# QuantDB - Intelligent Stock Data Caching
[](https://pypi.org/project/quantdb/)
[](https://pypi.org/project/quantdb/)
[](https://pypi.org/project/quantdb/)
[](https://opensource.org/licenses/MIT)
[](https://github.com/franksunye/quantdb)
**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
```bash
pip install quantdb # One command, instant 90%+ speed boost!
```
```python
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
```python
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
```
### AKShare Compatibility
```python
import qdb
# 100% compatible with AKShare API
df = qdb.stock_zh_a_hist("000001", start_date="20240101", end_date="20240201")
```
### Configuration
```python
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
## 🔄 Coming Soon (v2.3.0)
- **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](https://github.com/franksunye/quantdb)
- **Full Documentation**: [https://github.com/franksunye/quantdb/docs](https://github.com/franksunye/quantdb/docs)
- **Issue Tracker**: [https://github.com/franksunye/quantdb/issues](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](https://github.com/franksunye/quantdb/blob/main/CONTRIBUTING.md) for details.
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](https://github.com/franksunye/quantdb/blob/main/LICENSE) file for details.
## 🙏 Acknowledgments
- Built on top of the excellent [AKShare](https://github.com/akfamily/akshare) library
- Inspired by the need for high-performance financial data access in Python
---
**Note**: Package name is `quantdb`, import name is `qdb` (similar to scikit-learn → sklearn)
*Made with ❤️ for the Python quantitative finance community*
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"description": "# QuantDB - Intelligent Stock Data Caching\n\n[](https://pypi.org/project/quantdb/)\n[](https://pypi.org/project/quantdb/)\n[](https://pypi.org/project/quantdb/)\n[](https://opensource.org/licenses/MIT)\n[](https://github.com/franksunye/quantdb)\n\n**Intelligent caching wrapper for AKShare with 90%+ performance boost** - Complete stock data ecosystem with smart SQLite caching for Chinese financial markets.\n\n> \ud83d\udcca **Note**: Download statistics may take 24-48 hours to appear on PyPI and third-party services after a new release.\n\n## \ud83d\ude80 Quick Start\n\n```bash\npip install quantdb # One command, instant 90%+ speed boost!\n```\n\n```python\nimport qdb # Note: import name is 'qdb' for simplicity\ndf = qdb.get_stock_data(\"000001\", days=30) # 90%+ faster than AKShare!\n```\n\n## \u2728 Key Features\n\n- **\ud83d\ude80 90%+ Performance Boost**: Local SQLite cache avoids repeated network requests\n- **\ud83e\udde0 Smart Incremental Updates**: Only fetch missing data, maximize cache efficiency \n- **\u26a1 Millisecond Response**: Cache hit response time < 10ms\n- **\ud83d\udcc5 Trading Calendar Integration**: Smart data acquisition based on real trading calendar\n- **\ud83d\udd27 Zero Configuration**: Automatically initialize local cache database\n- **\ud83d\udd04 Full AKShare Compatibility**: Drop-in replacement with same API interface\n- **\ud83c\udf0d 100% English Codebase**: International developer friendly\n- **\ud83c\udfaf Multi-Market Support**: A-shares + Hong Kong stocks unified API\n\n## \ud83d\udcca Performance Comparison\n\n| Operation | AKShare | QuantDB | Improvement |\n|-----------|---------|---------|-------------|\n| First Request | ~1000ms | ~1000ms | Same |\n| Cache Hit | ~1000ms | ~18ms | **98.1% faster** |\n| Bulk Operations | Very Slow | Lightning Fast | **90%+ faster** |\n\n## \ud83d\udd27 Core API\n\n### Basic Usage\n```python\nimport qdb\n\n# Get stock data (with intelligent caching)\ndf = qdb.get_stock_data(\"000001\", days=30)\ndf = qdb.get_stock_data(\"600000\", start_date=\"20240101\", end_date=\"20240201\")\n\n# Multiple stocks\nstocks_data = qdb.get_multiple_stocks([\"000001\", \"000002\"], days=30)\n\n# Asset information\nasset_info = qdb.get_asset_info(\"000001\")\n\n# Cache management\nstats = qdb.cache_stats() # View cache statistics\nqdb.clear_cache() # Clear cache if needed\n```\n\n### AKShare Compatibility\n```python\nimport qdb\n\n# 100% compatible with AKShare API\ndf = qdb.stock_zh_a_hist(\"000001\", start_date=\"20240101\", end_date=\"20240201\")\n```\n\n### Configuration\n```python\nimport qdb\n\n# Custom cache directory\nqdb.set_cache_dir(\"./my_custom_cache\")\n\n# Logging level\nqdb.set_log_level(\"INFO\") # DEBUG, INFO, WARNING, ERROR\n```\n\n## \ud83c\udfaf Use Cases\n\n- **Quantitative Research**: Frequent backtesting with cached historical data\n- **Algorithm Trading**: Real-time data access with minimal latency\n- **Financial Analysis**: Large-scale data processing with performance optimization\n- **Portfolio Management**: Multi-asset data retrieval and analysis\n- **Academic Research**: Reliable data source for financial studies\n\n## \ud83d\udd04 Coming Soon (v2.3.0)\n\n- **Real-time Stock Quotes**: Live market data with smart caching\n- **Stock List API**: Complete market coverage and filtering\n- **Index Data**: Major indices support (SSE, SZSE, etc.)\n- **Financial Metrics**: Key financial indicators and ratios\n\n## \ud83d\udcda Documentation & Support\n\n- **GitHub Repository**: [https://github.com/franksunye/quantdb](https://github.com/franksunye/quantdb)\n- **Full Documentation**: [https://github.com/franksunye/quantdb/docs](https://github.com/franksunye/quantdb/docs)\n- **Issue Tracker**: [https://github.com/franksunye/quantdb/issues](https://github.com/franksunye/quantdb/issues)\n- **API Reference**: Complete API documentation with examples\n\n## \ud83c\udfd7\ufe0f Architecture\n\nQuantDB provides multiple deployment options:\n\n1. **\ud83d\udce6 Python Package** (This Package): Local caching for individual developers\n2. **\ud83d\ude80 API Service**: Enterprise-grade REST API with advanced features \n3. **\u2601\ufe0f Cloud Platform**: Web interface with visualization and monitoring\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](https://github.com/franksunye/quantdb/blob/main/CONTRIBUTING.md) for details.\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the MIT License - see the [LICENSE](https://github.com/franksunye/quantdb/blob/main/LICENSE) file for details.\n\n## \ud83d\ude4f Acknowledgments\n\n- Built on top of the excellent [AKShare](https://github.com/akfamily/akshare) library\n- Inspired by the need for high-performance financial data access in Python\n\n---\n\n**Note**: Package name is `quantdb`, import name is `qdb` (similar to scikit-learn \u2192 sklearn)\n\n*Made with \u2764\ufe0f for the Python quantitative finance community*\n\n\n",
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