Portfolio Optimization and Quantitative Strategic Asset Allocation in Python

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

Portfolio optimization and back-testing.

Portfolio optimization with deep learning.

🤖 Advanced AI-powered trading system for Kalshi prediction markets. Features Grok-4 integration, multi-agent decision making, portfolio optimization, and real-time market analysis. Educational/research purposes only.

Financial Portfolio Optimization Routines in Python

NVIDIA Quantitative Portfolio Optimization developer example

Entropy Pooling views and stress-testing combined with Conditional Value-at-Risk (CVaR) portfolio optimization in Python.

FactorHub is an open-source modern quantitative factor analysis platform designed specifically for the Chinese A-share market. FactorHub = Factor + Hub A full-stack quantitative investment research system integrating factor management, analysis, mining, portfolio optimization, and strategy backtesting.

Financial pipeline for the data-driven investor to research, develop and deploy robust strategies. Big Data ingestion, risk factor modeling, stock screening, portfolio optimization, and broker API.

Python CPU/GPU implementation of the Simulated Bifurcation (SB) algorithm to solve quadratic optimization problems (QUBO, Ising, TSP, optimal asset allocations for a portfolio, etc.).

Build a statistical risk model using PCA. Optimize the portfolio using the risk model and factors using multiple optimization formulations.

Deep Reinforcement Learning for Portfolio Optimization

Module for portfolio optimization, prices and options

Quantitative portfolio construction and optimization platform built on skfolio and scikit-learn.

A python application, that demonstrates optimizing a portfolio using machine learning.

A simple Python package for optimizing investment portfolios using historical return data from Yahoo Finance. Users can easily determine the optimal portfolio allocation among a given set of tickers based on the mean-variance optimization method or other algorithms.

Quadratic Programming based Python Package for Portfolio Optimization

Material accompanying the MOSEK Portfolio Optimization Cookbook

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A Streamlit based application to predict future Stock Price and pipeline to let anyone train their own multiple Machine Learning models on multiple stocks to generate Buy/Sell signals. This is a WIP and I will keep on adding new ideas to this in future.

Python for Portfolio Optimization: The Ascent! First working lessons to ascend the hilly terrain of Portfolio Optimization in seven strides (Lessons), beginning with the fundamentals (Lesson 1) and climbing slope after slope (Lessons 2-6), to reach the first peak of constrained portfolio optimization models (Lesson 7), amongst a range of peaks waiting beyond!

A tool for portfolio managers: use the Black-Litterman model to view optimal portfolio allocations using several of the most popular optimization methods.

Democratizing Index Tracking for Small Investors in Europe: A Meta-Learning Method for Sparse Portfolio Optimization

Markowitz portfolio optimization on synthetic and real stocks

This repository is the result of our work for the course CSCI-SHU 360 Machine Learning

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Built a practical Multi-Factor Backtesting Framework from scratch based on Huatai Security's(One of China's largest sell side) financial engineering report. Steps include factor data collection and preprocessing, factor combination, portfolio optimization and risk return analysis.

Built a smart beta portfolio and compared it to a benchmark index by calculating the tracking error. Built a portfolio using quadratic programming to optimize the weights..

Python library for high frequency portfolio analysis, intraday backtesting and optimization