Conditional-range High/Low adoptive-MA Crossover Strategy

Developed from the doctoral research of Abu-Kadunagra at ****** University on topic of Digital Finance and Crypto in Australia, this strategy implements a "Campaign-Based Adaptive Execution" framework. It moves beyond simple entries and exits by treating each market engagement as a multi-phase campaign with distinct operational states. The system intelligently identifies cyclical turning points, then employs a feedback-driven approach to capital allocation—reinforcing successful momentum with pyramiding while deploying controlled defensive averaging during temporary setbacks. By anchoring its exit mechanism to dynamically updated market structure rather than static profit targets, the algorithm seeks to capture cyclical momentum while maintaining disciplined risk parameters. This research-driven approach represents an evolution toward state-aware algorithmic systems that adapt their tactics in real-time based on market phase recognition.