Professional portfolio terminal — 14 optimization methods, walk-forward backtesting, factor analysis, regime detection, tax-loss harvesting, AI copilot. Python + PySide6.
Adaptive portfolio optimization using Kalman Filter estimation of time-varying expected returns — working paper with backtests, regime analysis, and statistical testing
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.
FastAPI service for Markowitz mean-variance portfolio optimization — efficient frontier + max-Sharpe allocation via SciPy SLSQP
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).
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.
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