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quant-real-factor-5d-smoothed-risk-adjusted-trend

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Actualizado11 de junio de 2026 a las 06:55

Use when computing the 5D Smoothed Risk-Adjusted Trend factor from user-supplied real OHLCV data or reviewing its bundled real-data validation metrics.

Instalación

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

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Fuente

quantskills

quantskills/skill-quant-factor-directional-alpha

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Ocupaciones relacionadasSOC

Basado en la clasificación ocupacional SOC

Científicos de datosOcupaciones informáticas y matemáticas·SOC 15-2051

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SKILL.md

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name	quant-real-factor-5d-smoothed-risk-adjusted-trend
description	Use when computing the 5D Smoothed Risk-Adjusted Trend 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":"R084","category":"Trend","license":"GPL-3.0-only","copyright":"Copyright (C) 2026 QuantSkills"}

5D Smoothed Risk-Adjusted Trend

Use this Skill to compute 5日平滑风险调整趋势 / 5D Smoothed Risk-Adjusted Trend 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.