Showing 61-80 of 94 projects
A Python library for portfolio optimization using scikit-learn and convex optimization techniques.
This Python project uses symbolic regression to efficiently extract factors in the Chinese stock and crypto markets.
A curated list of resources for machine learning-based algorithmic trading and quantitative finance.
A Python-based quantitative trading platform for developers interested in algorithmic trading.
fastquant is a Python library for backtesting and optimizing ML trading strategies with just a few lines of code.
A Python-based personal quantitative investment research and analysis package for financial market data and backtesting.
A machine learning repository for quantitative finance and asset management strategies.
A newsletter focused on quantitative and algorithmic trading, portfolio analysis, and investing.
A Rust library for quantitative finance, including tools for machine learning, option pricing, and trading.
Open-source tools for computational pathology and digital pathology research using deep learning and weakly-supervised learning.
A curated collection of resources for economists and students of economics.
An Interview Primer for Quantitative Finance in TeX format
A video conversation model that combines LLM capabilities with pretrained visual encoders for video-based chatbots.
Open-source algorithmic trading platform for building and deploying automated trading strategies.
A Python framework for the Wondertrader quantitative trading platform, focused on high-frequency trading and finance.
Personae is a repo for implementing and experimenting with Deep Reinforcement Learning and Supervised Learning for Quantitative Trading.
A high-frequency quantitative trading platform in C++ for quant developers.
Educational notebooks on quantitative finance, algorithmic trading, financial modeling, and investment strategy.
A Python library for easy backtesting and trading automation across various financial markets.
Cython QuantLib wrappers for quantitative finance applications.
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