volatility

Name: volatility
Availability: InStock
Author: wudye

Convert OHLCV price data into long, short, or neutral signals using annualized historical volatility percentiles.

Updated May 5, 2026

Search Tags:#backtesting #OHLCV #trading signals #mean reversion #historical volatility #percentile ranking

Ready to run

npx skills add https://github.com/wudye/traderAssistHK --skill volatility-wudye

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: volatility
Source: https://github.com/wudye/traderAssistHK/tree/main/backend/src/skills/volatility
Command: npx skills add https://github.com/wudye/traderAssistHK --skill volatility-wudye

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, requests.

What problem does it solve?

This Skill helps you systematically turn historical volatility behavior into actionable long/short/neutral trading signals, reducing reliance on intuition when volatility regimes shift.

Core Features & Use Cases

Historical Volatility (HV) computation: Calculates annualized HV from rolling return standard deviation using a configurable window.
HV percentile ranking: Ranks current HV against a rolling lookback period to quantify whether volatility is unusually low or high.
Mean-reversion style signal output: Produces 1 (long), -1 (short/exit), or 0 (neutral) based on configurable percentile thresholds.
Use Case: For a daily OHLCV feed, generate signals for multiple assets (e.g., BTC/ETH) where low-volatility regimes trigger long exposure and high-volatility regimes trigger exit/short exposure while waiting for volatility expansion or contraction.

Quick Start

Use the volatility skill to generate long/short signals from an OHLCV DataFrame with a close column.

Frequently Asked Questions about volatility

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate trading signals from historical volatility using OHLCV data?▼

You can generate trading signals from OHLCV data by computing annualized historical volatility from rolling returns, ranking it against a lookback period, and outputting long, short, or neutral signals based on percentile thresholds.

What is a historical volatility percentile ranking strategy for mean reversion?▼

A historical volatility percentile ranking strategy for mean reversion identifies market regimes by comparing current volatility to its rolling history, triggering long positions during low volatility and exits during high volatility.

Can I backtest multiple assets with a historical volatility mean reversion strategy?▼

Yes, you can backtest multiple assets by applying the historical volatility mean reversion strategy across multiple symbols, generating an independent integer signal series for each asset based on its specific OHLCV close prices.

How do I configure percentile thresholds for historical volatility trading signals?▼

You configure percentile thresholds by setting a rolling lookback window to rank the annualized historical volatility, mapping low percentile values to a long-biased regime and high percentile values to a short or exit regime.

Do I need pandas and numpy to calculate historical volatility for trading signals?▼

Yes, you need pandas and numpy because the skill relies on these libraries to process OHLCV data, calculate rolling return standard deviations, and produce the final annualized historical volatility signal series.