BT4103 Business Analytics Capstone Project Proposal

Industry Partner: Dymon Asia

Part 1: Project Information

CFTC Positioning Nowcast and Price-Action Crowding Dashboard

Develop a cross-asset analytics platform that combines weekly CFTC Commitments of Traders data with daily price action to estimate how speculative positioning evolves between official releases.
Create a standardized data pipeline across FX, rates, equity indices, energy, metals and agricultural futures, mapping relevant participant categories such as Managed Money and Leveraged Funds.
Engineer features from returns, momentum, volume, open interest, volatility, futures-curve structure and cross-asset macro variables; compare regularized regression and tree-based models using time-series cross-validation.
Produce interpretable positioning and crowding indicators, including historical percentile/z-score, new-long versus short-covering classification, long-liquidation versus new-short classification, price-positioning divergence and squeeze/reversal-risk scores.
The platform will be a research and decision-support tool, not an autonomous trading or execution system.

Approximately 16 hours per week per student for 12 weeks (team of 3-5 students).

Public CFTC historical Commitments of Traders data; Price/Volume per instrument yfinance api.

Python (pandas, NumPy, scikit-learn, statsmodels, XGBoost/LightGBM, SHAP), SQL, Git, Jupyter, and Streamlit or Power BI for dashboard development.

Automated and reusable data ingestion, cleaning and contract-mapping pipeline.
Validated positioning-nowcast models with walk-forward testing, confidence ranges and feature interpretation.
Interactive cross-asset dashboard showing reported positions, estimated current positions, crowding scores, divergences and regime classifications.
Reusable codebase, technical documentation, model evaluation report and final presentation.