QuantDinger

🇺🇸 English | 🇨🇳 简体中文 | 🇹🇼 繁體中文 | 🇯🇵 日本語 | 🇰🇷 한국어

⚡ Run your own local TradingView + AI quant research lab in 5 minutes.

🤖 AI-Native · 🔒 Privacy-First · 🚀 All-in-One Quant Workspace

The Next-Gen Local Quant Platform: Multi-Market Data, AI Research, Visual Backtesting, and Automated Trading.

Official Community · Live Demo · 📺 Video Demo · 🌟 Join Us

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🌟 Join the QuantDinger DAO Community - Early Contributors Get QDT Tokens!

We're building a decentralized, community-driven trading platform. Early contributors receive QDT governance tokens!

📖 Learn More · 💬 Join Telegram · 🎮 Join Discord

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📖 Introduction

QuantDinger is a Local-First quantitative trading workspace designed for traders, researchers, and geeks.

Unlike expensive SaaS platforms, QuantDinger returns data ownership to you. It features a built-in LLM-based Multi-Agent Research Team that autonomously gathers financial intelligence from the web, combines it with local market data, generates professional analysis reports, and seamlessly integrates with your strategy development, backtesting, and live trading workflows.

Core Value

• 🛡️ Privacy First: All strategies, trading logs, and API keys are stored locally in your SQLite database.
• 🧠 AI Empowered: Not just code completion, but a real AI Research Analyst (powered by OpenRouter/LLM).
• ⚡ Multi-Market: Native support for Crypto, US Stocks, CN/HK Stocks, Forex, and Futures.
• 🔌 Out-of-the-Box: One-click deployment via Docker. No complex environment setup required.

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📺 Video Demo

Click the video above to watch the QuantDinger project introduction

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📚 Documentation

• Python Strategy Development Guide

📸 Visual Tour

📊 Professional Quant Dashboard

Real-time monitoring of market dynamics, assets, and strategy status.

🤖 AI Deep Research Multi-agent collaboration for market sentiment & technical analysis.	💬 Smart Trading Assistant Natural language interface for instant market insights.
📈 Interactive Indicator Analysis Rich library of technical indicators with drag-and-drop analysis.	🐍 Python Strategy Gen Built-in editor with AI-assisted strategy coding.

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✨ Key Features

1. Universal Data Engine

Stop worrying about data APIs. QuantDinger features a powerful Data Source Factory pattern:

• Crypto: Direct API connection for trading (10+ exchanges) combined with CCXT for market data (100+ sources).
• Stocks: Integrates Yahoo Finance, Finnhub, Tiingo (US), and AkShare (CN/HK).
• Futures/Forex: Supports OANDA and major futures data sources.
• Proxy Support: Built-in proxy configuration for restricted network environments.

2. AI Multi-Agent Research

Your tireless team of analysts:

• Coordinator Agent: Decomposes tasks and manages workflows.
• Research Agent: Performs full-web searches (Google/Bing) for macro news.
• Crypto/Stock Agent: Specializes in technical and capital flow analysis for specific markets.
• Report Generation: Automatically produces structured Daily/Weekly research reports.

2.1 🧠 Memory-Augmented Agents (Local RAG + Reflection Loop)

QuantDinger’s agents don’t start from scratch every time. The backend includes a local memory store and an optional reflection/verification loop:

• What it is: RAG-style experience retrieval injected into agent prompts (NOT model fine-tuning).
• Where it lives: Local SQLite files under backend_api_python/data/memory/ (privacy-first).

flowchart TB
    %% ===== 🌐 Entry Layer =====
    subgraph Entry["🌐 API Entry"]
        A["📡 POST /api/analysis/multi"]
        A2["🔄 POST /api/analysis/reflect"]
    end

    %% ===== ⚙️ Service Layer =====
    subgraph Service["⚙️ Service Orchestration"]
        B[AnalysisService]
        C[AgentCoordinator]
        D["📊 Build Context<br/>price · kline · news · indicators"]
    end

