Iyengar-Lab/E2E-DRO
End-to-end distributionally robust optimization
This project helps quantitative analysts and portfolio managers build investment portfolios that are more resilient to unpredictable market changes. It takes historical asset return data and, by considering potential model inaccuracies, produces optimal portfolio allocations. This ensures your portfolio decisions account for the risk that your predictions might not be perfectly accurate.
No commits in the last 6 months.
Use this if you are a quantitative portfolio manager who wants to build robust investment portfolios that explicitly account for model risk and learn optimal risk parameters directly from data.
Not ideal if you are looking for a simple, off-the-shelf trading system or if your primary concern is solely maximizing predictive accuracy without considering the impact of model uncertainty on investment decisions.
Stars
38
Forks
11
Language
Python
License
Apache-2.0
Category
Last pushed
Apr 15, 2023
Commits (30d)
0
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