Building open-source AI-native trading infrastructure for crypto, stocks, forex, and global quant teams.
From market research to executable trading strategies — all in one workflow.
I'm the founder of QuantDinger, an open-source AI-native quantitative trading platform.
I focus on building trading systems that connect the full workflow:
Market Data → AI Research → Strategy Logic → Backtesting → Risk Control → Live Execution
My work is centered around:
- AI-assisted market research
- Quantitative strategy development
- Backtesting and strategy optimization
- Multi-agent trading analysis
- Live trading infrastructure
- Private deployment and open-source financial tools
QuantDinger is an open-source AI-native quant platform designed for traders, developers, and quant teams.
It supports crypto, stocks, forex, and multi-market research workflows.
- AI-assisted market analysis
- Indicator and strategy development
- Professional backtesting
- Strategy optimization
- Multi-agent research
- Live trading execution
- Multi-exchange connectivity
- Alerts via email, webhook, Telegram, Discord, and more
- Docker-based private deployment
- Full workflow from research to execution
QuantDinger is built to turn trading ideas, market signals, and AI research into executable strategies.
Most trading tools only solve one part of the problem.
Some tools help you write indicators. Some help you backtest. Some help you execute trades. Some help you analyze markets.
QuantDinger connects the whole loop.
Idea → Indicator → Strategy → Backtest → Optimize → Execute → Monitor
This is the workflow I believe the next generation of AI-native trading systems should provide.
I'm building QuantDinger as an open trading infrastructure layer for:
- Individual traders
- Quant developers
- AI trading researchers
- Crypto traders
- Stock and forex strategy builders
- Private trading teams
- Developers who want full control over their trading system
The long-term goal is to make professional-grade quant infrastructure more open, accessible, and extensible.
I work mainly with:
Python · Vue · Docker · PostgreSQL · Flask · Lightweight Charts · CCXT
AI Agents · Backtesting · Quant Research · Live Trading · Trading Infrastructure
Open-source AI-native quant platform for market research, strategy development, backtesting, and live execution.
Frontend source code for QuantDinger's AI-native quant research, strategy, trading, and operations workspace.
Mobile frontend for QuantDinger.
- Website: https://www.quantdinger.com
- AI Workspace: https://ai.quantdinger.com
- GitHub: https://github.com/brokermr810/QuantDinger
Quant trading should not be locked behind closed SaaS platforms.
Developers and traders should be able to:
- Own their trading infrastructure
- Inspect and modify the system
- Run strategies privately
- Connect their own data and brokers
- Build custom research workflows
- Move from idea to execution without switching five different tools
That is why QuantDinger is open-source.
I'm currently focused on improving:
- AI strategy generation
- Multi-agent market analysis
- Strategy backtesting workflow
- Live execution reliability
- Multi-market support
- SaaS and private deployment experience
- Developer-friendly strategy APIs
- Trading system automation
I believe open-source is the fastest way to build serious infrastructure.
Open-source gives developers:
- Transparency
- Flexibility
- Trust
- Community feedback
- Faster iteration
- Real-world validation
If you're interested in AI trading systems, quant research, or open financial infrastructure, feel free to follow the project.
If you like open-source AI quant trading infrastructure, please consider giving QuantDinger a star.
Every star helps the project reach more developers, traders, and builders.