arbitrage.py

#!/bin/python3
""" Optimize redundant predictions on hypermind """
import numpy
from collections import namedtuple
from itertools import product

Proba = namedtuple("Proba", ["hollande", "sarkozy"])

possibilities = [Proba(h, s) for (h, s) in product(numpy.linspace(0, 1, 101),
                                                   numpy.linspace(0, 1, 101))]

HOLLANDE_ET_SARKOZY = 0.24
HOLLANDE_SEUL = 0.60
SARKOZY_SEUL = 0.02
AUCUN = 0.18


def metric(p):
    """ Deviation from hypermind predictions """
    return (p.hollande * p.sarkozy - HOLLANDE_ET_SARKOZY) ** 2 +\
        (p.hollande * (1 - p.sarkozy) - HOLLANDE_SEUL) ** 2 +\
        (p.sarkozy * (1 - p.hollande) - SARKOZY_SEUL) ** 2 +\
        ((1 - p.sarkozy) * (1 - p.hollande) - AUCUN) ** 2

possibilities.sort(key=metric, reverse=True)
for p in possibilities:
    print("%s : %.5f" % (p, metric(p)))
print()
p = possibilities[-1]
print("Best: %s (%.5f)" % (p, metric(p)))

print("p.hollande*p.sarkozy      ", p.hollande * p.sarkozy)
print("p.hollande*(1-p.sarkozy)  ", p.hollande * (1 - p.sarkozy))
print("p.sarkozy*(1-p.hollande)  ", p.sarkozy * (1 - p.hollande))
print("(1-p.sarkozy)*(1-p.hollande)", (1 - p.sarkozy) * (1 - p.hollande))