모멘텀 투자와 R

1.
논문과 전략구현을 함께 다루는 교육 프로그램에 대한 꿈을 적었습니다. 몇 분이 관심을 가져주셨지만 진전이 없습니다.

새로운 교육 프로그램에 대한 고민?

만약 능력이 있었다면 위와 같은 프로그램의 주제로 삼고 싶었던 3부작이 있습니다.궁금해서 정리했던 내용입니다.

Direction, Trend 그리고 Momentum (1)
Direction, Trend 그리고 Momentum (2)
Direction, Trend 그리고 Momentum (3)

위의 글을 정리한 이후 특별한 노력을 기울이지 않았지만 우연히 한 블로거의 글에서 논문을 보았습니다.

Fact, Fiction and Momentum Investing

It’s been over 20 years since the academic discovery of momentum investing (Jegadeesh and Titman (1993), Asness (1994)), yet much confusion and debate remains regarding its efficacy and its use as a practical investment tool. In some cases “confusion and debate” is us attempting to be polite, as it is near impossible for informed practitioners and academics to still believe some of the myths uttered about momentum — but that impossibility is often belied by real world statements. In this article, we aim to clear up much of the confusion by documenting what we know about momentum and disproving many of the often-repeated myths. We highlight ten myths about momentum and refute them, using results from widely circulated academic papers and analysis from the simplest and best publicly available data.

Download (PDF, 539KB)

2.
위의 논문을 소개할 요량이면 긴 서론이 필요없습니다. 본론입니다. 위의 논문을 읽고 어떤 분이 논문중에 나오는 세가지 믿음을 검증하였습니다.

Myth 1: Momentum returns are too “small and sporadic”.
Myth 2: Momentum cannot be captured by long-only investors as “momentum can only be exploited on the short side”.
Myth 3: Momentum is much stronger among small cap stocks than large caps

R로 검증한 코드중 일 부분입니다. 나머지는 아래를 참조해주세요.

rCharts factor analytics

require(quantmod)

#use monthly French data to replicate research on momentum in R
# data source : Kenneth French Data Library
#               http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html 
# paper       : Fact, Fiction and Momentum Investing
#               Asness, Clifford S. and Frazzini, Andrea and Israel, Ronen and Moskowitz, Tobias J.
#               May 9, 2014
#               http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2435323

#gather french factor data
my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors.zip"
my.tempfile<-paste(tempdir(),"\\frenchfactors.zip",sep="")
my.usefile<-paste(tempdir(),"\\F-F_Research_Data_Factors.txt",sep="")
download.file(my.url, my.tempfile, method="auto", 
              quiet = FALSE, mode = "wb",cacheOK = TRUE)
unzip(my.tempfile,exdir=tempdir(),junkpath=TRUE)
#read space delimited text file extracted from zip
french_factors <- read.table(file=my.usefile,
                             header = TRUE, sep = "",
                             as.is = TRUE,
                             skip = 3, nrows=1054)
#get xts for analysis
french_factors_xts <- as.xts(
  french_factors,
  order.by=as.Date(
    paste0(rownames(french_factors),"01"),
    format="%Y%m%d"
  )
)

#now get the momentum factor
my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Momentum_Factor.zip"
my.usefile<-paste(tempdir(),"\\F-F_Momentum_Factor.txt",sep="")
download.file(my.url, my.tempfile, method="auto", 
              quiet = FALSE, mode = "wb",cacheOK = TRUE)
unzip(my.tempfile,exdir=tempdir(),junkpath=TRUE)
#read space delimited text file extracted from zip
french_momentum <- read.table(file=my.usefile,
                              header = TRUE, sep = "",
                              as.is = TRUE,
                              skip = 12, nrows=1048)
#get xts for analysis
french_momentum_xts <- as.xts(
  french_momentum,
  order.by=as.Date(
    paste(rownames(french_momentum),"01"),
    format="%Y%m%d"
  )
)

#merge UMD (momentum) with other french factors
french_factors_xts <- na.omit( merge( french_factors_xts, french_momentum_xts ) )
french_factors_xts <- french_factors_xts/100


