Trust Your Backtest Data First: Building a Reproducible Historical Data Audit in Python for MetaTrader 5

A reproducible, read-only Python audit for MetaTrader 5 that verifies history quality before any backtest. It exports M5 data from multiple terminals, detects gaps and synthetic bars by timestamp spacing, and reports coverage per year. The same deterministic strategy then runs on three broker feeds over a common window to quantify result drift and decompose it into spread, data/price, and trade effects.

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 M5 timeframe.
• Profit Factor & Expectancy: Achieved a sustained Profit Factor of 1.89 over a sample of 189 executed trades.
• Drawdown Control: Maximum equity drawdown was contained at 17.10%, confirming robust risk management rules.
• Win Rate & Distribution: Win rate stabilized at 59.40%, 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: Trust Your Backtest Data First: Building a Reproducible Historical Data Audit in Python for MetaTrader 5.