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skills/tushare-data-1.0.5/references/数据接口/股票数据/行情数据/港股通十大成交股.md

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Initial commit: merged, deduplicated, and vetted skill collection 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>
2026-07-13 14:47:12 +08:00
## 港股通十大成交股
----
接口：ggt_top10
描述：获取港股通每日成交数据，其中包括沪市、深市详细数据，每天18~20点之间完成当日更新
**输入参数**
名称 | 类型 | 必选 | 描述
---- | ----- | ---- | ----
ts_code | str | N | 股票代码（二选一）
trade_date | str | N | 交易日期（二选一）
start_date | str | N | 开始日期
end_date | str | N | 结束日期
market_type | str | N | 市场类型 2：港股通（沪） 4：港股通（深）
**输出参数**
名称 | 类型 | 描述
--- | ---- | ----
trade_date | str | 交易日期
ts_code | str | 股票代码
name | str | 股票名称
close | float | 收盘价
p_change | float | 涨跌幅
rank | str | 资金排名
market_type | str | 市场类型 2：港股通（沪） 4：港股通（深）
amount | float | 累计成交金额（元）
net_amount | float | 净买入金额（元）
sh_amount | float | 沪市成交金额（元）
sh_net_amount | float | 沪市净买入金额（元）
sh_buy | float | 沪市买入金额（元）
sh_sell | float | 沪市卖出金额
sz_amount | float | 深市成交金额（元）
sz_net_amount | float | 深市净买入金额（元）
sz_buy | float | 深市买入金额（元）
sz_sell | float | 深市卖出金额（元）
**接口用法**
```python
pro = ts.pro_api()
pro.ggt_top10(trade_date='20180727')
```
或者
```python
pro.query('ggt_top10', ts_code='00700', start_date='20180701', end_date='20180727')
```
**数据样例**
trade_date ts_code name close p_change rank market_type \
0 20180727 00175 吉利汽车 18.42 -3.2563 4.0 2
1 20180727 00175 吉利汽车 18.42 -3.2563 4.0 4
2 20180727 00581 中国东方集团 6.60 5.9390 NaN 4
3 20180727 00607 丰盛控股 3.48 -2.5210 NaN 4
4 20180727 00700 腾讯控股 373.00 -0.4803 1.0 2
5 20180727 00700 腾讯控股 373.00 -0.4803 1.0 4
6 20180727 00763 中兴通讯 13.74 0.8811 NaN 4
7 20180727 00914 海螺水泥 49.10 2.1852 NaN 4
8 20180727 00939 建设银行 7.11 -0.5594 2.0 2
9 20180727 01088 中国神华 18.24 3.2843 9.0 2
10 20180727 01288 农业银行 3.81 0.0000 5.0 2
11 20180727 01299 友邦保险 68.65 0.5124 6.0 2
12 20180727 01317 枫叶教育 7.07 1.1445 NaN 4
13 20180727 01398 工商银行 5.82 0.0000 3.0 2
14 20180727 01448 福寿园 7.60 -4.6424 NaN 4
15 20180727 01918 融创中国 25.25 -0.3945 10.0 2
16 20180727 02208 金风科技 10.30 4.9949 NaN 4
17 20180727 02382 舜宇光学科技 138.60 0.8734 8.0 2
18 20180727 02382 舜宇光学科技 138.60 0.8734 8.0 4
19 20180727 03988 中国银行 3.69 0.0000 7.0 2
amount net_amount sh_amount sh_net_amount sh_buy \
0 476991220.0 -71294840.0 182183940.0 -30957820.0 75613060.0
1 294807280.0 -71294840.0 182183940.0 -30957820.0 75613060.0
2 49196800.0 23544640.0 NaN NaN NaN
3 44903050.0 -36431000.0 NaN NaN NaN
4 519061900.0 -219372420.0 383183900.0 -189541460.0 96821220.0
5 654939900.0 -219372420.0 383183900.0 -189541460.0 96821220.0
6 94728576.0 5410088.0 NaN NaN NaN
7 97702200.0 97505000.0 NaN NaN NaN
8 379189670.0 -294782730.0 379189670.0 -294782730.0 42203470.0
9 75536270.0 30045150.0 75536270.0 30045150.0 52790710.0
10 143294570.0 19808330.0 143294570.0 19808330.0 81551450.0
11 114038360.0 -112839500.0 114038360.0 -112839500.0 599430.0
12 50733740.0 13866820.0 NaN NaN NaN
13 237510790.0 162518450.0 237510790.0 162518450.0 200014620.0
14 54901320.0 24257620.0 NaN NaN NaN
15 75175350.0 -4871850.0 75175350.0 -4871850.0 35151750.0
16 83730480.0 775296.0 NaN NaN NaN
17 272358740.0 130884350.0 108526330.0 85936290.0 97231310.0
18 163832410.0 130884350.0 108526330.0 85936290.0 97231310.0
19 108853650.0 -106781530.0 108853650.0 -106781530.0 1036060.0
sh_sell sz_amount sh_net_amount sz_buy sz_sell
0 106570880.0 112623340.0 -40337020.0 36143160.0 76480180.0
1 106570880.0 112623340.0 -40337020.0 36143160.0 76480180.0
2 NaN 49196800.0 23544640.0 36370720.0 12826080.0
3 NaN 44903050.0 -36431000.0 4236025.0 40667025.0
4 286362680.0 135878000.0 -29830960.0 53023520.0 82854480.0
5 286362680.0 135878000.0 -29830960.0 53023520.0 82854480.0
6 NaN 94728576.0 5410088.0 50069332.0 44659244.0
7 NaN 97702200.0 97505000.0 97603600.0 98600.0
8 336986200.0 NaN NaN NaN NaN
9 22745560.0 NaN NaN NaN NaN
10 61743120.0 NaN NaN NaN NaN
11 113438930.0 NaN NaN NaN NaN
12 NaN 50733740.0 13866820.0 32300280.0 18433460.0
13 37496170.0 NaN NaN NaN NaN
14 NaN 54901320.0 24257620.0 39579470.0 15321850.0
15 40023600.0 NaN NaN NaN NaN
16 NaN 83730480.0 775296.0 42252888.0 41477592.0
17 11295020.0 55306080.0 44948060.0 50127070.0 5179010.0
18 11295020.0 55306080.0 44948060.0 50127070.0 5179010.0
19 107817590.0 NaN NaN NaN NaN
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