a65adcc2e5
Sources: extracted from two upstream archives (skill-repo, skills-main),
merged with the following policy:
- 15 broken symlinks (pointing to /Users/jameslee/.cc-switch/skills or
../../.agents/skills on a foreign machine) discarded
- 3 real name collisions with identical content (ai-pair, ifind-http-api,
zhipu-websearch) kept as one copy
- Functional overlaps deduped keeping the strongest variant:
- docx family: kept docx (official, full toolchain) + docx-cn
(GB/T 9704 Chinese official-document constants),
dropped docx_writer (no scripts, name collided with docx)
- humanizer family: kept humanizer-zh (6 zh reference docs),
dropped humanizer (en, redundant for CN workflow)
- Skills that only ran in a foreign environment removed:
ablemind-ops, app-publish, hlb-design-system, openclaw-adj-skill,
claude-driver
- alphapai excluded from this public repo because its SKILL.md hard-coded
live credentials
Result: 25 skills, 572 files, ~7.5 MB.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
78 lines
2.9 KiB
Markdown
78 lines
2.9 KiB
Markdown
## 美股行情
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----
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接口：us_daily
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描述：获取美股行情（未复权），包括全部股票全历史行情，以及重要的市场和估值指标
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限量：单次最大6000行数据，可根据日期参数循环提取，开通正式权限后也可支持分页提取全部历史
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要求：120积分可以试用查看数据，开通正式权限请参考[权限说明文档](https://tushare.pro/document/1?doc_id=290)。
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<br>
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<br>
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**输入参数**
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名称 | 类型 | 必选 | 描述
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---- | ----- | ---- | ----
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ts_code | str | N | 股票代码（e.g. AAPL）
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trade_date | str | N | 交易日期（YYYYMMDD）
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start_date | str | N | 开始日期（YYYYMMDD）
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end_date | str | N | 结束日期（YYYYMMDD）
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<br>
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<br>
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**输出参数**
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名称 | 类型 | 默认显示 | 描述
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--- | ---- | ---- | ----
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ts_code | str | Y | 股票代码
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trade_date | str | Y | 交易日期
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close | float | Y | 收盘价
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open | float | Y | 开盘价
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high | float | Y | 最高价
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low | float | Y | 最低价
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pre_close | float | Y | 昨收价
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change | float | N | 涨跌额
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pct_change | float | Y | 涨跌幅
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vol | float | Y | 成交量
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amount | float | Y | 成交额
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vwap | float | Y | 平均价
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turnover_ratio | float | N | 换手率
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total_mv | float | N | 总市值
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pe | float | N | PE
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pb | float | N | PB
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**接口示例**
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```python
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pro = ts.pro_api()
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#获取单一股票行情
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df = pro.us_daily(ts_code='AAPL', start_date='20190101', end_date='20190904')
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#获取某一日所有股票
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df = pro.us_daily(trade_date='20190904')
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```
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<br>
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<br>
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**数据示例**
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ts_code trade_date close open high low pre_close pct_change vol amount vwap
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0 AAPL 20190904 209.19 208.39 209.48 207.32 205.70 1.70 19216821 4008342529.970000 208.59
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1 AAPL 20190903 205.70 206.43 206.98 204.22 208.74 -1.46 20059575 4120106317.760000 205.39
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2 AAPL 20190830 208.74 210.16 210.45 207.20 209.01 -0.13 21162563 4410472824.780000 208.41
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3 AAPL 20190829 209.01 208.50 209.32 206.66 205.53 1.69 21007653 4380322743.230000 208.51
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4 AAPL 20190828 205.53 204.10 205.72 203.32 204.16 0.67 15957633 3269889907.950000 204.91
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.. ... ... ... ... ... ... ... ... ... ... ...
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165 AAPL 20190108 150.75 149.56 151.82 148.52 147.93 1.91 41025313 6159076907.780000 150.13
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166 AAPL 20190107 147.93 148.70 148.83 145.90 148.26 -0.22 54777766 8071925608.900000 147.36
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167 AAPL 20190104 148.26 144.53 148.55 143.80 142.19 4.27 58607071 8605786116.450000 146.84
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168 AAPL 20190103 142.19 143.98 145.72 142.00 157.92 -9.96 91312188 13108586866.810000 143.56
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169 AAPL 20190102 157.92 154.89 158.85 154.23 157.74 0.11 37039739 5814198206.330000 156.97
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