Verified project record

bbfamily/abu

abu is a quantitative trading library that applies machine learning to optimize trading strategies across stocks, futures, options, and Bitcoin. It provides technical analysis, backtesting, and a non-programming UI so ordinary users can work without a complex code-heavy workflow.

Project overview

abu lowers the barrier to quantitative trading by combining machine-learning strategy optimization with a non-programming UI, making systematic trading accessible to users beyond those who write code.

Project type: Workflow · Data Processing
Use cases: Data Analysis
Deployment: Refer to project documentation
License: GPL-3.0

Best for

Ordinary users who want quantitative trading without a complex code-heavy workflow
Traders who want machine learning applied to strategy optimization and live trading guidance

Key capabilities

Uses multiple machine learning techniques to optimize strategies and guide their use in live trading.
Provides automatic support and resistance line drawing, gap analysis, and traditional technical-indicator analysis.
Backtests market-timing strategies against historical data and accounts for execution-price deviations and trading fees.
Includes an abupy_ui directory for operating the quantitative system through a non-programming interface.
Supports switching data modes, storage, and sources and connecting external sources for stocks, futures, Bitcoin, and Litecoin.

Getting started

Deploy a Python environment using Anaconda, then import abupy in Python to verify the installation. The README recommends Anaconda but does not document the complete installation sequence in the supplied section.

Evidence and sources

GitHub project description: 阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构
README: | 阿布量化系统源代码 | abupy目录 | | 阿布量化使用教程 | abupy_lecture目录 | | 阿布量化非编程界面操作 | abupy_ui目录 |
README: 推荐使用Anaconda部署Python环境，详见 [量化环境部署](https://github.com/bbfamily/abu/blob/master/abupy_lecture/0-abupy%E9%87%8F%E5%8C%96%E7%8E%AF%E5%A2%83%E9%83%A8%E7%BD%B2(ABU%E9%87%8F%E5%8C%96%E4…
README: * 使用多种机器学习技术智能优化策略 * 在实盘中指导策略进行交易，提高策略的实盘效果，战胜市场
README: 彻底跨越用户复杂的代码量化阶段, 更适合普通人群使用, 迈向量化2.0时代.

Reviewed at: 2026-07-17T03:56:12Z