# Quant Real Factor 10d Compressed Range Position

> Use when computing the 10D Compressed Range Position factor from user-supplied OHLCV data or reviewing its bundled real-data validation metrics.

- Skill: `quantskills/quant-real-factor-10d-compressed-range-position` (Agent Skill, multi-file: 8 files)
- Install (CLI): `npx skillmds@latest add quantskills/quant-real-factor-10d-compressed-range-position`
- Raw SKILL.md: https://api.skillmd.com/api/skills/quantskills/quant-real-factor-10d-compressed-range-position/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-10d-compressed-range-position

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# 10D Compressed Range Position

Use this Skill to compute `10日压缩区间位置` / `10D Compressed Range Position` 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.