Discussing the article: "MetaTrader 5 Machine Learning Blueprint (Part 11): Kelly Criterion, Prop Firm Integration, and CPCV Dynamic Backtesting"
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Check out the new article: MetaTrader 5 Machine Learning Blueprint (Part 11): Kelly Criterion, Prop Firm Integration, and CPCV Dynamic Backtesting.
The bet-sizing signal from Part 10 is concurrency-corrected but carries no payoff-ratio adjustment, no response to a hard drawdown budget, and no validation across combinatorial paths. This article covers three additions: a two-stage architecture in which a Kelly payoff multiplier is applied on top of get_signal, preserving the concurrency correction while incorporating win/loss asymmetry; a prop firm integration layer that calibrates the sigmoid w parameter continuously from the remaining drawdown budget under FundedNext Stellar 2-Step rules; and a CPCV backtest framework that simulates a fresh account state across all φ[N, k] paths, producing a Sharpe distribution and a PBO audit.
You have a bet-sizing signal from the toolkit introduced in Part 10. The signal is confidence-aware, concurrency-corrected, and discretized. What it is not yet is payoff-aware, budget-constrained, or validated across combinatorial paths. Three concrete gaps remain.
First, the sizing methods in Part 10 treat wins and losses symmetrically. A strategy that wins three dollars for every one it loses warrants a fundamentally different allocation than a symmetric bet at the same probability, and get_signal has no mechanism to express this. Second, none of the AFML methods incorporate a hard drawdown limit. When a prop firm account has consumed 70% of its daily loss capacity, the same model signal should produce a much smaller position than it would at the start of the day, and a static sizing function cannot respond to that. Third, a single backtest of a dynamically-sized strategy is a misleading performance summary. The position sizes at each bar depend on the P&L history up to that bar, which depends on every prior sizing decision, so the result is as much a function of the specific historical path as of the strategy's genuine edge.
All three gaps are addressed here. After reading, you will have: a precise account of when the Kelly criterion should replace or supplement get_signal, including the numerical crossover point at which the two methods diverge and the five structural conditions Kelly cannot satisfy in live financial markets; a two-stage hybrid architecture in which get_signal handles confidence-aware, concurrency-corrected signal sizing and a Kelly payoff multiplier applies asymmetric win/loss adjustment as a second stage, preserving the concurrency correction while adding what Kelly alone can express; a prop firm risk integration layer (PropFirmAwareSizer) in which the sigmoid w parameter is calibrated continuously from the remaining drawdown budget under the FundedNext Stellar 2-Step rules, so that as daily or overall loss capacity is consumed, the sizing function flattens automatically without threshold logic or manual override; and a CPCV dynamic backtest framework that simulates a fresh account state bar-by-bar through each of the φ[N, k] combinatorial paths, producing a distribution of equity curves and a PBO audit rather than the single path-dependent result that standard backtesting provides. Each component comes with practical limitations that the relevant sections make explicit.
Author: Patrick Murimi Njoroge