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Data Engineering

Edgar SEC Parser

Advanced Financial Document Processing System

16.51 MB/s
Peak Throughput
100%
Error Recovery Rate
3 Engines
Parser Coverage

Project Objective

Edgar is a production-ready SEC filing extraction and parsing system that intelligently processes regulatory documents using advanced parser integration. Built with specialized SEC parsing libraries (secsgml v0.3.1 and secxbrl v0.5.0), Edgar provides robust, scalable financial document processing capabilities for extracting structured metadata and financial facts from complex regulatory filings.

System Architecture

Core Extraction

Integrated SEC parsers with processing engine, database models, and content extractors

Hybrid Parsing

Seamless combination of SGML, XBRL, and legacy system parsers with intelligent fallback

Data Storage

PostgreSQL with SQLAlchemy ORM for structured metadata and financial facts persistence

Discovery System

SEC feed discovery for automated filing identification and retrieval

Key Features

High-Performance Processing

  • • Peak throughput of 16.51 MB/s with intelligent content detection
  • • Realistic performance of 1.77 MB/s with actual SEC documents
  • • Memory-efficient parsing for large-scale document processing
  • • Optimized database operations with batch inserts

Production-Ready Reliability

  • • 100% error case handling with graceful fallback mechanisms
  • • Comprehensive testing with unit and integration test suites
  • • Performance validation and benchmarking tools included
  • • Production-ready deployment with Docker support

Multi-Format Support

  • • Native SGML processing for legacy SEC filings
  • • Advanced XBRL parsing for modern financial statements
  • • Integrated parser for seamless format switching
  • • Automatic document type detection and optimal parser selection

Advanced Data Extraction

  • • Structured metadata extraction from filing headers
  • • Financial facts extraction with context preservation
  • • Document relationship tracking and hierarchy analysis
  • • Customizable extraction rules and patterns

Performance Results

Processing Speed

16.51 MB/s

Peak document throughput achieved

Error Recovery

100%

Successful malformed document handling

Parser Coverage

3 Engines

SGML, XBRL, and integrated parsing

Database Integration

Complete

Full metadata and facts storage

Technical Challenges

Multi-Format Document Parsing

Implemented hybrid parser architecture to handle SGML, XBRL, and legacy formats seamlessly with intelligent format detection and automatic parser selection.

Error Handling at Scale

Designed comprehensive error recovery system with graceful fallbacks to handle malformed documents, missing metadata, and parser failures without data loss.

Performance Optimization

Achieved 16.51 MB/s peak throughput through memory-efficient parsing, batch database operations, and optimized content detection algorithms.

Database Schema Design

Created flexible schema to store diverse filing metadata, financial facts, and document relationships while maintaining query performance and data integrity.

Production Deployment

Built production-ready infrastructure with Docker containerization, comprehensive testing, and deployment automation for reliable operation at scale.

Technologies Used

Python 3.11+SQLAlchemy 2.0+PostgreSQL 13+Dockersecsgml v0.3.1secxbrl v0.5.0pytest

View the Source Code

Explore the complete implementation with comprehensive documentation and test suites

View on GitHub← Back to Portfolio

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