Sigma Sentinel

Volatility-driven fee optimization for Uniswap V4 hooks using Pyth price data A Foundry-based project that integrates Pyth Network price feeds to calculate real-time volatility metrics for Uniswap V4 hook implementations. The system processes historical price data to generate a volatility index, enabling hooks to dynamically adjust trading fees based on market volatility patterns.

Volatility-driven fee optimization for Uniswap V4 hooks using Pyth price data A Foundry-based project that integrates Pyth Network price feeds to calculate real-time volatility metrics for Uniswap V4 hook implementations. The architecture includes a VolatilityOracle contract that processes historical price data using logarithmic return calculations with 1e18 fixed-point precision, generating both per-second and annualized volatility metrics. A companion VolatilityFeeHook implements the Uniswap V4 hook interface with beforeSwap and afterSwap callbacks that query the oracle for current volatility levels. The hook applies a sigmoid-based fee adjustment curve: fee = baseFee * (1 + sigmoid(volatility - threshold) * maxAdjustment), ensuring smooth fee transitions and preventing extreme fee spikes while maintaining responsiveness to market volatility changes