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Calculation of the relevant data for the AxE-model from a financial time series: trend, volatiliy, change in quotes and risk level.
axe(quotes)
data frame
the given time series
5-day trend
10-day trend
20-day trend
5-day volatility
10-day volatility
20-day volatility
5-day price change
10-day price change
20-day price change
5-day risk level
10-day risk level
20-day risk level
financial time series
Dr. Lars Metzner
Dr. Lars Metzner (2020) Trendbasierte Prognostik. Independently Published.
set.seed(1234) s <- 13000 + cumsum(rnorm(100)) df_axe <- axe(s) op <- par(mfrow=c(3,1)) plot(s, type = "l") plot(df_axe$trend5, type = "l") abline(a = 0, b = 0) plot(df_axe$vola5, type = "l") par(op)
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