http://code.activestate.com/recipes/tags/optimise/top/2014-04-28T08:41:49-07:00ActiveState Code Recipes2014-04-28T08:41:49-07:00alexander bakerhttp://code.activestate.com/recipes/users/4166679/http://code.activestate.com/recipes/578869-monte-carlo-engine-how-to-find-the-optimised-wager/

<p style="color: grey"> Python recipe 578869 by <a href="/recipes/users/4166679/">alexander baker</a> (<a href="/recipes/tags/finance/">finance</a>, <a href="/recipes/tags/optimise/">optimise</a>, <a href="/recipes/tags/python/">python</a>, <a href="/recipes/tags/tree/">tree</a>). </p> <p>Simple Engine to help understand how to best wager your next bet, given that you just made a loss. The engine uses the modified Powell method to optimise the weight to apply to your wager on the next position.</p> <p>{'My Simple Heads And Tails Model': &lt;BackTest.Simulation object at 0x0583D410&gt;} participants [100] survivors [90.0%] losers [10.0%] weight [0.073858] solving for r: [ 0.07385806] simulations 100, trials 100 starting pot 1000 calling initialise {'My Simple Heads And Tails Model': &lt;BackTest.Simulation object at 0x0583D430&gt;} participants [100] survivors [86.0%] losers [14.0%] weight [0.072220] solving for r: [ 0.07221954] Optimization terminated successfully. Current function value: 8.000000 Iterations: 2 Function evaluations: 30 highest survivability following loss, multiply wager by 7.2949 %</p> <h5 id="">.</h5> <p>Ran 2 tests in 25.545s</p> <p>OK</p>