Algorithm Summary: Bayesian Geometric Trading System

Mathematical Foundation: Bayesian Inference: Calculates probability of price reversion vs breakout using Bayes' theorem Geometric Levels: Identifies support/resistance via pivot point clustering and Fibonacci retracements Feature Engineering: Distance to levels, clustering strength, RSI momentum

Learning Process: Continuous Bayesian Updates: Adjusts prior probabilities based on market trend Reinforcement Learning: Records every trade outcome to optimize confidence thresholds Feature Performance Tracking: Analyzes win rates by RSI ranges and distance categories

Core Mechanism: Trades when posterior probability exceeds adaptive thresholds: Greater than 75%: Expect reversion to geometric levels Less than 25%: Expect breakout from geometric levels

The system self-optimizes by learning which market conditions (RSI extremes, strong clustering, close proximity to levels) produce the highest win rates.