Developed and deployed a Python-based allocation verification system, ensuring 100% accuracy for fund allocations across 400+ algorithmic trading accounts and preventing misallocations valued up to 5 Cr.
Automated daily generation and compilation of 50+ MTM reports, Orderbook, and Gridlog files using Python, Pandas, and Google Drive API, resulting in savings of over 20 operator hours per week.
Designed and implemented robust validation checks to proactively detect and flag operator errors in daily files, significantly reducing manual reporting mistakes by 95%.
Implemented a MySQL database backend for storing daily compiled data, which improved data retrieval time by 60% and enabled seamless querying for in-depth analysis.
Collaborated with the Team Lead to create 5+ dynamic Power BI dashboards, visualizing AUM, P&L trends, server-wise returns, and win/loss patterns for enhanced monitoring of 400+ trading accounts.
Utilized Regex, Pandas, and automation libraries to efficiently extract, clean, and transform raw financial files into accurate, business-ready datasets.