Hey dev.to community
If you've ever tried building algorithmic trading strategies, you probably know the pain:Backtesting tools are either too basic (no real AI help) or locked in proprietary platforms
Data privacy is a constant worry when using cloud-based services
Switching between crypto exchanges, stocks, forex brokers feels like juggling different APIs
Strategy development in Pine Script or limited DSLs is frustrating if you're a Python person
After years of frustration with fragmented tools, I decided to build something better: QuantDinger — a fully open-source, local-first quantitative trading platform powered by AI.It's designed for quants, algo traders, and Python developers who want full control, privacy, and modern AI assistance — without sending your strategies or data to any vendor.What is QuantDinger?QuantDinger is a complete end-to-end quant workflow in one Python-native app:AI-assisted strategy creation → Use LLMs (OpenAI, Grok, Gemini, DeepSeek, local models via Ollama) as co-pilot to generate, debug, and optimize Python strategies
High-performance backtesting → Vectorized + multi-threaded engine, supports parameter optimization, walk-forward analysis
Live trading execution → Unified interface for crypto (10+ exchanges via CCXT), US/HK stocks (Interactive Brokers), forex (MT5 bridge)
Multi-agent research system → LLM agents with reflection loops, local RAG for market sentiment, news, fundamentals
Beautiful dashboard → Real-time charts (Plotly), portfolio tracking, global market overview, multi-language UI (English/中文/日本語/한국어)
Everything runs locally or on your own server. Your API keys, historical data, and trading logic never leave your machine.Why Local-First + Privacy Matters in 2025With increasing regulations (GDPR, data sovereignty laws) and rising AI privacy concerns, many traders are moving away from cloud-only platforms. QuantDinger gives you:Zero vendor lock-in
No telemetry or forced updates
Full auditability of every line of code (Apache 2.0 license)
Key Technical HighlightsBackend: FastAPI + SQLModel + Celery for async tasks
Frontend: React + Vite + Tailwind + shadcn/ui (responsive, mobile-friendly)
AI Layer: LangChain + local RAG + multi-LLM support
Data Handling: DuckDB for fast local storage, Parquet for historical data
Deployment: One-command Docker Compose (backend + frontend + Redis + Postgres optional)
Quick Start in 3 StepsClone the repo:bash
git clone https://github.com/brokermr810/QuantDinger.git
cd QuantDinger
Start with Docker (recommended):bash
docker compose up -d
Open http://localhost:5173 → Create account → Connect your exchange/broker → Start building!
Or run without Docker (needs Python 3.11+, Node 18+):bash
Backend
poetry install
poetry run python main.py
Frontend (separate terminal)
cd frontend
pnpm install
pnpm dev
Demo & ScreenshotsHere's a quick intro video (3 mins): https://youtu.be/HPTVpqL7knM
Live hosted demo (for quick try, but local is recommended): https://ai.quantdinger.com
Recent updates: Added DeepSeek LLM support, fixed IBKR position sync, improved mobile UI
Next up: More built-in strategies, reinforcement learning agents, better optimization UI, community strategy marketplace.I Need Your Help!This is a solo + community project, and I'd love your input:Try it out and share your experience (good/bad)
What quant pain points does this solve (or miss)?
Contribute code, docs, translations, or strategy examples
Star/fork on GitHub if it looks interesting
Drop a comment below — tell me what kind of trading setup you use, or what feature you'd kill for in a tool like this.Thanks for reading, and happy (profitable) coding! #opensource #python #quant #trading #algotrading #ai #fintech #backtesting
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