Monte Carlo Simulation (200 Random Walks)

7. Aug. 2018

Understanding the Monte Carlo Simulation

This indicator uses Monte Carlo methods to predict the future price of a security using 200 random walks.

Monte Carlo methods rely on repeated random sampling to create a data set that has the same characteristics as the sample source, representing examples of alternate possible outcomes. The data set created using random sampling is called a “random walk”. Obtaining a mean from 200 random walks allows us to benchmark the performance of the source against the random walks obtained from the source.



Monte Carlo Utility

This Monte Carlo simulator plots a single line that represents 200 random walks across any security and time stamp. The line is red if most of the random walks are lower than the price of the security, and blue if the walks are higher.

Input Values

Select the “Format”, button located next to the indicator label to adjust the input values and the style.

The Monte Carlo indicator has only one user-defined input value that can be changed.

The Random_Variable determines set of random walks. If this variable is changed, it will run a fresh set
of 200 random walks which will result in a slightly different outcome. 200 random walks will load
relatively quick and produce roughly the same outcome as 10,000 random walks.

Adding the indicator to your chart multiple times using many different random variables will allow you
to achieve a more accurate reading.

For more information on this indicator view the PDF here: kenzing.com/product/monte-carlo-simulation-200-random-walks/