    %% ===== 🤖 Multi-Agent Workflow =====
    subgraph Agents["🤖 Multi-Agent Workflow"]

        subgraph P1["📈 Phase 1 · Analysis (Parallel)"]
            E1["🔍 MarketAnalyst<br/><i>Technical</i>"]
            E2["📑 FundamentalAnalyst<br/><i>Fundamentals</i>"]
            E3["📰 NewsAnalyst<br/><i>News & Events</i>"]
            E4["💭 SentimentAnalyst<br/><i>Market Mood</i>"]
            E5["⚠️ RiskAnalyst<br/><i>Risk Assessment</i>"]
        end

        subgraph P2["🎯 Phase 2 · Debate (Parallel)"]
            F1["🐂 BullResearcher<br/><i>Bullish Case</i>"]
            F2["🐻 BearResearcher<br/><i>Bearish Case</i>"]
        end

        subgraph P3["💹 Phase 3 · Decision"]
            G["🎰 TraderAgent<br/><i>Final Verdict → BUY / SELL / HOLD</i>"]
        end

    end

    %% ===== 🧠 Memory Layer =====
    subgraph Memory["🧠 Local SQLite Memory (data/memory/)"]
        M1[("market_analyst")]
        M2[("fundamental")]
        M3[("news_analyst")]
        M4[("sentiment")]
        M5[("risk_analyst")]
        M6[("bull_researcher")]
        M7[("bear_researcher")]
        M8[("trader_agent")]
    end

    %% ===== 🔄 Reflection Loop =====
    subgraph Reflect["🔄 Reflection Loop (Optional)"]
        R[ReflectionService]
        RR[("reflection_records.db")]
        W["⏰ ReflectionWorker"]
    end

    %% ===== Main Flow =====
    A --> B --> C --> D
    D --> P1 --> P2 --> P3

    %% ===== Memory Read/Write =====
    E1 <-.-> M1
    E2 <-.-> M2
    E3 <-.-> M3
    E4 <-.-> M4
    E5 <-.-> M5
    F1 <-.-> M6
    F2 <-.-> M7
    G <-.-> M8

    %% ===== Reflection Flow =====
    C --> R --> RR
    W --> RR
    W -.->|"verify + learn"| M8
    A2 -.->|"manual review"| M8

Retrieval ranking (simplified):

[ score = w_{sim}\cdot sim + w_{recency}\cdot recency + w_{returns}\cdot returns_score ]

Config lives in .env (see backend_api_python/env.example): ENABLE_AGENT_MEMORY, AGENT_MEMORY_TOP_K, AGENT_MEMORY_ENABLE_VECTOR, AGENT_MEMORY_HALF_LIFE_DAYS, and ENABLE_REFLECTION_WORKER.

3. Robust Strategy Runtime

• Thread-Based Executor: Independent thread pool management for strategy execution.
• Auto-Restore: Automatically resumes running strategies after system restarts.
• Pending Order Worker: Reliable background queue ensures precise signal execution and prevents slippage.

4. Modern Tech Stack

• Backend: Python (Flask) + SQLite + Redis (Optional) — Simple, powerful, extensible.
• Frontend: Vue 2 + Ant Design Vue + KlineCharts/ECharts — Responsive and interactive.
• Deployment: Docker Compose orchestration.

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🏦 Supported Exchanges & Rebates

QuantDinger supports direct connection to major cryptocurrency exchanges for low-latency execution, while using CCXT for broad market data coverage.

💡 Exclusive Benefits: Create accounts through our partner links below to enjoy reduced trading fees and exclusive bonuses. It helps support the project at no extra cost to you!

Exchange	Features	Sign Up Bonus
	🥇 World's Largest Spot, Futures, Margin
	🚀 Web3 & Derivatives Spot, Perpetual, Options
	👥 Social Trading Copy Trading, Futures

Also Supported (Direct/CCXT):

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Multi-Language Support

QuantDinger is built for a global audience with comprehensive internationalization:

All UI elements, error messages, and documentation are fully translated. Language is auto-detected based on browser settings or can be manually switched in the app.

