# Quant Real Factor 7d Time Series Ranked Intraday Return

> Use when computing the 7D Time-Series Ranked Intraday Return factor from user-supplied real OHLCV data or reviewing its bundled real-data validation metrics.

- Skill: `quantskills/quant-real-factor-7d-time-series-ranked-intraday-return` (Agent Skill, multi-file: 8 files)
- Install (CLI): `npx skillmds@latest add quantskills/quant-real-factor-7d-time-series-ranked-intraday-return`
- Raw SKILL.md: https://api.skillmd.com/api/skills/quantskills/quant-real-factor-7d-time-series-ranked-intraday-return/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: quantskills (https://skillmd.com/u/quantskills)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/quantskills/quant-real-factor-7d-time-series-ranked-intraday-return

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# 7D Time-Series Ranked Intraday Return

Use this Skill to compute `7日时序排名日内收益` / `7D Time-Series Ranked Intraday Return` from caller-provided OHLCV data.

## Workflow

1. Load a `pandas.DataFrame` with `open`, `high`, `low`, `close`, `volume`; include `date` and `symbol` for cross-sectional research.
2. Call `scripts/factor.py::compute_factor(df)` to compute the factor column.
3. Call `generate_signals(df)` for a simple rank-based long/short signal.
4. Review `validation_real/report.md` before using the factor in a model.

## Runtime Contract

- Framework-neutral Python: `pandas` and `numpy`.
- The caller owns data vendor, universe, calendar, costs, slippage, and execution modeling.