy Validation, Deep Backtesting & Walk-Forward Optimization

Objective: I am looking for an experienced MQL5 quantitative developer to code an Expert Advisor (EA) from scratch based on a fully defined, rule-based strategy, and then rigorously validate, optimize, and stress-test it before I deploy it on a live VPS environment.

Note: The exact mathematical rules, indicator names, and chart screenshots will be shared privately with shortlisted candidates.

Strategy Overview:

• Platform: MetaTrader 5

• Target Instruments: Applicable to all currency pairs and indices.

• Timeframe: H1 (utilizing simulated higher-timeframe momentum data calculated within the code).

• Core Logic: Trend-following and momentum alignment using standard MT5 indicators. Entries are triggered by a strict 5-point alignment checklist and specific indicator flip/touch events.

• Risk Management: Fixed 1:1 Risk/Reward ratio. Stop Loss and Take Profit distances are dynamically calculated based on indicator levels at the exact moment of entry. Fixed micro lots (0.01) are used, and trades can be held overnight. No martingale and no grid.

Scope of Work:

1. Code Creation & Debugging: Code the .mq5 EA from scratch based on my exact rules. Ensure order execution, dynamic SL/TP placement, and risk parameters trigger exactly as designed without latency issues.

2. High-Quality Backtesting: Perform backtesting using 100% real tick data (Every tick based on real ticks) over a 5 to 10-year historical period on major pairs and indices.

3. Robustness & Stress Testing:

   • Conduct Walk-Forward Optimization (WFO) to avoid curve-fitting.

   • Perform Monte Carlo simulations (randomizing slippage, spread, and tick data) to test the strategy's survival under adverse market conditions.

4. Optimization: Identify the optimal parameter sets for the indicators while maintaining a strict focus on minimizing maximum drawdown and maximizing the recovery factor.

Deliverables Required:

• The fully commented, compiled, and open-source MQL5 code (.mq5 ).

• Comprehensive HTML backtest reports containing Sharpe ratio, profit factor, max drawdown, and recovery factor.

• Set files (.set ) containing the optimized parameters for the tested instruments.

• A brief summary of the Walk-Forward Optimization and Monte Carlo results confirming the strategy's statistical edge.

Applicant Requirements:

• Please provide examples of previous optimization and stress-testing reports you have generated for other clients.

• Must have deep knowledge of algorithmic risk management, WFO, and quantitative statistics.

Budget & Timeline:

• Budget: $100 - $200 (Depending on code complexity and computational time required for Monte Carlo/WFO sweeps).

• Timeline: 5 to 7 days.