Dynamic mean-variance allocation under regime-switching jump-diffusions with wealth floors - fully analytical (SSRN 6534579)
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Updated
Jul 25, 2026 - Python
Dynamic mean-variance allocation under regime-switching jump-diffusions with wealth floors - fully analytical (SSRN 6534579)
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Mean-Variance Optimization using DL (pytorch)
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.
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Adaptive portfolio optimization using Kalman Filter estimation of time-varying expected returns — working paper with backtests, regime analysis, and statistical testing
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).
Interactive S&P 500 portfolio allocation app — Markowitz framework, CVXPY optimization, Ledoit-Wolf covariance, Streamlit UI
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.
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.
Full-stack quantum portfolio optimizer using QAOA to solve asset allocation. React frontend, FastAPI backend, real-time stock data via yfinance.
Mean-variance portfolio optimization and factor model performance evaluation across mutual funds, smart beta ETFs, and hedge fund indices
Factor-structured mean-variance portfolio optimization for Rust and Python
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