Metadata-Version: 2.1
Name: winning
Version: 0.0.3
Summary: Fast algorithm inferring relative ability from contest winning probabilities
Home-page: https://github.com/microprediction/winning
Author: microprediction
Author-email: pcotton@intechinvestments.com
License: MIT
Description: 
        A fast numerical algorithm for inferring relative ability from multi-entrant contest winning probabilities. This 
        repo includes code and draft paper accepted for publication into SIAM Journal on Financial Mathematics. 
        
        https://www.overleaf.com/read/qwnkrstmdwtn
        
        ### Usage
        
        To use a default skew-normal performance distribution:
        
            from winning.skew_calibration import skew_dividend_implied_ability
            dividends = [2.0, 3.0, 6.0] 
            ability  = skew_dividend_implied_ability(dividends=dividends)
            prices   = skew_ability_implied_dividends(ability)
        
        Alternatively see winning.lattice_calibration and use functions such as state_price_implied_ability(prices, density) which allow
        you to specify whatever performance distribution you like. 
        
        ### Practical use
        
        See the  [paper](https://github.com/microprediction/winning/blob/main/docs/Horse_Race_Problem__SIAM_.pdf) for why this is useful in lots of places.
        
        
        ![](https://i.imgur.com/83iFzel.png)
        
        
        ### Overview 
        
        The lattice_calibration module allows the user to infer relative abilities from state prices in a multi-entrant contest. The assumption
        made is that the performance distribution of one competitor is a translation of the performance distribution of another. 
        
        At the racetrack, this would mean looking at the win odds and infering a relative ability of horses. The algorithm is:
        
        - Fast 
        
        - Scalable (to contests with hundreds of thousands of entrants)
        
        - General (it works for any performance distribution). 
        
        
        ### Nomenclature 
        
        The algorithm takes state prices as inputs. These are for practical purposes equivalent to winning probabiliites (as the lattice size grows and ties are less common).
        
        - State prices. The expectation of an investment that has a payoff equal to 1 if there is only one winner, 1/2 if two are tied, 1/3 if three are tied and so forth. 
        
        - Relative ability refers to how much one performance distribution needs to be 
        translated in order to match another. 
        
        - Implied abilities are vectors of relative abilities consistent with a collection of state prices.
        
        - Dividends are the inverse of state prices.   
        
        
        ### Special cases
        
        Two natural choices are:
        
        - Standard normal, as per normal_calibration module. 
        
        - Skew-normal, as per skew_calibration module.  
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
