R/fxpro.R
In genpack/foxer:
# Header
# Filename: fxpro.R
# Description: Contains functions for reading forein exchange data from csv files with fxpro format
# and optimum lots recommendation
# Author: Nima Ramezani Taghiabadi
# Email : N.RamezaniTaghiabadi@uws.edu.au
# Date: 26 September 2013
# Version: 3.0
# Changes from previous version:
# function generate.figures modified completely:
# makes a lattice of given sets for commindex, lag, action, func, and gain and returns all elements of the lattice in figures dataframe
gen.fig.default = function(n){
# This function genertes a list of default figure settings to be used in read.forex.files
# It is convenient that you generate a default setting with this function and then modify it if you want
options(stringsAsFactors = FALSE)
df = data.frame(commindex= 1:n, column=rep("close", n), lag = array(0,n), action=rep("shift",n), func=rep("IDTY", n), gain=array(1.0,n))
rownames(df)= paste("Figure",1:n,":")
return(df)
}
generate.figures <- function (comm=c(1), col=c("close"), lgs=c(0), act=c("shift"), fun=c("IDTY"), gn=c(1.0)) {
# This function generates a data frame of figures containing all elements of a lattice of figures generated by the given sets
# Input 1: comm: A vector of integers containing the commodities involved
# Input 2: col : A vector of char strings containing the name of columns involved
# Input 3: lgs : A vevtor of signed integers containing the lags
# Input 4: act : A vevtor of char strings containing the actions
# Input 5: fun : A vevtor of char strings containing the functions
# Input 6: gn : A vevtor of real numbers containing the gains
fig = gen.fig.default(1)
# Make the explanatory variable:
fig$commindex[1] = 1
fig$lag[1] = 1
fig$action[1] = "delta"
fig$gain[1] = - 100000
# Generate the lattice of figures and insert each element before the explanatory variable
for (i in comm){
for (j in col){
for (k in lgs){
for (l in act){
for (m in fun){
for (n in gn){
if (l == "average"){kp = - 15*(k+1)} else{kp = - k}
fig = insert.figure(fig, i, j, kp, l, m, n)
}
}
}
}
}
}
return(fig)
}
insert.figure <- function (fig, cmmindx, clmn, lg, actn, fnc, gn) {
# This function inserts one figure before the explanatory variable with given fields
n.fig = dim(fig)[1]
f = data.frame(commindex = c(fig$commindex[sequence(n.fig - 1)], cmmindx, fig$commindex[n.fig]),
column = c(fig$column[sequence(n.fig - 1)] , clmn , fig$column[n.fig]),
lag = c(fig$lag[sequence(n.fig - 1)] , lg , fig$lag[n.fig]),
action = c(fig$action[sequence(n.fig - 1)] , actn , fig$action[n.fig]),
func = c(fig$func[sequence(n.fig - 1)] , fnc , fig$func[n.fig]),
gain = c(fig$gain[sequence(n.fig - 1)] , gn , fig$gain[n.fig]))
}
read.forex.data <- function (commodity, start.date.time, final.date.time, date.time.format="%Y.%m.%d %H:%M", period="H", path = fxpro.data.path) {
# This function reads one single Forex .csv file in a given time interval
# and returns a matrix containing raw data
# Input 1: commodity: A charachter strings containing the name of the currency-pair
# or commodity. Example: commodity = "EURUSD"
# Input 2: start.date.time : A charachter string containing the start date of the interval
# Input 3: final.date.time : A charachter string containing the final date of the interval
# Input 4: date.time.format: A charachter string containing the format of the dates given for inputs 4&5
# Input 5: period : A charachter string containing the period of prices
# "H": Hourly (Default), "D": Daily, "M": Minute, "5M": 5 minutes and etc.
# Input 6: path : A charachter string containing the path name where the files are located
# Output : A Matrix (n X 6) containing the forex data
# where n is the number of data rows found in the given
# interval. The first column contains date.time of each row in POSIXlt format.
# The next columns contain in order: open, high, low, close, vol
#
if (commodity == ""){
print("read.forex.data Error: Empty commodity given")
return(o)
}
# concatenates the path , commodity name, period and file extension to make the full filename
period.str=switch(period, "M"='1',"M5"='5',"M15"='15',"H"='60',"H4"='240',"D"='1440',"W"='10080')
filename = paste(path, "/", commodity, period.str,".csv",sep="")
raw.data = read.csv(filename, as.is=TRUE)
# Extract the dates
dates=raw.data[,1]
times=raw.data[,2]
# and convert them to POSIXlt
date.times.str = paste(dates,times)
date.times = strptime(date.times.str, format = date.time.format)
start = strptime(start.date.time, format = date.time.format)
final = strptime(final.date.time, format = date.time.format)
if (is.na(start) | is.na(final)) {
print("read.forex.data Error: Given date-times not in the given format")
return(0)
}
index = which(date.times >= start & date.times <= final)
R= cbind(date.times[index],raw.data[index,3:7])
rownames(R)=date.times.str[index]
colnames(R)=c("time","open","high","low","close","volume")
return(R)
}
read.forex.files <- function (commodities, start.date.time, final.date.time, date.time.format="%Y.%m.%d %H:%M", period="H", path= fxpro.data.path, figures=gen.fig.default(length(commodities))) {
