AI Hedge Fund

What It Is

A proof-of-concept multi-agent trading system. Each agent implements a distinct investment strategy (value, technical, sentiment). A central orchestrator aggregates signals, weights by confidence, and generates trade recommendations.

This is a research project, not a live fund.

The Problem

Trading systems typically:

• Use single models or fixed rule sets
• Lack multi-perspective analysis
• Cannot explain decision rationale
• Slow to adapt to changing conditions

The Solution

Multiple specialized agents:

• Each agent analyzes markets using its strategy
• Signals scored by confidence
• Central aggregation weights and compares
• Dashboard shows reasoning and consensus

Agents

Each agent operates independently and produces confidence-scored signals.

Architecture

Agent Layer

• Containerized microservices per agent
• REST APIs for inter-agent communication
• Custom logic per strategy

Data Layer

• Alpha Vantage and Yahoo Finance for market data
• NLP pipeline for news and social sentiment
• Historical fundamental datasets
• Redis caching

Processing Layer

• Signal scoring engine
• Cross-agent comparison
• Anomaly detection
• Decision audit trail

Decision Engine

• Trade simulation
• Position sizing based on confidence
• Portfolio constraint validation
• Performance feedback to agents

Technology Stack

• Python, FastAPI, Docker
• Kafka for event streaming
• MongoDB and PostgreSQL
• LangChain and CrewAI for agent orchestration
• GPT-4, FinBERT, Mistral for reasoning

Development Status

Use Cases

Signal Research Analyze agent consensus and individual signal performance.

Strategy Testing Plug in custom agent logic and compare against existing agents.

Education Understand multi-factor analysis through transparent reasoning.