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yutiansut logoQUANTAXIS

QUANTAXIS 支持任务调度 分布式部署的 股票/期货/期权 数据/回测/模拟/交易/可视化/多账户 纯本地量化解决方案

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Quick Overview

QUANTAXIS is an open-source quantitative trading and investment research platform developed in Python. It provides a comprehensive set of tools for financial data acquisition, processing, analysis, and strategy backtesting, aimed at both individual investors and institutional traders.

Pros

  • Comprehensive ecosystem with modules for data fetching, analysis, backtesting, and live trading
  • Supports multiple asset classes including stocks, futures, and cryptocurrencies
  • Integrates with various data sources and exchanges
  • Active community and regular updates

Cons

  • Steep learning curve for beginners due to its extensive features
  • Documentation is primarily in Chinese, which may be challenging for non-Chinese speakers
  • Some users report occasional stability issues with certain modules
  • Performance can be slow for large-scale backtests without optimization

Code Examples

  1. Fetching stock data:
from QUANTAXIS import QA_fetch_stock_day_adv

data = QA_fetch_stock_day_adv('000001', '2020-01-01', '2021-01-01')
print(data.data)
  1. Creating a simple moving average strategy:
from QUANTAXIS import QA_Strategy

class MAStrategy(QA_Strategy):
    def on_bar(self, data):
        if data.close > data.close.rolling(20).mean():
            self.buy(data.code, 100)
        elif data.close < data.close.rolling(20).mean():
            self.sell(data.code, 100)

strategy = MAStrategy(start='2020-01-01', end='2021-01-01', code='000001')
strategy.run()
  1. Backtesting a strategy:
from QUANTAXIS import QA_Backtest

backtest = QA_Backtest(strategy=MAStrategy, start='2020-01-01', end='2021-01-01', code_list=['000001', '600000'])
result = backtest.run()
backtest.analyze()

Getting Started

  1. Install QUANTAXIS:
pip install quantaxis
  1. Initialize the QUANTAXIS environment:
import QUANTAXIS as QA
QA.QA_util_log_info('Welcome to QUANTAXIS')
  1. Fetch and analyze data:
stock_data = QA.QA_fetch_stock_day_adv('000001', '2020-01-01', '2021-01-01')
print(stock_data.data.head())
  1. Run a simple backtest:
from QUANTAXIS.QAARP.QAStrategy import QA_Strategy

class SimpleStrategy(QA_Strategy):
    def on_bar(self, data):
        self.buy(data.code, 100)

backtest = QA.QA_Backtest(strategy=SimpleStrategy, start='2020-01-01', end='2021-01-01', code_list=['000001'])
result = backtest.run()
backtest.analyze()

Competitor Comparisons

QuantConnect logo

Lean

12,743

Lean Algorithmic Trading Engine by QuantConnect (Python, C#)

Pros of Lean

  • More extensive documentation and community support
  • Broader range of supported asset classes and data sources
  • Better integration with cloud platforms and live trading systems

Cons of Lean

  • Steeper learning curve for beginners
  • Requires more computational resources
  • Less focus on Chinese markets compared to QUANTAXIS

Code Comparison

QUANTAXIS example:

import QUANTAXIS as QA
data = QA.QA_fetch_stock_day_adv('000001', '2019-01-01', '2019-12-31')
QA.QA_SU_save_stock_day('tdx')

Lean example:

public class MyAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2019, 1, 1);
        SetEndDate(2019, 12, 31);
        AddEquity("SPY", Resolution.Daily);
    }
}

QUANTAXIS focuses on simplicity and ease of use for Chinese markets, while Lean offers a more comprehensive and flexible framework for algorithmic trading across various markets. QUANTAXIS provides straightforward data fetching and saving functions, whereas Lean requires a more structured approach with algorithm initialization and asset addition.

microsoft logo

qlib

33,629

Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.

