#Portfolio-optimization
Showing 60 of 74 repositories tagged #portfolio-optimization, ranked by stars
The backtesting engine that gives you an unfair advantage. Run thousands of trading ideas before others finish one.
Financial portfolio optimization in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
Portfolio Optimization in Python
Statistical and Algorithmic Investing Strategies for Everyone
The Operator Splitting QP Solver
Python library for portfolio optimization built on top of scikit-learn
Machine Learning in Asset Management (by @firmai)
Portfolio optimization with deep learning.
An Open Source Portfolio Backtesting Engine for Everyone | 面向所有人的开源投资组合回测引擎
Quantitative Investment Strategies (QIS) package implements Python analytics for visualisation of financial data, performance reporting, analysis of quantitative strategies.
The Open-Source Backtesting Engine/ Trading Simulator by Bertram Enterprises.
A toolkit for building AI-automated trading strategies on Kalshi prediction markets.
Applied an ARIMA-LSTM hybrid model to predict future price correlation coefficients of two assets
cuFOLIO is a GPU-accelerated portfolio optimization toolkit for building, backtesting, and scaling modern investment workflows with NVIDIA cuOpt and CUDA-X Data Science.
📈This repo contains detailed notes and multiple projects implemented in Python related to AI and Finance. Follow the blog here: https://purvasingh.medium.com
An open source library for portfolio optimisation
Entropy Pooling views and stress testing combined with Conditional Value-at-Risk (CVaR) portfolio optimization in Python.
Investment portfolio and stocks analyzing tools for Python with free historical data
Portfolio Construction and Risk Management book's Python code.
A JavaScript library to allocate and optimize financial portfolios.
PortfolioLab is a python library that enables traders to take advantage of the latest portfolio optimisation algorithms used by professionals in the industry.
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.
Оптимизация долгосрочного портфеля акций
Autonomous quantitative trading research platform that transforms stock lists into fully backtested strategies using AI agents, real market data, and mathematical formulations, all without requiring any coding.
🤖 基于深度学习的AI量化投资系统 | Vision-Based Quantitative Trading System with Deep Learning
This repository contains the customized trading algorithms that I have created using the Quantopian IDE.
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.
This repository is the result of our work for the course CSCI-SHU 360 Machine Learning
Implementation of optimisation analytics for constructing and backtesting optimal portfolios in Python
Python financial widgets with okama and Dash (plotly)
Portfolio optimization using Genetic algorithm.
Mean Variance (Markowitz) Portfolio Optimization and Beyond
I have been deeply interested in algorithmic trading and systematic trading algorithms. This Repository contains the code of what I have learnt on the way. It starts form some basic simple statistics and will lead up to complex machine learning algorithms.
Financial Portfolio Optimization Algorithms
Financial analysis, algorithmic trading, portfolio optimization examples with Python (DISCLAIMER - No Investment Advice Provided, YASAL UYARI - Yatırım tavsiyesi değildir).
Python based Quant Finance Models, Tools and Algorithmic Decision Making
applications for risk management through computational portfolio construction methods
Open-source Hyperliquid trading client with portfolio analytics and vault analytics
finance
Determine optimal rebalancing of a passive stock portfolio.
Developing a long/short equity investment portfolio with Machine Learning predictions using data acquired from web-scraping. Flatiron Module 5 Project.
Attribution and optimisation using a multi-factor equity risk model.
Dynamic portfolio optimization
PyTorch research stack for ML multi-factor trading: 213 factors, bias correction, portfolio optimization, and vectorized backtesting.
The goal of this project is to develop a statistical arbitrage strategy for cryptocurrencies using Python
Advanced ML-powered analyzer for hyperliquid.xyz vaults with portfolio optimization and risk analysis. Features include intelligent weight allocation, risk-adjusted return optimization, performance prediction, and comprehensive reporting
'Portfolio Analysis, methods for portfolio optimization'
3rd Year University Undergraduate dissertation project, supervised by https://www.nottingham.ac.uk/computerscience/people/thomas.gaertner focusing on using Natural Language Processing, AI Portfolio Optimisation and Machine Learning to produce an Automated Trading Agent. Received 89% overall.
Codes for the paper 'Clustering Approaches for Global Minimum Variance Portfolio'
Quantitative strategy for the Ibovespa that combines Topological Data Analysis (with Persistent Homology & Mapper), classical factors and meta-models, regime-sensitive HRP. Achieved top 4%.
Automatic optimal sequential investment decisions. Forecasts made using advanced stochastic processes with Monte Carlo simulation. Dependency is handled with vine copulas.
Multi-Agent AI Investment Analysis Engine
Fast MVSK portfolio optimization: no tensor storage, solves 800 assets in 0.05s
chameleonQuant was born as an open-source Java framework to help enthusiast quants to implement system trading strategies and dynamic portfolio trading systems using advanced optimization techniques, machine learning, and deep learning techniques.
AI-powered fundamental analysis platform. TimescaleDB, CrewAI agents, portfolio optimization, pair trading, and automated alpha extraction.
Implementing a first hurdle for expected returns
A student Investment portfolio web app built with various optimization techniques and screening parameters from core finance
This code accompanies the paper DeePM: Regime-Robust Deep Learning for Systematic Macro Portfolio Management (https://arxiv.org/abs/2601.05975)