AdvancedQuantitative MethodsPython
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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.
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