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Use when computing the 5D Time-Series Ranked Upper Breakout factor from user-supplied real OHLCV data or reviewing its bundled real-data validation metrics.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/quantskills/skill-quant-factor-directional-alpha --skill quant-real-factor-5d-time-series-ranked-upper-breakout命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Use when an agent needs a verified library of directional OHLCV alpha factor Skills for trend, breakout, reversal, channel-position, or price-direction research across A-share and US equity samples.
Use when computing the 5D Z-Scored Return Momentum factor from user-supplied real OHLCV data or reviewing its bundled real-data validation metrics.
Use when computing the 5D Z-Scored Skip-Period Momentum factor from user-supplied real OHLCV data or reviewing its bundled real-data validation metrics.
基于 SOC 职业分类
正在显示 SKILL.md
| name | quant-real-factor-5d-time-series-ranked-upper-breakout |
| description | Use when computing the 5D Time-Series Ranked Upper Breakout factor from user-supplied real OHLCV data or reviewing its bundled real-data validation metrics. |
| metadata | {"organization":"QuantSkills","organization_url":"https://github.com/quantskills","repository":"skill-quant-factor-directional-alpha","repository_url":"https://github.com/quantskills/skill-quant-factor-directional-alpha","collection":"directional-alpha","factor_id":"R108","category":"Breakout","license":"GPL-3.0-only","copyright":"Copyright (C) 2026 QuantSkills"} |
Use this Skill to compute 5日时序排名上轨突破 / 5D Time-Series Ranked Upper Breakout from caller-provided OHLCV data.
pandas.DataFrame with open, high, low, close, volume; include date and symbol for cross-sectional research.scripts/factor.py::compute_factor(df) to compute the factor column.generate_signals(df) for a simple rank-based long/short signal.validation_real/report.md before using the factor in a model.pandas and numpy.