quantdb 2.2.7

Intelligent caching wrapper for AKShare with 90%+ performance boost - 100% English codebase (import as 'qdb')

QuantDB - Intelligent Stock Data Caching

Intelligent caching wrapper for AKShare with 90%+ performance boost - Complete stock data ecosystem with smart SQLite caching for Chinese financial markets.

📊 Note: Download statistics may take 24-48 hours to appear on PyPI and third-party services after a new release.

🚀 Quick Start

pip install quantdb  # One command, instant 90%+ speed boost!

import qdb  # Note: import name is 'qdb' for simplicity
df = qdb.get_stock_data("000001", days=30)  # 90%+ faster than AKShare!

✨ Key Features

• 🚀 90%+ Performance Boost: Local SQLite cache avoids repeated network requests
• 🧠 Smart Incremental Updates: Only fetch missing data, maximize cache efficiency
• ⚡ Millisecond Response: Cache hit response time < 10ms
• 📅 Trading Calendar Integration: Smart data acquisition based on real trading calendar
• 🔧 Zero Configuration: Automatically initialize local cache database
• 🔄 Full AKShare Compatibility: Drop-in replacement with same API interface
• 🌍 100% English Codebase: International developer friendly
• 🎯 Multi-Market Support: A-shares + Hong Kong stocks unified API

📊 Performance Comparison

Operation	AKShare	QuantDB	Improvement
First Request	~1000ms	~1000ms	Same
Cache Hit	~1000ms	~18ms	98.1% faster
Bulk Operations	Very Slow	Lightning Fast	90%+ faster

🔧 Core API

Basic Usage

import qdb

# Get stock data (with intelligent caching)
df = qdb.get_stock_data("000001", days=30)
df = qdb.get_stock_data("600000", start_date="20240101", end_date="20240201")

# Multiple stocks
stocks_data = qdb.get_multiple_stocks(["000001", "000002"], days=30)

# Asset information
asset_info = qdb.get_asset_info("000001")

# Cache management
stats = qdb.cache_stats()  # View cache statistics
qdb.clear_cache()         # Clear cache if needed

New Features (v2.2.6)

import qdb

# Real-time stock quotes
realtime = qdb.get_realtime_data("000001")
batch_realtime = qdb.get_realtime_data_batch(["000001", "000002"])

# Complete stock list
stock_list = qdb.get_stock_list()

# Financial data
financial_summary = qdb.get_financial_summary("000001")
financial_indicators = qdb.get_financial_indicators("000001")

AKShare Compatibility

import qdb

# 100% compatible with AKShare API
df = qdb.stock_zh_a_hist("000001", start_date="20240101", end_date="20240201")

Configuration

import qdb

# Custom cache directory
qdb.set_cache_dir("./my_custom_cache")

# Logging level
qdb.set_log_level("INFO")  # DEBUG, INFO, WARNING, ERROR

🎯 Use Cases

• Quantitative Research: Frequent backtesting with cached historical data
• Algorithm Trading: Real-time data access with minimal latency
• Financial Analysis: Large-scale data processing with performance optimization
• Portfolio Management: Multi-asset data retrieval and analysis
• Academic Research: Reliable data source for financial studies

🎉 New in v2.2.6

• ✅ Real-time Stock Quotes: Live market data with smart caching
• ✅ Stock List API: Complete market coverage and filtering
• ✅ Index Data: Major indices support (SSE, SZSE, etc.)
• ✅ Financial Metrics: Key financial indicators and ratios

📚 Documentation & Support

• GitHub Repository: https://github.com/franksunye/quantdb
• Full Documentation: https://github.com/franksunye/quantdb/docs
• Issue Tracker: https://github.com/franksunye/quantdb/issues
• API Reference: Complete API documentation with examples

🏗️ Architecture

QuantDB provides multiple deployment options:

1. 📦 Python Package (This Package): Local caching for individual developers
2. 🚀 API Service: Enterprise-grade REST API with advanced features
3. ☁️ Cloud Platform: Web interface with visualization and monitoring

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built on top of the excellent AKShare library
• Inspired by the need for high-performance financial data access in Python

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Note: Package name is quantdb, import name is qdb (similar to scikit-learn → sklearn)

Made with ❤️ for the Python quantitative finance community