Aggregate financial market data into structured pandas DataFrames via akshare.
npx skills add https://github.com/hxhyyy/Vibe-Trading --skill akshare-hxhyyy
Please help me install this Agent Skill. Skill: akshare Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/akshare Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill akshare-hxhyyy
This Skill solves the challenge of accessing high-quality, real-time financial market data without the need for expensive subscriptions or complex API key management.
Use the akshare skill to fetch the daily historical price data for the stock with symbol 000001 from January 1st 2024 to January 1st 2026.
High-intent search queries and answers about installing and using this skill.
To get free real-time stock market data for quantitative analysis, you can use this Skill to interface with the akshare library. It aggregates historical and real-time price feeds from public financial portals and returns structured pandas DataFrames without requiring API keys.
Yes, you can pull historical OHLCV data for A-share and US stocks without an API key. This Skill leverages the open-source akshare library to aggregate data from major public financial portals, returning structured pandas DataFrames for immediate use in backtesting.
This data aggregator supports diverse financial instruments and macroeconomic indicators. You can retrieve historical and real-time data for A-shares, US stocks, HK stocks, futures, and macroeconomic indicators, facilitating comprehensive quantitative research.
Yes, this tool works seamlessly with pandas for backtesting and financial data analysis. It requires the akshare library to interface with financial data providers and outputs structured pandas DataFrames directly, ensuring immediate compatibility with standard quantitative workflows.
The best free way to access global futures and macroeconomic data programmatically is using the akshare library. This Skill aggregates diverse market data from public sources without expensive subscriptions, providing structured pandas DataFrames suitable for quantitative analysis.
Limitations of using free open-source libraries for real-time financial data feeds include dependency on public portal availability and potential rate limits. This Skill aggregates from public sources via akshare, so data stability depends on external providers rather than paid premium APIs.
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