QuantitativeData

Financial Metrics Processing System

Financial Workflow Diagram : https://github.com/Finsights-MLOps/QuantitativeData/blob/main/FinancialMetrics_MLOps.png

Overview

This project processes SEC financial metrics from EDGAR using the EDGAR SDK to extract, derive, and structure financial data for downstream analysis and querying. The system is designed to create a queryable database or support a RAG (Retrieval-Augmented Generation) model for natural language queries on financial metrics.

Workflow

1. Data Ingestion

• Source: SEC EDGAR database via EDGAR SDK
• Data Type: Financial metrics from company filings (10-K)
• Process: Read and extract raw financial data from SEC filings

2. Metrics Processing

• Raw Metrics: Direct extraction of reported financial metrics

  • Revenue, expenses, assets, liabilities
  • Cash flow statements
  • Balance sheet items
  • Income statement components

• Derived Metrics: Calculate additional financial indicators

  • Financial ratios (P/E, debt-to-equity, current ratio)
  • Growth rates (YoY revenue growth, earnings growth)
  • Profitability metrics (ROE, ROA, profit margins)
  • Efficiency metrics (asset turnover, inventory turnover)
  • Liquidity metrics (quick ratio, cash ratio)

3. Data Structuring

Two potential output paths:

Option A: Database Creation

• Structure processed metrics into a queryable database
• Enable SQL-based queries for financial analysis
• Support time-series analysis and company comparisons

Option B: RAG Model Preparation

• Transform raw data into natural language sentences
• Create contextual descriptions around metrics
• Examples:
  • "Apple Inc. reported revenue of $394.3B in fiscal year 2023, representing a 7.8% increase from the previous year."
  • "The company's debt-to-equity ratio stands at 1.73, indicating moderate leverage."

Project Structure

DataPipeline/
├── src_metrics/
│   ├── data_ingestion.py      # EDGAR SDK integration
│   ├── data_preprocessing.py       # Metric calculation logic
│   ├── data_loading.py      # Loading data into S3
│   ├── config.py     # RAG statements on the metrics
│   ├── README.md   

Key Features

• Automated Data Extraction: Seamless integration with SEC EDGAR
• Comprehensive Metrics: Both standard and derived financial indicators
• Flexible Output: Support for both structured database and NLP-ready formats
• Scalable Architecture: Process multiple companies and time periods

Use Cases

1. Financial Analysis Dashboard: Query historical trends and compare companies
2. AI-Powered Financial Assistant: Natural language queries on financial data
3. Investment Research: Automated screening and analysis
4. Compliance Monitoring: Track regulatory filing metrics over time

Next Steps

• Choose database schema (SQL/NoSQL)
• Design sentence templates for RAG model
• Implement data validation and quality checks
• Create API for metric queries
• Build visualization layer

Technical Stack

• Data Source: SEC EDGAR SDK
• Processing: Python
• Potential Database: PostgreSQL or vector database
• RAG Integration: LangChain or custom implementation

Notes

This system bridges traditional financial data processing with modern AI capabilities, enabling both structured queries and natural language interactions with financial metrics.