Metadata-Version: 2.3
Name: neuralstockprophet
Version: 0.0.2
Summary: LSTM-ARIMA with attention mechanisms and multiplicative decomposition for sophisticated stock forecasting.
Project-URL: Repository, https://github.com/lucaswychan/neural-stock-prophet
Author-email: Lucas Chan <chanconan@gmail.com>
Maintainer-email: Lucas Chan <chanconan@gmail.com>
License: MIT License
        
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License-File: LICENSE
Keywords: ARIMA,LSTM,attention,forecasting,multiplicative decomposition,stock
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
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Classifier: Programming Language :: Python :: 3.8
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Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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Requires-Dist: jax
Requires-Dist: jaxlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: quadprog
Requires-Dist: riskparityportfolio
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: statsmodels
Requires-Dist: ta
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Requires-Dist: yfinance
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Description-Content-Type: text/markdown

# Neural Stock Prophet

[![PyPI version](https://badge.fury.io/py/neuralstockprophet.svg)](https://badge.fury.io/py/neuralstockprophet)
[![Downloads](https://pepy.tech/badge/neuralstockprophet)](https://pepy.tech/project/neuralstockprophet)

**neuralstockprophet** combines several techniques and algorithms to enhance the robustness, stability, and interoperability of the stock price prediction algorithm. Stock Price Prediction using a machine learning algorithm helps discover the future value of company stock and other financial assets traded on an exchange. Whereas, the existing methods relied highly on model setup and tuning, without considering the variation of data. Also, the machine learning model faces the problems of overfitting and performance limitations.

Combined techniques:

-   LSTM model with attention mechanisms
-   Multiplicative decomposition
-   ARIMA model

## Installation

-   Stable version

```
pip install neuralstockprophet
```

## Getting Started

Simple

```python
nsp = NeuralStockProphet(
        stock_names=["AAPL", "GOOGL"],
        scaler_func=lambda: MinMaxScaler(feature_range=(0, 1)),
        train_start_date="2010-01-01",
        train_end_date="2019-12-31",
        test_start_date="2020-01-01",
        test_end_date="2020-12-31",
    )
```

## License

This project is licensed under the MIT License - see the [LICENSE](https://github.com/lucaswychan/neural-stock-prophet/blob/main/LICENSE) file for details.

## TODO

There are further improvements that can be made. Please have a look at the [TODO](https://github.com/lucaswychan/neural-stock-prophet/blob/main/TODO).
