shresthish.in · New Delhi · 28.61°N 77.21°E · IST
Quantitative analysis and financial modeling, applied to real decisions. Methods stated, assumptions documented, results reproducible.
Ledoit–Wolf shrinkage · CVXPY Sharpe maximization · 250-day annualization — running live at the NSE Portfolio Optimizer ↗
My work sits at the boundary between data and strategy — between what a model says and what a room decides.
I started in data science at Publicis Sapient: 25 months of building analytics pipelines for stakeholders who had more data than they knew what to do with. The lesson that stayed with me: the gap between insight and decision is a strategy problem, not a math problem.
That observation pushed me toward the MBA — not to move away from data, but to understand capital, organisations, and the decisions that happen above the spreadsheet. The PGDM also forced me to formalize something I had been doing informally: thinking in financial models.
The FMVA certification, the NSE Portfolio Optimizer, and an advisory internship that converted into a pre-placement offer followed from the same approach: quantitative work is most useful when it is connected to an actual decision.
Using a 252-day annualization factor on NSE is a small error that compounds into a large one. Why the RBI repo rate changes the efficient frontier more than most models acknowledge.
The British looted our wealth. Platforms now loot our attention. And we loot each other by hoarding knowledge. On India's three extractions, and how to flow the other way again.
A motorcycle route from Delhi that follows the five sacred confluences of Uttarakhand — geography that shaped a civilisation, surveyed from the saddle of a Royal Enfield Himalayan.
Open to conversations on data, markets, and strategy.
If you have a dataset, a model, or a question about Indian markets, the fastest route is email. Analysis requests go through the form.