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

Overview

Beibo, predict the stock market πŸ’Έ



Beibo logo

Quickstart



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.

How to install πŸ“₯

pip install beibo

How to use πŸ’»

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


Beibo output

About Accuracy

MAPE Interpretation
<10 Highly accurate forecasting πŸ‘Œ
10-20 Good forecasting πŸ†—
20-50 Reasonable forecasting πŸ˜”
>50 Inaccurate forecasting πŸ‘Ž

Models available

Models Availability
Exponential Smoothing βœ…
Facebook Prophet βœ…
ARIMA βœ…
AutoARIMA βœ…
Theta βœ…
4 Theta βœ…
Fast Fourier Transform (FFT) βœ…
Naive Drift βœ…
Naive Mean βœ…
Naive Seasonal βœ…

Stargazers over time

θΏ½ζ˜Ÿζ—ηš„ζ—Άι—΄

Contribution and Issues

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.

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    If your fork is out of date, then will you need to manually sync your fork: Synchronization method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!

Acknowledgments

  • 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

Contact

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

License

MIT

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Comments
  • dart not loading uppercase/lowercase issue?

    dart not loading uppercase/lowercase issue?

    Did pip install beibo and it seems to be unable to find dart library, looks like it loaded it, but it calls it DART not dart... all other libs show up in lowercase

    opened by themindfactory 3
  • new

    new

    My name is Luis, I'm a big-data machine-learning developer, I'm a fan of your work, and I usually check your updates.

    I was afraid that my savings would be eaten by inflation. I have created a powerful tool that based on past technical patterns (volatility, moving averages, statistics, trends, candlesticks, support and resistance, stock index indicators). All the ones you know (RSI, MACD, STOCH, Bolinger Bands, SMA, DEMARK, Japanese candlesticks, ichimoku, fibonacci, williansR, balance of power, murrey math, etc) and more than 200 others.

    The tool creates prediction models of correct trading points (buy signal and sell signal, every stock is good traded in time and direction). For this I have used big data tools like pandas python, stock market libraries like: tablib, TAcharts ,pandas_ta... For data collection and calculation. And powerful machine-learning libraries such as: Sklearn.RandomForest , Sklearn.GradientBoosting, XGBoost, Google TensorFlow and Google TensorFlow LSTM.

    With the models trained with the selection of the best technical indicators, the tool is able to predict trading points (where to buy, where to sell) and send real-time alerts to Telegram or Mail. The points are calculated based on the learning of the correct trading points of the last 2 years (including the change to bear market after the rate hike).

    I think it could be useful to you, to improve, I would like to share it with you, and if you are interested in improving and collaborating I am also willing, and if not file it in the box.

    opened by Leci37 0
  • data from predict() does not return as shown

    data from predict() does not return as shown

    In beibo.py in def oracle(): there is a def predict(): find the lines b = b.split("array([[[") c = b[1].split("]]])") d = c[0][ : -3]

    I had to change them to d = b.split('\n')[1].split(' ')[1] for some reason the output of the model.predict() was not of the format it expected...

    opened by themindfactory 1
Owner
Santosh
The 17-year-old not so part-time coder
Santosh
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