tushare

Name: tushare
Availability: InStock
Author: 20YN04

Retrieve financial market data into pandas DataFrames via a standardized API.

Updated Jun 30, 2026

Search Tags:#quantitative analysis #finance #market data #stocks #etf #tushare

Ready to run

npx skills add https://github.com/20YN04/vibe-trading-macos --skill tushare-20yn04

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: tushare
Source: https://github.com/20YN04/vibe-trading-macos/tree/main/agent/src/skills/tushare
Command: npx skills add https://github.com/20YN04/vibe-trading-macos --skill tushare-20yn04

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tushare, pandas, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill solves the challenge of accessing fragmented, high-quality financial data by providing a unified, standardized interface for stocks, funds, bonds, and macroeconomic indicators.

Core Features & Use Cases

Comprehensive Data Access: Retrieve historical and real-time data for stocks, ETFs, funds, and global economic events.
Analytical Integration: Seamlessly feed financial data into pandas DataFrames for immediate quantitative analysis.
Use Case: A researcher needs to analyze the historical performance of all ETFs tracking the Shanghai 300 index; this Skill allows them to fetch the list of ETFs, their daily net values, and historical minute-level trading data in a single workflow.

Quick Start

Use the tushare skill to fetch the daily historical data for the ETF with code 510330.SH for the last 30 days.

Frequently Asked Questions about tushare

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I get historical stock market data into a pandas DataFrame for quantitative analysis?▼

To get historical stock market data into a pandas DataFrame for quantitative analysis, you can use a standardized financial data API that seamlessly feeds structured market data directly into DataFrames. This supports querying historical and real-time data across multiple asset classes.

Can I retrieve ETF historical trading data and daily net values programmatically?▼

Yes, you can retrieve ETF historical trading data and daily net values programmatically through a standardized API interface. It supports fetching ETF lists, daily net values, and minute-level trading data to facilitate comprehensive quantitative research workflows.

Do I need a tushare API token to access real-time financial and market data?▼

Yes, accessing real-time financial and market data requires an API token, as the interface includes integrated authentication management. You must configure your token to authenticate requests for retrieving stocks, funds, bonds, and macroeconomic indicators.

What is the best way to fetch macroeconomic indicators and bond data for automated trading workflows?▼

The best way to fetch macroeconomic indicators and bond data for automated trading workflows is through a unified API interface that delivers structured data directly into pandas DataFrames. This approach streamlines quantitative research across fragmented financial data sources.

Does this Skill support querying real-time market data for multiple asset classes like funds and stocks?▼

Yes, this Skill supports querying real-time market data for multiple asset classes, including stocks, funds, and bonds. It provides a standardized interface to retrieve comprehensive financial market data, facilitating both historical and real-time queries.

What are the limitations when using tushare with pandas for quantitative research?▼

When using tushare with pandas for quantitative research, limitations depend on the API's integrated authentication management and data access permissions. Users should be aware of query constraints and ensure their token permissions align with the required real-time and historical market data scope.