Graph Theory: Network Flow of Commodities (Ford-Fulkerson Algorithm), Used as a Liquidity-Capacity Engine

The article presents an MQL5 Expert Advisor that adapts the Ford–Fulkerson max-flow method into a liquidity-capacity filter. Market structures—Swing Highs/Lows, Fair Value Gaps, Order Blocks, and Liquidity Pools—form a directed graph with edge capacities from volume, price reaction, distance, and structure quality. Maximum flow qualifies ICT setups, filters weak paths, and drives dynamic position sizing for a consistent, two-stage decision process.

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 Commodities & FX across the M15 / H1 timeframe.
• Profit Factor & Expectancy: Achieved a sustained Profit Factor of 1.82 over a sample of 517 executed trades.
• Drawdown Control: Maximum equity drawdown was contained at 17.90%, confirming robust risk management rules.
• Win Rate & Distribution: Win rate stabilized at 60.20%, 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: Graph Theory: Network Flow of Commodities (Ford-Fulkerson Algorithm), Used as a Liquidity-Capacity Engine.