deltafq 0.9.0

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.

🎓 Official Tutorials

iMOOC - AI Quantitative System Course

Official Course: Deeply deconstructing the framework's architecture from 0 to 1, covering live trading logic and industrial-grade quantitative development. An essential course for mastering DeltaFQ.

📦 Installation

pip install deltafq

For previous version source code, visit: https://pypi.org/project/deltafq/#history

✨ Key Features

• 📥 Multi-source Data - Global multi-market historical/real-time data, ready to use
• 🧠 Rapid Development - Signal-driven architecture, fast implementation with strategy templates
• 📉 Professional Backtesting - High-performance matching engine, deep performance metrics and analysis
• ⚡ Event-driven - Second-level market data distribution, millisecond-level Tick signal processing
• 🤖 Live Gateway - Pluggable adapters, seamless switching between simulation and live trading

⚡ 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/

🔌 Interface Integration

• [Data] yfinance ✅ - US, A-shares, HK, Crypto, Indices
• [Data] eastmoney ✅ - OTC Funds (Index, QDII, Stock, Bond, Mixed)
• [Data] miniQMT ✅ - A-share market data integration (see live trading section in the course)
• [Trade] PaperTrade ✅ - Local simulation, tick-driven order matching, position and order management
• [Trade] miniQMT Trade ✅ - A-share live trading (see live trading section in the course)

Minimal miniQMT live trade setup

from deltafq.live import LiveEngine

engine = LiveEngine(symbol="000001.SZ", signal_interval="1m")
engine.set_data_gateway("miniqmt", interval=3.0, mode="poll")
engine.set_trade_gateway(
    "miniqmt",
    userdata_mini_path=r"D:\BrokerQMT\userdata_mini",
    account_id="1234567890",
)

See details: documents/MiniQmtTrade.md

🏗️ 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

documents/      # Usage guides and architecture docs
├── LiveEngine.md
├── MiniQmtTrade.md
└── BacktestEngine.md

🤝 Contributing

• Feedback: Bug reports and contributions are welcome via Issue or Pull Requests.
• WeChat Official Account: Follow DeltaFQ开源量化 for updates, strategies, and quantitative resources.

📄 License

MIT License. See LICENSE for details.