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Intelligent caching wrapper for AKShare with 90%+ performance boost - Complete stock data ecosystem with smart SQLite caching for Chinese financial markets. Perfect for quantitative trading, financial analysis, and algorithmic trading in Python.

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

🎉 NOW AVAILABLE ON PyPI!

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)

# New in v2.2.9: Multi-market support!
df_china = qdb.get_stock_data("000001", days=30)  # China A-shares
df_hk = qdb.get_stock_data("00700", days=30)      # Hong Kong stocks
realtime = qdb.get_realtime_data("000001")        # Real-time quotes
financials = qdb.get_financial_summary("000001")  # Financial data

Three product formats: Python Package, API Service, and Cloud Platform for different user needs.

pip install quantdb

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

Perfect for: Quantitative researchers, Python developers, data scientists Import Note: Install as quantdb, import as qdb (like scikit-learn → sklearn)

curl "https://your-api.com/api/v1/stocks/000001/data?days=30"

Perfect for: Enterprise teams, multi-user applications, production systems

Visit: QuantDB Cloud Platform Perfect for: Individual investors, data analysis, visualization

• 🚀 90%+ Performance Boost: Smart SQLite caching, millisecond response time
• 📦 Multiple Product Forms: Python package, API service, cloud platform
• 🔄 Full AKShare Compatibility: Same API interface, seamless replacement
• 💾 Local Caching: Offline available, intelligent incremental updates
• 📅 Trading Calendar Integration: Smart data fetching based on real trading days
• 🛠️ Zero Configuration: pip install and ready to use
• ☁️ Cloud Deployment Ready: Supports Railway, Render, Alibaba Cloud, etc.
• 🧠 Intelligent Updates: Automatic missing data detection and fetching
• ⚡ Real-time Data: Live stock quotes and market data
• 📊 Financial Analytics: Complete financial indicators and ratios
• 📈 Index Data: Major market indices support (SSE, SZSE, etc.)
• 📋 Stock Lists: Complete market coverage and filtering

Important: Package name and import name are different (common practice in Python ecosystem)

# Install the package
pip install quantdb

# Import the package (note: import name is 'qdb')
import qdb

# Start using immediately
df = qdb.get_stock_data("000001", days=30)
stats = qdb.cache_stats()

Why different names?

• Package name: quantdb (descriptive, searchable on PyPI)
• Import name: qdb (concise, easy to type)
• Similar to: scikit-learn → sklearn, beautifulsoup4 → bs4

# Install the package
pip install quantdb

# Import and use (note the different import name)
python -c "
import qdb  # Package: quantdb, Import: qdb
df = qdb.get_stock_data('000001', days=30)
print(f'Got {len(df)} records with 90%+ speed boost!')
print('✅ QuantDB package working perfectly!')
"

Direct access to deployed Streamlit Cloud version:

• Frontend Interface: QuantDB Cloud
• Complete Features: Stock data query, asset information, cache monitoring, watchlist management

# Clone repository
git clone https://github.com/franksunye/quantdb.git
cd quantdb

# Install dependencies
pip install -r requirements.txt

# Initialize database
python src/scripts/init_db.py

Method 1: One-click Start (Recommended)

# Enter frontend directory and run startup script
cd quantdb_frontend
python start.py
# Script will automatically start backend API and frontend interface

Method 2: Manual Start

# 1. Start backend API (in project root)
python src/api/main.py

# 2. Start frontend interface (in new terminal)
cd quantdb_frontend
streamlit run app.py

# Access URLs
# Frontend Interface: http://localhost:8501
# API Documentation: http://localhost:8000/docs

Method 3: Cloud Version Local Run

# Run Streamlit Cloud version (integrated backend services)
cd cloud/streamlit_cloud
streamlit run app.py
# Access URL: http://localhost:8501

# Health check
curl http://localhost:8000/api/v1/health

# Get stock data (auto-cached, displays real company names)
curl "http://localhost:8000/api/v1/historical/stock/600000?start_date=20240101&end_date=20240131"

# Get asset information (includes financial metrics)
curl "http://localhost:8000/api/v1/assets/symbol/600000"

# View cache status
curl http://localhost:8000/api/v1/cache/status

# Run backend tests
python scripts/test_runner.py --all

# Run frontend tests
cd quantdb_frontend
python run_tests.py

# Run performance tests
python scripts/test_runner.py --performance

QuantDB is optimized for AI agent integration with comprehensive machine-readable documentation and standardized APIs.

