AdvancedQuantitative MethodsPython
Run this module
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.
Continue in Quantitative Methods¶
-
Quantitative Methods - Bayesian Inference
A strategy wins 7 of its first 10 trades. Is its true win rate 70%? Almost
-
Quantitative Methods - Bootstrap
The bootstrap estimates the sampling distribution of any statistic by resampling the observed data with replacement — no normality assumption required. It is the honest way to put confidence intervals around backtest metrics like Sharpe ratio, mean return, or maximum drawdown.
-
Quantitative Methods - Cointegration
Cointegration: two non-stationary series whose linear combination is stationary. Backbone of statistical arbitrage and pairs trading.
-
Quantitative Methods - Copulas
This module demonstrates the concept of Copulas, specifically the Gaussian Copula, used in quantitative finance to model the dependency structure between multivariate random variables.
-
Quantitative Methods - Entropy
Entropy measures how surprised you should expect to be. A coin that always
-
Quantitative Methods - Extreme Value Theory
Most risk models assume returns are normally distributed. They are not —