Financial portfolio optimisation 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 and Quantitative Strategic Asset Allocation in Python

A list of online resources for quantitative modeling, trading, portfolio management

Quantitative analysis, strategies and backtests

A program for financial portfolio management, analysis and optimisation.

An Open Source Portfolio Backtesting Engine for Everyone | 面向所有人的开源投资组合回测引擎

Attempting to replicate "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" https://arxiv.org/abs/1706.10059 (and an openai gym environment)

The Open-Source Backtesting Engine/ Trading Simulator by Bertram Enterprises.

AI-Hedge-Fund for Crypto 🚀 AI-powered hedge fund for cryptocurrency trading, leveraging LLM agents for intelligent decision-making.

Backtrader-powered backtesting framework for algorithmic trading, featuring 20+ strategies, multi-market support, CLI tools, and an integrated MCP server for professional traders.

An open source library for portfolio optimisation

Applying Reinforcement Learning in Quantitative Trading

Оценка эффективности инвестиций и трейдинга с учетом комиссий, налогов (удержанных и ожидающихся), дивидендов и купонов.

PortfolioLab is a python library that enables traders to take advantage of the latest portfolio optimisation algorithms used by professionals in the industry.

CSCI 599 deep learning and its applications final project

This repository contains the customized trading algorithms that I have created using the Quantopian IDE.

Options and Option Strategies analytics for educational purpose using the Black-Scholes Model

OHLC,news,economic event restfull api and WebSocket API for specifically designed for AI quantitative trading/training.Multiple Timeframes,Multiple-Symbols-Multiple-Timeframes