The trading system "Paimon Bless V17.7" integrates a hybrid machine learning and Bayesian approach to manage model uncertainty and dynamically allocate risk. It employs a three-model ensemble: a shallow neural network with Monte Carlo Dropout for uncertainty estimation, a Bayesian Gaussian Naive Bayes Classifier for robust predictions, and a Four-Moment Kelly Criterion Engine for dynamic risk allocation. The system prioritizes models based on their real-time confidence, with higher uncertainty resulting in lower model weight, and incorporates a feedback loop for continuous learning and adaptation to market conditions. This approach aims to enhance trade selectivity and risk management, acknowledging the noisy and non-stationary nature of market data. This matters because it offers a sophisticated method for improving trading strategies by explicitly addressing uncertainty and adapting to changing market environments, potentially leading to more stable and profitable outcomes.
