casino strategy

casino_strategy.py

"""
This script simulates a common casino strategy when playing roulette:

bet on odd/even, and stick to your choice. bet $20 at start, if you lose, 
double the bet; if you win, start with the $20 bet again.

This simulation suggests that if you follow this strategy for a few rounds (say 50), 
more than 90% of the people will make money, with only a slight chance of bankrupt

    $ python casino_strategy.py
    Simulating 50 rolls per trial and generate 10000 trials
    mean: 10006,  median: 10480
    93% of the samples earned money
    3% of the samples got bankrupt

However if you adopt this strategy long enough, eventually you'll lose money.
See "gambler's ruin" <http://en.wikipedia.org/wiki/Gambler's_ruin>
"""

from random import choice
import sys
w = lambda x: sys.stderr.write("%s\n" % x)

class Gambler:

    __slots__ = ("money",)
    
    def __init__(self, money=10000):
        self.money = money
        
    reset = __init__
        
    def roll(self, times=50, verbose=False):
        
        bet = 20
        for i in range(times):
            self.money -= bet
            won = choice((True, False)) # binary choice
            
            # the gambling strategy
            if won:
                self.money += bet * 2
                next_bet = 20
            else:
                next_bet = bet * 2

            if verbose: 
                w("bet: %d, %s, balance: %d" % \
                   (bet, "won" if won else "lost", self.money))
            
            # can't bet more than you have
            if next_bet > self.money:
                next_bet = self.money
            
            bet = next_bet
        
        return self.money
        
        
if __name__ == "__main__":
    
    ITERATIONS = 10000
    ROLLS = 50
    finals = []
    g = Gambler()

    for i in range(ITERATIONS):
        g.reset()
        finals.append(g.roll(times=ROLLS))
    
    import matplotlib.pyplot as plt
    import numpy as np
    finals = np.array(finals, dtype=int)
    w("Simulating %d rolls per trial and generate %d trials" % (ROLLS, ITERATIONS))
    w("mean: %d,  median: %d" % (np.mean(finals), np.median(finals)))
    w("%d%% of the samples earned money" % (len(finals[finals > 10000]) * 100 / ITERATIONS))
    w("%d%% of the samples got bankrupt" % (len(finals[finals == 0]) * 100 / ITERATIONS))
    plt.hist(finals, bins=100)
    plt.savefig(__file__.replace(".py", ".png"))