deltafq 0.4.0

A comprehensive Python quantitative finance library

DeltaFQ

Modern Python library for strategy research, backtesting, paper/live trading, and streamlined reporting.

Highlights

• Clean architecture: data → strategy (signals) → backtest (execution + metrics)
• Execution engine: Unified order routing for paper/live trading via a Broker abstraction
• Indicators: Rich TechnicalIndicators (SMA/EMA/RSI/KDJ/BOLL/ATR/…)
• Signals: Simple, composable SignalGenerator helpers (e.g., Bollinger touch/cross/cross_current)
• Reports: Console-friendly summary with i18n (Chinese/English) powered by PerformanceReporter
• Charts: PerformanceChart delivers Matplotlib or Plotly (optional) performance dashboards

Install

pip install deltafq

60-second Quick Start (Bollinger strategy)

import deltafq as dfq

symbol = "AAPL"
fetcher = dfq.data.DataFetcher()
indicators = dfq.indicators.TechnicalIndicators()
generator = 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)
signals = generator.boll_signals(price=data["Close"], bands=bands, method="cross_current")

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

# Text summary (zh/en available)
reporter.print_summary(
    symbol=symbol,
    trades_df=trades_df,
    values_df=values_df,
    title=f"{symbol} BOLL Strategy",
    language="en",
)

# Optional performance dashboard (Matplotlib by default; set use_plotly=True for interactive charts)
chart.plot_backtest_charts(values_df=values_df, benchmark_close=data["Close"], title=f"{symbol} BOLL Strategy")

What’s inside

• deltafq/data: fetching, cleaning, validation
• deltafq/indicators: classic TA indicators
• deltafq/strategy: signal generation + BaseStrategy helpers
• deltafq/backtest: execution via ExecutionEngine; reporting via PerformanceReporter
• deltafq/charts: signal and performance charts (Matplotlib + optional Plotly)

Examples

See the examples/ folder for ready-to-run scripts:

• 04_backtest_result.py: Bollinger strategy summary + charts
• 05_visualize_charts.py: standalone visualization demos
• 06_base_strategy: implement a moving-average cross using BaseStrategy

Contributing

Contributions are welcome! Please open an issue or submit a PR.

License

MIT License – see LICENSE.