Pros of Qlib

  • More extensive documentation and examples
  • Stronger focus on machine learning and AI-driven quantitative investment
  • Backed by Microsoft, potentially leading to better long-term support and development

Cons of Qlib

  • Steeper learning curve for beginners in quantitative finance
  • Less comprehensive in terms of traditional financial analysis tools
  • Primarily focused on the Chinese stock market, which may limit its applicability for global investors

Code Comparison

QUANTAXIS:

import QUANTAXIS as QA
data = QA.QA_fetch_stock_day_adv('000001', '2017-01-01', '2017-12-31')
QA.QA_SU_save_stock_day('tdx')

Qlib:

from qlib.data import D
from qlib.config import REG_CN
D.calendar(start_time='2010-01-01', end_time='2017-12-31', freq='day')
D.features(["SH600000"], ["$close", "$volume"], start_time='2010-01-01', end_time='2017-12-31')

Both repositories offer powerful tools for quantitative finance, but they cater to slightly different audiences. QUANTAXIS provides a more comprehensive suite of traditional financial analysis tools, while Qlib focuses on leveraging machine learning for quantitative investment strategies. The choice between the two depends on the user's specific needs and level of expertise in both finance and machine learning.

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Pros of Catalyst

  • Focuses on cryptocurrency trading and backtesting
  • Provides integration with popular exchanges like Binance and Coinbase
  • Offers a more specialized toolset for crypto-specific strategies

Cons of Catalyst

  • Limited to cryptocurrency markets, less versatile for traditional assets
  • Smaller community and fewer resources compared to QUANTAXIS
  • Less frequent updates and maintenance

Code Comparison

QUANTAXIS:

from QUANTAXIS import QA_Backtest
backtest = QA_Backtest()
backtest.strategy = myStrategy
backtest.run()

Catalyst:

from catalyst import run_algorithm
run_algorithm(
    capital_base=10000,
    data_frequency='daily',
    initialize=initialize,
    handle_data=handle_data,
    exchange_name='poloniex'
)

Both repositories provide quantitative trading frameworks, but they cater to different markets and use cases. QUANTAXIS offers a more comprehensive solution for Chinese markets and traditional assets, while Catalyst specializes in cryptocurrency trading. QUANTAXIS has a larger community and more frequent updates, making it potentially more suitable for general quantitative trading needs. Catalyst, on the other hand, provides a more focused approach for crypto enthusiasts and traders looking to implement strategies specifically for digital assets.

ranaroussi logo

quantstats

6,590

Portfolio analytics for quants, written in Python

Pros of QuantStats

  • Focused on performance analytics and risk metrics
  • Generates detailed HTML reports for portfolio analysis
  • Lightweight and easy to integrate into existing Python projects

Cons of QuantStats

  • Limited to post-trade analysis and reporting
  • Lacks real-time data fetching and trading capabilities
  • Smaller community and fewer contributors compared to QUANTAXIS

Code Comparison

QuantStats:

import quantstats as qs

# Extend pandas functionality with metrics
qs.extend_pandas()

# Generate tearsheet
qs.reports.html(returns, output='tearsheet.html')

QUANTAXIS:

import QUANTAXIS as QA

# Fetch stock data
data = QA.QA_fetch_stock_day_adv('000001', '2019-01-01', '2020-01-01')

# Perform backtest
backtest = QA.QA_Backtest(strategy=MyStrategy, benchmark_code='000300')
backtest.run()

Summary

QuantStats excels in performance analysis and reporting, making it ideal for portfolio managers and analysts. QUANTAXIS offers a more comprehensive suite of tools for quantitative trading, including data fetching, backtesting, and real-time trading capabilities. QuantStats is more accessible for beginners and those focused on post-trade analysis, while QUANTAXIS caters to more advanced users requiring a full-stack quantitative trading platform.

kernc logo

backtesting.py

7,782

🔎 📈 🐍 💰 Backtest trading strategies in Python.

