A quantitative finance platform that integrates financial modeling with market intelligence. Built with Python/FastAPI backend, React/Next.js frontend, and AI-powered analysis.
- STA (Scaled Total Accruals): Measures earnings quality and cash flow reliability
- SNOA (Scaled Net Operating Assets): Operating efficiency and working capital productivity
- M-Score (Beneish Model): Detects potential earnings manipulation with 8-factor analysis
- Enterprise Earnings Yield: True profitability relative to enterprise value
- Franchise Power: Competitive advantage and economic moat assessment
- Financial Strength: Comprehensive balance sheet analysis
- Probability of Financial Distress: Bankruptcy risk modeling
- Quality Score: Holistic business quality assessment
- Executive Narrative: AI-generated market summaries with sentiment analysis
- Market Outlook: Multi-timeframe analysis (1D, 3M, 1Y+) with confidence scoring
- Top Movers Analysis: Automated identification of market catalysts
- Claim Cards: Key market insights with supporting data and implications
- Risk Assessment: Data-driven opportunity and risk analysis
- SEC EDGAR CompanyFacts: Direct integration with official financial filings
- Yahoo Finance: Real-time stock prices and market data
- FRED (Federal Reserve): Economic indicators and macroeconomic data
- Polymarket: Prediction market data for sentiment analysis
Market intelligence dashboard with AI-generated executive narrative, key insights with confidence scoring, top market movers with catalysts, and risk-opportunity analysis
Stock screening with value-quality composite rankings, risk filtering (excludes manipulation risks, high leverage, financial distress), and company performance cards
Fundamental analysis dashboard showing risk metrics (Scaled Total Accruals, M-Score manipulation detection), valuation analysis (Enterprise Earnings Yield), quality scoring (Financial Strength), and peer percentile rankings
AI-powered chatbot accessible via the floating chat button (💬) provides real-time financial analysis, explains metrics, and answers questions about companies and market data
- API Design: FastAPI with OpenAPI documentation and type validation
- Calculator Framework: Object-oriented financial metrics engine
- Data Integration: SEC EDGAR, Yahoo Finance, FRED economic data pipelines
- AI Integration: Pydantic AI for structured market analysis
- Database: Peewee ORM with SQLite for data storage
- React Components: Server/client components, custom hooks
- State Management: TanStack Query for server state, React Context for client state
- TypeScript: Full type safety with generated API clients
- UI Components: Reusable library with Radix UI and Tailwind CSS
- Build System: Turbopack bundling with code splitting
- Python 3.13+
- Node.js 18+
- uv package manager
# Clone the repository
git clone https://github.com/yourusername/fynance.git
cd fynance
# Install Python dependencies with uv
uv sync
# Install frontend dependencies
cd fynance/frontend
npm install# Start both backend and frontend (recommended)
cd fynance
make dev
# Or start services individually:
# Backend API (Port 8000)
uv run uvicorn fynance.backend.app:app --reload --host 127.0.0.1 --port 8000
# Frontend (Port 3000)
cd fynance/frontend && npm run dev# Ingest SEC CompanyFacts data
uv run python -m fynance.backend.ingestion.ingest_all_calculator_metrics
# Update market data
uv run python -m fynance.backend.sources.market.market_clientGET /api/v1/companies # List all tracked companies
GET /api/v1/companies/{ticker} # Company details
GET /api/v1/companies/{ticker}/metrics # All metrics for company
GET /api/v1/metrics/matrix # Bulk metrics matrix
GET /api/v1/metrics/{key}/rankings # Metric rankings
GET /api/v1/market/summary # AI market intelligenceimport requests
# Get company metrics
response = requests.get("http://localhost:8000/api/v1/companies/AAPL/metrics")
metrics = response.json()
# Get market intelligence
market_summary = requests.get("http://localhost:8000/api/v1/market/summary").json()- Stock Screening: Filter companies using value-quality composite metrics
- Risk Assessment: Identify earnings manipulation and financial distress signals
- Company Analysis: Deep-dive fundamental analysis with interactive charts
- Market Intelligence: AI-powered market summaries and sentiment analysis
- Peer Comparison: Percentile rankings across industry peers
- SEC EDGAR Integration: Official financial statement data from company filings
