Open-source investment analytics platform bridging academic research and retail finance. Features include portfolio risk decomposition [Fama-French Five Factor Model], retirement sustainability modeling [Block Bootstrap Monte Carlo], max drawdown/CVaR dashboards, and risk-return optimisation [Markowitz, Ledoit-Wolf] via an intuitive user interface.

Financial engineering from a signal processing perspective

Asset allocation and portfolio optimization implementations to examine how each one differs and affects the overall portfolio.

Final Project of THU course Investment

Implemented the Porfolio optimization using Markowitz Model on VOO(NYSE) and BLV(NYSE) initally in Google Sheets

A mean-variance analysis of a portfolio of risky assets, visualising the Markowitz bullet and the efficient frontier. We also compare the performance of a randomly selected portfolio within the Markowitz bullet, with that of an efficient portfolio of the same variance.

Project on Markowitz Portfolio Management offered by the Finance and Analytics Club, IIT Kanpur in 2020-21 II

Plot the efficient frontier of any combination of assets using Yahoo Finance Historical Data.

Markowitz mean-variance criterion in R

Projects and reusable code from my time completing Codecademy's Analyze Financial Data with Python Skill Path. August 2021.

ConsciousCapital aims to help beginner investors curate portfolios that align with their ESG values.

Quantitative analysis of DAX 40 – fundamental screening, Risk Parity portfolio optimization and Monte Carlo simulation | Python

Bachelor’s Thesis. Classic and RL portfolio optimization methods.

Course Project for the course Intro to Financial Engineering

The system follows a clean client–server architecture where all computationally intensive quantitative logic is executed in a high-performance C++ backend. The React frontend acts purely as a visualization layer, communicating with the backend via a lightweight REST/JSON API. The Quant Engine encapsulates optimization, risk analysis and backtesting

This project is developed as part of the Computational Finance course. It covers not only classical and dynamic Markowitz portfolio optimization but also static and dynamic portfolio optimization based on Genetic Algorithms.

Using Shiny for Python to visualize mean-variance portfolio optimization, using mean monthly returns.

Portfolio allocation optimization via the efficient frontier

Stock Portfolio Management and Performance Analysis