Implementation of top solutions of Optiver - Trading at the Close 2023 with
ML engineering best practices extension
| Stage | Status | |
|---|---|---|
| 1 | EDA w/ target as stock wap regression restore | ✅ |
| 2 | Feature generation, 431 total | ✅ |
| 3 | Revealed target handling | ⏳ |
| 4 | Batch learning on daily h5 data | ✅ |
| 5 | Check aligment of inference and train features | ⏳ |
| 6 | Feature selection with Catboost | ✅ |
| 7 | Params optimization with Optuna and MlFlow on selected 300 features |
✅ |
| 8 | Kaggle submission code | ⏳ |
| 9 | NannyMl to check perfomance decay for ts splits | ⏳ |
| 10 | Retrain options and DVC each fited porion | ⏳ |