deltafq 0.5.2

A comprehensive Python quantitative finance library

DeltaFQ

中文 | English

A Python-based quantitative trading system development framework focused on strategy research, backtesting execution, and performance visualization. Simulated trading and live trading features are under development.

Installation

pip install deltafq

• Optional components like Plotly and TA-Lib can be installed via pip install deltafq[viz] and pip install deltafq[talib].

Core Modules

deltafq/
├── data        # Data acquisition, cleaning, storage interfaces
├── indicators  # Technical indicators and factor calculations
├── strategy    # Signal generators and strategy base classes
├── backtest    # Backtest execution, performance metrics, reporting
├── charts      # Signal/performance chart components
└── trader      # Trading execution and risk control (ongoing expansion)

Quick Start (BOLL Strategy)

import deltafq as dfq

symbol = "AAPL"
fetcher = dfq.data.DataFetcher()
indicators = dfq.indicators.TechnicalIndicators()
signals = dfq.strategy.SignalGenerator()
engine = dfq.backtest.BacktestEngine(initial_capital=100_000)
reporter = dfq.backtest.PerformanceReporter()
chart = dfq.charts.PerformanceChart()

data = fetcher.fetch_data(symbol, "2023-01-01", "2023-12-31", clean=True)
bands = indicators.boll(data["Close"], period=20, std_dev=2)
signal_series = signals.boll_signals(price=data["Close"], bands=bands, method="cross_current")

trades_df, values_df = engine.run_backtest(symbol, signal_series, data["Close"], strategy_name="BOLL")

reporter.print_summary(symbol, trades_df, values_df, title=f"{symbol} BOLL Strategy", language="en")
chart.plot_backtest_charts(values_df=values_df, benchmark_close=data["Close"], title=f"{symbol} BOLL Strategy")

• More example scripts: examples

Community & Contributing

• Welcome to provide feedback and submit improvements via Issue or PRs.

License

MIT License. See LICENSE for details.