Most “AI trading projects” fall into one of three categories:
notebook experiments
single-model pipelines
or black-box systems with no observability
They don’t resemble real trading systems.
So I built something closer to production reality:
A free, portable, AI-native hedge fund prototype with:
multi-agent decision making
backtesting + paper execution
full audit infrastructure
and zero paid API dependencies
Project:
https://github.com/td-02/ai-native-hedge-fund
What this actually is
This is not just a model.
It’s a complete trading runtime with:
data ingestion
research layer
strategy ensemble
risk management
execution system
audit + tracing
All wired together into a single pipeline.
From the README:
“A production-grade multi-agent trading system with backtesting, paper execution, and full audit infrastructure. No paid APIs required.”
Why I built this
The gap is simple:
Most people optimize models.
Real systems fail on integration, control, and visibility.
Discussion
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