New Release of {healthyR.ts}
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Introduction
Hello R users!
I am excited to announce a new update to the {healthyR.ts}
package: the ts_brownian_motion()
function.
This function allows you to easily simulate brownian motion, also known as a Wiener process, using just a few parameters. You can specify the length of the simulation using the ‘.time’ parameter, the number of simulations to run using the ‘.num_sims’ parameter, the time step size (standard deviation) using the ‘.delta_time’ parameter, and the initial value (which is set to 0 by default) using the ‘.initial_value’ parameter.
But what is brownian motion, and why might you want to simulate it? Brownian motion is a random process that describes the movement of particles suspended in a fluid. It is named after the botanist Robert Brown, who observed the random movement of pollen grains suspended in water under a microscope in the 19th century.
In finance, brownian motion is often used to model the movement of stock prices over time. By simulating brownian motion, you can get a sense of how prices might fluctuate in the future, and use this information to inform your investment decisions.
I hope that the ts_brownian_motion()
function will be a useful tool for anyone interested in simulating brownian motion, whether for financial modeling or any other application. Give it a try and see what you can do with it!
Right now the function is a bit slow at .num_sims
> 500 so I am working on optimizing it. I will also later on be introducing the Geometric Brownian Motion to {healthyR.ts}
As always, we welcome feedback and suggestions for new features and improvements. Thank you for using the {healthyR.ts} package, and happy simulating!
Function
Here is the full function call:
ts_brownian_motion( .time = 100, .num_sims = 10, .delta_time = 1, .initial_value = 0 )
Example
A simple example of the output.
library(healthyR.ts) ts_brownian_motion()
# A tibble: 1,010 × 3 sim_number t y <fct> <dbl> <dbl> 1 1 0 0 2 1 1 1.46 3 1 2 2.68 4 1 3 2.78 5 1 4 3.07 6 1 5 3.43 7 1 6 3.05 8 1 7 4.43 9 1 8 6.04 10 1 9 6.89 # … with 1,000 more rows
Voila!
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