Metadata-Version: 2.1
Name: throne-trader
Version: 0.3
Summary: Python module that provides collection of algorithms to analyze, categorize and predict stocks.
Author-email: Vignesh Sivanandha Rao <svignesh1793@gmail.com>
License: MIT License
        
        Copyright (c) 2023 Vignesh Rao
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://github.com/thevickypedia/throne-trader
Project-URL: Docs, https://thevickypedia.github.io/throne-trader
Project-URL: Source, https://github.com/thevickypedia/throne-trader
Project-URL: Bug Tracker, https://github.com/thevickypedia/throne-trader/issues
Project-URL: Release Notes, https://github.com/thevickypedia/throne-trader/blob/main/release_notes.rst
Keywords: investment,stock-prediction,stock-market
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Information Technology
Classifier: Topic :: Office/Business :: Financial :: Investment
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: yfinance
Requires-Dist: tensorflow
Requires-Dist: matplotlib
Requires-Dist: scikit-learn
Requires-Dist: pyfinviz
Provides-Extra: dev
Requires-Dist: sphinx ==5.1.1 ; extra == 'dev'
Requires-Dist: pre-commit ; extra == 'dev'
Requires-Dist: recommonmark ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'

[![made-with-python](https://img.shields.io/badge/Made%20with-Python-blue?style=for-the-badge&logo=Python)](https://python.org)

![Python](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-orange)

[![pypi-publish](https://github.com/thevickypedia/throne-trader/actions/workflows/python-publish.yml/badge.svg)](https://github.com/thevickypedia/throne-trader/actions/workflows/python-publish.yml)
[![pages-build-deployment](https://github.com/thevickypedia/throne-trader/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/thevickypedia/throne-trader/actions/workflows/pages/pages-build-deployment)

# ThroneTrader

A collection of algorithms to analyze, categorize and predict stocks.

These algorithms are used to assess stocks, and make predictions about future stock prices.

The collection of algorithms leverage data analysis, machine learning, and statistical methods to achieve its objectives in the context of financial markets and investments.

## Installation
```shell
python -m pip install throne-trader
```

## Usage
**Predict future stock prices using machine learning**
```python
from thronetrader import Predictions

predictions = Predictions(symbol="AAPL")
print(predictions.linear_regression_prediction())
print(predictions.gradient_boosting_prediction())
```

**Generate buy/sell/hold signals based on real-time data**
```python
from thronetrader import RealTimeSignals

realtime_signals = RealTimeSignals(symbol="AAPL")

print(realtime_signals.get_financial_signals())
print(realtime_signals.get_insider_signals())

series1, series2 = realtime_signals.get_trading_volume()
print(series1.name)
print(series1.to_dict())
print(series2.name)
print(series2.to_dict())
```

**Generate buy/sell/hold signals based on strategic algorithms**
```python
from thronetrader import StrategicSignals

strategic_signals = StrategicSignals(symbol="AAPL")

print(strategic_signals.get_bollinger_bands_signals())
print(strategic_signals.get_breakout_signals())
print(strategic_signals.get_crossover_signals())
print(strategic_signals.get_macd_signals())
print(strategic_signals.get_rsi_signals())
```

> :bulb: While individual algorithms may lack optimal accuracy, the aggregation of multiple algorithms proves valuable and effective in enhancing overall prediction accuracy.

> :warning: Please note that stock prediction is inherently challenging, and the accuracy of any prediction model will depend on the quality and relevance of the data used, the choice of algorithms, and the changing dynamics of the stock market. Continuous evaluation and improvement of the model are essential to enhance its predictive capabilities.

## Components
- [**Predict stock price using deep learning models**][dl_trade]
- [**Analyze stock price using machine learning models**][ml_trade]
- [**Generate buy/sell/hold signals using real time data**][realtime]
- [**Generate buy/sell/hold signals using financial strategies**][strategies]

## Sample Notebooks
- [**Long Short-Term Memory**][lstm]
- [**Gradient Boosting**][gradient]
- [**Linear Regression**][linear]

## Disclaimer
Remember to thoroughly backtest and paper trade any strategy before using real funds, and always exercise caution and risk management when trading stocks.

<br>

<details>
<summary>Why <code>throne-trader</code>?</summary>

<br>

<i>This name draws inspiration from the "Game of Thrones" series, where various characters vie for the Iron Throne, 
symbolizing power, wealth, and influence.
<br><br>
"ThroneTrader" signifies the algorithm's quest for dominance in the financial markets, much like the characters in the 
show strive to sit upon the Iron Throne.</i>

</details>

## Linting
`PreCommit` will ensure linting, and the doc creation are run on every commit.

**Requirement**
```shell
pip install sphinx==5.1.1 pre-commit recommonmark pytest
```

**Usage**
```shell
pre-commit run --all-files
```

## Pypi Package
[![pypi-module](https://img.shields.io/badge/Software%20Repository-pypi-1f425f.svg)][pypi]

## Runbook
[![made-with-sphinx-doc](https://img.shields.io/badge/Code%20Docs-Sphinx-1f425f.svg)][docs]

## License & copyright

&copy; Vignesh Rao

Licensed under the [MIT License][license]

[dl_trade]: https://github.com/thevickypedia/throne-trader/blob/main/markdown/DL_ALGORITHMS.md
[ml_trade]: https://github.com/thevickypedia/throne-trader/blob/main/markdown/ML_ALGORITHMS.md
[realtime]: https://github.com/thevickypedia/throne-trader/blob/main/markdown/REALTIME.md
[strategies]: https://github.com/thevickypedia/throne-trader/blob/main/markdown/STRATEGIES.md
[license]: https://github.com/thevickypedia/throne-trader/blob/main/LICENSE
[docs]: https://thevickypedia.github.io/throne-trader/
[pypi]: https://pypi.org/project/throne-trader
[lstm]: https://github.com/thevickypedia/throne-trader/blob/main/notebook/lstm.ipynb
[gradient]: https://github.com/thevickypedia/throne-trader/blob/main/notebook/gradient_boosting.ipynb
[linear]: https://github.com/thevickypedia/throne-trader/blob/main/notebook/linear_regression.ipynb
