Comments (2)
firmai
commented on August 11, 2024
This is a really cool project. What do you think about creating more finance functions for numpy? What else do you think is possible? We can maybe combine our expertise to build something.
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polakowo
commented on August 11, 2024
Sorry for a late response. I checked the pandapy project and it's great. I think your library could truly shine for general use, while vectorbt is more tailored to backtesting and hyperparameter optimization and does things in a way unique for trading. I made vectorbt out of curiosity and the first iteration was built entirely on NumPy - you can find some crazy things done in vectorized way in the commit history, but now I mostly use Numba to make things simpler.
I'm now at the point where everything fits everything and I have no more ideas what to improve and just keep adding technical indicators. In the long term I would love to have some machine learning involved, such as identifying trading patterns in charts (pennants, triangles, etc.). So if you're up to do something similar, I'm open for collaboration.
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Related Issues (20)
- portfolio stats calculation for dca strategies. not appear all buy orders only first HOT 1
- Error specifying `benchmark_rets` arg in `Portfolio.plot_cum_returns()` HOT 2
- Doc Bug report HOT 1
- how to plot the trades of each symbol in portfolio HOT 1
- slippages are not applied to stop orders HOT 1
- How to import 3rd party data HOT 1
- Open short position right after closing long position with full capital HOT 3
- Installing vectorbt on Google Colab HOT 1
- plot with 1m granularity takes forever HOT 5
- portfolio.from_signals reporting HIGH variations with fees vs no fees
- set sl/tp to be triggered on exact values
- Portfolio.stats consumes large amount of memory. HOT 1
- Need to add requirement: nbformat>=4.2.0
- Vectorbt documentation code examples throws unsupported error. HOT 1
- Assertion Error with VectorBT Portfolio from MultiIndex DataFrame
- Reverse position with from_signals HOT 1
- tp_stop with accumulate gives wrong result
- Data mismatch
- Incorrect Position Size Allocation Across Multiple Assets HOT 3
- For the same precision data, there is an accuracy error in the results.
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