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JacobHsu
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volatility

Generate mean-reversion trading signals from percentile-ranked historical volatility.

Updated Apr 9, 2026
Search Tags:#volatility#ohlcv#trading-strategy#mean-reversion#signal-generation#historical-volatility
One-click install
npx skills add https://github.com/JacobHsu/vibe-trading-agent --skill volatility-jacobhsu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: volatility
Source: https://github.com/JacobHsu/vibe-trading-agent/tree/main/agent/src/skills/volatility
Command: npx skills add https://github.com/JacobHsu/vibe-trading-agent --skill volatility-jacobhsu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill captures volatility mean reversion opportunities by signaling when historical volatility regimes are unusually low or high so traders can avoid guesswork about timing.

Core Features & Use Cases

  • HV percentile detection: Computes rolling historical volatility and ranks it within a lookback window to spot regime shifts.
  • Thresholded signals: Emits long orders when volatility percentiles drop below the low threshold, and shorts or exits when they exceed the high threshold, with neutral holds in between.
  • Use Case: Apply it to daily OHLCV data for equities or crypto to build positions in calm periods and trim risk as volatility expands.

Quick Start

Run the volatility skill on your latest OHLCV dataset to flag low-volatility entries and high-volatility exits.

Frequently Asked Questions about volatility

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

FAQPage Schema
How do I generate mean reversion trading signals using historical volatility?▼

Mean reversion trading signals are generated by calculating rolling annualized historical volatility (HV), ranking it within a lookback window, and issuing threshold-based long, short, or neutral orders based on those percentile ranks.

Can I apply volatility regime analysis to daily crypto OHLCV data?▼

Yes, volatility regime analysis applies directly to daily OHLCV datasets across both equities and crypto markets, using historical volatility percentiles to identify calm entry periods and high-volatility exit points.

What is the best way to detect volatility regime shifts for trading entries and exits?▼

Detecting volatility regime shifts involves computing rolling historical volatility and ranking it within a lookback window to spot unusually low or high regimes, emitting long orders when percentiles drop below a low threshold and shorts or exits when exceeding a high threshold.

How do historical volatility percentile ranks translate into long and short positions?▼

Historical volatility percentile ranks translate into positions by triggering long orders when volatility drops below the low threshold, issuing shorts or exits when volatility exceeds the high threshold, and holding neutral positions in between.

When should I avoid using a volatility percentile strategy?▼

You should avoid using a volatility percentile strategy when your dataset lacks sufficient OHLCV history for an accurate rolling lookback window, or when trading assets that do not exhibit mean-reverting volatility characteristics.

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