| name | quant-real-factor-5d-smoothed-sma-gap |
| description | Use when computing the 5D Smoothed SMA Gap 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":"R070","category":"Trend","license":"GPL-3.0-only","copyright":"Copyright (C) 2026 QuantSkills"} |
5D Smoothed SMA Gap
Use this Skill to compute 5日平滑均线偏离 / 5D Smoothed SMA Gap from caller-provided OHLCV data.
Workflow
- Load a
pandas.DataFrame with open, high, low, close, volume; include date and symbol for cross-sectional research.
- Call
scripts/factor.py::compute_factor(df) to compute the factor column.
- Call
generate_signals(df) for a simple rank-based long/short signal.
- 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.
