Multi-Agent Fundamental Analysis System

Autonomous AI agent system for stock fundamental analysis. Mimics human analyst teams through collaborative intelligence, parallel processing, and human-in-the-loop oversight.

⚠️ Status: Design Phase Only

This repository contains comprehensive architectural documentation and design specifications. No implementation code exists yet. Implementation planned for Phases 1-4.

Quick Start

# Install dependencies
uv sync

# Run placeholder
python main.py  # Currently prints "Hello from fundamental-analysis-system!"

Requirements: Python 3.14+

Architecture

5-Layer System:

• Human Interface: Dashboard, notifications, feedback, analytics
• Memory & Learning: Central knowledge graph, learning engine, patterns
• Coordination: Lead coordinator, debate facilitator, QC
• Specialists: Screening, business, financial, strategy, valuation (with memory)
• Support: Data collector, news monitor, knowledge base, report writer

14 Specialized Agents → Memory-enhanced pipeline → 6 human decision gates

Analysis Pipeline

1. Screening (Days 1-2): Quantitative filters → Human Gate 1
2. Parallel Analysis (Days 3-7): Business/Financial/Strategy/News research
3. Debate & Synthesis (Days 8-9): Agent findings challenged → Human Gate 2
4. Valuation (Days 10-11): DCF, scenarios → Human Gate 3
5. Documentation (Day 12): Reports, watchlists → Human Gates 4 & 5

Tech Stack

• Backend: Agent services, API layer
• Frontend: Dashboard, visualization
• Orchestration: Workflow management
• Analysis: Data processing, statistical analysis, time series forecasting
• AI: Agent framework, LLM integration

Data Sources

• SEC EDGAR (10-K, 10-Q, 8-K)
• Financial providers (Koyfin, Bloomberg, Refinitiv)
• News feeds (Reuters, Bloomberg)
• Alternative data (web traffic, social sentiment)

Project Structure

• /docs - Architecture and design documentation
• /docs/design-decisions - Architectural decision records
• /examples - Code samples (to be populated)

Roadmap

• MVP: Initial stocks end-to-end
• Beta: Expanded coverage, 80% accuracy
• Production: Operational workload, <24hr turnaround
• Scale: Large-scale coverage (1000+ stocks)

Documentation

📚 Complete Documentation - Full design documentation:

• Architecture - 5-layer system, 14 agents, memory system, collaboration protocols
• Operations - 12-day analysis pipeline, 6 human gates, data management
• Learning - Pattern recognition, feedback loops, performance metrics
• Implementation - Roadmap, tech stack, compliance, glossary
• Design Decisions - DD-XXX architectural decision records
• Design Flaws - Active issues tracking, resolved flaws
• Examples - Code samples (to be populated)

Quick Links: System Overview | Roadmap | CLAUDE.md

Key Features

• Parallel Processing: Multiple agents work simultaneously
• Collaborative Intelligence: Agents debate and validate findings
• Human Augmentation: Expert input at critical decision points
• Transparency: All reasoning auditable
• Continuous Learning: System improves through feedback loops

Compliance

• SEC investment advisor regulations
• GDPR/CCPA data privacy
• Complete audit trails
• No material non-public info

License

MIT License. See LICENSE for details.

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Disclaimer

Educational project demonstrating multi-agent systems architecture. Not financial advice. Not an investment service. For personal research and portfolio demonstration only.

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Status: v3.0 Design Phase (Documentation Only - No Implementation) | Last Updated: 2025-11-19