Pros of backtesting.py

  • Lightweight and easy to use, with a focus on simplicity
  • Fast execution due to optimized Cython implementation
  • Extensive documentation and examples for quick start

Cons of backtesting.py

  • Limited to backtesting only, lacks real-time trading capabilities
  • Fewer built-in indicators and analysis tools compared to QUANTAXIS
  • Less comprehensive ecosystem and community support

Code Comparison

QUANTAXIS:

import QUANTAXIS as QA

data = QA.QA_fetch_stock_day_adv('000001', '2010-01-01', '2019-01-01')
QA.QA_Risk_analysis(data)

backtesting.py:

from backtesting import Backtest, Strategy
from backtesting.lib import crossover

class SmaCross(Strategy):
    def init(self):
        self.sma1 = self.I(SMA, self.data.Close, 10)
        self.sma2 = self.I(SMA, self.data.Close, 20)

    def next(self):
        if crossover(self.sma1, self.sma2):
            self.buy()
        elif crossover(self.sma2, self.sma1):
            self.sell()

Summary

QUANTAXIS offers a more comprehensive suite of tools for quantitative analysis and trading, including real-time capabilities and a broader ecosystem. backtesting.py, on the other hand, provides a lightweight and fast solution specifically for backtesting trading strategies, with a focus on simplicity and ease of use. The choice between the two depends on the specific needs of the project and the user's preference for either a more comprehensive toolkit or a specialized backtesting solution.

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README

QUANTAXIS 2.1.0-alpha2

⭐ 如果这个项目对您有帮助，请点击Star支持我们！

🔄 Fork本项目开始您的量化交易之旅！

Made with ❤️ by @yutiansut and contributors

© 2016-2025 QUANTAXIS. Released under the MIT License.

🚀 全新升级: Python 3.9+、QARS2 Rust核心集成、100x性能提升

最新版本: v2.1.0-alpha2 | Python: 3.9-3.12 | 更新日期: 2025-10-25

🌟 新特性 (v2.1.0)

⚡ QARS2 Rust核心集成 - 性能飞跃

  • 100x账户操作加速: 创建账户从50ms降至0.5ms
  • 10x回测速度提升: 10年日线回测从30秒降至3秒
  • 90%内存优化: 大规模持仓内存占用降低90%
  • 无缝集成: 完全兼容QIFI协议，自动回退Python实现

🔧 Python 3.9-3.12 现代化

  • 依赖升级: 60+核心依赖现代化 (pymongo 4.10+, pandas 2.0+, pyarrow 15.0+)
  • 性能优化: 利用Python 3.11+的性能提升
  • 类型安全: 更好的类型提示支持

📦 QARSBridge - Rust桥接层

from QUANTAXIS.QARSBridge import QARSAccount, has_qars_support

# 自动检测并使用Rust高性能版本
if has_qars_support():
    print("✨ 使用QARS2 Rust版本 (100x性能)")
account = QARSAccount("my_account", init_cash=1000000)

# API完全兼容，无需修改代码
account.buy("000001", 10.5, "2025-01-15", 1000)

🔗 相关项目生态

核心项目

  • 🦀 QARS2 - QUANTAXIS Rust核心 (高性能账户、回测引擎)
  • ⚡ QADataSwap - 跨语言零拷贝通信 (Python/Rust/C++)
  • 🏛️ QAEXCHANGE-RS - Rust交易所 + HTAP混合数据库

扩展实现

  • 📊 QAUltra-cpp - QUANTAXIS C++实现
  • 🔥 QAUltra-rs - QUANTAXIS Rust实现 (部分开源)

Github workers GitHub stars GitHub forks

[点击右上角Star和Watch来跟踪项目进展! 点击Fork来创建属于你的QUANTAXIS!]

QUANTAXIS_LOGO_LAST_small.jpg

📞 联系方式

更多文档在QABook Release

Quantitative Financial FrameWork

📚 核心模块

1. 🦀 QARSBridge - Rust桥接层 (v2.1新增)

QARS2 Rust核心的Python包装器，提供100x性能提升

  • QARSAccount: 高性能QIFI账户系统

    • 股票交易: buy(), sell()
    • 期货交易: buy_open(), sell_open(), buy_close(), sell_close()
    • 账户查询: get_qifi(), get_positions(), get_account_info()
    • 完全兼容QIFI协议，跨语言一致性 (Python/Rust/C++)

