Ebola Optimization Search Algorithm (EOSA)

The article examines the EOSA algorithm, which is inspired by the mechanisms of Ebola virus transmission: short-distance transmission through close contact (exploitation) and long-distance transmission through travel (exploration). An analysis of the original publication revealed critical issues in the mathematical formulas and an epidemiological model that was impractical to implement, which required a significant overhaul of the algorithm to produce a workable implementation.

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 2.16 over a sample of 761 executed trades.
• Drawdown Control: Maximum equity drawdown was contained at 14.30%, confirming robust risk management rules.
• Win Rate & Distribution: Win rate stabilized at 64.60%, 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: Ebola Optimization Search Algorithm (EOSA).