- Implemented AFML part A for testing the dip-z book against search artifacts, including PBO, DSR, and CPCV metrics.
- Developed AFML part B to generate time and tick bars from M1 broker data, including return distribution statistics.
- Created AFML part C to build a pipeline for dip-z primary analysis, incorporating features and a random forest model for classification.
- Added HCC history decoder to read and process broker M1 `.hcc` files, ensuring proper handling of data structure and integrity.