  • QARSBacktest: Rust回测引擎

    • 10x回测速度提升
    • 支持自定义策略 (QARSStrategy基类)
    • 内存占用降低90%

  • 自动回退机制: QARS2未安装时自动使用纯Python实现

# 完整示例
from QUANTAXIS.QARSBridge import QARSAccount

account = QARSAccount("test", init_cash=1000000)
account.buy("000001", 10.5, "2025-01-15", 1000)      # 股票买入
account.buy_open("IF2512", 4500.0, "2025-01-15", 2)  # 期货开仓
positions = account.get_positions()                   # 查询持仓

📖 详细文档: QARSBridge README

2. 🔄 QADataBridge - 零拷贝数据交换 (v2.1新增)

基于QADataSwap的跨语言零拷贝数据传输，5-10x性能提升

  • 零拷贝转换:

    • Pandas ↔ Polars (2.5x加速)
    • Pandas ↔ Arrow (零拷贝)
    • Polars ↔ Arrow (零拷贝)
    • 批量转换支持

  • 共享内存通信:

    • 跨进程数据传输 (7x加速)
    • 实时行情分发
    • 策略间数据共享

  • 自动回退机制: QADataSwap未安装时自动使用标准转换

# 零拷贝转换示例
from QUANTAXIS.QADataBridge import convert_pandas_to_polars
import pandas as pd

df_pandas = pd.DataFrame({'price': [10.5, 20.3], 'volume': [1000, 2000]})
df_polars = convert_pandas_to_polars(df_pandas)  # 零拷贝，2.5x加速

# 共享内存示例
from QUANTAXIS.QADataBridge import SharedMemoryWriter, SharedMemoryReader

# 进程A：写入数据
writer = SharedMemoryWriter("market_data", size_mb=50)
writer.write(df_polars)

# 进程B：读取数据
reader = SharedMemoryReader("market_data")
df = reader.read(timeout_ms=5000)  # 零拷贝，7x加速

📖 详细文档: QADataBridge README

3. 💾 QASU / QAFetch - 多市场数据

  • 支持MongoDB / ClickHouse存储
  • 自动运维和数据更新
  • Tick / L2 Order / Transaction数据格式
  • 因子化数据结构

4. 🕐 QAUtil - 工具函数

  • 交易时间、交易日历
  • 时间向前向后推算
  • 市场识别、DataFrame转换

5. 💼 QIFI / QAMarket - 统一账户体系

多市场、多语言统一账户协议

  • qifiaccount: 标准QIFI账户，与Rust/C++版本保持100%一致
  • qifimanager: 多账户管理系统
  • qaposition: 单标的精准仓位管理 (套利/CTA/股票)
  • marketpreset: 市场预制基类 (tick大小/保证金/手续费)

QIFI协议特点:

  • 跨语言兼容 (Python/Rust/C++)
  • 完整账户状态 (账户/持仓/订单/成交)
  • 增量更新支持 (Diff机制)
  • MongoDB友好

6. 📊 QAFactor - 因子研究

  • 单因子研究入库
  • 因子管理、测试
  • 因子合并
  • 优化器 [开发中]

7. 📈 QAData - 内存数据库

多标的多市场数据结构，支持：

  • 实时计算
  • 回测引擎
  • 高性能数据访问

8. 📉 QAIndicator - 自定义指标

  • 支持自定义指标编写
  • 批量全市场apply
  • 因子表达式构建

9. ⚙️ QAEngine - 异步计算

  • 自定义线程/进程基类
  • 异步计算支持
  • 局域网分布式计算agent

10. 📮 QAPubSub - 消息队列

基于RabbitMQ的消息系统：

  • 1-1 / 1-n / n-n 消息分发
  • 计算任务分发收集
  • 实时订单流

11. 🎯 QAStrategy - 回测套件

  • CTA策略回测
  • 套利策略回测
  • 完整QIFI模式支持

12. 🌐 QAWebServer - 微服务

  • Tornado Web服务器
  • 中台微服务构建
  • RESTful API

13. 📅 QASchedule - 任务调度

  • 后台任务调度
  • 自动运维
  • 远程任务调度

🆕 版本更新说明

v2.1.0 (2025-10-25) - 重大性能升级

🚀 核心升级

1. QARS2 Rust核心集成

  • ✅ QARSBridge桥接层 - 100x性能提升
  • ✅ 完全兼容QIFI协议
  • ✅ 自动fallback到Python实现
  • ✅ 账户操作: 50ms → 0.5ms
  • ✅ 回测速度: 30s → 3s (10年日线)
  • ✅ 内存优化: -90%

