<p>Nepali investors and fund managers often find themselves wrestling with outdated spreadsheets, manually tracking mutual fund performance against benchmarks. This ad-hoc approach is not only prone to human error but also scales poorly, leaving critical investment decisions vulnerable to stale data. I recently tackled this challenge head-on, building a production-ready pipeline that automates the entire process: from ingesting raw, often messy, data to calculating advanced performance metrics and visualizing the results. This post will walk you through the engineering decisions and practical code needed to construct a reproducible, robust mutual fund performance engine, showing you how to normalize disparate time series, compute critical financial indicators, and confidently evaluate investment strategies. The core judgment I came to is that a well-engineered data pipeline is not a luxury, but a necessity for informed financial decision-making in today's dynamic markets.</p>
Key Takeaways Meticulous data normalization is paramount when integrating financial data from disparate sources with different reporting calenda…