deltafq 0.2.0

A comprehensive Python quantitative finance library

DeltaFQ

A comprehensive Python quantitative finance library for strategy development, backtesting, and live trading.

Features

• Data Management: Efficient data fetching, cleaning, and storage
• Strategy Framework: Flexible strategy development framework
• Backtesting: High-performance historical data backtesting
• Paper Trading: Risk-free strategy testing with simulated trading
• Live Trading: Real-time trading with broker integration
• Technical Indicators: Rich library of technical analysis indicators
• Risk Management: Built-in risk control modules

Installation

pip install deltafq

Quick Start

import deltafq as dfq

# Fetch market data
fetcher = dfq.data.DataFetcher()
fetcher.initialize()
data = fetcher.fetch_stock_data('AAPL', '2023-01-01', '2023-12-31')

# Clean and validate data
cleaner = dfq.data.DataCleaner()
cleaner.initialize()
cleaned_data = cleaner.clean_price_data(data)

validator = dfq.data.DataValidator()
validator.initialize()
validator.validate_price_data(cleaned_data)

# Create and test a strategy
class SimpleMAStrategy(dfq.strategy.BaseStrategy):
    def __init__(self, fast_period=10, slow_period=20):
        super().__init__()
        self.fast_period = fast_period
        self.slow_period = slow_period
    
    def generate_signals(self, data):
        fast_ma = data['close'].rolling(window=self.fast_period).mean()
        slow_ma = data['close'].rolling(window=self.slow_period).mean()
        import numpy as np
        signals = np.where(fast_ma > slow_ma, 1, np.where(fast_ma < slow_ma, -1, 0))
        return pd.Series(signals, index=data.index)

strategy = SimpleMAStrategy()
strategy.initialize()
results = strategy.run(cleaned_data)

# Run backtest
engine = dfq.backtest.BacktestEngine(initial_capital=100000)
engine.initialize()
backtest_results = engine.run_backtest(strategy, cleaned_data)

# Run paper trading
simulator = dfq.trading.PaperTradingSimulator(initial_capital=100000)
simulator.initialize()
portfolio_summary = simulator.run_strategy(strategy, cleaned_data)

Documentation

• API Reference
• Tutorials
• Examples

Contributing

Please read our Contributing Guide for details on our code of conduct and the process for submitting pull requests.

License

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