Wanted to implement something that shows how the simple framework can be used to investigate a basic heads and tail game. The betting strategy can be varied simply by changing the logic in the ontrail() method. This currently tests the double or quits strategy. Run it to see how many
Python, 133 lines
Download
Copy to clipboard1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | '''
monte carlo tool for simple strategy, more generally for use with any
strategy that I want to back test...
Engine takes a class instance, derived from a base class wih two methods
initialise()
onsimulation()
aftersimulation()
ontrial()
finalise()
'''
import unittest, datetime
import numpy as np
from engine.BackTest import MonteCarloModel, MonteCarloEngine, Simulation
import matplotlib.pyplot as plt
from random import randint
class SimpleHeadTailModel(MonteCarloModel):
'''
model wager
previous bet success or not, impact to profit.
'''
def toss(self):
'''
1 - win
-1 - loss
Each toss determines whether the position is successful or not. This way no need to keep track of a decision
and an associated variable. Simply do I win or not.
'''
coin_toss = randint(1,2)
if coin_toss == 1:
return 1
else:
return -1
def initialise(self, context):
self.name = 'My Simple Heads And Tails Model'
# start with 10 USD bet
self.wager= 100
self.wager_initial= 100
self.starting_pot = 1000
self.previous_value = 1 # default to 1 on first round
self.simulations = 10 # MC simulation trials
self.trials = 100 # subintervals
self.r = np.zeros(shape=(self.simulations, self.trials), dtype=float) # matrix to hold all results
self.pnl = np.zeros(shape=(self.simulations, self.trials), dtype=float) # matrix to hold all results
# Tell the engine where to associate the data to security.
context[self.name] = Simulation(self.simulations, self.trials, self.toss)
self.fig = plt.figure()
self.ax = self.fig.add_subplot(211)
self.ax1 = self.fig.add_subplot(212)
self.ax.autoscale_view(True,True,True)
def onsimulation(self, model, simulation, engine):
self.r[simulation,0] = 0
# assume starting pot here
self.pnl[simulation,0] = self.starting_pot
def aftersimulation(self, model, simulation, engine):
self.ax.plot(np.arange(0, self.trials, 1), self.r[simulation])
self.ax1.plot(np.arange(0, self.trials, 1), self.pnl[simulation])
def reset_wager(self):
self.wager = self.wager_initial
def ontrial(self, model, simulation, trial, value, engine):
'''
want to test some strategies for betting
set wager for each bet
if previous bet
value : float
sample from model
'''
# if we lost last time then double up
if self.previous_value == -1:
self.wager += self.wager
# keep track of coin toss paths
self.r[simulation,trial] = self.r[simulation,trial-1] + value
# if we won, add the wager
# else subtract the wager
if self.pnl[simulation,trial-1] > 0:
if value == 1 :
self.pnl[simulation,trial] = self.pnl[simulation,trial-1] + self.wager
else:
self.pnl[simulation,trial] = self.pnl[simulation,trial-1] - self.wager
else:
# no bet to be made here
self.pnl[simulation,trial] = self.pnl[simulation,trial-1]
# always reset wager
self.reset_wager()
# keep track of the previous value for next time around
self.previous_value = value
def finalise(self, model, engine):
plt.title('Simulations %d Steps %d' % (int(self.simulations), int(self.trials)))
plt.xlabel('steps')
plt.ylabel('profit and loss')
plt.show()
class TestNode(unittest.TestCase):
def setUp(self):
pass
def test_engine(self):
'''
example of how to launch the MontoCarloTestEngine
this is modelled on the quantopian style interface.
'''
e = MonteCarloEngine(moduleName='MonteCarloHeadsTailsExample', className='SimpleHeadTailModel')
e.start()
if __name__ == '__main__':
unittest.main()
|
Simple example of a H & T game.
