Multi-Purpose Kalman Filter
Overview
This module provides a pure Python implementation of a 1-Dimensional Kalman Filter. Kalman filters are recursive algorithms used to estimate the state of a linear dynamic system from a series of noisy measurements.
Applications in Quant
- Price Smoothing: Filtering out high-frequency noise from price data to see the underlying trend.
- Pairs Trading: Estimating the dynamic hedge ratio (beta) between two cointegrated assets.
- Volatility Estimation: Smoothing realized volatility estimates.
Usage
Run the script directly to see a simple example of tracking a constant value with noise.