A comprehensive Python quantitative finance library
Project description
DeltaFQ
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]andpip install deltafq[talib].
Core Modules
deltafq/
├── data # Data acquisition, cleaning, storage interfaces (stocks, funds)
├── 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
- Stock data fetching:
01_fetch_yahoo_data.py - Fund data fetching:
11_fetch_fund_data.py
- Stock data fetching:
Community & Contributing
- Welcome to provide feedback and submit improvements via Issue or PRs.
License
MIT License. See LICENSE for details.
Project details
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