https://github.com/franksunye/quantdb

High-performance stock data caching service based on AKShare data source, providing intelligent caching, RESTful API, and professional frontend interface. 🚀 Cloud Deployment Ready! Now provides complete cloud deployment solutions, including local development environment and Streamlit Cloud deployment version.
https://github.com/franksunye/quantdb
streamlit webapp
Last synced: 9 months ago
JSON representation
Host: GitHub
URL: https://github.com/franksunye/quantdb
Owner: franksunye
License: mit
Created: 2025-05-13T10:57:23.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2025-08-11T14:19:43.000Z (11 months ago)
Last Synced: 2025-08-11T14:54:22.123Z (11 months ago)
Topics: streamlit, webapp
Language: Python
Homepage: https://quantdb-cloud.streamlit.app/
Size: 6.39 MB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
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README

          # QuantDB - High-Performance Python Stock Data Toolkit 🚀

*English | [中文版本](README.zh-CN.md)*

> **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.

![Version](https://img.shields.io/badge/version-2.2.9-blue)

![License](https://img.shields.io/badge/license-MIT-green)

![Python Package](https://img.shields.io/badge/PyPI-quantdb-blue)

[![codecov](https://codecov.io/gh/franksunye/quantdb/branch/main/graph/badge.svg)](https://codecov.io/gh/franksunye/quantdb)

![API](https://img.shields.io/badge/API-FastAPI-009688)

![Database](https://img.shields.io/badge/Database-SQLite-4169E1)

![Tests](https://img.shields.io/badge/Tests-259/259-success)

![Frontend](https://img.shields.io/badge/Frontend-Streamlit-FF4B4B)

![Cloud](https://img.shields.io/badge/Cloud-Ready-brightgreen)

![Performance](https://img.shields.io/badge/Cache-90%25_faster-brightgreen)

![Integration](https://img.shields.io/badge/Integration-Complete-success)

![Status](https://img.shields.io/badge/Status-Production_Ready-success)

**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!**

[![PyPI version](https://badge.fury.io/py/quantdb.svg)](https://pypi.org/project/quantdb/)

[![Downloads](https://pepy.tech/badge/quantdb)](https://pepy.tech/project/quantdb)

```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)

# 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.

## 🎯 Product Matrix

### 📦 **QuantDB Python Package** - For Developers

```bash

pip install quantdb

```

```python

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)

### 🚀 **API Service** - For Enterprises

```bash

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

```

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

### ☁️ **Cloud Platform** - For Individual Investors

Visit: [QuantDB Cloud Platform](https://quantdb-cloud.streamlit.app)

**Perfect for**: Individual investors, data analysis, visualization

## ✨ Core Features

- **🚀 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

## ⚡ Performance Highlights

| Metric | Direct AKShare Call | QuantDB Package | Performance Improvement |

|--------|-------------------|-------------|------------------------|

| **Response Time** | ~1000ms | ~10ms | **99%** ⬆️ |

| **Cache Hit** | N/A | 90%+ | **Smart Cache** ✅ |

| **Trading Day Recognition** | Manual | Automatic | **Intelligent** 🧠 |

| **Installation** | Complex setup | `pip install quantdb` | **One Command** 🚀 |

## 📦 Installation & Import

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

```bash

# Install the package

pip install quantdb

```

```python

# 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`

## 🚀 Quick Start

### Option 1: Python Package (Recommended)

```bash

# 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!')

"

```

### Option 2: Cloud Platform Access

Direct access to deployed Streamlit Cloud version:

- **Frontend Interface**: [QuantDB Cloud](https://quantdb-cloud.streamlit.app)

- **Complete Features**: Stock data query, asset information, cache monitoring, watchlist management

### Option 3: Local API Service

#### 1. Installation and Setup

```bash

# 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

```

#### 2. Start Services

**Method 1: One-click Start (Recommended)**

```bash

# 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**

```bash

# 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**

