#!/usr/bin/env python

"""
This script was made to understand probabilities behind SatoshiDice game
http://satoshidice.com/

ToDo: implement MoneyManagement
see fix lot
see martingale
"""

import numpy as np
import pandas as pd
import matplotlib.pylab as plt

class SatoshiDiceGameTester:
    def __init__(self):
        print("="*5+" Satoshidice "+"="*5)
        toss_nb = 60000
        max_nb = 2**16-1 # 65535
        less_than = 8000
        win_odds = float(less_than) / float(max_nb) * 100.0
        price_multiplier = 8
        house_percent = 1.9
        expected_rate_of_return = 100.0 - house_percent
        min_bet = 0.01
        max_bet = 500

        print("""Playing {toss_nb} times to Satoshidice game less than {less_than}
    win_odds = {win_odds} %
    price_multiplier = {price_multiplier}
    house_percent = {house_percent} %
    expected_rate_of_return = {expected_rate_of_return} %
    min_bet = {min_bet}
    max_bet = {max_bet}""".format(
        toss_nb=toss_nb,
        less_than=less_than,
        win_odds=win_odds,
        price_multiplier=price_multiplier,
        house_percent=house_percent,
        expected_rate_of_return=expected_rate_of_return,
        min_bet=min_bet,
        max_bet=max_bet,
        ))

        self.df = pd.DataFrame(np.random.randint(0,max_nb,toss_nb), columns=['Toss'])
        self.df['TossResults'] = np.where(self.df['Toss']<less_than, 1, -1) # 1 = win -1=lose
        #self.df['TossResults'] = np.where(self.df['Toss']<less_than, 1, -1) # 1 = win -1=lose

        self.df['TossResultsCumsum'] = self.df['TossResults'].cumsum()

        # Fix size
        self.df['Size'] = 0.01
        initial_balance = 1
        self.df['BalanceVar'] = np.where(self.df['TossResults']>0, self.df['Size']*price_multiplier, -self.df['Size']+0.005 * self.df['Size'])
        self.df['Balance'] = initial_balance+self.df['BalanceVar'].cumsum()

        print(self.df.head())
        print(self.df)
        print(self.df.tail())

        exp_win = len(self.df[self.df['TossResults']>0])
        exp_loss = toss_nb - exp_win
        exp_win_pc = float(exp_win)/float(toss_nb)*100
        exp_loss_pc = float(exp_loss)/float(toss_nb)*100
        relative_difference = (exp_win_pc-win_odds)/win_odds*100
        print("""Results:
    toss_nb={toss_nb}
    win={win}
    loss={loss}
    win={win_pc} %
    loss={loss_pc} %
    relative difference = {rel_diff} %
    min balance = {balance_min}
    max balance = {balance_max}
    initial deposit = {balance_initial}""".format(
  toss_nb=toss_nb,
  win=exp_win,
  loss=exp_loss,
  win_pc=exp_win_pc,
  loss_pc=exp_loss_pc,
  rel_diff = relative_difference,
  balance_min = self.df['Balance'].min(),
  balance_max = self.df['Balance'].max(),
  balance_initial = initial_balance
  ))

        #self.df.to_csv('out.csv')
        self.df.to_excel('out.xls')

        fig = plt.figure()

        fig.subplots_adjust(bottom=0.1)
        #ax = fig.add_subplot(311)
        #plt.title("Toss")
        #self.df.plot(x=self.df.index, y='Toss', style='*')

        #ax = fig.add_subplot(312)
        #plt.title("Toss Results")
        #self.df.plot(x=self.df.index, y='TossResults', style='*')

        #ax = fig.add_subplot(313)
        ax = fig.add_subplot(111)
        #plt.title("Toss Results Cumsum")
        #self.df.plot(x=self.df.index, y='TossResultsCumsum')
        plt.title("Balance")
        self.df.plot(x=self.df.index, y='Balance')

        #fileOut='out/fig/fig_{0}.png'.format(filename)
        #print("Generating {0}".format(fileOut))
        #plt.savefig(fileOut)

        plt.show() # pause


g = SatoshiDiceGameTester()