Calculate annualized historical volatility and percentile rankings from OHLCV data.
npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill volatility-0xzknw
Please help me install this Agent Skill. Skill: volatility Source: https://github.com/0xZKnw/vibe-trading-tap/tree/main/agent/src/skills/volatility Command: npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill volatility-0xzknw
This skill addresses the challenge of identifying optimal entry and exit points in financial markets by quantifying volatility regimes, helping traders avoid emotional decision-making during market fluctuations.
Use the volatility skill to analyze the provided OHLCV data and generate trading signals for the specified asset.
High-intent search queries and answers about installing and using this skill.
To calculate historical volatility for OHLCV data, this skill computes annualized volatility using rolling windows and percentile rankings to classify mean reversion signals. It processes standard OHLCV datasets to generate long or short signals based on volatility regimes.
Mean reversion trading using volatility percentile ranking generates long signals during low-volatility periods anticipating expansion, and short signals during high-volatility periods anticipating contraction. The skill ranks current volatility against historical lookback windows to classify these regimes.
Yes, you can automate mean reversion signal generation for crypto assets using pandas and numpy. This skill leverages these libraries for rolling window statistical computations to calculate annualized historical volatility and output trading signals.
This volatility analysis approach works for both crypto and equity markets. The skill applies its annualized historical volatility calculations and percentile ranking to any provided OHLCV financial dataset to identify mean reversion opportunities across asset classes.
A limitation of using historical volatility for automated trading is that it relies entirely on past OHLCV data. It assumes volatility will revert to a historical mean, which may not hold during trending markets or structural breaks, potentially generating false signals.
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