• 📋 Standardized Docstrings: All functions use Google Style format with detailed parameter constraints
• 🔧 Machine-Readable Schema: Complete API specification in JSON format
• 💡 Usage Examples: Comprehensive examples for financial and index data analysis
• ⚠️ Error Handling: Detailed exception documentation with recovery strategies
• 🎯 Type Safety: Full type hints for all function parameters and return values

import qdb

# AI agents can access complete documentation
help(qdb.get_stock_data)  # Detailed function documentation
help(qdb.get_financial_summary)  # Financial analysis functions
help(qdb.get_index_data)  # Market index functions

# Example: AI agent can generate this code confidently
df = qdb.get_stock_data("000001", days=30)  # Get recent stock data
summary = qdb.get_financial_summary("000001")  # Get financial metrics
realtime = qdb.get_realtime_data("000001")  # Get current quotes

• API Schema: qdb-ai-agent-schema.json - Machine-readable API specification
• Usage Examples: AI Agent Examples - Complete usage patterns
• Financial Analysis: Financial Examples - Advanced analysis workflows
• Documentation Guide: AI Agent Guide - Integration best practices

With QuantDB, AI agents can:

• ✅ Understand precise API functionality with detailed parameter constraints
• ✅ Generate correct code with proper error handling
• ✅ Perform advanced financial analysis with 80+ financial indicators
• ✅ Access comprehensive market data including stocks, indices, and real-time quotes
• ✅ Optimize performance using intelligent caching strategies

• Stock Analysis: Historical data, real-time quotes, technical indicators
• Financial Analysis: Quarterly reports, financial ratios, profitability metrics
• Index Analysis: Market indices, sector performance, trend analysis
• Portfolio Management: Multi-stock analysis, performance comparison

QuantDB adopts modern microservice architecture with the following core components:

• 🔧 Core Services: Unified business logic layer supporting multiple deployment modes
• 📡 FastAPI Backend: High-performance REST API service
• 📱 Streamlit Frontend: Interactive data analysis interface
• ☁️ Cloud Deployment: Cloud deployment version supporting Streamlit Cloud
• 🧪 Comprehensive Testing: Complete test suite covering unit, integration, API, E2E tests
• 📊 Smart Caching: Intelligent caching system based on trading calendar

For detailed architecture design, please refer to System Architecture Documentation.

• Backend: FastAPI + SQLAlchemy + SQLite
• Frontend: Streamlit + Plotly + Pandas
• Data Source: AKShare (Official Stock Data)
• Caching: Smart database caching + trading calendar
• Testing: pytest + unittest (259 tests, 100% pass rate)
• Monitoring: Real-time performance monitoring and data tracking
• Logging: Unified logging system with completely consistent recording
• Integration: Complete frontend-backend integration solution

We welcome your feedback and are here to help! Choose the best channel for your needs:

• GitHub Issues: Report bugs or request features
• Please include version info, error messages, and reproduction steps

• GitHub Discussions: Join the conversation
  • 🙏 Q&A - Get help with usage questions
  • 💡 Ideas - Share feature ideas and suggestions
  • 🙌 Show and Tell - Share your projects using QuantDB
  • 📣 Announcements - Stay updated with latest news

• Quick Help: FAQ
• Migration Guide: AKShare → QuantDB
• Full Documentation: https://franksunye.github.io/quantdb/

• PyPI Package: https://pypi.org/project/quantdb/
• Live Demo: https://quantdb-cloud.streamlit.app/

Response time: We aim to respond to issues within 24-48 hours. For urgent matters, please use GitHub Issues.

Current Version: v2.2.8 (Complete Multi-Feature Extension) Next Version: v2.3.0 (Enhanced Analytics and Performance) MVP Score: 10/10 (Core features complete, cloud deployment ready) Test Coverage: 259/259 passed (100%) - 222 backend + 37 frontend Data Quality: ⭐⭐⭐⭐⭐ (5/5) - Real company names and financial metrics Frontend Experience: ⭐⭐⭐⭐⭐ (5/5) - Professional quantitative data platform interface Integration Status: ✅ Complete frontend-backend integration, cloud deployment ready Production Ready: ⭐⭐⭐⭐⭐ (5/5) - Cloud deployment version complete Cloud Deployment: ✅ Streamlit Cloud version, directly using backend services

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

• GitHub: https://github.com/franksunye/quantdb
• API Documentation: http://localhost:8000/docs (access after starting service)
• Project Maintainer: frank

⭐ If this project helps you, please give it a Star!