Monte Carlo Simulation and Python 5 - Martingale Strategy
176Monte Carlo Simulation with Python Playlist: http://www.youtube.com/watch?v=9M_KPXwnrlE&feature=share&list=PLQVvvaa0QuDdhOnp-FnVStDsALpYk2hk0
In this video, we create a bettor who doubles down on every loss. This is known as the Martingale 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.
There are many purposes for a monte carlo simulator. Some people use them as a form of brute force to solve complex mathematical equations. A popular example used is to have a monte carlo simulator solve for pi. In our case, we are using the Monte Carlo simulator to account for randomness and the degree of risk associated with a betting strategy. In the world of stock trading and investing, people can use the Monte Carlo simulator to test a given strategy's risk.
It used to be very much the case that only performance was considered, for the most part, to decide on a trader's value. Only until recently has the paradigm shifted to consider a strategy's risk more closely. Through this series, you will be able to see just how much random variability can affect the outcome, regardless of how "good" or "bad" a strategy might have been.
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