Beibo is a Python library that uses several AI prediction models to predict stocks returns over a defined period of time.

It was firstly introduced in one of my previous package called Empyrial.

Disclaimer: Information is provided 'as is' and solely for informational purposes, not for trading purposes or advice.

pip install beibo

from beibo import oracle
  
oracle(
      portfolio=["TSLA", "AAPL", "NVDA", "NFLX"], #stocks you want to predict
      start_date = "2020-01-01", #date from which it will take data to predict
      weights = [0.3, 0.2, 0.3, 0.2], #allocate 30% to TSLA and 20% to AAPL...(equal weighting  by default)
      prediction_days=30 #number of days you want to predict
)
  

Output

About Accuracy

Models available

Beibo uses GitHub to host its source code. Learn more about the Github flow.

For larger changes (e.g., new feature request, large refactoring), please open an issue to discuss first.

• If you wish to create a new Issue, then click here to create a new issue.

Smaller improvements (e.g., document improvements, bugfixes) can be handled by the Pull Request process of GitHub: pull requests.

• To contribute to the code, you will need to do the following:

• Fork Beibo - Click the Fork button at the upper right corner of this page.

• Clone your own fork. E.g., git clone https://github.com/ssantoshp/Beibo.git
  If your fork is out of date, then will you need to manually sync your fork: Synchronization method

• Create a Pull Request using your fork as the compare head repository.

You contributions will be reviewed, potentially modified, and hopefully merged into Beibo.

Contributions of any kind are welcome!

• Unit8 for Darts
• @ranroussi for yfinance
• This random guy on Python's Discord server who helped me
• @devnull10 on Reddit who warned me when I called the package The Oracle

You are welcome to contact us by email at [email protected] or in Beibo's discussion space

MIT