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Supported Markets

Market Type	Data Sources	Trading
Cryptocurrency	Binance, OKX, Bitget, + 100 exchanges	✅ Full support
US Stocks	Yahoo Finance, Finnhub, Tiingo	✅ Via broker API
CN/HK Stocks	AkShare, East Money	⚡ Data only
Forex	Finnhub, OANDA	✅ Via broker API
Futures	Exchange APIs, AkShare	⚡ Data only

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Architecture (Current Repo)

┌─────────────────────────────┐
│      quantdinger_vue         │
│   (Vue 2 + Ant Design Vue)   │
└──────────────┬──────────────┘
               │  HTTP (/api/*)
               ▼
┌─────────────────────────────┐
│     backend_api_python       │
│   (Flask + strategy runtime) │
└──────────────┬──────────────┘
               │
               ├─ SQLite (quantdinger.db)
               ├─ Redis (optional cache)
               └─ Data providers / LLMs / Exchanges

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Repository Layout

.
├─ backend_api_python/         # Flask API + AI + backtest + strategy runtime
│  ├─ app/
│  ├─ env.example              # Copy to .env for local config
│  ├─ requirements.txt
│  └─ run.py                   # Entrypoint
└─ quantdinger_vue/            # Vue 2 UI (dev server proxies /api -> backend)

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Quick Start

Option 1: Docker Deployment (Recommended)

The fastest way to get QuantDinger running.

1. Start Services

Linux / macOS

git clone https://github.com/brokermr810/QuantDinger.git && \
cd QuantDinger && \
cp backend_api_python/env.example backend_api_python/.env && \
docker-compose up -d --build

Windows (PowerShell)

git clone https://github.com/brokermr810/QuantDinger.git
cd QuantDinger
Copy-Item backend_api_python\env.example -Destination backend_api_python\.env
docker-compose up -d --build

2. Configuration & Access

• Frontend UI: http://localhost:8888
• Default Account: quantdinger / 123456

Note

: For production or AI features, edit backend_api_python/.env (add OPENROUTER_API_KEY, change passwords) and restart with docker-compose restart backend.

3. Access the Application

• Frontend UI: http://localhost
• Backend API: http://localhost:5000

Docker Commands Reference

# View running status
docker-compose ps

# View logs
docker-compose logs -f

# View backend logs only
docker-compose logs -f backend

# View frontend logs only
docker-compose logs -f frontend

# Stop services
docker-compose down

# Stop and remove volumes (WARNING: deletes database!)
docker-compose down -v

# Restart services
docker-compose restart

# Rebuild and restart
docker-compose up -d --build

# Enter backend container
docker exec -it quantdinger-backend /bin/bash

# Enter frontend container
docker exec -it quantdinger-frontend /bin/sh

Docker Architecture

┌─────────────────┐     ┌─────────────────┐
│   Frontend      │     │    Backend      │
│   (Nginx)       │────▶│   (Python)      │
│   Port: 80      │     │   Port: 5000    │
└─────────────────┘     └─────────────────┘
        │                       │
        └───────────────────────┘
              Docker Network

• Frontend: Vue.js app served by Nginx, proxies API requests to backend
• Backend: Python Flask API service

Data Persistence

The following data is mounted to the host and persists across container restarts:

volumes:
  - ./backend_api_python/quantdinger.db:/app/quantdinger.db   # Database
  - ./backend_api_python/logs:/app/logs                       # Logs
  - ./backend_api_python/data:/app/data                       # Data directory
  - ./backend_api_python/.env:/app/.env                       # Configuration

Customization

Change ports - Edit docker-compose.yml:

services:
  frontend:
  ports:
    - "8080:80"  # Change to port 8080
  
  backend:
  ports:
    - "5001:5000"  # Change to port 5001

Configure HTTPS - Use a reverse proxy (like Caddy/Nginx):

# Using Caddy (automatic HTTPS)
caddy reverse-proxy --from yourdomain.com --to localhost:80

Production Recommendations

Security:

# Generate strong SECRET_KEY
openssl rand -hex 32

# Set secure admin password
ADMIN_PASSWORD=your-very-secure-password

Resource limits - Add to docker-compose.yml:

services:
  backend:
  deploy:
    resources:
    limits:
      cpus: '2'
      memory: 2G
    reservations:
      cpus: '0.5'
      memory: 512M

Log management:

services:
  backend:
  logging:
    driver: "json-file"
    options:
    max-size: "100m"
    max-file: "3"

Docker Troubleshooting

Frontend can't connect to backend:

docker-compose logs backend
curl http://localhost:5000/api/health

Database permission issues:

chmod 666 backend_api_python/quantdinger.db

Build failures:

# Clear Docker cache and rebuild
docker-compose build --no-cache

Out of memory:

# Check memory usage
docker stats

# Add swap space (Linux)
sudo fallocate -l 2G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile

Updating

# Pull latest code
git pull

# Rebuild and restart
docker-compose up -d --build

Backup

# Backup database
cp backend_api_python/quantdinger.db backup/quantdinger_$(date +%Y%m%d).db

# Backup configuration
cp backend_api_python/.env backup/.env_$(date +%Y%m%d)

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Option 2: Local Development

Prerequisites

• Python 3.10+ recommended
• Node.js 16+ recommended

1. Start the backend (Flask API)

cd backend_api_python
pip install -r requirements.txt
cp env.example .env   # Windows: copy env.example .env
python run.py

Backend will be available at http://localhost:5000.

