yfinance-data

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yfinance Data Skill

yfinance 数据技能

Fetches financial and market data from Yahoo Finance using the yfinance Python library.

Important: yfinance is not affiliated with Yahoo, Inc. Data is for research and educational purposes.

使用yfinance Python库从Yahoo Finance获取金融与市场数据。

重要提示：yfinance与Yahoo公司无关联。数据仅供研究和教育使用。

Step 1: Ensure yfinance Is Available

步骤1：确保yfinance可用

Before running any code, install yfinance if needed:

python

import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])

Always include this at the top of your script.

运行任何代码前，若需要请安装yfinance：

python

import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])

请始终将此代码放在脚本顶部。

Step 2: Identify What the User Needs

步骤2：明确用户需求

Match the user's request to one or more data categories below, then use the corresponding code from

references/api_reference.md

User Request	Data Category	Primary Method
Stock price, quote	Current price	`ticker.info` or `ticker.fast_info`
Price history, chart data	Historical OHLCV	`ticker.history()` or `yf.download()`
Balance sheet	Financial statements	`ticker.balance_sheet`
Income statement, revenue	Financial statements	`ticker.income_stmt`
Cash flow	Financial statements	`ticker.cashflow`
Dividends	Corporate actions	`ticker.dividends`
Stock splits	Corporate actions	`ticker.splits`
Options chain, calls, puts	Options data	`ticker.option_chain()`
Earnings, EPS	Analysis	`ticker.earnings_history`
Analyst price targets	Analysis	`ticker.analyst_price_targets`
Recommendations, ratings	Analysis	`ticker.recommendations`
Upgrades/downgrades	Analysis	`ticker.upgrades_downgrades`
Institutional holders	Ownership	`ticker.institutional_holders`
Insider transactions	Ownership	`ticker.insider_transactions`
Company overview, sector	General info	`ticker.info`
Compare multiple stocks	Bulk download	`yf.download()`
Screen/filter stocks	Screener	`yf.Screener` + `yf.EquityQuery`
Sector/industry data	Market data	`yf.Sector` / `yf.Industry`
News	News	`ticker.news`

将用户的请求与以下一个或多个数据类别匹配，然后使用

references/api_reference.md

中的对应代码。

用户请求	数据类别	主要方法
股票价格、报价	当前价格	`ticker.info` 或 `ticker.fast_info`
价格历史、图表数据	历史OHLCV数据	`ticker.history()` 或 `yf.download()`
资产负债表	财务报表	`ticker.balance_sheet`
利润表、营收	财务报表	`ticker.income_stmt`
现金流量表	财务报表	`ticker.cashflow`
股息	公司行动	`ticker.dividends`
股票拆分	公司行动	`ticker.splits`
期权链、认购期权、认沽期权	期权数据	`ticker.option_chain()`
收益、每股收益（EPS）	分析数据	`ticker.earnings_history`
分析师目标价	分析数据	`ticker.analyst_price_targets`
建议、评级	分析数据	`ticker.recommendations`
评级上调/下调	分析数据	`ticker.upgrades_downgrades`
机构持股人	持股情况	`ticker.institutional_holders`
内部人交易	持股情况	`ticker.insider_transactions`
公司概况、行业板块	基本信息	`ticker.info`
多只股票对比	批量下载	`yf.download()`
股票筛选	筛选工具	`yf.Screener` + `yf.EquityQuery`
板块/行业数据	市场数据	`yf.Sector` / `yf.Industry`
新闻	新闻资讯	`ticker.news`

Step 3: Write and Execute the Code

步骤3：编写并执行代码

General pattern

通用模式

python

import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])

import yfinance as yf

ticker = yf.Ticker("AAPL")

python

import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])

import yfinance as yf

ticker = yf.Ticker("AAPL")

... use the appropriate method from the reference

... 使用参考文档中的合适方法

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Key rules

关键规则

Always wrap in try/except — Yahoo Finance may rate-limit or return empty data
Use
yf.download()
for multi-ticker comparisons — it's faster with multi-threading
For options, list expiration dates first with
```
ticker.options
```
before calling
```
ticker.option_chain(date)
```

For quarterly data, use

quarterly_

prefix:

ticker.quarterly_income_stmt

ticker.quarterly_balance_sheet

ticker.quarterly_cashflow

For large date ranges, be mindful of intraday limits — 1m data only goes back ~7 days, 1h data ~730 days
Print DataFrames clearly — use
```
.to_string()
```
or
```
.to_markdown()
```
for readability, or select key columns

始终使用try/except包裹代码——Yahoo Finance可能会限制请求频率或返回空数据
多股票对比使用
yf.download()
——它支持多线程，速度更快
获取期权数据时，先通过
ticker.options
列出到期日期，再调用
```
ticker.option_chain(date)
```

获取季度数据，使用

quarterly_

前缀：

ticker.quarterly_income_stmt

、

ticker.quarterly_balance_sheet

、

ticker.quarterly_cashflow

处理大日期范围时，注意日内数据限制——1分钟数据仅可追溯约7天，1小时数据约730天
清晰打印DataFrame——使用
```
.to_string()
```
或
```
.to_markdown()
```
提升可读性，或选择关键列展示

Valid periods and intervals

有效周期与时间间隔

Periods	`1d` , `5d` , `1mo` , `3mo` , `6mo` , `1y` , `2y` , `5y` , `10y` , `ytd` , `max`
Intervals	`1m` , `2m` , `5m` , `15m` , `30m` , `60m` , `90m` , `1h` , `1d` , `5d` , `1wk` , `1mo` , `3mo`

周期	`1d` , `5d` , `1mo` , `3mo` , `6mo` , `1y` , `2y` , `5y` , `10y` , `ytd` , `max`
时间间隔	`1m` , `2m` , `5m` , `15m` , `30m` , `60m` , `90m` , `1h` , `1d` , `5d` , `1wk` , `1mo` , `3mo`

Step 4: Present the Data

步骤4：呈现数据

After fetching data, present it clearly:

Summarize key numbers in a brief text response (current price, market cap, P/E, etc.)
Show tabular data formatted for readability — use markdown tables or formatted DataFrames
Highlight notable items — earnings beats/misses, unusual volume, dividend changes
Provide context — compare to sector averages, historical ranges, or analyst consensus when relevant

If the user seems to want a chart or visualization, combine with an appropriate visualization approach (e.g., generate an HTML chart or describe the trend).

获取数据后，需清晰呈现：

用简短文字总结关键数据（当前价格、市值、市盈率等）
以可读格式展示表格数据——使用Markdown表格或格式化后的DataFrame
突出重要信息——收益超预期/未达预期、异常成交量、股息变化等
提供上下文信息——必要时与行业平均水平、历史区间或分析师共识进行对比

若用户需要图表或可视化，可结合合适的可视化方法（例如生成HTML图表或描述趋势）。

Reference Files

参考文件

```
references/api_reference.md
```
— Complete yfinance API reference with code examples for every data category

Read the reference file when you need exact method signatures or edge case handling.

```
references/api_reference.md
```
—— 完整的yfinance API参考文档，包含每个数据类别的代码示例

当你需要准确的方法签名或处理边缘情况时，请查阅此参考文件。