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CVaR portfolio optimisation and Entropy Pooling in Python
What it is
A Python package for Conditional Value-at-Risk portfolio optimisation and Entropy Pooling. Entropy Pooling lets you blend subjective views or stress scenarios into a market distribution, which then feeds a CVaR-based optimisation. Quant researchers and portfolio managers who need allocations under tail-risk constraints are the intended users. The methods work on scenario data, so results depend on how well your simulated or historical scenarios represent the risks you care about.
At a glance
Worth watchingOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Professional quants |
|---|---|
| Used for | Strategy research, Data analysis |
| Markets | Multi-market |
| Stack | Python |
| Learning curve | Moderate learning curve |
| Practical value | Medium practical value |
| Cost | Free and open source |
| Hardware | No GPU needed |
| Maintenance | Last commit 19 days ago |
GitHub stars, last 30 days
Daily snapshots since 2026-09-28 (up to 30 days): +2 over the period, now 312. Gaps mean no snapshot was taken that day.
Conditional Value-at-Risk (CVaR) portfolio optimization and Entropy Pooling views / stress-testing in Python.
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