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>
73 lines
2.9 KiB
Markdown
73 lines
2.9 KiB
Markdown
## 神奇九转指标
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----
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接口：stk_nineturn（由于涉及分钟数据每天21点更新）
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描述：神奇九转（又称“九转序列”）是一种基于技术分析的股票趋势反转指标，其思想来源于技术分析大师汤姆·迪马克（Tom DeMark）的TD序列。该指标的核心功能是通过识别股价在上涨或下跌过程中连续9天的特定走势，来判断股价的潜在反转点，从而帮助投资者提高抄底和逃顶的成功率，日线级别配合60min的九转效果更好，数据从20230101开始。
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限量：单次提取最大返回10000行数据，可通过股票代码和日期循环获取全部数据
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权限：达到6000积分可以调用
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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
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trade_date | str | N | 交易日期 （格式：YYYY-MM-DD HH:MM:SS)
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freq | str | N | 频率(日daily)
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start_date | str | N | 开始时间
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end_date | str | N | 结束时间
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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 | datetime | Y | 交易日期
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freq | str | Y | 频率(日daily)
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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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close | float | Y | 收盘价
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vol | float | Y | 成交量
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amount | float | Y | 成交额
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up_count | float | Y | 上九转计数
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down_count | float | Y | 下九转计数
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nine_up_turn | str | Y | 是否上九转)+9表示上九转
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nine_down_turn | str | Y | 是否下九转-9表示下九转
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<br>
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<br>
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**接口用法**
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```python
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pro = ts.pro_api()
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df=pro.stk_nineturn(ts_code='000001.SZ',freq='daily',fields='ts_code,trade_date,freq,up_count,down_count,nine_up_turn,nine_down_turn')
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```
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<br>
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<br>
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**数据样例**
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ts_code trade_date freq up_count down_count nine_up_turn nine_down_turn
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0 000001.SZ 2025-01-17 00:00:00 daily 3.0 0.0 None None
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1 000001.SZ 2025-01-16 00:00:00 daily 2.0 0.0 None None
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2 000001.SZ 2025-01-15 00:00:00 daily 1.0 0.0 None None
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3 000001.SZ 2025-01-14 00:00:00 daily 0.0 3.0 None None
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4 000001.SZ 2025-01-13 00:00:00 daily 0.0 2.0 None None
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.. ... ... ... ... ... ... ...
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491 000001.SZ 2023-01-09 00:00:00 daily 1.0 0.0 None None
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492 000001.SZ 2023-01-06 00:00:00 daily 0.0 0.0 None None
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493 000001.SZ 2023-01-05 00:00:00 daily 0.0 0.0 None None
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494 000001.SZ 2023-01-04 00:00:00 daily 0.0 0.0 None None
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495 000001.SZ 2023-01-03 00:00:00 daily 0.0 0.0 None None |