rprices: Generate time series data based on different generators

In zatonovo/fractalrock: Generate fractal time series with non-normal returns distribution

Description

This function will construct a portfolio of asset returns based on the time range specified or the number of 'observations' requested. The resulting time series will be based on the specified calendar, as defined by trading_dates that uses the timeDate package under the hood.

Arguments

process	The process to use
start	Start date
ohlc	Whether to include OHLC values
volume	Whether to include volume
...	Additional arguments to date generator

Value

An xts object with either obs rows or points in the range [start,end] and a time series for each symbol provided.

Usage

rprices(process, start=Sys.Date(), ohlc=FALSE, volume=FALSE, ...)

Details

The main entry point is getPortfolioPrices, which generates a TxM xts object based on the symbols provided. Prices generated by this function can be used in risk modeling, as a substitute for brownian motion in Monte Carlo simulations, and backtesting applications. Studying fractal generation of time series can be accomplished more directly by calling rfractal.

In addition to the arguments above, it is necessary to pass the appropriate arguments to the the underlying fractal call. This includes passing in a seed and generator patterns. If none are provided predefined sets will be used, although users of this package are encouraged to create their own initiators and generators.

The trading_dates function is a utility to generate proper business days for a given calendar. This is used to be compatible with other applications that load actual asset data.

Examples

mygbm <- function(x) gbm(x, 40, .03/1440)
ps <- rprices(mygbm, obs=100)

zatonovo/fractalrock documentation built on May 15, 2021, 7:28 p.m.