risk-parity

Portfolio Optimization in Python

Python library for portfolio optimization built on top of scikit-learn

Fast and scalable construction of risk parity portfolios

A JavaScript library to allocate and optimize financial portfolios.

Constrained and Unconstrained Risk Budgeting / Risk Parity Allocation in Python

Design of Risk Parity Portfolios

Cluster-based portfolio allocation on an explicit, inspectable tree: hierarchical risk parity, Schur complementary allocation and hierarchical 1/N

Risk_Parity strategy 风险平价

Portfolio optimisation library for Julia. Over 50 risk measures (CVaR, EVaR, RLVaR, drawdown, OWA), hierarchical risk parity, HERC, nested clustered optimisation, risk budgeting, near-optimal centering, four Black-Litterman variants, entropy pooling, factor and high-order priors, denoising, and JuMP-backed convex and non-convex optimization.

Backtesting of different trading strategies by applying different Modern Portfolio Theory (MPT) approaches on long-only ETFs portfolios in Python.

Bridgewater All Weather Strategy (China Edition) — Risk Parity backtesting with real A-share equity, bond & commodity ETF data

Factor Risk Parity Portfolio Construction algorithm. Built during my Master's. final project. Backtested on the S&P500.

End-to-end portfolio optimization (MVO), Risk Parity, Black–Litterman, regime targeting

Constructing a portfolio of crypto and stock assets utlizing ESG scores as well as machine learning models to predict buy / sell signals after establishing asset weights using hierarchical risk parity models.

Quantitative Risk and Asset Management Project - HEC Lausanne

Streamlit app to simulate/optimize different portfolio allocations based on mathematical methods.

LSTM-ARIMA with attention mechanism and multiplicative decomposition for sophisticated stock forecasting.

Adaptive regime estimation of market conditions (Maewal & Bock, 2018)

End-to-End Python implementation of Ang et al's (2026) Agentic 'Self-Driving Portfolio'. Implements: Black-Litterman equilibrium priors, Grinold-Kroner building blocks, Campbell-Shiller CAPE analysis, Ledoit-Wolf covariance shrinkage, Risk Parity, Hierarchical Risk Parity, and Robust Mean-Variance optimization across 18 asset classes.

We Design a PCA Cluster Risk Parity Portfolio