A Causal Approach to Bitcoin Performance Modeling
Oct 2025 – Apr 2026, extension Aug 2026
Pi2 industrial research project, ESILV, with asset manager Ginjer-AM
Technical pipeline lead on a causal ML research project with asset manager Ginjer-AM: reduced 288 features to the 10 validated causal drivers of Bitcoin returns, then built a monthly exposure strategy (Sharpe 1.03 vs 1.00 buy-and-hold, max drawdown -49% vs -83%).
- Python
- pandas
- scikit-learn
- statsmodels
- causal-learn
- NOTEARS
- tigramite (PCMCI)
- DoWhy
- networkx/pyvis
- Plotly