Hierarchical Research Agent for Quantitative Trading

The Problem

In quantitative trading, milliseconds matter. A hedge fund’s research team was bottlenecked by the speed of human synthesis—by the time an analyst connected breaking news to relevant SEC filings and historical patterns, the trading window had closed. They needed machine-speed insight with analyst-quality reasoning.

The Architecture

flowchart TB
  subgraph feeds [Real-Time Feeds]
      Bloomberg[Bloomberg Terminal]
      News[News Wires]
      SEC[SEC EDGAR]
  end
  
  subgraph specialists [Specialist Agents]
      Equities[Equities Agent]
      FixedIncome[Fixed Income Agent]
      Commodities[Commodities Agent]
      Macro[Macro Agent]
  end
  
  subgraph synthesis [Synthesis Layer]
      Synthesizer[Synthesizer Agent]
      Reasoner[o1-preview Reasoner]
  end
  
  subgraph output [Trading Output]
      Recommendations[Trade Recommendations]
      Confidence[Confidence Scores]
      Audit[Reasoning Audit Trail]
  end
  
  Bloomberg --> Equities
  Bloomberg --> FixedIncome
  Bloomberg --> Commodities
  News --> Macro
  SEC --> Equities
  SEC --> FixedIncome
  
  Equities --> Synthesizer
  FixedIncome --> Synthesizer
  Commodities --> Synthesizer
  Macro --> Synthesizer
  
  Synthesizer --> Reasoner
  Reasoner --> Recommendations
  Reasoner --> Confidence
  Reasoner --> Audit

Hierarchical Multi-Agent System

The architecture mirrors how a trading desk operates:

1. Specialist Agents: Each agent monitors a specific asset class—equities, fixed income, commodities, macro indicators. They maintain domain-specific context and historical pattern libraries.
2. Synthesizer Agent: Aggregates signals from specialists, identifies cross-asset correlations, and surfaces potential opportunities
3. o1-preview Reasoner: Applies deep reasoning to synthesized signals, generating trade recommendations with explicit confidence scores and reasoning chains

The hierarchy ensures specialists stay focused while the synthesizer maintains the big picture.

Tech Stack

• LlamaIndex — Document indexing and retrieval for SEC filings
• OpenAI o1-preview — Complex reasoning and synthesis
• Bloomberg Terminal API — Real-time market data
• Python — High-performance async pipeline

The Impact

The system now runs continuously during market hours, surfacing opportunities faster than any human analyst could.