Building a Local AI-Native Hedge Fund: Multi-Agent Architecture and Auditability

I Built a Fully Local, AI-Native Hedge Fund System (Multi-Agent, Auditable, No Paid APIs)

Tapesh Chandra Das engineered an AI-native hedge fund prototype that operates entirely on local infrastructure with zero paid API dependencies. The system achieved a 0.61 Sharpe ratio and 7.6% CAGR during backtesting of its momentum and trend strategy.

Why This Matters

Most AI trading projects remain isolated notebook experiments or black-box systems that lack the observability required for institutional-grade operations. Real-world trading systems frequently fail on integration, control, and visibility rather than just model performance; this project addresses these technical realities by prioritizing system design and traceability, implementing essential production features like circuit breakers and data quality gates to ensure reliability over mere demos.

Key Insights

• Multi-agent coordination using a Research Council for data ingestion and Strategy Ensembles including trend following and mean reversion for signal generation.
• Auditability by design using TraceLM-based execution tracing and decision snapshots stored in Postgres to ensure every trade is traceable and reproducible.
• Local LLM integration via Ollama for research tasks and Alpaca for paper execution, eliminating dependencies on paid subscription-based APIs.
• Infrastructure built on a FastAPI service layer with Celery worker queues and Redis for managing distributed research and execution tasks.
• Production-style reliability features such as dead-man heartbeats and stage-specific circuit breakers to prevent system failure during execution.

Practical Applications

• Use Case: Deploying a portable trading runtime on local Docker or Oracle Always Free instances for cost-effective strategy validation. Pitfall: Using hobbyist scripts without data quality validation leads to ‘garbage in, garbage out’ execution errors.
• Use Case: Implementing structured audit logs for internal strategy debugging and performance attribution in multi-agent environments. Pitfall: Scaling black-box AI systems without decision-level introspection makes identifying the root cause of drawdowns impossible.

References:

• https://dev.to/tapesh_chandradas_5f7919/i-built-a-fully-local-ai-native-hedge-fund-system-multi-agent-auditable-no-paid-apis-k8f
• https://github.com/td-02/ai-native-hedge-fund