Discussing the article: "Building a Position Sizing Engine in MQL5 with Multiple Risk Models"

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Check out the new article: Building a Position Sizing Engine in MQL5 with Multiple Risk Models.

The article presents a position sizing engine for MQL5 Expert Advisors that separates risk policy from lot conversion. Four models—fixed fractional, fixed monetary, ATR-based volatility scaling, and equity-curve scaling—share a CLotConverter that uses OrderCalcProfit() to measure real money per point. A unified CPositionSizer interface exposes CalculateLots(), making model changes straightforward while producing broker-compliant volumes across symbols.

Two different questions hide inside "what lot size should I use":

1. How much am I willing to lose if the stop is hit? This is a policy question. A percentage of balance, a flat dollar figure, a number that shrinks during a drawdown. It has nothing to do with the symbol being traded.
2. Given that dollar figure and a stop distance, what lot size produces it? This is a conversion question. It depends entirely on the symbol's tick value, tick size, contract size, and volume constraints.

Mixing these two ideas is exactly what makes sizing code fragile. A formula like balance * risk_pct / 100 / stop_points / 10.0 assumes a flat $10 per point value baked directly into the risk logic. That assumption holds for a handful of USD forex pairs and breaks on everything else. Switch the symbol to an index or a metal and the formula is simply wrong, even though the risk policy itself, "risk 1% per trade," never changed.

This engine keeps the two apart. Each risk model computes a risk_amount and hands it, along with a stop distance, to one shared CLotConverter. The converter's only job is turning that pair of numbers into a lot size the broker will accept. Adding a fifth model later means writing a small class that produces a risk_amount. The conversion math never needs touching.

Author: Ushana Kevin Iorkumbul