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.
python -m pip install throne-trader
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.
- Predict stock price using deep learning models
- Analyze stock price using machine learning models
- Generate buy/sell/hold signals using real time data
- Generate buy/sell/hold signals using financial strategies
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.
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'
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
© Vignesh Rao
Licensed under the MIT License