Hi Product Hunt 👋 I’m the maker behind QuantDinger. I built it after getting tired of stitching together charts, notebooks, LLM chats, backtest scripts, and broker APIs—only to discover that research code and live code often behaved differently. QuantDinger is an open-source, self-hosted AI quant operating system that lets you: • Research markets with multiple AI models • Turn ideas into auditable Python strategies • Run deterministic backtests with saved code and configuration snapshots • Move from paper trading to live execution through explicit safety controls • Connect Cursor, Claude Code, and Codex through 25 scoped MCP tools Privacy and safety are core to the product. Your strategies, credentials, and trading history stay on your infrastructure. The hosted environment is paper-only, live agent trading must be explicitly enabled on a self-hosted deployment, and agent activity is scoped and audited. I’d especially love feedback from independent quants, Python strategy developers, and small research teams: 1. Is the path from idea → backtest → paper/live execution clear? 2. Which market or broker integration matters most to your workflow? 3. What evidence would you need before trusting an AI-assisted backtest? You can try the hosted paper environment or self-host the stack from GitHub. QuantDinger is research and trading infrastructure, not financial advice. Please start with paper trading. Thanks for taking a look—I’ll be here to answer technical, security, and backtesting questions.

QuantDinger

Self-hosted AI quant OS for research, backtests & trading

Self-hosted AI quant OS for research, backtests & trading