momentum

Name	momentum JSON
Version	0.2.7 JSON
	download
home_page	https://github.com/microprediction/momentum
Summary	Running estimates of moments
upload_time	2022-03-05 15:30:05
maintainer
docs_url	None
author	microprediction
requires_python
license	MIT
keywords
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # momentum ![tests](https://github.com/microprediction/momentum/workflows/tests/badge.svg) ![deploy](https://github.com/microprediction/momentum/workflows/deploy/badge.svg)
A trivial mini-package for computing the running univariate mean, variance, kurtosis and skew

- No dependencies ... not even numpy.
- No classes ... unless you want them.
- State is a dict, for trivial serialization. 
- Tested against scipy, creme, statistics

For multivariate covariance updating, maybe see [precise](https://github.com/microprediction/precise). 

### Install 

    pip install momentum

### Usage: running mean, var

    from momentum import var_init, var_update
    from pprint import pprint
    
    m = var_init()
    for x in [5,3,2.4,1.0,5.0]:
        m = var_update(m,x)
    pprint(m)
    
    

### Usage: running mean, var, kurtosis and skew 

    from momentum import kurtosis_init, kurtosis_update
    
    m = kurtosis_init()
    for x in [5,3,2.4,1.0,5.0]:
        m = kurtosis_update(m,x)
    pprint(m)
    
    
File an issue if you need more help using this. 
    
  
### Usage: running recency-weighted mean, var

    from momentum import rvar_init, rvar_update
    from pprint import pprint
    
    m = rvar_init(rho=0.01,n=15)
    for x in [5,3,2.4,1.0,5.0]:
        m = rvar_update(m,x)
    pprint(m)
    
This will switch from running variance to a weighted variance after 15 data points. 
    



            

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    "description": "# momentum ![tests](https://github.com/microprediction/momentum/workflows/tests/badge.svg) ![deploy](https://github.com/microprediction/momentum/workflows/deploy/badge.svg)\nA trivial mini-package for computing the running univariate mean, variance, kurtosis and skew\n\n- No dependencies ... not even numpy.\n- No classes ... unless you want them.\n- State is a dict, for trivial serialization. \n- Tested against scipy, creme, statistics\n\nFor multivariate covariance updating, maybe see [precise](https://github.com/microprediction/precise). \n\n### Install \n\n    pip install momentum\n\n### Usage: running mean, var\n\n    from momentum import var_init, var_update\n    from pprint import pprint\n    \n    m = var_init()\n    for x in [5,3,2.4,1.0,5.0]:\n        m = var_update(m,x)\n    pprint(m)\n    \n    \n\n### Usage: running mean, var, kurtosis and skew \n\n    from momentum import kurtosis_init, kurtosis_update\n    \n    m = kurtosis_init()\n    for x in [5,3,2.4,1.0,5.0]:\n        m = kurtosis_update(m,x)\n    pprint(m)\n    \n    \nFile an issue if you need more help using this. \n    \n  \n### Usage: running recency-weighted mean, var\n\n    from momentum import rvar_init, rvar_update\n    from pprint import pprint\n    \n    m = rvar_init(rho=0.01,n=15)\n    for x in [5,3,2.4,1.0,5.0]:\n        m = rvar_update(m,x)\n    pprint(m)\n    \nThis will switch from running variance to a weighted variance after 15 data points. \n    \n\n\n",
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