ARGUS is an AI-powered quantitative research terminal designed to unify:
- 📈 Market Intelligence
- 📊 Quantitative Analytics
- 💼 Portfolio Intelligence
- 🤖 Multi-Agent Research
- 📚 Financial Knowledge Retrieval
- 🔍 Evidence-Based Decision Support
into a single research ecosystem.
Unlike conventional "ChatGPT + Stock API" applications, ARGUS follows a layered architecture where deterministic financial analytics, retrieval systems, portfolio intelligence, and specialized research agents collaborate to generate explainable and evidence-backed investment insights.
The long-term objective is to build an institutional-grade research platform inspired by:
- Bloomberg Terminal
- BlackRock Aladdin
- FactSet
- Capital IQ
- TradingView
- Internal Quant Research Platforms
while remaining transparent, reproducible, and engineer-friendly.
ARGUS is built on five foundational principles:
All quantitative metrics are computed mathematically.
No performance metric is generated by an LLM.
Every research output should be traceable to:
- Financial statements
- SEC filings
- Earnings calls
- Investor presentations
- Market data
Instead of a single general-purpose assistant, ARGUS employs specialized agents that focus on specific domains.
Every metric, recommendation, and conclusion must be explainable and reproducible.
ARGUS prioritizes research quality over conversational fluency.
┌──────────────────────────┐
│ ARGUS Terminal │
└────────────┬─────────────┘
│
▼
┌────────────────────────────────────────────────────────────────────────────┐
│ FastAPI Backend │
└────────────────────────────────────────────────────────────────────────────┘
│ │
▼ ▼
┌─────────────────┐ ┌────────────────────┐
│ Quant Engine │ │ Portfolio Engine │
└─────────────────┘ └────────────────────┘
│ │
└──────────────┬────────────┘
▼
┌──────────────────────┐
│ Intelligence Layer │
└──────────────────────┘
┌──────────────┬──────────────┬──────────────┐
▼ ▼ ▼ ▼
Market Agent Risk Agent News Agent Strategy Agent
│ │ │ │
└──────────────┴───────┬──────┴──────────────┘
▼
┌────────────────────────┐
│ Knowledge Layer │
│ ChromaDB + Embeddings │
└────────────────────────┘
│
▼
SEC Filings • Earnings Calls • Annual Reports
Investor Presentations • Research Documents
- Live Yahoo Finance Integration
- Async Data Retrieval
- Provider-Based Architecture
- Dependency Injection
- Configurable Market Data Sources
- Typed Response Contracts
- Structured Validation
POST /analyzeReturns:
- Company Information
- Market Statistics
- Quantitative Metrics
- Research Context
The Quant Engine is responsible for all deterministic financial computations.
- Annualized Return
- Volatility
- Sharpe Ratio
- Beta
- Maximum Drawdown
✅ Deterministic
✅ Reproducible
✅ Explainable
✅ Unit Tested
✅ Independent of LLMs
Analyze portfolios using weighted allocations and risk-adjusted performance metrics.
- Portfolio Return
- Portfolio Volatility
- Portfolio Sharpe Ratio
- Portfolio Beta
- Portfolio Drawdown
- Allocation Analysis
POST /portfolio/analyzeARGUS utilizes specialized research agents instead of a monolithic assistant.
Analyzes:
- Price Trends
- Performance
- Volatility
- Technical Context
Produces:
- Market Intelligence Summary
Analyzes:
- Beta
- Volatility
- Drawdowns
- Sharpe Ratio
Produces:
- Risk Assessment Report
Analyzes:
- Company News
- Market Events
- Financial Headlines
Produces:
- News Intelligence Summary
Aggregates insights from all other agents.
Produces:
- Bull Case
- Bear Case
- Neutral Case
- Key Risks
- Growth Opportunities
The Knowledge Layer enables evidence-backed research through retrieval-augmented generation.
- 10-K Filings
- 10-Q Filings
- Annual Reports
- Earnings Call Transcripts
- Investor Presentations
PDF / Filing / Transcript
│
▼
Document Loader
│
▼
Chunking
│
▼
Embeddings
│
▼
ChromaDB
│
▼
Semantic Retrieval
│
▼
Agent Context
│
▼
Research Report
- Source Citations
- Page-Level Provenance
- Chunk Traceability
- Ticker Filtering
- Offline Deterministic Testing
Current Test Status:
56+ Tests Passing
Coverage Includes:
- Quant Engine
- Portfolio Analytics
- Agent Framework
- Knowledge Layer
- API Contracts
- Validation Rules
- Concurrency Handling
- Error Scenarios
- Python 3.11+
- FastAPI
- Pydantic
- AsyncIO
- NumPy
- Pandas
- Yahoo Finance
- ChromaDB
- Embeddings
- PDF Processing
- Semantic Retrieval
- Multi-Agent Architecture
- Retrieval-Augmented Generation (RAG)
- React
- TypeScript
- Vite
- TradingView Charts
- TanStack Query
- Project Foundations
- FastAPI Backend
- Quant Engine
- Portfolio Analytics
- Intelligence Layer
- Knowledge Layer
- Retrieval Pipeline
- PostgreSQL Persistence
- Authentication System
- Production Hardening
- Research Report Generation
- Research Terminal UI
- Portfolio Workspace
- Strategy Workspace
- Agent Workspace
- Monitoring & Observability
- Cloud Deployment
Most finance projects stop at:
Stock API + ChatGPT
ARGUS is designed differently.
The objective is to build a platform where:
- Analytics are deterministic.
- Research is evidence-backed.
- Agents are specialized.
- Intelligence is explainable.
- Conclusions are traceable.
- Insights are grounded in real financial data.
The end goal is not to create another chatbot.
The goal is to build a modern research terminal capable of supporting serious quantitative research and investment analysis workflows.
AI & Machine Learning Engineer • Quant Research Enthusiast • Data Science Practitioner
Building ARGUS as a long-term exploration of:
- Quantitative Finance
- AI Engineering
- Multi-Agent Systems
- Retrieval-Augmented Generation
- Financial Research Infrastructure
- Institutional Analytics