finfeatures 0.2.0

Advanced Financial Feature Engineering Library for Quantitative Finance and Algorithmic Trading

FinFeatures

Advanced Financial Feature Engineering Library for Quantitative Finance and Algorithmic Trading

FinFeatures is a comprehensive Python library designed for financial technical analysis and feature engineering. It provides a wide range of technical indicators, statistical transformations, and utility functions optimized for quantitative finance applications.

🚀 Key Features

Technical Indicators

• Moving Averages: Simple (SMA) and Exponential (EMA) Moving Averages
• Momentum Indicators: RSI, MACD with Signal and Histogram
• Volatility Indicators: Bollinger Bands, Average True Range (ATR)
• Oscillators: Stochastic Oscillator, Williams %R
• Other Indicators: Commodity Channel Index (CCI)

Statistical Transformations

• Volatility Analysis: Rolling volatility with annualization options
• Returns Calculation: Simple and logarithmic returns
• Statistical Measures: Rolling correlation, beta, z-scores, skewness, kurtosis
• Normalization: MinMax, Z-score, and Robust scaling methods

Advanced Features

• Signal Generation: Automated buy/sell signal generation
• Multi-timeframe Analysis: Batch processing for multiple timeframes
• Memory Efficient: Optional in-place operations
• Type Safety: Full type hints and validation
• Robust Error Handling: Comprehensive input validation

📦 Installation

From PyPI (Recommended)

📊 Available Functions

Technical Indicators

Function	Description	Key Parameters
simple_moving_average()	Simple Moving Average	window, min_periods
exponential_moving_average()	Exponential Moving Average	window
relative_strength_index()	RSI with Wilder's smoothing	window (default: 14)
macd()	MACD with signal and histogram	fast, slow, signal
bollinger_bands()	Bollinger Bands with position	window, num_std
average_true_range()	Average True Range	window (default: 14)
stochastic_oscillator()	Stochastic %K and %D	k_window, d_window
williams_r()	Williams %R	window (default: 14)
commodity_channel_index()	Commodity Channel Index	window (default: 20)

Statistical Transformations

Function	Description	Key Parameters
rolling_volatility()	Rolling volatility calculation	window, annualize
percent_change()	Simple/log returns	periods, method
rolling_correlation()	Rolling correlation	column1, column2, window
rolling_beta()	Rolling beta calculation	asset_col, market_col, window
z_score()	Rolling z-score	window
rolling_skewness()	Rolling skewness	window
rolling_kurtosis()	Rolling kurtosis	window
price_normalization()	Various normalization methods	method, window

Convenience Functions

Function	Description
basic_feature_set()	Apply basic technical indicators
premium_feature_set()	Apply comprehensive indicator set
multi_timeframe_sma()	SMA for multiple timeframes
generate_sma_signals()	Generate trading signals

🔧 Configuration

Default Parameters

Custom Indicator Pipeline

Trend indicators

df = ff.multi_timeframe_sma(df, price_col, windows=) df = ff.exponential_moving_average(df, price_col, window=12) df = ff.exponential_moving_average(df, price_col, window=26)

Momentum indicators

df = ff.relative_strength_index(df, price_col, window=14) df = ff.macd(df, price_col)

Volatility indicators

df = ff.bollinger_bands(df, price_col, window=20) df = ff.rolling_volatility(df, price_col, window=20)

Generate signals

df = ff.generate_sma_signals(df, price_col, fast_window=10, slow_window=30)

return df

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Development Setup

🐛 Bug Reports

If you encounter any bugs, please file an issue on GitHub Issues with:

• A clear description of the bug
• Steps to reproduce
• Expected vs actual behavior
• Your environment details

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with pandas and numpy
• Inspired by various financial analysis libraries
• Thanks to the quantitative finance community

📚 Additional Resources

• Technical Analysis Explained
• Pandas Documentation
• Quantitative Finance with Python

Happy Trading! 📈

Made with ❤️ by Advait Dharmadhikari