2. Start the frontend (Vue UI)

cd quantdinger_vue
npm install
npm run serve

Frontend dev server runs at http://localhost:8000 and proxies /api/* to http://localhost:5000 (see quantdinger_vue/vue.config.js).

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Configuration (.env)

Use backend_api_python/env.example as a template. Common settings include:

• Auth: SECRET_KEY, ADMIN_USER, ADMIN_PASSWORD
• Server: PYTHON_API_HOST, PYTHON_API_PORT, PYTHON_API_DEBUG
• Database: SQLITE_DATABASE_FILE (optional; default is backend_api_python/data/quantdinger.db)
• AI / LLM: OPENROUTER_API_KEY, OPENROUTER_MODEL, timeouts
• Web search: SEARCH_PROVIDER, SEARCH_GOOGLE_*, SEARCH_BING_API_KEY
• Proxy (optional): PROXY_PORT or PROXY_URL
• Workers: ENABLE_PENDING_ORDER_WORKER, DISABLE_RESTORE_RUNNING_STRATEGIES

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API

The backend provides REST endpoints for login, market data, indicators, backtesting, strategies, and AI analysis.

• Health: GET /health (also supports GET /api/health for deployment probes)
• Auth (frontend-compatible): POST /api/user/login, POST /api/user/logout, GET /api/user/info

For the full route list, see backend_api_python/app/routes/.

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License

Licensed under the Apache License 2.0. See LICENSE.

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🤝 Community & Support

Join our global community for strategy sharing and technical support:

• 🌟 Want to Contribute?: Join as a Contributor - Early contributors receive QDT governance tokens!
• Telegram (Group): Join QuantDinger Telegram Group
• Discord: Join Server
• 📺 Video Demo: Watch Project Introduction Video
• YouTube: @quantdinger
• Email: brokermr810@gmail.com
• GitHub Issues: Report bugs / Request features

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☕ Support the Project

If QuantDinger helps you profit, consider buying the developers a coffee. Your support keeps the project alive!

ERC-20 / BEP-20 / Polygon / Arbitrum

0x96fa4962181bea077f8c7240efe46afbe73641a7

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Commercial Services

We offer professional services to help you get the most out of QuantDinger:

Service	Description
Deployment & Setup	One-on-one assistance with server deployment, configuration, and optimization
Custom Strategy Development	Tailored trading strategies designed for your specific needs and markets
Enterprise Upgrade	Commercial license, priority support, and advanced features for businesses
Training & Consulting	Hands-on training sessions and strategic consulting for your trading team

Interested? Contact us via:

• 📧 Email: brokermr810@gmail.com
• 💬 Telegram: QuantDinger Group

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Acknowledgements

QuantDinger stands on the shoulders of great open-source projects:

Project	Description	Link
Flask	Lightweight WSGI web framework	flask.palletsprojects.com
flask-cors	Cross-Origin Resource Sharing extension	GitHub
Pandas	Data analysis and manipulation library	pandas.pydata.org
CCXT	Cryptocurrency exchange trading library	github.com/ccxt/ccxt
yfinance	Yahoo Finance market data downloader	github.com/ranaroussi/yfinance
akshare	China financial data interface	github.com/akfamily/akshare
requests	HTTP library for Python	requests.readthedocs.io
Vue.js	Progressive JavaScript framework	vuejs.org
Ant Design Vue	Enterprise-class UI components	antdv.com
KlineCharts	Lightweight financial charting library	github.com/klinecharts/KLineChart
Lightweight Charts	TradingView charting library	github.com/nicepkg/lightweight-charts
ECharts	Apache data visualization library	echarts.apache.org

Thanks to all maintainers and contributors across these ecosystems! ❤️