quant-real-factor-5d-time-series-ranked-downside-volatility

name	quant-real-factor-5d-time-series-ranked-downside-volatility
description	Use when computing the 5D Time-Series Ranked Downside Volatility 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-risk-pattern-alpha","repository_url":"https://github.com/quantskills/skill-quant-factor-risk-pattern-alpha","collection":"risk-pattern-alpha","factor_id":"R116","category":"Volatility","license":"GPL-3.0-only","copyright":"Copyright (C) 2026 QuantSkills"}

5D Time-Series Ranked Downside Volatility

Use this Skill to compute 5日时序排名下行波动率 / 5D Time-Series Ranked Downside Volatility 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.