ledoit-wolf

Ledoit-Wolf covariance matrix estimator of stock returns

Estimation error in portfolio construction — Ledoit-Wolf shrinkage from the paper, walk-forward horserace vs 1/N, and a 2×2 VaR backtest with Kupiec, Christoffersen and Basel traffic-light tests

Robust portfolio optimization in Python using rolling out-of-sample validation, covariance shrinkage, concentration constraints, risk parity, turnover and transaction costs.

Unsupervised market-structure analytics - shrinkage/RMT-filtered correlation networks, minimum spanning trees, and a PCA systemic-risk index - plus a pre-registered research program that found no tradeable edge.

Paper VII of Statistical Pharmacology via Kakutani Dichotomy: kakutani_pharma, a Python pipeline for Kakutani indices of MD conformational ensembles. Ledoit-Wolf regularized CKI with an exact three-way decomposition, split-trajectory null subtraction, within-half block bootstrap, and pocket-centred shell-scaling exponents. Validated on a synthetic

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.

From-scratch mean-variance portfolio optimization toolkit reproducing canonical literature results.

SIM Swap Fraud Prevention via Behavioral Biometrics

Build, backtest, and serve Sharpe-optimised cryptocurrency portfolios using consensus Louvain clustering. Includes ARIMA / GARCH / LSTM forecasting, yfinance + ccxt data, a stateless REST API, and Docker.

Covariance matrix cleaning using Random Matrix Theory vs Ledoit-Wolf, with walk-forward backtests

Robust portfolio construction using Ledoit-Wolf covariance shrinkage and Hierarchical Risk Parity (HRP) for stable, risk-aware asset allocation

Interactive S&P 500 portfolio allocation app — Markowitz framework, CVXPY optimization, Ledoit-Wolf covariance, Streamlit UI

Paper VIII of Statistical Pharmacology via Kakutani Dichotomy: validation on real PDB ensembles (ubiquitin NMR, adenylate kinase), Isserlis bound and influence-function block selection, bootstrap coverage calibration. Code, data, paper.

A modular Python framework for covariance estimation, portfolio optimization, and rolling out-of-sample backtesting.

Constraint-aware portfolio optimization with covariance shrinkage and stress tests

End-to-End Python implementation of Azzone et al's (2026) Physical Climate Risk Engine for equity portfolios. Elements include: 2σ temperature-anomaly events, quadratic-trend logits and Fréchet–Hoeffding dependence feed portfolio-level Climate Risk Exposure (CRE) & Climate Exposure Volatility (CEV). It is optimized with return & variance via MOPSO.