Closed MhmdFasihi closed 3 weeks ago
Hi @MhmdFasihi
Can you send a reproducible code?
Best, Dany
Hi @dcajasn thank you for your response here is link of my Google Colab code. https://colab.research.google.com/drive/0C2V0O_d2x4jYxlBqNRoASSa6Z?usp=sharing best, mhmd
This example not works for me, I need a simple example, like the examples in the example section. That shows where is the problem.
sorry, could you please have a look to this https://colab.research.google.com/drive/0C2V0O_d2x4jYxlBqNRoASSa6Z?usp=sharing one more time, it should works for you. and ok now I'm working on writing a sample code.
I told you a simple example, your code have too many things that I don't have time to check, I need a simple example that shows where is the problem.
Sorry about that @dcajasn Here is simple code based on your sample tutorial. https://colab.research.google.com/drive/1SSWCr
Hi @dcajasn could you please guide me about this issue? best, Mhmd
Hi @MhmdFasihi,
I solved the bug (that happens mainly because your data has too extreme values and too zeros returns), but I will released the fix in next riskfolio-lib version in two or three weeks.
Best regards, Dany
Hi @MhmdFasihi,
Download version 6.3.0 to fix the problem.
Best, Dany
I am currently working on a Hierarchical Risk Parity (HRP) optimization using the riskfolio-lib library and have encountered an issue specifically related to the Mean Variance (MV) risk measure. Here are the details: I have implemented HRP optimization with constraints, and while I am obtaining good results with all other risk measure methods, I consistently receive NaN values for some of my assets when I set MV as the risk measure.
I have attempted to adjust various code dependencies and configurations, but regardless of the changes made, the NaN results persist specifically for the MV risk measure. The issue seems to be isolated to this risk measure, as other methods function correctly.