# Two interesting ideas here: “trading time” price impact of a…

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Two interesting ideas here:

- “trading time”
- price impact of a trade proportional to
`exp( √size )`

Code follows:

require(quantmod) getSymbols("MER") #Merrill Lynch #Gatheral's model HiLo <- function(symbol) log( Hi(symbol) / Lo(symbol) ) **2 UpDay <- function(symbol) Cl(symbol) > Op(symbol) #munging mer <- merge( MER, UpDay(MER), HiLo(MER) ) mer <- data.frame(mer) names(mer)[7] = "UpDay" names(mer)[8] = "HiLo" mer <- subset(mer, Vo(mer) > 0) #data cleaning #plot layers require(ggplot2) base <- ggplot(data=mer, aes(x=log(MER.Volume), y=HiLo, col=as.factor(UpDay))) points <- geom_point(alpha=.5) #no more I(.5) ! line <- geom_smooth(method='lm', col='red', fill='red') labels <- labs(y="Volatility", x="Volume", colour="Up day?", title="Jim Gatheral's model") #more munging winners <- subset(mer, UpDay>0) losers <- subset(mer, UpDay

(This doesn’t run as is. I think you can fix that by combining the ggplot pieces differently. I just gave the pieces semantic names combing through my history.

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