QuantDinger

AI-Native Quantitative Trading Platform

Vibe Coding Meets Algo Trading

🇨🇳 中文  ·  🇺🇸 English

🌐 Live Demo  ·  📺 Video  ·  💬 Community  ·  🚀 Quick Start

7 AI Agents · Python Strategies · 10+ Exchanges · Prediction Markets · Your Server, Your Keys

Describe your trading idea in natural language → AI writes the Python strategy → Backtest → Live trade.
Zero coding required. Self-hosted — your API keys and strategies never leave your machine.

_{🇺🇸 English · 🇨🇳 简体中文 · 🇹🇼 繁體中文 · 🇯🇵 日本語 · 🇰🇷 한국어 · 🇩🇪 Deutsch · 🇫🇷 Français · 🇹🇭 ไทย · 🇻🇳 Tiếng Việt · 🇸🇦 العربية}

• 🚀 Quick Start (2 Minutes)
• 🎯 Why QuantDinger?
• 📸 Visual Tour
• ✨ Key Features
• 🔌 Supported Exchanges & Brokers
• 🏗️ Architecture & Configuration
• 📚 Documentation Index
• 💼 License & Commercial
• 🤝 Community & Support

Only need: Docker installed. Nothing else.

# 1. Clone
git clone https://github.com/brokermr810/QuantDinger.git
cd QuantDinger

# 2. Configure (edit admin password & AI API key)
cp backend_api_python/env.example backend_api_python/.env

# 3. Launch!
docker-compose up -d --build

Windows PowerShell: use Copy-Item backend_api_python\env.example -Destination backend_api_python\.env

🎉 Done! Open http://localhost:8888 | Login: quantdinger / 123456

# Required — Change for production!
ADMIN_USER=quantdinger
ADMIN_PASSWORD=your_secure_password
SECRET_KEY=your_random_secret_key

# Optional — Enable AI features (pick one)
OPENROUTER_API_KEY=your_key        # Recommended: 100+ models
OPENAI_API_KEY=your_key            # GPT-4o
DEEPSEEK_API_KEY=your_key          # Cost-effective
GOOGLE_GEMINI_API_KEY=your_key     # Gemini

docker-compose ps                  # View service status
docker-compose logs -f backend     # View backend logs (real-time)
docker-compose restart backend     # Restart backend only
docker-compose up -d --build       # Rebuild & restart all
docker-compose down                # Stop all services

git pull && docker-compose up -d --build

docker exec quantdinger-db pg_dump -U quantdinger quantdinger > backup.sql
cat backup.sql | docker exec -i quantdinger-db psql -U quantdinger quantdinger

FRONTEND_PORT=3000          # Default: 8888
BACKEND_PORT=127.0.0.1:5001 # Default: 5000

Vibe Coding for Trading — Describe your trading idea in plain English (or any language). AI writes the Python strategy, backtests it, and deploys it to live markets. No manual coding. No SaaS lock-in. Everything runs on your own server.

📊 Professional Quant Dashboard
🤖 AI Deep Research	💬 Smart Trading Assistant
📈 Indicator Analysis	🐍 AI Strategy Coding
📊 Portfolio Monitor

No coding required. Tell AI what you want in natural language — it generates production-ready Python strategies. Or write your own with the full Python ecosystem (Pandas, NumPy, TA-Lib, scikit-learn). Visualize everything on professional K-line charts.

💬 "I want a MACD crossover strategy with RSI filter on BTC 15min"
    ↓ AI generates Python code
    ↓ 📈 Visualize on K-line charts
    ↓ 🔄 Backtest with rich metrics
    ↓ 🤖 AI suggests optimizations
    ↓ 🚀 One-click deploy to live trading

Not just one AI call. QuantDinger deploys 7 specialized agents that collaborate like a research team — analyze, debate, and reach consensus:

Phase 1 (Parallel):  📊 Technical · 📑 Fundamental · 📰 News · 💭 Sentiment · ⚠️ Risk
Phase 2 (Debate):    🐂 Bull vs 🐻 Bear — structured argumentation
Phase 3 (Decision):  🎯 TraderAgent → BUY / SELL / HOLD (with confidence %)

• 🎵 Natural Language Analysis — Ask "Analyze BTC trend for next week" → 7 agents deliver a full report
• 📡 AI Trading Radar — Auto-scans Crypto/Stocks/Forex hourly, surfaces opportunities
• ⚡ Quick Trade Panel — See a signal? One-click to execute. No page switching.
• 🧠 Memory-Augmented — Agents learn from past analyses (local RAG, not cloud)
• 🔌 5+ LLM Providers: OpenRouter (100+ models), OpenAI, Gemini, DeepSeek, Grok
• 📊 Polymarket Prediction Markets — On-demand AI analysis for prediction markets. Input a market link or title → AI analyzes probability divergence, opportunity score, and trading recommendations. Full history tracking and billing integration.

On-demand AI analysis for prediction markets. Input a Polymarket link or market title → AI analyzes probability divergence, opportunity score, and provides trading recommendations.

