/throne-trader

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

Primary LanguageJupyter NotebookMIT LicenseMIT

made-with-python

Python

pypi-publish 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

python -m pip install throne-trader

Usage

Predict future stock prices using machine learning

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

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

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())

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

⚠️ 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

Sample Notebooks

Disclaimer

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


Why throne-trader?

This name draws inspiration from the "Game of Thrones" series, where various characters vie for the Iron Throne, symbolizing power, wealth, and influence.

"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.

Coding Standards

Docstring format: Google
Styling conventions: PEP 8
Clean code with pre-commit hooks: flake8 and isort

Requirement

python -m pip install gitverse

Usage

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

pip install sphinx==5.1.1 pre-commit recommonmark pytest

Usage

pre-commit run --all-files

Pypi Package

pypi-module

Runbook

made-with-sphinx-doc

License & copyright

© Vignesh Rao

Licensed under the MIT License