{"repo":"WYFHHH/QuantGplearn","free":true,"listed":false,"github":"https://github.com/WYFHHH/QuantGplearn","clone":"git clone https://github.com/WYFHHH/QuantGplearn.git","description":"Interpretable quantitative factor mining with genetic programming, NumPy/Pandas, and a Torch GPU panel backend.","language":"Python","stars":11,"topics":["algorithmic-trading","factor-mining","genetic-programming","gpu","machine-learning","pytorch","quantitative-finance","symbolic-regression"],"license":"MIT","category":"trading","readme_excerpt":"QuantGplearn Evolve interpretable quantitative factors. Evaluate them on CPU or GPU. Keep the formula. Quick start · Features · Operators · Architecture · Documentation --- QuantGplearn is a genetic-programming framework for quantitative factor research. Instead of fitting an opaque set of weights, it searches for human-readable expressions built from market features, rolling operators, and cross-sectional transformations: The same symbolic program representation can run through the original NumPy/Pandas engine or through a Torch tensor backend designed for dense panel data. The result is a practical bridge between explainable symbolic research and GPU-accelerated factor evaluation. QuantGplearn discovers candidate signals; it is not a promise of investment performance. Validate every factor with leakage-aware, out-of-sample research and a realistic execution model. 中文简介 QuantGplearn 是一个面向量化因子研究的遗传规划框架。它不输出难以解释的黑盒权重， 而是进化出由行情特征、时序算子和截面算子组成的可读公式。项目同时保留原有 NumPy/Pandas CPU 路径，并提供适用于 [时间, 标的, 特征] 面板数据的 Torch/GPU 执行后端，支持 IC、RankIC、ICIR 和多空组合 Sharpe 代理目标，以及因子相关性过滤。 Why QuantGplearn? Capability What it gives you --- --- Formula-native research Every candidate is an inspectable expression tree, not a hidden parameter vector. Two execution backends Keep the mature NumPy/Pandas workflow or evaluate dense panel expressions with Torch. Time-series + cross-section semantics Mix rolling logic within each security with ranking and normalization across securities. Finance-aware objectives Se","default_branch":null,"files":null,"tree":[],"storefront":"/r/WYFHHH","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/WYFHHH/QuantGplearn/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}