| Name | throne-trader JSON |
| Version |
0.4
JSON |
| download |
| home_page | |
| Summary | Python module that provides collection of algorithms to analyze, categorize and predict stocks. |
| upload_time | 2023-08-18 20:48:02 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| 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. |
| keywords |
investment
stock-prediction
stock-market
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://python.org)
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# 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>
## Coding Standards
Docstring format: [`Google`](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) <br>
Styling conventions: [`PEP 8`](https://www.python.org/dev/peps/pep-0008/) <br>
Clean code with pre-commit hooks: [`flake8`](https://flake8.pycqa.org/en/latest/) and
[`isort`](https://pycqa.github.io/isort/)
## [Release Notes](https://github.com/thevickypedia/throne-trader/blob/main/release_notes.rst)
**Requirement**
```shell
python -m pip install gitverse
```
**Usage**
```shell
gitverse-release reverse -f release_notes.rst -t 'Release Notes'
```
## 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]
## Runbook
[][docs]
## License & copyright
© 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
Raw data
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"description": "[](https://python.org)\n\n\n\n[](https://github.com/thevickypedia/throne-trader/actions/workflows/python-publish.yml)\n[](https://github.com/thevickypedia/throne-trader/actions/workflows/pages/pages-build-deployment)\n\n# ThroneTrader\n\nA collection of algorithms to analyze, categorize and predict stocks.\n\nThese algorithms are used to assess stocks, and make predictions about future stock prices.\n\nThe collection of algorithms leverage data analysis, machine learning, and statistical methods to achieve its objectives in the context of financial markets and investments.\n\n## Installation\n```shell\npython -m pip install throne-trader\n```\n\n## Usage\n**Predict future stock prices using machine learning**\n```python\nfrom thronetrader import Predictions\n\npredictions = Predictions(symbol=\"AAPL\")\nprint(predictions.linear_regression_prediction())\nprint(predictions.gradient_boosting_prediction())\n```\n\n**Generate buy/sell/hold signals based on real-time data**\n```python\nfrom thronetrader import RealTimeSignals\n\nrealtime_signals = RealTimeSignals(symbol=\"AAPL\")\n\nprint(realtime_signals.get_financial_signals())\nprint(realtime_signals.get_insider_signals())\n\nseries1, series2 = realtime_signals.get_trading_volume()\nprint(series1.name)\nprint(series1.to_dict())\nprint(series2.name)\nprint(series2.to_dict())\n```\n\n**Generate buy/sell/hold signals based on strategic algorithms**\n```python\nfrom thronetrader import StrategicSignals\n\nstrategic_signals = StrategicSignals(symbol=\"AAPL\")\n\nprint(strategic_signals.get_bollinger_bands_signals())\nprint(strategic_signals.get_breakout_signals())\nprint(strategic_signals.get_crossover_signals())\nprint(strategic_signals.get_macd_signals())\nprint(strategic_signals.get_rsi_signals())\n```\n\n> :bulb: While individual algorithms may lack optimal accuracy, the aggregation of multiple algorithms proves valuable and effective in enhancing overall prediction accuracy.\n\n> :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.\n\n## Components\n- [**Predict stock price using deep learning models**][dl_trade]\n- [**Analyze stock price using machine learning models**][ml_trade]\n- [**Generate buy/sell/hold signals using real time data**][realtime]\n- [**Generate buy/sell/hold signals using financial strategies**][strategies]\n\n## Sample Notebooks\n- [**Long Short-Term Memory**][lstm]\n- [**Gradient Boosting**][gradient]\n- [**Linear Regression**][linear]\n\n## Disclaimer\nRemember to thoroughly backtest and paper trade any strategy before using real funds, and always exercise caution and risk management when trading stocks.\n\n<br>\n\n<details>\n<summary>Why <code>throne-trader</code>?</summary>\n\n<br>\n\n<i>This name draws inspiration from the \"Game of Thrones\" series, where various characters vie for the Iron Throne, \nsymbolizing power, wealth, and influence.\n<br><br>\n\"ThroneTrader\" signifies the algorithm's quest for dominance in the financial markets, much like the characters in the \nshow strive to sit upon the Iron Throne.</i>\n\n</details>\n\n## Coding Standards\nDocstring format: [`Google`](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) <br>\nStyling conventions: [`PEP 8`](https://www.python.org/dev/peps/pep-0008/) <br>\nClean code with pre-commit hooks: [`flake8`](https://flake8.pycqa.org/en/latest/) and \n[`isort`](https://pycqa.github.io/isort/)\n\n## [Release Notes](https://github.com/thevickypedia/throne-trader/blob/main/release_notes.rst)\n**Requirement**\n```shell\npython -m pip install gitverse\n```\n\n**Usage**\n```shell\ngitverse-release reverse -f release_notes.rst -t 'Release Notes'\n```\n\n## Linting\n`PreCommit` will ensure linting, and the doc creation are run on every commit.\n\n**Requirement**\n```shell\npip install sphinx==5.1.1 pre-commit recommonmark pytest\n```\n\n**Usage**\n```shell\npre-commit run --all-files\n```\n\n## Pypi Package\n[][pypi]\n\n## Runbook\n[][docs]\n\n## License & copyright\n\n© Vignesh Rao\n\nLicensed under the [MIT License][license]\n\n[dl_trade]: https://github.com/thevickypedia/throne-trader/blob/main/markdown/DL_ALGORITHMS.md\n[ml_trade]: https://github.com/thevickypedia/throne-trader/blob/main/markdown/ML_ALGORITHMS.md\n[realtime]: https://github.com/thevickypedia/throne-trader/blob/main/markdown/REALTIME.md\n[strategies]: https://github.com/thevickypedia/throne-trader/blob/main/markdown/STRATEGIES.md\n[license]: https://github.com/thevickypedia/throne-trader/blob/main/LICENSE\n[docs]: https://thevickypedia.github.io/throne-trader/\n[pypi]: https://pypi.org/project/throne-trader\n[lstm]: https://github.com/thevickypedia/throne-trader/blob/main/notebook/lstm.ipynb\n[gradient]: https://github.com/thevickypedia/throne-trader/blob/main/notebook/gradient_boosting.ipynb\n[linear]: https://github.com/thevickypedia/throne-trader/blob/main/notebook/linear_regression.ipynb\n",
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