Simulating stock market prices and returns can be accomplished using a number of techniques. Most commonly, geometric brownian motion (aka a random walk) is used to simulate stock prices. Using this technique results in a normal distribution of price returns. As an alternative technique, it is possible to generate price series using fractals. The advantage is that price returns tend to have volatility that clusters, similar to actual returns.
The basic principle driving fractal generation of time series is that data is generated iteratively based on increasing levels of resolution. The initial series is defined by a so-called initiator pattern and then generators are used to replace each segment of the initial pattern. Regular, repeatable patterns can be produced by using the same seed and generators. By using a set of generators, non-repeatable time series can be produced. This technique is the basis of the fractal time series process in this package.
At a later date, implementation of the [modified] rescaled range statistic will be included to provide more analytical insight into the time series data produced by this package.
To generate a set of asset prices, the function
getPortfolioPricesis the most direct way to accomplish this. An xts object will be returned with
one time series per 'asset' provided. In addition, the dates will be coerced
to fit a given business day calendar based on timeDate.
Investigation into fractals via this package is best accomplished by calling the
fractal function. This function produces raw values
useful for analysis of the fractal generation process.
Brian Lee Yung Rowe <email@example.com>
M. Frame, B. Mandelbrot, N. Neger. Fractal Geometry. 2009. http://classes.yale.edu/fractals/
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