A bit more context on why I focused on a local-first design:
In my own trading and research, I found it hard to trust hosted quant platforms with proprietary strategies and exchange API keys. QuantDinger was built so that execution logic, credentials, and most data processing never leave the local environment.
The AI components are used mainly for research assistance (e.g. generating strategy ideas or indicators), not for opaque “black-box” execution. All strategies remain inspectable Python code.
Happy to answer technical questions about how the backtesting engine works, how markets are abstracted, or how the AI agents are integrated.