skills.rest
hanlinlibham
hanlinlibhamCOMMUNITY
Explore all skills by this author →
GitHub

windpy-sdk

Query WindPy endpoints for time-series and cross-sectional data with pandas-friendly outputs.

1|2|Updated Feb 6, 2026
Search Tags:#pandas#data-analytics#finance-data#windpy#windpy-sdk#windpy-python
One-click install
npx skills add https://github.com/hanlinlibham/skills --skill windpy-sdk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: windpy-sdk
Source: https://github.com/hanlinlibham/skills/tree/main/windpy-sdk
Command: npx skills add https://github.com/hanlinlibham/skills --skill windpy-sdk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires WindPy, pandas, pywin32, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Access WindPy data from Python without boilerplate. This Skill provides a robust, Python-friendly wrapper around WindPy, enabling straightforward retrieval of time-series and cross-sectional data for equities, bonds, funds, futures, and macro indicators.

Core Features & Use Cases

  • Unified Python wrappers for common WindPy endpoints (wsd, wss, wset, wsq, edb, wsi, tdays, tdaysoffset) with pandas-friendly outputs.
  • Automatic connection management and robust error handling, including a lightweight health() helper.
  • DataFrame-first results, consistent data shapes, and easy composition into analytics pipelines.
  • Real-world use cases include fetching stock prices, sector constituents, ETF data, and macro indicators across multiple instruments.

Quick Start

Install windpy-sdk, import wind_client and execute a simple query to fetch time-series data.

Frequently Asked Questions about windpy-sdk

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

FAQPage Schema
How do I fetch WindPy time-series data for stocks directly into a pandas DataFrame?▼

Yes, you can retrieve cross-sectional and time-series data for equities, bonds, and funds by calling wrapped WindPy endpoints. It handles the connection automatically and outputs consistent pandas DataFrames for your analytics pipelines.

What is the best way to manage WindPy connection errors when querying macro indicators?▼

The best way to manage WindPy connection errors is using a wrapper with built-in health checks and robust error handling. It automatically manages connections and includes a lightweight health() helper to ensure reliable data retrieval.

Do I need to install pywin32 to query WindPy endpoints from Python?▼

Yes, you need pywin32 installed along with WindPy and pandas to use this wrapper. These dependencies are required to execute the Python wrappers that retrieve financial data and output pandas-friendly results.

Can I use WindPy to fetch ETF data and sector constituents across multiple instruments?▼

Yes, you can fetch ETF data and sector constituents across multiple instruments using wrapped endpoints like wset and wsd. The wrapper delivers consistent cross-sectional data shapes that easily compose into broader analytics pipelines.

Why does my WindPy query return inconsistent data shapes for different financial instruments?▼

WindPy queries can return inconsistent shapes without a dedicated wrapper. This wrapper enforces DataFrame-first results with consistent data shapes across equities, bonds, funds, and futures, preventing misaligned data exports.

Related Skills

Explore More Data & Analytics Skills→
Awannaphasch2016Awannaphasch2016

mcp-integration

Register MCP servers via .mcp.json and escalate configuration through CLAUDE.md and hooks.

Community
Advanced
xiechyxiechy
3

dask

Parallelize pandas and NumPy workflows with Dask DataFrames and Arrays.

Community
Intermediate
644408071-design644408071-design
1

factor-research

Evaluate cross-sectional factors using IC/IR analysis and quantile backtesting.

Community
Advanced
20YN0420YN04

akshare

Aggregate historical and real-time financial market data via akshare.

Community
Intermediate