#test our numbers
periods <- c("1927::2013","1963::2013","1991::2013")
t(sapply(
  periods,
  function(x){
    return(data.frame(Return.annualized(french_factors_xts[x,-4],geometric=F)))
  }
))
t(do.call(cbind,lapply(
  periods,
  function(x){
    df <- data.frame(
      SharpeRatio.annualized(
        french_factors_xts[x,-4],
        Rf = 0,
        geometric=F
      )[1,]
    )
    colnames(df) <- x
    return(df)
  }
)))

#ok Table 1 matches so now let's move on to other calculations
#Table 2
do.call(rbind,lapply(
  periods,
  function(x){
    df <- data.frame(lapply(
      rollapply(french_factors_xts[x,-4], width = 12, by = 1, FUN = Return.cumulative, geometric=F),
      function(y){sum(na.omit(y)>=0)/nrow(na.omit(y))}
    ))
    rownames(df) <- x
    return(df)
  }
))

do.call(rbind,lapply(
  periods,
  function(x){
    df <- data.frame(lapply(
      rollapply(french_factors_xts[x,-4], width = 60, by = 1, FUN = Return.cumulative, geometric=F),
      function(y){sum(na.omit(y)>=0)/nrow(na.omit(y))}
    ))
    rownames(df) <- x
    return(df)
  }
))


#Table 3
SharpeRatio.annualized(
  apply(
    french_factors_xts[x,c(3,5)],
    MARGIN = 1,
    function(x){ x[1] * 0.6 + x[2] * 0.4}
  ),
  Rf = 0,
  geometric=F
)

lapply(
  c(12,60),
  function(width){
    y = rollapply(
      apply(
        french_factors_xts[,c(3,5)],
        MARGIN = 1,
        function(x){ x[1] * 0.6 + x[2] * 0.4}
      ),
      width = width,
      by = 1,
      FUN = Return.cumulative,
      geometric=F
    )
    return(sum(na.omit(y)>=0)/length(na.omit(y)))
  }
)


#Table 4
#need some additional data here
#http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_Prior_12_2.zip
my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_Prior_12_2.zip"
my.tempfile<-paste(tempdir(),"\\frenchfactors.zip",sep="")
my.usefile<-paste(tempdir(),"\\6_Portfolios_ME_Prior_12_2.txt",sep="")
download.file(my.url, my.tempfile, method="auto", 
              quiet = FALSE, mode = "wb",cacheOK = TRUE)
unzip(my.tempfile,exdir=tempdir(),junkpath=TRUE)
#read space delimited text file extracted from zip
french_hml_smb <- read.table(file=my.usefile,
                             header = TRUE, sep = "",
                             as.is = TRUE,
                             skip = 12, nrows=1048)
colnames(french_hml_smb) <- c(
  paste0(
    "Small.",
    colnames(french_hml_smb)[1:3]
  ),
  paste0(
    "Big.",
    colnames(french_hml_smb)[1:3]
  )
)
#get xts for analysis
french_hml_smb_xts <- as.xts(
  french_hml_smb/100,
  order.by=as.Date(
    paste0(rownames(french_hml_smb),"01"),
    format="%Y%m%d"
  )
)

#so short side will be average of Small.Low and Big.Low
#and long side will be the average of Small.High and Big.High
french_short_long_xts <- as.xts(do.call(rbind,apply(
  french_hml_smb_xts,
  MARGIN=1,
  function(period){
    data.frame(
      shortside = mean(period[c(1,4)]),
      longside = mean(period[c(3,6)])
    )
  }
)),order.by = index(french_hml_smb_xts))

#add umd from the factors
#as check this should be same as sum of -short and long
french_short_long_xts <- merge(
  french_short_long_xts,
  french_factors_xts[,c(5,1,4)] #Market and Mom (UMD)
)


#UMD market-adjusted returns (alpha)
do.call(rbind,lapply(
  periods,
  function(period){
    df <- data.frame(
      shortside = -((CAPM.alpha(
        Ra = french_short_long_xts[period,]$shortside,
        Rb = french_short_long_xts[period,]$Mkt.RF + french_short_long_xts[period,]$RF,
        Rf = french_short_long_xts[period,]$RF
      )+1)^12-1),
      longside = (CAPM.alpha(
        Ra = french_short_long_xts[period,]$longside,
        Rb = french_short_long_xts[period,]$Mkt.RF + french_short_long_xts[period,]$RF,
        Rf = french_short_long_xts[period,]$RF
      )+1)^12-1
    )
    df$UMD <- df$shortside + df$longside
    rownames(df) <- period
    return(df)
  }
))

