Risk-Constrained Trade Sizing via Calibrated ML Probabilities and Linear Programming on SMC/ICT-Driven Structural Signals

Abstract

Smart Money Concepts (SMC) and the Inner Circle Trader (ICT) provide a rich vocabulary of market-structure events, but translating these discretionary ideas into robust, cost-aware execution remains challenging. This paper proposes a structure-aware trading system that (i) detects SMC/ICT events on OHLC data, (ii) learns calibrated probabilities of favorable resolution over a fixed horizon, and (iii) maps the probabilistic edge to a risk-aware position via linear programming (LP) with exposure, turnover, and Conditional Value-at-Risk (CVaR) constraints. On XAUUSD H1 data, the hybrid method achieves a cost-inclusive development return of 46.7% with Sharpe 1.18 and maximum drawdown (MDD) 6.8%, outperforming rules-only (38.6%, Sharpe 0.86) and ML-only (34.1%, Sharpe 0.79) baselines. On a held-out validation window, calibration and tail risk improve further, reaching Brier score [32] 0.186, Expected Calibration Error (ECE) 6.1%, ES0.95 0.036, and MDD 6.3%. Ablations indicate that probability calibration and the CVaR constraint contribute materially to risk-adjusted performance, while session conditioning improves robustness across volatility regimes.