```bash

# Run Streamlit Cloud version (integrated backend services)

cd cloud/streamlit_cloud

streamlit run app.py

# Access URL: http://localhost:8501

```

### 3. Using API

```bash

# 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

```

### 4. Run Tests

```bash

# 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

```

## 🤖 For AI Agents

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

### ✨ AI Agent Features

- **📋 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

### 🚀 Quick AI Agent Integration

```python

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

```

### 📚 AI Agent Resources

- **API Schema**: [qdb-ai-agent-schema.json](docs/ai-agent/qdb-ai-agent-schema.json) - Machine-readable API specification

- **Usage Examples**: [AI Agent Examples](examples/ai_agent_usage_examples.py) - Complete usage patterns

- **Financial Analysis**: [Financial Examples](examples/financial_and_index_analysis.py) - Advanced analysis workflows

- **Documentation Guide**: [AI Agent Guide](docs/ai-agent/ai-agent-documentation-guide.md) - Integration best practices

### 🎯 AI Agent Capabilities

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

### 📊 Supported Analysis Types

- **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

## 🏗️ Architecture Overview

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](./docs/10_ARCHITECTURE.md).

## 🔧 Technology Stack

- **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

## 📚 Documentation

| Document | Description |

|----------|-------------|

| [📋 Project Status](./docs/00_BACKLOG.md) | Current progress and priorities |

| [📅 Changelog](./docs/01_CHANGELOG.md) | Version history and changes |

| [🏗️ System Architecture](./docs/10_ARCHITECTURE.md) | Architecture design and components |

| [🗄️ Database Architecture](./docs/11_DATABASE_ARCHITECTURE.md) | Database design and models |

| [📊 API Documentation](./docs/20_API.md) | Complete API usage guide |

| [🛠️ Development Guide](./docs/30_DEVELOPMENT.md) | Development environment and workflow |

| [🧪 Testing Guide](./docs/31_TESTING.md) | Test execution and writing |

| [📅 Trading Calendar Upgrade](./docs/TRADING_CALENDAR_UPGRADE.md) | Multi-market trading calendar system upgrade |

| [📖 **DeepWiki Knowledge Base**](https://deepwiki.com/franksunye/quantdb) | Comprehensive knowledge base and documentation |

## 💬 Support & Feedback

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

### 🐛 Bug Reports & Feature Requests

- **GitHub Issues**: [Report bugs or request features](https://github.com/franksunye/quantdb/issues)

- Please include version info, error messages, and reproduction steps

### 💭 Community Discussion

- **GitHub Discussions**: [Join the conversation](https://github.com/franksunye/quantdb/discussions)

  - [🙏 Q&A](https://github.com/franksunye/quantdb/discussions/categories/q-a) - Get help with usage questions

  - [💡 Ideas](https://github.com/franksunye/quantdb/discussions/categories/ideas) - Share feature ideas and suggestions

  - [🙌 Show and Tell](https://github.com/franksunye/quantdb/discussions/categories/show-and-tell) - Share your projects using QuantDB

  - [📣 Announcements](https://github.com/franksunye/quantdb/discussions/categories/announcements) - Stay updated with latest news

### 📚 Documentation & Resources

- **Quick Help**: [FAQ](./docs/faq.md)

- **Migration Guide**: [AKShare → QuantDB](./docs/guides/migration_akshare_to_quantdb.md)

- **Full Documentation**: [https://franksunye.github.io/quantdb/](https://franksunye.github.io/quantdb/)

### 🚀 Stay Connected

- **PyPI Package**: [https://pypi.org/project/quantdb/](https://pypi.org/project/quantdb/)

- **Live Demo**: [https://quantdb-cloud.streamlit.app/](https://quantdb-cloud.streamlit.app/)

---

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

## 🎯 Project Status

**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

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🔗 Links

- **GitHub**: [https://github.com/franksunye/quantdb](https://github.com/franksunye/quantdb)

- **API Documentation**: http://localhost:8000/docs (access after starting service)

- **Project Maintainer**: frank

---

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