2. Python现代化

  • ✅ Python版本: 3.5-3.10 → 3.9-3.12
  • ✅ 依赖升级: 60+核心依赖现代化
    • pymongo: 3.11.2 → 4.10.0+
    • pandas: 1.1.5 → 2.0.0+
    • pyarrow: 6.0.1 → 15.0.0+
    • tornado: 6.3.2 → 6.4.0+
  • ✅ 移除过时依赖: delegator.py, six, pyconvert

3. 新增模块

  • ✅ QARSBridge/: QARS2桥接层
    • qars_account.py: 高性能账户包装器
    • qars_backtest.py: Rust回测引擎
    • QIFI_PROTOCOL.md: 完整协议规范
  • ✅ examples/qarsbridge_example.py: 完整使用示例

4. 安装方式优化

# 基础安装
pip install -e .

# 包含Rust组件 (推荐)
pip install -e .[rust]

# 包含性能优化包
pip install -e .[performance]

# 完整安装
pip install -e .[full]

📝 升级文档

v2.0.0 - 架构重构

本版本为不兼容升级，涉及重大架构改变：

数据层改进

  • ✅ ClickHouse客户端集成
  • ✅ Tabular数据支持
  • ✅ 因子化数据结构
  • ✅ Tick / L2 Order / Transaction格式

微服务架构

  • ✅ QAWebServer - Tornado Web服务
  • ✅ QASchedule - 动态任务调度
  • ✅ DAG Pipeline模型
  • ✅ QAPubSub - RabbitMQ消息队列

账户系统升级

  • ⚠️ 移除QAARP (不再维护老版本)
  • ✅ 完整QIFI模块
    • 保证金模型
    • 股票/期货支持
    • 期权 [开发中]

实盘/模拟盘

  • ✅ QIFI结构对接
  • ✅ CTP接口 (期货/期权)
  • ✅ QMT对接 (股票)
  • ✅ 母子账户OMS
  • ✅ OrderGateway风控

多语言集成

  • ✅ QUANTAXIS Rust版本通信
  • ✅ Apache Arrow跨语言数据交换
    • pyarrow (Python)
    • arrow-rs (Rust)
    • libarrow (C++)
  • ✅ Rust/C++账户支持
  • ✅ Rust Job Worker

🚀 快速开始

系统要求

  • Python: 3.9 - 3.12 (推荐3.11+)
  • 操作系统: Linux / macOS / Windows
  • 内存: 最低4GB，推荐8GB+
  • 数据库: MongoDB 4.0+ / ClickHouse 20.0+ (可选)

安装

1. 基础安装

# 克隆仓库
git clone https://github.com/QUANTAXIS/QUANTAXIS.git
cd QUANTAXIS

# 安装依赖
pip install -e .

2. 包含Rust组件 (推荐 - 100x性能)

# 安装QUANTAXIS + QARS2
pip install -e .[rust]

# 或手动安装QARS2
cd /home/quantaxis/qars2
pip install -e .

3. 完整安装

# 安装所有组件
pip install -e .[full]

# 包含:
# - QARS2 Rust核心
# - QADataSwap跨语言通信
# - Polars高性能DataFrame
# - 所有可选依赖

4. 验证安装

import QUANTAXIS as QA
from QUANTAXIS.QARSBridge import has_qars_support

print(f"QUANTAXIS版本: {QA.__version__}")
print(f"QARS2支持: {has_qars_support()}")

# 预期输出:
# QUANTAXIS版本: 2.1.0.alpha2
# QARS2支持: True

快速示例

from QUANTAXIS.QARSBridge import QARSAccount

# 创建高性能账户 (自动使用Rust核心)
account = QARSAccount(
    account_cookie="my_strategy",
    init_cash=1000000.0
)

# 股票交易
account.buy("000001", 10.5, "2025-01-15", 1000)
account.sell("000001", 10.8, "2025-01-16", 500)

# 期货交易
account.buy_open("IF2512", 4500.0, "2025-01-15", 2)
account.sell_close("IF2512", 4520.0, "2025-01-16", 1)

# 查询持仓
positions = account.get_positions()
print(positions)

# 获取QIFI格式账户数据
qifi = account.get_qifi()
print(f"账户权益: {qifi['accounts']['balance']}")
print(f"可用资金: {qifi['accounts']['available']}")

