Tools: I Built an AI-Powered Quant Trading Platform That Runs 100% Locally – Introducing QuantDinger

Backend ## Frontend (separate terminal) 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 Open http://localhost:5173 → Create account → Connect your exchange/broker → Start building! Or run without Docker (needs Python 3.11+, Node 18+):bash poetry install poetry run python main.py 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 Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse