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

Santosh

Last update: Dec 10, 2022

Related tags

Finance finance machine-learning ai stock artificial-intelligence stock-market stock-price-prediction quant stocks quantitative-finance investment investment-analysis portfolio-management

Overview

Beibo, predict the stock market 💸

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 📥

pip install beibo

How to use 💻

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

MAPE	Interpretation
<10	Highly accurate forecasting 👌
10-20	Good forecasting 🆗
20-50	Reasonable forecasting 😔
>50	Inaccurate forecasting 👎

Models available

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

Stargazers over time

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.

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If your fork is out of date, then will you need to manually sync your fork: Synchronization method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

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?

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

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

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

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

Related tags

Overview

Beibo, predict the stock market 💸

How to install 📥

How to use 💻

Stargazers over time

Contribution and Issues

Acknowledgments

Contact

License

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