Python Trading Indicators

A comprehensive Python library for technical analysis indicators used in algorithmic trading. Built with performance and extensibility in mind, this library provides a suite of technical indicators with a clean, consistent API for analyzing market data and generating buy/sell signals.

✨ Features

• Modular Architecture: Built around an abstract Indicator base class for consistency and extensibility
• Performance Optimized: Efficient calculations using pandas and numpy
• Complete Indicator Suite: RSI, VIX, Candlestick patterns, Price drops, and more
• Easy Integration: Simple API for incorporating indicators into trading strategies
• Type Safety: Full type hints for better development experience
• Extensible: Easy to add custom indicators following the established patterns

🚀 Quick Start

Installation

pip install Python.Trading.Indicators

For development installation:

git clone https://github.com/venantvr/Python.Trading.Indicators.git
cd Python.Trading.Indicators
pip install -e .

Basic Usage

import pandas as pd
from python_trading_indicators.rsi import RSIIndicator
from python_trading_indicators.candlestick import CandlestickIndicator

# Sample market data
data = {
    'close': [100, 102, 105, 103, 108, 115, 110, 109, 107, 102, 95],
    'open': [98, 100, 102, 105, 103, 108, 115, 110, 109, 107, 102],
    'volume': [1000, 1100, 1200, 900, 1500, 2500, 1800, 1000, 1100, 2200, 3000]
}
candles = pd.DataFrame(data)

# RSI Indicator
rsi = RSIIndicator(period=14, buy_threshold=30, sell_threshold=70)
rsi.calculate(candles)

if rsi.check_buy_condition():
    print("🟢 RSI Buy signal detected")
if rsi.check_sell_condition():
    print("🔴 RSI Sell signal detected")

# Candlestick Pattern Analysis
candlestick = CandlestickIndicator(lookback_period=3)
candlestick.calculate(candles)

if candlestick.check_buy_condition():
    print("🟢 Bullish candlestick pattern with volume confirmation")

📊 Available Indicators

RSI (Relative Strength Index)

Identifies overbought and oversold conditions in the market.

from python_trading_indicators.rsi import RSIIndicator

rsi = RSIIndicator(
    period=14,           # Calculation period
    buy_threshold=30,    # Oversold threshold
    sell_threshold=70    # Overbought threshold
)

Candlestick Pattern Analyzer

Analyzes recent candlestick patterns to determine bullish/bearish trends with volume confirmation.

from python_trading_indicators.candlestick import CandlestickIndicator

candlestick = CandlestickIndicator(
    lookback_period=3,    # Number of candles to analyze
    volume_threshold=1.2  # Volume confirmation multiplier
)

Sudden Price Drop Detector

Detects significant price drops that might indicate selling opportunities or rebounds.

from python_trading_indicators.drop import SuddenPriceDropIndicator

drop_detector = SuddenPriceDropIndicator(
    drop_percentage=10,   # Minimum drop percentage
    lookback_period=20   # Period to check for highest price
)

VIX (Volatility Index)

Measures market volatility and identifies panic conditions.

from python_trading_indicators.vix import VIXIndicator

vix = VIXIndicator(
    period=20,           # Calculation period
    panic_threshold=25   # Volatility panic threshold
)

PassThrough Indicator

A utility indicator for testing or temporarily disabling indicator logic.

from python_trading_indicators.passthrough import PassThroughIndicator

passthrough = PassThroughIndicator(enabled=False)

🏗️ Architecture

All indicators inherit from the abstract Indicator base class, ensuring a consistent interface:

from abc import ABC, abstractmethod
from pandas import DataFrame

class Indicator(ABC):
    def __init__(self, enabled: bool = True):
        self.is_enabled = enabled
    
    @abstractmethod
    def compute_indicator(self, candles: DataFrame):
        """Compute the indicator based on the provided candles."""
        pass
    
    @abstractmethod
    def evaluate_buy_condition(self) -> bool:
        """Evaluate if buy conditions are met."""
        pass
    
    @abstractmethod
    def evaluate_sell_condition(self) -> bool:
        """Evaluate if sell conditions are met."""
        pass

🧪 Testing

Run the complete test suite:

make test

🔧 Development

Install dependencies for development:

make install

Update dependencies:

make update

Code formatting:

make format

Format and check code:

make format

Run all quality checks:

make check

📈 Performance Considerations

• All indicators are optimized for pandas DataFrame operations
• Calculations are vectorized where possible for better performance
• Memory usage is minimized through efficient data handling
• Suitable for both real-time and batch processing scenarios

🤝 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.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

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

📞 Support

If you encounter any issues or have questions, please file an issue on the GitHub repository.

🔗 Links

• Repository: https://github.com/venantvr/Python.Trading.Indicators
• PyPI Package: https://pypi.org/project/Python.Trading.Indicators/
• Documentation: https://Python.Trading.Indicators.readthedocs.io/

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Disclaimer: This library is for educational and research purposes. Always do your own research before making any trading decisions. Past performance is not indicative of future results.