- Real-time Market Data: Live stock prices and market indicators
- Economic Data: Federal Reserve economic indicators and macroeconomic trends
- Historical Analysis: Multi-year trend analysis and metric calculations
- Interactive Visualizations: Charts, tables, and dashboards for data exploration
fynance/
├── backend/ # FastAPI backend
│ ├── app/
│ │ ├── api/v1/ # API routes
│ │ │ ├── companies/ # Company endpoints
│ │ │ ├── market_intelligence/ # AI market analysis
│ │ │ └── metrics/ # Financial metrics
│ ├── calculators/ # Financial metrics engine
│ ├── sources/ # Data source integrations
│ │ ├── edgar/ # SEC EDGAR client
│ │ ├── yahooFinance/ # Yahoo Finance client
│ │ ├── FRED/ # Federal Reserve data
│ │ └── polymarket/ # Prediction markets
│ ├── database/ # SQLite models
│ ├── ai/ # AI analysis components
│ └── ingestion/ # Data pipeline
├── frontend/ # Next.js React app
│ ├── src/
│ │ ├── app/ # Next.js app router
│ │ │ ├── overview/ # Market overview page
│ │ │ ├── ranking/ # Stock rankings
│ │ │ ├── company/[slug]/ # Company detail pages
│ │ │ └── compare/ # Multi-company comparison
│ │ ├── components/ # Reusable UI components
│ │ └── lib/ # Utilities and API clients
├── data/ # SQLite database and cache
└── docs/ # Documentation
The Overview page serves as the main dashboard that aggregates real-time market intelligence using AI analysis:
What it does:
- AI-Powered Executive Narrative: Generates structured market summaries with sentiment analysis
- Market Intelligence Integration: Combines SEC EDGAR data, Yahoo Finance prices, FRED economic indicators, and Polymarket sentiment
- Top Movers Analysis: Identifies biggest gainers and losers with contextual explanations
- Key Market Insights: Presents claim cards with confidence scoring and actionable implications
- Risk-Opportunity Framework: Provides balanced view of market opportunities and risks
How it works:
- Data Collection: Aggregates market data from multiple sources in real-time
- AI Processing: Uses Pydantic AI with structured prompts to generate coherent market narratives
- Sentiment Analysis: Applies multi-factor sentiment tagging (positive/negative/neutral)
- Confidence Scoring: Rates each insight's reliability based on data consistency
- Contextual Reasoning: Links market movements to fundamental drivers and catalysts
The AI chatbot is a sophisticated conversational interface powered by advanced prompt engineering:
What it does:
- Contextual Financial Analysis: Answers questions about companies, markets, and financial metrics
- Data-Driven Responses: Bases all answers on actual SEC EDGAR and market data
- Structured Conversations: Uses streaming responses for real-time interaction
- Financial Expertise: Provides professional-grade financial analysis and explanations
How it works:
- Backend Integration: Connects to FastAPI endpoint at
/api/v1/agent/chat/stream - Streaming Architecture: Uses AI SDK React for real-time text streaming
- Context Preservation: Maintains conversation history for coherent multi-turn dialogue
- Error Handling: Gracefully handles API failures and provides user feedback
- Modal Interface: Floating chat widget that doesn't interfere with main navigation
The platform implements various financial metrics for company analysis:
Example Metrics:
- M-Score (Beneish Model): Detects earnings manipulation using financial ratios
- Enterprise Earnings Yield: Measures profitability relative to enterprise value
- Scaled Total Accruals: Compares reported earnings to actual cash flow
- Franchise Power: Assesses competitive advantage and business quality
- Financial Strength: Evaluates balance sheet health and stability
The rankings page screens stocks using quantitative metrics:
Features:
- Value-Quality Scoring: Combines valuation and quality metrics
- Risk Filtering: Excludes companies with manipulation risks or financial distress
- Peer Comparison: Shows percentile rankings vs industry peers
- Interactive Cards: Displays scores and company information
The company detail page shows fundamental analysis for individual stocks:
Features:
- Price Charts: Interactive stock price visualization
- Risk Metrics: Accruals analysis and manipulation detection
- Valuation: Enterprise earnings yield and valuation metrics
- Quality Scores: Financial strength and business quality assessment
- Peer Rankings: Performance percentiles vs industry competitors