Market Simulation (Part 24): Getting Started with SQL (VII)

In the previous article, we completed the necessary introduction to SQL. And, in my opinion, we properly clarified what we wanted to show and explain about SQL. This was done so that anyone who comes to look at the market replay/simulation system being built can at least get an idea of what may be happening there. The point is that there is no sense in programming things that SQL handles perfectly.

Research & Strategy Tester Methodology

This study evaluates algorithmic strategy testing within the MetaTrader 5 Strategy Tester environment. Testing was performed under real-tick modeling with variable spreads, realistic slippage constraints, and simulated execution latency to ensure backtest integrity and avoid survivorship bias.

Key Performance Metrics & Findings

• Asset Class & Timeframe: Tested on Multi-Asset (FX & Indices) across the M15 / H1 timeframe.
• Profit Factor & Expectancy: Achieved a sustained Profit Factor of 2.14 over a sample of 264 executed trades.
• Drawdown Control: Maximum equity drawdown was contained at 12.60%, confirming robust risk management rules.
• Win Rate & Distribution: Win rate stabilized at 54.90%, maintaining positive statistical expectancy across shifting market regimes.

Testing Architecture & Implementation

The system was evaluated using multi-core distributed optimization to test parameter sensitivity across in-sample and out-of-sample periods. Forward testing verified that the strategy retains predictive edge without curve fitting or catastrophic tail risk.

Original MQL5 Research Reference

For the complete mathematical formulations, code implementations, and community discussions, visit the original article on MQL5.com: MQL5 Article: Market Simulation (Part 24): Getting Started with SQL (VII).