Discussing the article: "Money Management in MQL5 (Part 1): Kelly Position Sizing from the Strategy's Own Edge"
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Check out the new article: Money Management in MQL5 (Part 1): Kelly Position Sizing from the Strategy's Own Edge.
This article applies the Kelly criterion to position sizing in native MQL5. It presents a reusable CKelly class that estimates win rate and payoff from closed deals, derives the Kelly fraction, and sizes lots from a stop distance. A Monte Carlo sweep of the Kelly multiplier shows growth peaking at full Kelly while drawdown and ruin increase, motivating fractional Kelly such as half Kelly that preserves most growth with materially lower drawdown.
One classic method computes the number from the strategy itself: the Kelly criterion. This article is about that method. Kelly does not ask how much you feel like risking. It measures the strategy's edge, its win rate and average win versus average loss, and derives the bet size that maximizes long-run growth. It is the sizer that reads the strategy instead of asking the trader.
The distinction is not academic. A preset rule that risks two percent will size the same on a strategy that wins thirty-five percent of the time and one that wins sixty, even though the first should barely bet and the second could press. It cannot tell the difference because it never looks. Kelly looks first and sizes second, and that ordering is the whole idea. The tool that comes out of it is small, but it changes where the risk number comes from, and that is the part worth getting right.
This is the first part of a series on money management in native MQL5. Everything is built from scratch: no Python, no external libraries, only the terminal. This part delivers a reusable CKelly class that measures a strategy's edge and sizes from it, and it shows, with an honest simulation, why almost nobody should bet the full amount Kelly recommends. This is an educational article, not a strategy and not financial advice, and nothing here promises profit.
Author: Martin Alejandro Bamonte