Metadata-Version: 2.1
Name: StatArbTools
Version: 0.0.1
Summary: A set of tools useful in exploring statistical arbitrage
Home-page: https://github.com/mattfirth7/cointegratedpairstest
Author: Matthew Firth
Author-email: mmf001x@yahoo.com
License: UNKNOWN
Description: # StatArbTools
        
        StatArbTools is a Python library primarily for determining if a pair of time series are cointegrated.
        It also includes tools for generating an array of log returns from a price array, looking for a linear relationship,
        and creating a potentially stationary distribution.
        
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install StatArbTools
        
        ```bash
        pip install StatArbTools
        ```
        
        ## Usage
        
        ```python
        import StatArbTools
        
        StatArbTools.gen_log_returns(numpy_time_series_1, numpy_time_series_2) # returns numpy arrays of the log returns for each time series
        StatArbTools.gen_linear_relationship(numpy_log_returns_1, numpy_log_returns_2) # returns the coefficient from a linear regression between the two log returns arrays
        StatArbTools.gen_stationary_distr(numpy_log_returns_1, numpy_log_returns_2, coefficient) # returns the linear combination of the two log returns arrays based on a linear regression coefficient
        StatArbTools.test_stationarity(numpy_time_series_1, numpy_time_series_2) # returns True if the null hypothesis of an Augmented Dickey Fuller test is rejected and False otherwise. It also returns the p-value of the ADF test.
        StatArbTools.plot(stationary_distribution) # plots the passed distribution
        ```
        
        ## Contributing
        For changes, please open an issue first to discuss what you would like to change.
        
        ## License
        [MIT](https://choosealicense.com/licenses/mit/)
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
