Python package for cointegration analysis.
Python library for cointegration analysis.
- Carry out cointegration test
- Evaluate spread between cointegrated time-series
- Generate cointegrated time-series artificially
- Based on scikit-learn API
$ pip install cointanalysis
What is cointegration?
See Hamilton's book.
How to use
Let us see how the main class
CointAnalysis works using two ETFs, HYG and BKLN, as examples.
Since they are both connected with liabilities of low-rated companies, these prices behave quite similarly.
test carries out a cointegration test.
The following code gives p-value for null-hypothesis that there is no cointegration.
from cointanalysis import CointAnalysis hyg = ... # Fetch historical price of high-yield bond ETF bkln = ... # Fetch historical price of bank loan ETF X = np.array([hyg, bkln]).T coint = CointAnalysis() coint.test(X) coint.pvalue_ # 0.0055
The test have rejected the null-hypothesis by p-value of 0.55%, which implies cointegration.
fit finds the cointegration equation.
coint = CointAnalysis().fit(X) coint.coef_ # np.array([-0.18 1.]) coint.mean_ # 7.00 coint.std_ # 0.15
This means that spread "-0.18 HYG + BKLN" has the mean 7.00 and standard deviation 0.15.
In fact, the prices adjusted with these parameters clarifies the similarities of these ETFs:
The time-series of spread is obtained by applying the method
The mean and the standard deviation are automatically adjusted (unless you pass parameters asking not to).
spread = coint.transform(X) # returns (-0.18 * hyg + 1. * bkln - 7.00) / 0.15 spread = coint.transform(X, adjust_mean=False, adjust_std=False) # returns -0.18 * hyg + 1. * bkln
fit_transform carries out
transform at once.
spread = coint.fit_transform(X)
The result looks like this:
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