QuantDinger

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

Next-Gen AI Quantitative Trading Platform

🤖 AI-Native · 🐍 Visual Python · 🌍 Multi-Market · 🔒 Privacy-First

Build, Backtest, and Trade with an AI Co-Pilot. Better than PineScript, Smarter than SaaS.

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

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📖 Introduction

What is QuantDinger?

QuantDinger is a local-first, privacy-first quantitative trading infrastructure. It runs entirely on your machine, giving you full control over your strategies, trading data, and API keys.

Why Local-First?

Unlike SaaS platforms that lock your data and strategies in the cloud, QuantDinger runs locally. Your strategies, trading logs, API keys, and analysis results stay on your machine. No vendor lock-in, no subscription fees, no data exfiltration.

Who is this for?

QuantDinger is built for traders, researchers, and engineers who:

• Value data sovereignty and privacy
• Want transparent, auditable trading infrastructure
• Prefer engineering over marketing
• Need a complete workflow: data, analysis, backtesting, and execution

Core Features

QuantDinger includes a built-in LLM-based multi-agent research system that gathers financial intelligence from the web, combines it with local market data, and generates analysis reports. This integrates with strategy development, backtesting, and live trading workflows.

Core Value

• 🔓 Apache 2.0 Open Source: Fully permissive and commercial-friendly. Unlike viral licenses (GPL/AGPL), you truly own your code and modifications.
• 🐍 Python-Native & Visual: Write indicators in standard Python (easier than PineScript) with AI assistance. Visualize signals directly on charts—a "Local TradingView" experience.
• 🤖 AI-Loop Optimization: It doesn't just run strategies; AI analyzes backtest results to suggest parameter tuning (Stop-Loss/TP/MACD settings), forming a closed optimization loop.
• 🌍 Universal Market Access: One unified system for Crypto (Live), US/CN Stocks, Forex, and Futures (Data/Notify).
• ⚡ Docker & Clean Arch: 4-line command deployment. Modern Tech Stack (Vue + Python) with a clean, separation-of-concerns architecture.

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📺 Video Demo

Click the video above to watch the QuantDinger project introduction

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📚 Documentation

Guides

• Python Strategy Development Guide

Notification Configuration

• Telegram Notification Setup
• Email (SMTP) Notification Setup
• SMS (Twilio) Notification Setup

📸 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.
📊 Portfolio Monitor Track positions, set alerts, and receive AI-powered analysis via Email/Telegram.

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✨ Key Features

1. Visual Python Strategy Workbench

Better than PineScript, Smarter than SaaS.

• Python Native: Write indicators and strategies in Python. Leverage the entire Python ecosystem (Pandas, Numpy, TA-Lib) instead of proprietary languages like PineScript.
• "Mini-TradingView" Experience: Run your Python indicators directly on the built-in K-line charts. Visually debug buy/sell signals on historical data.
• AI-Assisted Coding: Let the built-in AI write the complex logic for you. From idea to code in seconds.

2. Complete Trading Lifecycle

From Indicator to Execution, Seamlessly.

1. Indicator: Define your market entry/exit signals.
2. Strategy Config: Attach risk management rules (Position sizing, Stop-Loss, Take-Profit).
3. Backtest & AI Optimization: Run backtests, view rich performance metrics, and let AI analyze the result to suggest improvements (e.g., "Adjust MACD threshold to X").
4. Execution Mode:
   • Live Trading: Direct API execution for 10+ Crypto Exchanges (Binance, OKX, etc.).
   • Signal Notification: For non-executable markets (Stocks/Forex/Futures), send signals via Telegram, Discord, Email, SMS, or Webhook.

3. AI Multi-Agent Research

Your 24/7 AI Investment Committee.

The system employs a multi-agent team to act as a secondary filter for your strategies:

• Research Agents: Scrape web news and macro events (Google/Bing).
• Analysis Agents: Analyze technical indicators and capital flows.
• Strategic Integration: The AI judgment can serve as a "Market Filter"—only allowing your strategy to trade when the AI sentiment aligns (e.g., "Don't buy if AI Risk Analyst flags high macro danger").

4. Universal Data Engine

QuantDinger provides a unified data interface across multiple markets:

• Cryptocurrency: Direct API connections for trading (10+ exchanges) and CCXT integration for market data (100+ sources)
• Stocks: Yahoo Finance, Finnhub, Tiingo (US stocks), and AkShare (CN/HK stocks)
• Futures/Forex: OANDA and major futures data sources
• Proxy Support: Built-in proxy configuration for restricted network environments

5. 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.

6. Strategy Runtime

• Thread-Based Executor: Independent thread pool for strategy execution
• Auto-Restore: Resumes running strategies after system restarts
• Order Queue: Background worker for order execution

7. Tech Stack

• Backend: Python (Flask) + SQLite + Redis (optional)
• Frontend: Vue 2 + Ant Design Vue + KlineCharts/ECharts
• Deployment: Docker Compose

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🔌 Supported Exchanges

QuantDinger supports direct API connections to major cryptocurrency exchanges for execution, and uses CCXT for broad market data coverage.

Direct API Support

Exchange	Markets
Binance	Spot, Futures, Margin
OKX	Spot, Perpetual, Options
Bitget	Spot, Futures, Copy Trading

Also Supported via CCXT

Bybit, Gate.io, Kraken, KuCoin, HTX, and 100+ other exchanges for market data.

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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

• Contributing: Contributing Guide · Contributors
• Telegram: QuantDinger Group
• Discord: Join Server
• 📺 Video Demo: Project Introduction
• YouTube: @quantdinger
• Email: brokermr810@gmail.com
• GitHub Issues: Report bugs / Request features

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💰 Project Sustainability

QuantDinger is open-source and free to use. If you find it useful, here are ways to support ongoing development:

Direct Donations

ERC-20 / BEP-20 / Polygon / Arbitrum

0x96fa4962181bea077f8c7240efe46afbe73641a7

Exchange Referral Links

If you're signing up for supported exchanges, using the links below provides referral benefits that help support the project. These are optional and do not affect your trading fees or account functionality.

Exchange	Referral Link
Binance	Sign up with referral
OKX	Sign up with referral
Bitget	Sign up with referral

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Professional Services

Professional services are available:

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! ❤️