QuantDinger is an open-source, local-first AI-powered quantitative trading platform that I’ve been building for about six months. It’s designed to cover the full quant workflow — from research and strategy development to backtesting and live execution — while keeping everything running locally.

Most existing quant tools are cloud-based, which means strategies, indicators, and API keys often need to be uploaded to third-party servers. QuantDinger takes a different approach: it is local-first by default, so strategy logic and credentials stay on your own machine.

The platform currently supports multiple markets, including US equities, A-shares, Hong Kong stocks, crypto, forex, and futures.

Key features: - Local-first architecture with Docker-based deployment - AI-assisted strategy and indicator generation - Python-native strategy development - Visual indicators and K-line (candlestick) execution - Backtesting and live trading support - Multi-user support for self-hosted setups

QuantDinger is fully open source under the Apache 2.0 license and can be used commercially.

Demo: https://ai.quantdinger.com

GitHub: https://github.com/brokermr810/QuantDinger

I’d really appreciate feedback from people who’ve built or used trading systems, especially around architecture, backtesting design, and practical usability.