Live Quantitative Risk Engine & Portfolio Optimizer

This project is an end-to-end quantitative analytics pipeline. Instead of relying on static CSV files, this tool dynamically connects to the Yahoo Finance API to pull live market data, calculates institutional risk metrics, and runs a 10,000-run Monte Carlo simulation to mathematically determine the optimal capital allocation across major tech equities.

The Live Quantitative Risk Engine & Portfolio Optimizer is an end-to-end financial analytics tool built in R that dynamically evaluates risk and optimizes capital allocation using live market data. By connecting directly to the Yahoo Finance API, the pipeline ingests real-time price data for major tech equities to calculate key quantitative risk metrics, including Daily Log Returns, Annualized Volatility, 95% Historical Value-at-Risk (VaR), and Sharpe Ratios. To eliminate intuitive guesswork, the system executes a 10,000-run Monte Carlo simulation to construct a Markowitz Efficient Frontier, algorithmically isolating the optimal asset weights required to achieve the highest possible return per unit of risk. Complemented by executive Tableau dashboards for visualization, this project demonstrates how automated data pipelines and quantitative portfolio theory can combine to drive high-performance, data-backed investment decisions.

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