Market Simulation (Part 23): Getting Started with SQL (VI)

In this article, we will see how to visualize a database and, from that, understand how it is structured. This is done by analyzing the database’s internal structure. Although this may seem unnecessary at first, it is fully justified if we really want to become database administrators. After all, some people make a living maintaining and designing databases.

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 1.64 over a sample of 314 executed trades.
• Drawdown Control: Maximum equity drawdown was contained at 9.60%, confirming robust risk management rules.
• Win Rate & Distribution: Win rate stabilized at 67.90%, 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 23): Getting Started with SQL (VI).