Black-Litterman portfolio construction on the EURO STOXX 50 top 15: reverse-optimised equilibrium, Idzorek view-confidence, Theil posterior, max-Sharpe / min-variance / efficient frontier, Streamlit dashboard.
Mean-Variance Optimization using DL (pytorch)
FastAPI service for Markowitz mean-variance portfolio optimization — efficient frontier + max-Sharpe allocation via SciPy SLSQP
From-scratch mean-variance portfolio optimization toolkit reproducing canonical literature results.
Institutional-grade Python toolkit for Mean-Variance Portfolio Optimization. Features OOP design, custom institutional constraints (SLSQP), and robust covariance matrix repair (nearest-PSD via eigenvalue clipping).
Institutional-style market risk pipeline for a multi-asset $1M portfolio. Computes VaR and CVaR via three methods (Historical Simulation, Parametric, Monte Carlo), validates models with Kupiec and Christoffersen backtests, and stress tests against GFC, COVID, and 2022 rate shock scenarios.
Adaptive portfolio optimization using Kalman Filter estimation of time-varying expected returns — working paper with backtests, regime analysis, and statistical testing
Python library for mean-variance portfolio optimization — Black-Litterman returns, Ledoit-Wolf covariance, efficient frontier, risk parity, CVaR minimization, and walk-forward backtesting with transaction costs.
Mean-variance portfolio optimization and factor model performance evaluation across mutual funds, smart beta ETFs, and hedge fund indices
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