Wind Financial Data

Why This Data Query Skill Exists

When building investment research, risk monitoring, or backtesting prompts, the first hard part is often not asking a question, but defining what kind of financial data is in scope. A prompt like tell me about this ETF can quickly become ambiguous because the answer may mix price data, fund NAV, announcements, sector membership, and valuation metrics. This skill is framed around a narrower domain: querying financial data for A-shares, HK stocks, US stocks, funds and ETFs, index sectors, bonds, and announcements or news. It is useful for deciding early whether a question is coverable, which fields are needed, and how to ask for them in steps.

How It Works

The main behavior is to organize financial data requests into explicit scopes and field-level questions. You can start by breaking the request into three layers:

• Asset class: for example fund ETF, index sector, or bond
• Market scope: for example A-share, HK, or US
• Field requirements: for example price, NAV, announcements, or sector composition

The skill notes that fields may be requested in steps, so it is better to split the goal into checkable subquestions instead of sending one long prompt. For example, first confirm the instrument scope, then the metric definitions, then the time range and output structure. This helps reduce the chance that the model returns loosely related data.

Boundaries and Caveats

This skill does not cover European stocks, Japanese stocks, or crypto assets. Those markets should not be treated as in-scope for this skill. Financial data also often depends on exact dates, currency, adjustment method, and metric definitions; without explicit fields, the answer may become too generic. It is well suited for scoping queries, clarifying field requirements, and drafting structured data requests, but it should not be treated as a verified production data feed.