#UMD returns minus market
do.call(rbind,lapply(
  periods,
  function(period){
    df <- data.frame(
      shortside = -Return.annualized(
        french_short_long_xts[period,]$shortside - 
          french_short_long_xts[period,]$Mkt.RF - 
          french_short_long_xts[period,]$RF,
        geometric=F
      ),
      longside = Return.annualized(
        french_short_long_xts[period,]$longside - 
          french_short_long_xts[period,]$Mkt.RF - 
          french_short_long_xts[period,]$RF,
        geometric=F
      )
    )
    df$UMD <- df$shortside + df$longside
    rownames(df) <- period
    return(df)
  }
))




#table 5
table5 <- do.call(rbind,lapply(
  periods,
  function(period){
    df <- data.frame(Return.annualized(
      french_hml_smb_xts[period,],
      geometric = F
    ))
    rownames(df) <- period
    return(df)
  }
))
#umd small
umdsmall <- data.frame(table5$Small.High - table5$Small.Low)
rownames(umdsmall) <- periods
colnames(umdsmall) <- "UMD Small"
umdsmall

#umd big
umdbig <- data.frame(table5$Big.High - table5$Big.Low)
rownames(umdbig) <- periods
colnames(umdbig) <- "UMD Big"
umdbig

#by this point I hope you can do the Value piece of table 5 on your own
#if you really can't figure it out, let me know
umdbig










####################### with daily ####################################
#get Mkt.RF, SMB, HML, and RF
#UMD is in a different file
my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_daily.zip"
my.tempfile<-paste(tempdir(),"\\frenchfactors.zip",sep="")
my.usefile<-paste(tempdir(),"\\F-F_Research_Data_Factors_daily.txt",sep="")
download.file(my.url, my.tempfile, method="auto", 
              quiet = FALSE, mode = "wb",cacheOK = TRUE)
unzip(my.tempfile,exdir=tempdir(),junkpath=TRUE)
#read space delimited text file extracted from zip
french_factors <- read.table(file=my.usefile,
                             header = TRUE, sep = "",
                             as.is = TRUE,
                             skip = 4, nrows=23215)
#get xts for analysis
french_factors_xts <- as.xts(
  french_factors,
  order.by=as.Date(
    rownames(french_factors),
    format="%Y%m%d"
  )
)

#now get the momentum factor
my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Momentum_Factor_daily.zip"
my.usefile<-paste(tempdir(),"\\F-F_Momentum_Factor_daily.txt",sep="")
download.file(my.url, my.tempfile, method="auto", 
              quiet = FALSE, mode = "wb",cacheOK = TRUE)
unzip(my.tempfile,exdir=tempdir(),junkpath=TRUE)
#read space delimited text file extracted from zip
french_momentum <- read.table(file=my.usefile,
                              header = TRUE, sep = "",
                              as.is = TRUE,
                              skip = 13, nrows=23114)
#get xts for analysis
french_momentum_xts <- as.xts(
  french_momentum,
  order.by=as.Date(
    rownames(french_momentum),
    format="%Y%m%d"
  )
)

#merge UMD (momentum) with other french factors
french_factors_xts <- na.omit( merge( french_factors_xts, french_momentum_xts ) )
french_factors_xts <- french_factors_xts/100

#get price series from monthly returns
french_price<-as.xts(
  apply(1+coredata(french_factors_xts),MARGIN=2,cumprod),
  index(french_factors_xts))

#now we should have all the french factor data that we need
#we can start to do our exploration

require(PerformanceAnalytics)

Return.annualized(french_factors_xts["1991::2013"],geometric=F)




require(reshape2)
require(ggplot2)
require(dplyr)
require(lattice)

Omega(french_factors_xts)

