deltafq 0.6.2

A-share low-frequency quantitative trading framework covering research, backtesting, and execution

DeltaFQ

中文 | English

Python Open-source Quantitative Framework: Covering the full "Research, Backtest, Trade" lifecycle, building an industrial-grade closed-loop quantitative workflow from scratch to production.

Exclusive Tutorials

iMOOC - AI Quantitative System Course: https://class.imooc.com/sale/aiqwm

Installation

pip install deltafq

Key Features

• 📥 Fetch Historical Data - Built-in free data sources, supporting global markets.
• 🧪 Common Indicators - Fast calculation of MACD, Bollinger Bands, etc., with TA-Lib support.
• 🧠 Fast Prototyping - Write logic in a few lines using signal generators and templates.
• 📉 High-Performance Backtesting - Rapid testing with multi-strategy comparison and performance analysis.
• ⚡ Live Market Distribution - Event-driven architecture for second-level distribution and Tick processing.
• 🤖 Paper & Live Trading - Pluggable design for seamless switching between simulation and live brokers.
• 📊 Interactive Visualization - Auto-generated Plotly charts for precise insights into backtest details.
• 📝 System Logging - Unified status management with multi-level logging and file storage.

Interface Integration

DeltaFQ flexibly connects to various external interfaces through pluggable Adapters:

• ✅ yfinance - Integrated, supporting multi-market historical and real-time market data.
• ✅ PaperTrade - Integrated, supporting multi-market local simulation and position management.
• 🛠️ qmt - Planned, supporting A-share live market snapshots and broker execution.
• 🛠️ Tushare - Planned, providing richer financial fundamental data for A-shares.

Quick Start

import deltafq as dfq

# 1. Define strategy logic
class MyStrategy(dfq.strategy.BaseStrategy):
    def generate_signals(self, data):
        bands = dfq.indicators.TechnicalIndicators().boll(data["Close"])
        return dfq.strategy.SignalGenerator().boll_signals(data["Close"], bands)

# 2. Minimal backtest & results
engine = dfq.backtest.BacktestEngine()
engine.set_parameters("GOOGL", "2025-07-26", "2026-01-26")
engine.load_data()
engine.add_strategy(MyStrategy(name="BOLL"))
engine.run_backtest()
engine.show_report()
engine.show_chart(use_plotly=False)

Application Example

DeltaFStation is an open-source quantitative trading cloud platform based on deltafq, integrating data services, strategy management, and trading access with paper and live support. Project: https://github.com/Delta-F/deltafstation/

Project Architecture

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
├── live        # Event engine, gateway abstraction, routing
├── adapters    # Pluggable data/trade adapters
├── trader      # Execution with order/position management
└── charts      # Signal/performance chart components

Contributing

• Feedback: Contributions and bug reports are welcome via Issue or PRs.
• WeChat Account: Follow DeltaFQ开源量化 for updates, strategies, and resources.

License

MIT License. See LICENSE for details.