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volatility

Generate volatility-based trading signals from OHLCV time series using pandas and numpy.

Updated Apr 12, 2026
Search Tags:#pandas#trading#volatility#ohlcv#signal#mean-reversion
One-click install
npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill volatility-daddyelonmusk69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: volatility
Source: https://github.com/DaddyElonMusk69/motis-agent/tree/main/skills/finance/volatility
Command: npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill volatility-daddyelonmusk69

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Traders need a systematic way to detect when market volatility is unusually low or high so they can position for mean‑reversion moves without manually calculating complex statistics.

Core Features & Use Cases

  • Volatility Calculation: Computes annualized historical volatility (HV) over a configurable window using pandas.
  • Percentile Ranking: Ranks HV against a look‑back period to determine extreme low or high volatility regimes.
  • Signal Generation: Emits long, short, or neutral signals based on configurable percentile thresholds, suitable for equities, futures, or crypto assets.

Quick Start

Ask the agent to generate volatility‑based trading signals for a given ticker using the volatility skill.

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 for volatility mean-reversion?▼

Generate volatility trading signals by computing rolling historical volatility on OHLCV time series, ranking it against a look-back period, and classifying long, short, or neutral positions based on percentile thresholds.

What is volatility percentile ranking and how does it identify mean-reversion opportunities?▼

Volatility percentile ranking compares current historical volatility against a look-back period to identify extreme high or low regimes, signaling potential mean-reversion opportunities when volatility reaches configured threshold extremes.

Can I use pandas to calculate historical volatility for cryptocurrency OHLCV data?▼

Yes, pandas and numpy can calculate rolling historical volatility and generate trading signals for any OHLCV time series, including cryptocurrencies, stocks, and futures markets.

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

Configure percentile thresholds by setting cutoff levels in the signal generation logic, which classifies the rolling volatility percentile into long, short, or neutral signals to capture mean-reversion moves.

What's the best way to detect extreme volatility regimes in stock time series?▼

Detect extreme volatility regimes by computing annualized historical volatility over a rolling window, then applying percentile ranking against a look-back period to identify unusually high or low volatility states.

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