#the traditional way to get rolling
#but this takes a really long time except if we do as.matrix
system.time(
  df_matrix <- rollapply(as.matrix(french_factors_xts), width=200, by = 1, FUN=Omega)
)

system.time(
  df_lapply <- do.call(rbind,lapply(
    french_factors_xts,function(f){
      answer <- data.frame(rollapply(as.numeric(f),Omega,width=200,by=1))
      colnames(answer) <- "omega"
      answer$date = index(french_factors_xts)[seq(1,nrow(french_factors_xts)-199,by=1)]
      answer$mkt_factor = colnames(f)
      return(answer)
    }
  ))
)

xyplot(omega~date, groups = mkt_factor, data = df_lapply,type="l",ylim=c(-1,4))

require(tidyr)
system.time(
df_dplyr <- #melt(
#  data.frame(
#    date=as.Date(index(french_factors_xts)),
#    french_factors_xts
#  ),
#  id.vars = "date",
#  variable.name = "mkt_factor",
#  value.name = "roc"
#)
data.frame("date"=index(french_factors_xts),french_factors_xts) %>%
  gather(ff_factor,roc,-date) %.%
  group_by( ff_factor )  %.%
  do(
    data.frame(
      date = .$date[seq(1,nrow(.)-199,by=1)],
      omega = rollapply( as.numeric(.$roc) , Omega, width=200, by=1)
    )
  )
)

xyplot(omega~date, groups = ff_factor, data = df_dplyr,type="l",ylim=c(-1,4))


#play with tidyr
data.frame("date"=index(french_factors_xts),french_factors_xts) %>%
    gather(ff_factor,roc,-date)

data.frame("date"=index(french_factors_xts),french_factors_xts) %>%
  gather(ff_factor,roc,-date) %>%
  ggplot(data = .,aes(x=date,y=roc,colour=ff_factor)) + geom_line()

data.frame("date"=index(french_factors_xts),french_factors_xts) %>%
  gather(ff_factor,roc,-date) %>%
  group_by( ff_factor ) %>%
  mutate(cumul = cumsum(roc)) %>%
  ggplot(data = .,aes(x=date,y=cumul,colour=ff_factor)) + geom_line()

require(rCharts)
data.frame("date"=format(index(french_factors_xts)),french_factors_xts) %>%
  gather(ff_factor,roc,-date) %>%
  group_by( ff_factor ) %>%
  mutate(cumul = cumsum(roc)) %>%
  dPlot(
    cumul~date
    ,groups="ff_factor"    
    ,data = .
    ,type="line"
    ,xAxis = list( type = "addTimeAxis", inputFormat = '%Y-%m-%m', outputFormat = "%b %Y"  )
  )
  
#very hacky way of accomplishing
#need to iterate to something better
modifyChartList <- function( x, element, val ) {
  rTemp <- x$copy()
  rTemp[[element]] <- modifyList(rTemp[[element]], val)
  return(rTemp)
}

data.frame("date"=format(index(french_factors_xts)),french_factors_xts) %>%
  gather( ff_factor, roc, -date ) %>%
  group_by( ff_factor ) %>%
  mutate(cumul = cumsum(roc)) %>%
  dPlot(
    cumul~date
    ,groups="ff_factor"    
    ,data = .
    ,type="line"
    ,xAxis = list( type = "addTimeAxis", inputFormat = '%Y-%m-%m', outputFormat = "%b %Y"  )
  ) %>%
  modifyChartList(
    element = "templates",
    val = list(afterScript = '
      <script>
        myChart.axes[0].shapes.selectAll(".tick")[0].forEach(function(d,i){
          if (!(+d3.time.format("%Y")(new Date(+d3.select(d).datum())) % 10 == 0)) {
            d.remove()
          } else {
            d3.select(d).select("text")
              .attr("transform",null)
              .attr("y","0")
              .attr("dy","2em")
              .style("text-anchor","middle")
              .text(d3.time.format("%Y")(new Date(+d3.select(d).datum())))
          }
        });
      </script>
     '
    )
  )

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require(quantmod)

#use monthly French data to replicate research on momentum in R

# data source : Kenneth French Data Library

# http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

# paper : Fact, Fiction and Momentum Investing

# Asness, Clifford S. and Frazzini, Andrea and Israel, Ronen and Moskowitz, Tobias J.

# May 9, 2014

# http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2435323

#gather french factor data

my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors.zip"

my.tempfile<-paste(tempdir(),"\\frenchfactors.zip",sep="")

my.usefile<-paste(tempdir(),"\\F-F_Research_Data_Factors.txt",sep="")

download.file(my.url, my.tempfile, method="auto",

quiet = FALSE, mode = "wb",cacheOK = TRUE)

unzip(my.tempfile,exdir=tempdir(),junkpath=TRUE)