Features:

• 🔍 Smart Search — Supports market links, slugs, or natural language titles
• 🤖 AI Probability Prediction — Compares AI-predicted probability vs market probability
• 📈 Opportunity Scoring — Calculates opportunity score based on divergence and confidence
• 💡 Trading Recommendations — YES/NO/HOLD with detailed reasoning and key factors
• 📚 History Tracking — View all your past analyses with full details in a dedicated history tab
• 💰 Billing Integration — Configurable credit consumption per analysis (set via BILLING_COST_POLYMARKET_DEEP_ANALYSIS)
• 🌍 Multi-Language — AI responses match your frontend language (English/Chinese)
• 📊 Admin Statistics — All analyses tracked in user management dashboard

Usage:

1. Navigate to AI Asset Analysis → Prediction Markets tab
2. Input Polymarket link or market title
3. AI analyzes and returns:
   - Market probability vs AI-predicted probability
   - Divergence analysis
   - Opportunity score (0-100)
   - Trading recommendation (YES/NO/HOLD)
   - Detailed reasoning and key factors
4. View analysis history anytime

Most open-source projects need months of custom billing work. QuantDinger ships with a complete monetization system out of the box:

• 💳 Membership Plans — Monthly / Yearly / Lifetime tiers with configurable pricing & credits
• ₿ USDT On-Chain Payment — TRC20 scan-to-pay, HD Wallet (xpub) per-order addresses, auto-reconciliation via TronGrid
• 🏪 Indicator Marketplace — Users publish & sell Python indicators, you take commission
• ⚙️ Admin Dashboard — Order management, AI usage stats, user analytics

• Multi-User — PostgreSQL-backed accounts with role-based permissions
• OAuth — Google & GitHub one-click login
• Protection — Cloudflare Turnstile, IP/account rate limiting, email verification
• Demo Mode — Read-only mode for public showcases

flowchart TB
    subgraph Entry["🌐 API Entry"]
        A["📡 POST /api/analysis/multi"]
        A2["🔄 POST /api/analysis/reflect"]
    end
    subgraph Service["⚙️ Service Orchestration"]
        B[AnalysisService]
        C[AgentCoordinator]
        D["📊 Build Context<br/>price · kline · news · indicators"]
    end
    subgraph Agents["🤖 7-Agent Workflow"]
        subgraph P1["📈 Phase 1 · Parallel Analysis"]
            E1["🔍 MarketAnalyst"]
            E2["📑 FundamentalAnalyst"]
            E3["📰 NewsAnalyst"]
            E4["💭 SentimentAnalyst"]
            E5["⚠️ RiskAnalyst"]
        end
        subgraph P2["🎯 Phase 2 · Bull vs Bear Debate"]
            F1["🐂 BullResearcher"]
            F2["🐻 BearResearcher"]
        end
        subgraph P3["💹 Phase 3 · Final Decision"]
            G["🎰 TraderAgent → BUY / SELL / HOLD"]
        end
    end
    subgraph Memory["🧠 Local Memory Store"]
        M1[("Agent Memories (PostgreSQL)")]
    end
    subgraph Reflect["🔄 Reflection Loop"]
        R[ReflectionService]
        W["⏰ ReflectionWorker → verify + learn"]
    end
    A --> B --> C --> D
    D --> P1 --> P2 --> P3
    Agents <-.->|"RAG retrieval"| M1
    C --> R
    W -.->|"update memories"| M1

┌─────────────────────────────────────┐
│         Docker Compose              │
│                                     │
│  ┌───────────────────────────────┐  │
│  │  frontend (Nginx)  → :8888   │  │
│  └──────────────┬────────────────┘  │
│                 │ /api/* proxy       │
│  ┌──────────────▼────────────────┐  │
│  │  backend (Flask)   → :5000   │  │
│  └──────────────┬────────────────┘  │
│  ┌──────────────▼────────────────┐  │
│  │  postgres (PG 16)  → :5432   │  │
│  └───────────────────────────────┘  │
│                                     │
│  External: LLM APIs · Exchanges ·   │
│  TronGrid · Data providers          │
└─────────────────────────────────────┘

QuantDinger/
├── backend_api_python/          # 🐍 Backend (Open Source, Apache 2.0)
│   ├── app/routes/              #   API endpoints
│   ├── app/services/            #   Business logic (AI, trading, payment)
│   ├── migrations/init.sql      #   Database schema
│   ├── env.example              #   ⚙️ Config template → copy to .env
│   └── Dockerfile
├── frontend/                    # 🎨 Frontend (Pre-built)
│   ├── dist/                    #   Static files (HTML/JS/CSS)
│   ├── Dockerfile               #   Nginx image
│   └── nginx.conf               #   SPA routing + API proxy
├── docs/                        # 📚 Guides & tutorials
├── docker-compose.yml           # 🐳 One-click deployment
└── LICENSE                      # Apache 2.0

All detailed guides are in the docs/ folder:

Backend source code is licensed under Apache License 2.0. See LICENSE.

The frontend UI is provided as pre-built files. Trademark rights (name/logo/branding) are governed separately — see TRADEMARKS.md.

If you are a university, research institution, non-profit, community group, or educational program, you can apply for free authorization and full frontend source code:

• 🏫 Universities & academic research
• 🌍 Open-source communities & developer groups
• 🤝 Non-profit & public welfare organizations
• 📚 Educational programs & student hackathons

For commercial use, purchase a license to get:

• Full frontend source code + future updates
• Branding authorization — modify name/logo/copyright as agreed
• Operations support — deployment, upgrades, incident response
• Consulting — architecture review, performance tuning

• Contributing Guide · Contributors
• Report Bugs / Request Features
• Email: [email protected]

Crypto Donations (ERC-20 / BEP-20 / Polygon / Arbitrum)

0x96fa4962181bea077f8c7240efe46afbe73641a7

Quantitative Finance Society (QFS) Indiana University Bloomington

💡 Want to become a partner? Contact [email protected] or Telegram.

Built with ❤️ on the shoulders of: Flask · Pandas · CCXT · yfinance · Vue.js · Ant Design Vue · KlineCharts · ECharts · Capacitor · bip-utils

_{If QuantDinger helps you, consider ⭐ starring the repo — it means a lot!}