Built an automated financial screening system that analyzed 226 real companies in 2 seconds 📊
To see how I automated PE-grade due diligence →
What I discovered:
• eBay: 107% profit margin (highest performer)
• Moderna: 3,065% revenue growth (COVID vaccine impact)
• Only 2 companies have truly strong balance sheets
• Tech & semiconductors dominate profitability
The system calculates 20+ financial ratios automatically:
✅ Profitability: ROE, ROA, margins
✅ Liquidity: Current ratio, quick ratio, cash ratio
✅ Leverage: Debt-to-equity, interest coverage
✅ Efficiency: Asset turnover, DSO
✅ Investment quality scoring (0-100)
Built for PE/IB workflows with investor-grade standards:
• Complete audit trails for compliance
• Data quality scoring on every dataset
• Handles CSV, Excel, and PDF statements
• Defensive error handling throughout
• Metadata tracking at every pipeline stage
Tech stack: Python, pandas, numpy, matplotlib
Dataset: 226 companies, 40 metrics each
Processing time: ~2 seconds
Code: 1,050 lines production + 2,500 lines docs
The difference between a script and production code? Complete audit trails, stable interfaces, and handling real-world data messiness.
What financial metrics would you add to this analysis?
🔗 Code on GitHub: https://github.com/marutijhawar/Financial-Pipeline-
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