#read space delimited text file extracted from zip

french_factors <- read.table(file=my.usefile,

header = TRUE, sep = "",

as.is = TRUE,

skip = 3, nrows=1054)

#get xts for analysis

french_factors_xts <- as.xts(

french_factors,

order.by=as.Date(

paste0(rownames(french_factors),"01"),

format="%Y%m%d"

)

)

#now get the momentum factor

my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Momentum_Factor.zip"

my.usefile<-paste(tempdir(),"\\F-F_Momentum_Factor.txt",sep="")

download.file(my.url, my.tempfile, method="auto",

quiet = FALSE, mode = "wb",cacheOK = TRUE)

unzip(my.tempfile,exdir=tempdir(),junkpath=TRUE)

#read space delimited text file extracted from zip

french_momentum <- read.table(file=my.usefile,

header = TRUE, sep = "",

as.is = TRUE,

skip = 12, nrows=1048)

#get xts for analysis

french_momentum_xts <- as.xts(

french_momentum,

order.by=as.Date(

paste(rownames(french_momentum),"01"),

format="%Y%m%d"

)

)

#merge UMD (momentum) with other french factors

french_factors_xts <- na.omit( merge( french_factors_xts, french_momentum_xts ) )

french_factors_xts <- french_factors_xts/100

#test our numbers

periods <- c("1927::2013","1963::2013","1991::2013")

t(sapply(

periods,

function(x){

return(data.frame(Return.annualized(french_factors_xts[x,-4],geometric=F)))

}

))

t(do.call(cbind,lapply(

periods,

function(x){

df <- data.frame(

SharpeRatio.annualized(

french_factors_xts[x,-4],

Rf = 0,

geometric=F

)[1,]

)

colnames(df) <- x

return(df)

}

)))

#ok Table 1 matches so now let's move on to other calculations

#Table 2

do.call(rbind,lapply(

periods,

function(x){

df <- data.frame(lapply(

rollapply(french_factors_xts[x,-4], width = 12, by = 1, FUN = Return.cumulative, geometric=F),

function(y){sum(na.omit(y)>=0)/nrow(na.omit(y))}

))

rownames(df) <- x

return(df)

}

))

do.call(rbind,lapply(

periods,

function(x){

df <- data.frame(lapply(

rollapply(french_factors_xts[x,-4], width = 60, by = 1, FUN = Return.cumulative, geometric=F),

function(y){sum(na.omit(y)>=0)/nrow(na.omit(y))}

))

rownames(df) <- x

return(df)

}

))

#Table 3

SharpeRatio.annualized(

apply(

french_factors_xts[x,c(3,5)],

MARGIN = 1,

function(x){ x[1] * 0.6 + x[2] * 0.4}

),

Rf = 0,

geometric=F

)

lapply(

c(12,60),

function(width){

y = rollapply(

apply(

french_factors_xts[,c(3,5)],

MARGIN = 1,

function(x){ x[1] * 0.6 + x[2] * 0.4}

),

width = width,

by = 1,

FUN = Return.cumulative,

geometric=F

)

return(sum(na.omit(y)>=0)/length(na.omit(y)))

}

)

#Table 4

#need some additional data here

#http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_Prior_12_2.zip

my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_Prior_12_2.zip"

my.tempfile<-paste(tempdir(),"\\frenchfactors.zip",sep="")

my.usefile<-paste(tempdir(),"\\6_Portfolios_ME_Prior_12_2.txt",sep="")

download.file(my.url, my.tempfile, method="auto",

quiet = FALSE, mode = "wb",cacheOK = TRUE)

unzip(my.tempfile,exdir=tempdir(),junkpath=TRUE)

#read space delimited text file extracted from zip

french_hml_smb <- read.table(file=my.usefile,

header = TRUE, sep = "",

as.is = TRUE,

skip = 12, nrows=1048)

colnames(french_hml_smb) <- c(

paste0(

"Small.",

colnames(french_hml_smb)[1:3]

),

paste0(

"Big.",

colnames(french_hml_smb)[1:3]

)

)

#get xts for analysis

french_hml_smb_xts <- as.xts(

french_hml_smb/100,

order.by=as.Date(

paste0(rownames(french_hml_smb),"01"),

format="%Y%m%d"

)

)