# This function reads Forex data from a list of .csv files in a given time interval
# and returns a matrix of figures (current prices,lagged prices or volumes)
# and their equivalent future figures (candle lengths or prices)
# Input 1: path : A charachter string containing the path name where the files are located
# Input 2: commodities: A vector of charachter strings containing the names of the currency-pairs
# or commodities. Example: commodities=c("EURUSD", "AUDUSD", "GOLD")
# Input 3: period : A charachter string containing the period of prices
# "H": Hourly (Default), "D": Daily, "M": Minute, "5M": 5 minutes and etc.
# Input 4: start.date.time : A charachter string containing the start date of the interval
# Input 5: final.date.time : A charachter string containing the final date of the interval
# Input 6: date.time.format: A charachter string containing the format of the dates given for inputs 4&5
# Input 7: figures: A data frame of three iso-length vectors of chars and numbers
# containing the specifications of figures. The influence of these vectors will
# be applied in order:
# vector 1: figures$commindex: a vector of numbers specifying which commodity
# should be considered as the figure.
# vector 2: figures$column: a vector of char strings specifying which columns of the
# commodity should be read. Options are: ("open","high","low","close","volume")
# vector 3: figures$lag: a vector of signed integer numbers specifying the
# lag. Considered backward for negative and forward for positive lags
# vector 4: figures$action: a vector of char strings specifying what to replace the element:
# "shift": each row is replaced by its lag
# "delta": each row is replaced by the difference between that row and its lag.
# "average": each row is replaced by the moving average among that row and all "lag" rows before or after that.
# vector 5: figures$func: A vector of char containing the functions to
# be applied on the dtata for each figure
# The supported function names are:
# "IDTY" (default): identity function
# "SQR", "SQRT", "CUBE" , "CNT", "NRM","STDRD", "LOG", "EXPNT", "INV", "MA"
# vector 6: figures$gain: A vector of real numbers. All elements of the figure
# will be multiplied by this number. The value is 1.0 by thefault.
# rownames(figures): A vector of char containing labels of the figures
# by default,they contain a key string as a name generated by function gen.fig.default()
# for eample: "Figure 1"
# Example: If figures$commindex[1] = 4,
# figures$function[1] = "IDTY"
# figures$column[1] = "open"
# figures$lag[1] = 0
# and commodities[4]="EURUSD",
# the first figure is the current open prices of "EURUSD" with no lag
# Output : A Matrix (n X m) containing the figures in the given interval
# where n is the number of data rows found common in all given commodities in the given
# interval and m is total number of figures.
# Note: Predictor figures (lag 0 and negative) are usually arranged in the first columns
# and future figures (lag positive) are the next.
m = length(commodities)
if (m == 0){
print("read.forex.files Error: Empty list of commodities given")
return(o)
}
# Read all data and find common dates
raw.data.list=c()
for (i in 1:m){
R = read.forex.data(commodities[i], start.date.time, final.date.time, date.time.format, period=period, path=path)
if (i==1){common.date.times = R[,1]}
else {common.date.times = intersect(common.date.times, R[,1])}
raw.data.list[[i]]=R
}
X=c()
p = dim(figures)
# delete rows corresponding to non-sharing dates from all data and extract the figures
for (i in 1:p[1]){
comm.index = figures$commindex[i]
R = raw.data.list[[comm.index]]
index = which(R[,1] %in% common.date.times)
R = R[index,]
col = switch(figures$column[i], "open"=2,"high"=3,"low"=4,"close"=5,"volume"=6)
X = cbind(X, R[,col])
}
# Assign date-times as row labels
rownames(X)=rownames(R)
# Assign column labels (figures' titles)
# colnames(X)=paste(commodities[figures$commindex],".",figures$column,".lag:",figures$lag,figures$action,figures$func,".gain:",figures$gain,sep="")
# Applying lags and diffs
for (i in 1:p[1]){
if (figures$lag[i] > 0){
switch(figures$action[i],
"delta" = {X = column.delta.up(X, i,figures$lag[i], keep.rows = TRUE)},
"shift" = {X = column.shift.up(X, i,figures$lag[i], keep.rows = TRUE)},
"average" = {X = column.average.up(X, i,figures$lag[i], keep.rows = TRUE)})
} else if (figures$lag[i] < 0){
switch (figures$action[i],
