Monte Carlo Simulation and Python 17 - Using Monte Carlo to find best D'Alembert Increment
Monte Carlo Simulation with Python Playlist: http://www.youtube.com/watch?v=9M_KPXwnrlE&feature=share&list=PLQVvvaa0QuDdhOnp-FnVStDsALpYk2hk0
Here we use the monte carlo simulation to help us locate superior variables to use with our D'Alembert strategy.
In the monte carlo simulation with Python series, we test various betting strategies. A simple 50/50 strategy, a martingale strategy, and the d'alembert strategy. We use the monte carlo simulator to calculate possible paths, as well as to calculate preferred variables to use including wager size, how many wagers, and more.
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Build Your Own Supercomputer 3 - Installing operating system
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Build Your Own Supercomputer 4 - Downloading/Installing an MPI
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Build Your Own Supercomputer 6 - Copying Master Node
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Build Your Own Supercomputer 5 - Testing supercomputer process
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Build Your Own Supercomputer 7 - Setting up Master Login
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Build Your Own Supercomputer 8 - Completing our 2-Node supercomputer
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Getting Node Rank tutorial - Supercomputing and Parallel Programming in Python and MPI 2
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Monte Carlo Simulation and Python 1 - Intro
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Monte Carlo Simulation and Python 2 - Dice Function
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Monte Carlo Simulation and Python 3 - Simple Bettor Creation
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Monte Carlo Simulation and Python 5 - Martingale Strategy
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Monte Carlo Simulation and Python 7 - More comparison
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Monte Carlo Simulation and Python 8 - Graphing Results
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Monte Carlo Simulation and Python 9 Fixing Debt Issues
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Monte Carlo Simulation and Python 12 - Checking Results
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Monte Carlo Simulation and Python 11 - Using Monte Carlo to find best multiple
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Monte Carlo Simulation and Python 13 - D'Alembert Strategy
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Monte Carlo Simulation and Python 14 - 50/50 odds
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Monte Carlo Simulation and Python 15 - Analysis of D'Alembert
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Monte Carlo Simulation and Python 16 - Comparing profitability
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Monte Carlo Simulation and Python 17 - Using Monte Carlo to find best D'Alembert Increment
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Monte Carlo Simulation and Python 18 - 2D charting monte carlo variables
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