#so short side will be average of Small.Low and Big.Low

#and long side will be the average of Small.High and Big.High

french_short_long_xts <- as.xts(do.call(rbind,apply(

french_hml_smb_xts,

MARGIN=1,

function(period){

data.frame(

shortside = mean(period[c(1,4)]),

longside = mean(period[c(3,6)])

)

}

)),order.by = index(french_hml_smb_xts))

#add umd from the factors

#as check this should be same as sum of -short and long

french_short_long_xts <- merge(

french_short_long_xts,

french_factors_xts[,c(5,1,4)] #Market and Mom (UMD)

)

#UMD market-adjusted returns (alpha)

do.call(rbind,lapply(

periods,

function(period){

df <- data.frame(

shortside = -((CAPM.alpha(

Ra = french_short_long_xts[period,]$shortside,

Rb = french_short_long_xts[period,]$Mkt.RF + french_short_long_xts[period,]$RF,

Rf = french_short_long_xts[period,]$RF

)+1)^12-1),

longside = (CAPM.alpha(

Ra = french_short_long_xts[period,]$longside,

Rb = french_short_long_xts[period,]$Mkt.RF + french_short_long_xts[period,]$RF,

Rf = french_short_long_xts[period,]$RF

)+1)^12-1

)

df$UMD <- df$shortside + df$longside

rownames(df) <- period

return(df)

}

))

#UMD returns minus market

do.call(rbind,lapply(

periods,

function(period){

df <- data.frame(

shortside = -Return.annualized(

french_short_long_xts[period,]$shortside -

french_short_long_xts[period,]$Mkt.RF -

french_short_long_xts[period,]$RF,

geometric=F

),

longside = Return.annualized(

french_short_long_xts[period,]$longside -

french_short_long_xts[period,]$Mkt.RF -

french_short_long_xts[period,]$RF,

geometric=F

)

)

df$UMD <- df$shortside + df$longside

rownames(df) <- period

return(df)

}

))

#table 5

table5 <- do.call(rbind,lapply(

periods,

function(period){

df <- data.frame(Return.annualized(

french_hml_smb_xts[period,],

geometric = F

))

rownames(df) <- period

return(df)

}

))

#umd small

umdsmall <- data.frame(table5$Small.High - table5$Small.Low)

rownames(umdsmall) <- periods

colnames(umdsmall) <- "UMD Small"

umdsmall

#umd big

umdbig <- data.frame(table5$Big.High - table5$Big.Low)

rownames(umdbig) <- periods

colnames(umdbig) <- "UMD Big"

umdbig

#by this point I hope you can do the Value piece of table 5 on your own

#if you really can't figure it out, let me know

umdbig

####################### with daily ####################################

#get Mkt.RF, SMB, HML, and RF

#UMD is in a different file

my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_daily.zip"

my.tempfile<-paste(tempdir(),"\\frenchfactors.zip",sep="")

my.usefile<-paste(tempdir(),"\\F-F_Research_Data_Factors_daily.txt",sep="")

download.file(my.url, my.tempfile, method="auto",

quiet = FALSE, mode = "wb",cacheOK = TRUE)

unzip(my.tempfile,exdir=tempdir(),junkpath=TRUE)

#read space delimited text file extracted from zip

french_factors <- read.table(file=my.usefile,

header = TRUE, sep = "",

as.is = TRUE,

skip = 4, nrows=23215)

#get xts for analysis

french_factors_xts <- as.xts(

french_factors,

order.by=as.Date(

rownames(french_factors),

format="%Y%m%d"

)

)

#now get the momentum factor

my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Momentum_Factor_daily.zip"

my.usefile<-paste(tempdir(),"\\F-F_Momentum_Factor_daily.txt",sep="")

download.file(my.url, my.tempfile, method="auto",

quiet = FALSE, mode = "wb",cacheOK = TRUE)

unzip(my.tempfile,exdir=tempdir(),junkpath=TRUE)

#read space delimited text file extracted from zip

french_momentum <- read.table(file=my.usefile,

header = TRUE, sep = "",

as.is = TRUE,

skip = 13, nrows=23114)

#get xts for analysis

french_momentum_xts <- as.xts(

french_momentum,

order.by=as.Date(

rownames(french_momentum),

format="%Y%m%d"

)

)