数据库配置

# MongoDB配置
import QUANTAXIS as QA

# 设置MongoDB连接
QA.DATABASE = QA.QAUtil.QALogs.QA_Setting.MONGO_URI
# 默认: mongodb://localhost:27017/quantaxis

# ClickHouse配置
QA.CLICKHOUSE_HOST = 'localhost'
QA.CLICKHOUSE_PORT = 9000

📖 文档

📚 文档中心

完整文档请访问 文档中心 (Documentation Hub)

快速导航

🚀 入门指南

📘 API参考

🔧 高级功能

🐳 部署指南

📦 迁移指南

👨‍💻 开发者

📘 其他资源

🤝 社区与支持

GitHub

QUANTAXIS 是一个开放的项目, 在开源的3年中有大量的小伙伴加入了我, 并提交了相关的代码, 感谢以下的同学们

问题反馈:

社群

QQ群

  • 💬 QUANTAXIS交流群: 563280067 群链接
  • 👨‍💻 QUANTAXIS开发群: 773602202 (贡献代码请加此群，需备注GitHub ID)
  • 🔥 期货实盘部署群: 945822690 (仅限本地多账户部署用户)

Discord

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📊 性能对比

QARS2 Rust vs Python

操作Python版本QARS2 Rust加速比
创建1000个账户~50秒~0.5秒100x ⚡
发送10000个订单~50秒~0.5秒100x ⚡
账户结算~200ms~2ms100x ⚡
10年日线回测~30秒~3秒10x 🚀
内存占用(单账户)~2MB~200KB-90% 💾
内存占用(1000持仓)~50MB~5MB-90% 💾

Python版本性能

Python版本性能提升推荐度
Python 3.9基准⭐⭐⭐
Python 3.10+10%⭐⭐⭐⭐
Python 3.11+25%⭐⭐⭐⭐⭐ 最佳
Python 3.12+20%⭐⭐⭐⭐⭐ 最新

💰 项目支持

捐赠

写代码不易...请作者喝杯咖啡呗? ☕

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企业赞助

如需企业级支持、定制开发或技术咨询，请联系:

  • 📧 Email: yutiansut@qq.com
  • 💼 企业服务: 提供定制化量化交易解决方案

📜 许可证

本项目采用 MIT License 开源许可证。

Copyright (c) 2016-2025 yutiansut/QUANTAXIS

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction...

完整许可证请查看 LICENSE 文件。

👏 致谢

核心贡献者

特别感谢所有为QUANTAXIS做出贡献的开发者！

技术栈

QUANTAXIS得以实现离不开以下优秀的开源项目:

  • Python生态: pandas, numpy, scipy, matplotlib
  • 数据库: MongoDB, ClickHouse, Redis
  • Web框架: Tornado, Flask
  • 消息队列: RabbitMQ (pika)
  • Rust生态: PyO3, Polars, Arrow
  • 金融数据: tushare, pytdx

特别鸣谢

  • QARS2项目组: 提供高性能Rust核心
  • 社区贡献者: 所有提交PR和Issue的朋友们
  • 早期用户: 在项目初期就给予支持和反馈的用户

🗺️ 路线图

v2.1.x (当前)

  • ✅ QARS2 Rust核心集成
  • ✅ Python 3.9-3.12支持
  • ✅ QARSBridge桥接层
  • 🔄 QADataSwap跨语言通信 (进行中)
  • 📋 完善文档和示例

v2.2.0 (计划中)

  • 📊 完整的QADataSwap集成
  • 🔥 Polars全面替代pandas (可选)
  • ⚡ 更多Rust加速模块
  • 🧪 增强的回测引擎

v3.0.0 (未来)

  • 🤖 AI驱动的策略优化
  • 🌐 分布式回测系统
  • 📱 移动端支持
  • ☁️ 云原生部署