Chinese Language Resources for Systematic Trading: A Complete Guide to the Awesome Systematic Trading Repository

The awesome-systematic-trading repository provides dedicated Chinese-language documentation, native Python libraries for accessing A-share market data, and Mandarin research materials to support systematic trading workflows in China.

The paperswithbacktest/awesome-systematic-trading repository curates one of the most comprehensive collections of systematic trading tools and research available today. For Mandarin-speaking quantitative analysts and traders focusing on China's markets, the repository offers extensive Chinese language resources for systematic trading, including fully translated documentation and specialized data access libraries that integrate directly with Python backtesting pipelines.

Native Chinese Documentation

The repository maintains a complete Mandarin translation of its main documentation, ensuring accessibility for Chinese-speaking users.

The file README_zh.md serves as a full Chinese-language mirror of the English overview, listing the same structured categories of libraries, strategies, books, and videos. This translation enables users to read project guidelines, contribution instructions, and resource descriptions in their native language without losing technical accuracy or navigational structure.

Access the Chinese documentation directly at the repository root:

# The Chinese README is located at the repository root

https://github.com/paperswithbacktest/awesome-systematic-trading/blob/main/README_zh.md

Chinese Market Data Libraries

Systematic trading in China requires reliable access to A-share data, futures, and macroeconomic indicators. The repository specifically highlights two open-source Python packages designed for Chinese financial data.

TuShare for A-Share Equity Data

TuShare (tushare) is a widely-used data crawler that provides historical and real-time data for Chinese A-shares, indices, futures, and more. As referenced in the repository's data tools section, this library allows systematic traders to pull clean, structured price data directly into pandas DataFrames.

The following example demonstrates how to fetch daily price data for a Chinese stock using TuShare's professional API:

import tushare as ts

# Set your token (replace with your own token; a placeholder is shown here)

ts.set_token('YOUR_TUSHARE_TOKEN')
pro = ts.pro_api()

# Retrieve daily price data for a Chinese stock (e.g., 600519.SH – Kweichow Moutai)

df = pro.daily(ts_code='600519.SH', start_date='20240101', end_date='20240430')
print(df.head())

AkShare for Macroeconomic Indicators

AkShare (akshare) offers a comprehensive financial data interface specifically built for Chinese markets, covering equities, macroeconomics, fund information, and alternative datasets. This library is particularly valuable for systematic strategies that incorporate China-specific factors like CPI, PPI, or monetary policy indicators.

Use the following snippet to download China's Consumer Price Index (CPI) monthly series:

import akshare as ak

# Get China's macro-economic data: CPI (Consumer Price Index) monthly series

cpi_df = ak.macro_china_cpi_monthly()
print(cpi_df.tail())

Both libraries integrate seamlessly with the strategy implementations found in the repository's static/strategies/ directory, allowing traders to backtest algorithms using native Chinese market data rather than relying on manual web scraping.

Research Materials and Strategy Implementation

Beyond data access, the repository includes Chinese-language academic resources and executable strategy code.

The Books section within the curated list contains Chinese-language titles and translations on quantitative finance written in Mandarin, covering topics from statistical arbitrage to machine learning applications in A-share markets. Additionally, while the Python strategy scripts in static/strategies/ are language-agnostic, many underlying research papers originate from Chinese quantitative researchers and can be executed against Chinese market data using TuShare or AkShare connectors.

Summary

• The repository provides README_zh.md, a complete Chinese translation of the main documentation located at the repository root.
• TuShare and AkShare are the recommended Python libraries for accessing Chinese equity and macroeconomic data without manual scraping.
• Strategy implementations in static/strategies/ can be adapted for Chinese markets by sourcing data through these native libraries.
• Chinese-language books and research papers are cataloged in the repository's dedicated sections, supporting Mandarin-speaking quantitative researchers.

Frequently Asked Questions

Does the awesome-systematic-trading repository have a Chinese language README?

Yes. The repository maintains README_zh.md at the root level, which provides a full Mandarin translation of the English README.md. This file contains the same structured categories and resource links, making the entire curation accessible to Chinese-speaking users.

Which Python libraries are recommended for accessing Chinese stock data?

According to the repository's curated list, TuShare (tushare) and AkShare (akshare) are the primary recommended libraries. TuShare specializes in A-share historical and real-time price data, while AkShare provides broader coverage including macroeconomic indicators, fund data, and alternative datasets specific to China.

Can the strategy scripts in the repository be used with Chinese market data?

Yes. The Python implementations in static/strategies/ are data-source agnostic. You can adapt these scripts for Chinese markets by replacing standard data connectors with TuShare or AkShare API calls to fetch A-share prices or Chinese macro factors, then proceed with the same backtesting logic.

Are there Chinese language books on systematic trading listed in the repository?

Yes. The repository's Books section includes Chinese-language titles and Mandarin translations covering quantitative finance, algorithmic trading, and statistical methods. These resources are specifically highlighted to support Chinese-speaking practitioners building systematic trading systems.

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