volatility

Name: volatility
Availability: InStock
Author: liangwei-zhang

Generates long, short, or neutral signals from percentile-ranked historical volatility.

Updated Apr 14, 2026

Search Tags:#trading #volatility #crypto #ohlcv #mean-reversion #percentile #historical-volatility

Ready to run

npx skills add https://github.com/Liangwei-zhang/six-stock --skill volatility-liangwei-zhang

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: volatility
Source: https://github.com/Liangwei-zhang/six-stock/tree/main/Vibe-Trading/agent/src/skills/volatility
Command: npx skills add https://github.com/Liangwei-zhang/six-stock --skill volatility-liangwei-zhang

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the manual guesswork of volatility-driven trading by signaling low-volatility regimes for potential position entries and high-volatility regimes for exits or shorting.

Core Features & Use Cases

HV computation: annualized historical volatility using a rolling window.
Percentile ranking: relative HV percentile over a lookback window.
Signal generation: produce 1 for long in low-vol regimes, -1 for short in high-vol regimes, 0 otherwise.
Use Case: crypto or equity assets with OHLCV data to capture volatility mean reversion.

Quick Start

Feed your OHLCV data as a map of symbol to DataFrame, initialize the volatility SignalEngine with your preferred parameters, and call generate to obtain trading signals.

Frequently Asked Questions about volatility

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

FAQPage Schema

How do I generate volatility mean-reversion trading signals from OHLCV data?▼

You can generate volatility mean-reversion trading signals by feeding OHLCV data into a signal engine that computes rolling historical volatility, ranks it by percentile over a lookback window, and outputs long, short, or neutral positions based on configurable thresholds.

What is historical volatility percentile ranking and how does it identify trading regimes?▼

Historical volatility percentile ranking compares current rolling HV against past values over a lookback window. Low percentiles signal quiet regimes suitable for long entries, while high percentiles indicate volatile regimes for exits or shorting.

Can I use volatility mean-reversion signals for cryptocurrency daily data?▼

Yes, volatility mean-reversion signals apply to crypto assets using daily or higher-frequency OHLCV data. For cryptocurrencies, historical volatility annualization uses a factor of 365 to account for continuous trading.

How do I configure thresholds for low-volatility and high-volatility trading regimes?▼

You configure trading regime thresholds by setting percentile cutoffs in the signal engine. Low percentile thresholds trigger long positions in low-volatility regimes, while high percentile thresholds trigger short positions in high-volatility regimes.

Do I need additional dependencies or libraries to calculate historical volatility in Python?▼

No additional dependencies are required. The implementation supports Python Pandas-based computation for historical volatility calculation and includes a deterministic signal engine to generate trading outputs.

Does volatility mean-reversion work for stocks or is it limited to crypto?▼

Volatility mean-reversion works for any asset class with OHLCV data, including stocks and crypto. The historical volatility annualization factor can be adjusted to suit the specific market being analyzed.