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--- |
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license: apache-2.0 |
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tags: |
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- tabular-regression |
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- finance |
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- settlement-fails |
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--- |
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# Fails-Forecasting v1 (XGBoost + LightGBM) |
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Predicts next-day settlement-fail notional for US Treasuries & corporates. |
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| metric | value | |
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|--------|-------| |
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| MAE | **4.2 bn** | |
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| RMSE | 5.8 bn | |
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## Files |
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* `xgb_next_day_fails_model.joblib` – point prediction |
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* `lgb_quantile90_next_day_fails.joblib` – 90-percentile upper-bound |
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<img src="streaming forecaster.PNG" alt="Streaming Fail-Forecaster: training & inference flow" width="80%"> |
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Trained on FINRA FTD + TRACE (2009-2025). |
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**Usage** |
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```python |
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import joblib, lightgbm as lgb, pandas as pd |
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xgb = joblib.load("xgb_next_day_fails_model.joblib") |
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lgbm = lgb.Booster(model_file="lgb_quantile90_next_day_fails.txt") |
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y_hat = xgb.predict(X_new) |
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y_q90 = lgbm.predict(X_new) |
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alert = y_hat > y_q90 |
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``` |
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## Citation |
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> Musodza, K. (2025). Bond Settlement Automated Exception Handling and Reconciliation. Zenodo. https://doi.org/10.5281/zenodo.16828730 |
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> |
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> ➡️ Technical white-paper & notebooks: https://github.com/Coreledger-tech/Exception-handling-reconciliation.git |
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