#merge UMD (momentum) with other french factors

french_factors_xts <- na.omit( merge( french_factors_xts, french_momentum_xts ) )

french_factors_xts <- french_factors_xts/100

#get price series from monthly returns

french_price<-as.xts(

apply(1+coredata(french_factors_xts),MARGIN=2,cumprod),

index(french_factors_xts))

#now we should have all the french factor data that we need

#we can start to do our exploration

require(PerformanceAnalytics)

Return.annualized(french_factors_xts["1991::2013"],geometric=F)

require(reshape2)

require(ggplot2)

require(dplyr)

require(lattice)

Omega(french_factors_xts)

#the traditional way to get rolling

#but this takes a really long time except if we do as.matrix

system.time(

df_matrix <- rollapply(as.matrix(french_factors_xts), width=200, by = 1, FUN=Omega)

)

system.time(

df_lapply <- do.call(rbind,lapply(

french_factors_xts,function(f){

answer <- data.frame(rollapply(as.numeric(f),Omega,width=200,by=1))

colnames(answer) <- "omega"

answer$date = index(french_factors_xts)[seq(1,nrow(french_factors_xts)-199,by=1)]

answer$mkt_factor = colnames(f)

return(answer)

}

))

)

xyplot(omega~date, groups = mkt_factor, data = df_lapply,type="l",ylim=c(-1,4))

require(tidyr)

system.time(

df_dplyr <- #melt(

# data.frame(

# date=as.Date(index(french_factors_xts)),

# french_factors_xts

# ),

# id.vars = "date",

# variable.name = "mkt_factor",

# value.name = "roc"

#)

data.frame("date"=index(french_factors_xts),french_factors_xts) %>%

gather(ff_factor,roc,-date) %.%

group_by( ff_factor ) %.%

do(

data.frame(

date = .$date[seq(1,nrow(.)-199,by=1)],

omega = rollapply( as.numeric(.$roc) , Omega, width=200, by=1)

)

)

)

xyplot(omega~date, groups = ff_factor, data = df_dplyr,type="l",ylim=c(-1,4))

#play with tidyr

data.frame("date"=index(french_factors_xts),french_factors_xts) %>%

gather(ff_factor,roc,-date)

data.frame("date"=index(french_factors_xts),french_factors_xts) %>%

gather(ff_factor,roc,-date) %>%

ggplot(data = .,aes(x=date,y=roc,colour=ff_factor)) + geom_line()

data.frame("date"=index(french_factors_xts),french_factors_xts) %>%

gather(ff_factor,roc,-date) %>%

group_by( ff_factor ) %>%

mutate(cumul = cumsum(roc)) %>%

ggplot(data = .,aes(x=date,y=cumul,colour=ff_factor)) + geom_line()

require(rCharts)

data.frame("date"=format(index(french_factors_xts)),french_factors_xts) %>%

gather(ff_factor,roc,-date) %>%

group_by( ff_factor ) %>%

mutate(cumul = cumsum(roc)) %>%

dPlot(

cumul~date

,groups="ff_factor"

,data = .

,type="line"

,xAxis = list( type = "addTimeAxis", inputFormat = '%Y-%m-%m', outputFormat = "%b %Y" )

)

#very hacky way of accomplishing

#need to iterate to something better

modifyChartList <- function( x, element, val ) {

rTemp <- x$copy()

rTemp[[element]] <- modifyList(rTemp[[element]], val)

return(rTemp)

}

data.frame("date"=format(index(french_factors_xts)),french_factors_xts) %>%

gather( ff_factor, roc, -date ) %>%

group_by( ff_factor ) %>%

mutate(cumul = cumsum(roc)) %>%

dPlot(

cumul~date

,groups="ff_factor"

,data = .

,type="line"

,xAxis = list( type = "addTimeAxis", inputFormat = '%Y-%m-%m', outputFormat = "%b %Y" )

) %>%

modifyChartList(

element = "templates",

val = list(afterScript = '

<script>

myChart.axes[0].shapes.selectAll(".tick")[0].forEach(function(d,i){

if (!(+d3.time.format("%Y")(new Date(+d3.select(d).datum())) % 10 == 0)) {

d.remove()

} else {

d3.select(d).select("text")

.attr("transform",null)

.attr("y","0")

.attr("dy","2em")

.style("text-anchor","middle")

.text(d3.time.format("%Y")(new Date(+d3.select(d).datum())))

}

});

</script>

'

)

)

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