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>
83 lines
2.7 KiB
Markdown
83 lines
2.7 KiB
Markdown
## 港股通每月成交统计
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----
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接口：ggt_monthly
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描述：港股通每月成交信息，数据从2014年开始
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限量：单次最大1000
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积分：用户积5000积分可调取，请自行提高积分，具体请参阅[积分获取办法](https://tushare.pro/document/1?doc_id=13)
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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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month | str | N | 月度（格式YYYYMM，下同，支持多个输入）
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start_month | str | N | 开始月度
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end_month | str | N | 结束月度
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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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month | str | Y | 交易日期
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day_buy_amt | float | Y | 当月日均买入成交金额（亿元）
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day_buy_vol | float | Y | 当月日均买入成交笔数（万笔）
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day_sell_amt | float | Y | 当月日均卖出成交金额（亿元）
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day_sell_vol | float | Y | 当月日均卖出成交笔数（万笔）
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total_buy_amt | float | Y | 总买入成交金额（亿元）
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total_buy_vol | float | Y | 总买入成交笔数（万笔）
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total_sell_amt | float | Y | 总卖出成交金额（亿元）
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total_sell_vol | float | Y | 总卖出成交笔数（万笔）
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<br>
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<br>
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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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#获取单月全部统计
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df = pro.ggt_monthly(trade_date='201906')
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#获取多月统计信息
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df = pro.ggt_monthly(trade_date='201906,201907,201709')
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#获取时间段统计信息
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df = pro.ggt_monthly(start_date='201809', end_date='201908')
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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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month day_buy_amt ... total_sell_amt total_sell_vol
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0 201908 37.77 ... 450.97 96.62
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1 201907 21.84 ... 382.55 80.20
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2 201906 27.45 ... 379.76 84.01
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3 201905 32.58 ... 473.15 96.49
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4 201904 37.52 ... 574.37 107.81
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5 201903 40.92 ... 734.38 137.88
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6 201902 34.70 ... 601.37 102.96
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7 201901 21.44 ... 481.81 121.27
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8 201812 19.56 ... 299.61 65.57
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9 201811 20.44 ... 496.59 112.33
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10 201810 31.36 ... 453.75 96.50
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11 201809 26.58 ... 334.69 66.25
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12 201808 25.67 ... 772.85 122.83
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13 201807 25.25 ... 569.46 98.26
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14 201806 28.27 ... 689.56 119.53
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15 201805 29.71 ... 716.09 118.85
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16 201804 30.49 ... 502.29 86.25
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17 201803 38.74 ... 879.75 141.66
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18 201802 75.70 ... 787.44 105.01 |