"delta" = {X = column.delta.down(X, i, - figures$lag[i], keep.rows = TRUE)},
"shift" = {X = column.shift.down(X, i, - figures$lag[i], keep.rows = TRUE)},
"average" = {X = column.average.down(X, i, - figures$lag[i], keep.rows = TRUE)})
} else{ # This means the lag is 0
if (figures$action[i]=="delta"){
X[,i] = 0.0
}
}
}
# Removing empty top and bottom rows
n=dim(X)[1]
keep=c(-1:min(figures$lag), (-n + max(figures$lag)-1):-n)
X = X[keep,]
# Applying functions and gains
for (i in 1:p[1]){
switch(figures$func[i],
"SQR" = {X[,i]=X[,i]^2},
"SQRT" = {X[,i]=sqrt(X[,i])},
"CUBE" = {X[,i]=X[,i]^3},
"CNT" = {X[,i]=vector.centralize(X[,i])},
"STDRD" = {X[,i]=scale(X[,i], scale=TRUE)},
"NRM" = {X[,i]=vector.normalize(X[,i])},
"LOG" = {X[,i]=log(X[,i])},
"EXPNT" = {X[,i]=exp(X[,i])},
"INV" = {X[,i]=1.0/X[,i]},{}
)
X[,i]=figures$gain[i]*X[,i]
}
return(X)
}
prepare.environment <- function(currency_pair, from_date = "2000.01.01 00:00", until_date = "2014.04.01 00:00", period = "D", dt_format = "%Y.%m.%d %H:%M"){
D = read.forex.data(currency_pair, from_date, until_date, date.time.format=dt_format, period=period, path = fxpro.data.path)
vt = new("VIRTUAL.TRADER", data = as.data.frame(D))
if (currency_pair %in% c("EURJPY", "USDJPY", "GBPJPY", "AUDJPY", "NZDJPY", "CADJPY", "CHFJPY")){
vt$pip = 0.01
}
if (currency_pair %in% c("GOLD")){
vt$pip = 0.1
}
if (currency_pair %in% c("SILVER")){
vt$pip = 0.01
vt$spread = 5.0
}
if (currency_pair %in% c("#HPQ", "#INTC", "#MSFT","#XOM")){
vt$pip = 0.01
vt$spread = 4.0
}
if (currency_pair %in% c("AMP")){
vt$pip = 0.001
vt$spread = 0.0
}
return(vt)
}
genpack/foxer documentation built on May 23, 2019, 9:49 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# Header
# Filename: fxpro.R
# Description: Contains functions for reading forein exchange data from csv files with fxpro format
# and optimum lots recommendation
# Author: Nima Ramezani Taghiabadi
# Email : N.RamezaniTaghiabadi@uws.edu.au
# Date: 26 September 2013
# Version: 3.0
# Changes from previous version:
# function generate.figures modified completely:
# makes a lattice of given sets for commindex, lag, action, func, and gain and returns all elements of the lattice in figures dataframe
gen.fig.default = function(n){
# This function genertes a list of default figure settings to be used in read.forex.files
# It is convenient that you generate a default setting with this function and then modify it if you want
options(stringsAsFactors = FALSE)
df = data.frame(commindex= 1:n, column=rep("close", n), lag = array(0,n), action=rep("shift",n), func=rep("IDTY", n), gain=array(1.0,n))
rownames(df)= paste("Figure",1:n,":")
return(df)
}
generate.figures <- function (comm=c(1), col=c("close"), lgs=c(0), act=c("shift"), fun=c("IDTY"), gn=c(1.0)) {
# This function generates a data frame of figures containing all elements of a lattice of figures generated by the given sets
# Input 1: comm: A vector of integers containing the commodities involved
# Input 2: col : A vector of char strings containing the name of columns involved
# Input 3: lgs : A vevtor of signed integers containing the lags
# Input 4: act : A vevtor of char strings containing the actions
# Input 5: fun : A vevtor of char strings containing the functions
# Input 6: gn : A vevtor of real numbers containing the gains
fig = gen.fig.default(1)
# Make the explanatory variable:
fig$commindex[1] = 1
fig$lag[1] = 1
fig$action[1] = "delta"
fig$gain[1] = - 100000
# Generate the lattice of figures and insert each element before the explanatory variable
for (i in comm){
for (j in col){
for (k in lgs){
for (l in act){
for (m in fun){
for (n in gn){
if (l == "average"){kp = - 15*(k+1)} else{kp = - k}
fig = insert.figure(fig, i, j, kp, l, m, n)
}
}
}
}
}
}
return(fig)
}
insert.figure <- function (fig, cmmindx, clmn, lg, actn, fnc, gn) {
# This function inserts one figure before the explanatory variable with given fields
n.fig = dim(fig)[1]
f = data.frame(commindex = c(fig$commindex[sequence(n.fig - 1)], cmmindx, fig$commindex[n.fig]),
column = c(fig$column[sequence(n.fig - 1)] , clmn , fig$column[n.fig]),
lag = c(fig$lag[sequence(n.fig - 1)] , lg , fig$lag[n.fig]),
action = c(fig$action[sequence(n.fig - 1)] , actn , fig$action[n.fig]),
func = c(fig$func[sequence(n.fig - 1)] , fnc , fig$func[n.fig]),
gain = c(fig$gain[sequence(n.fig - 1)] , gn , fig$gain[n.fig]))
}
read.forex.data <- function (commodity, start.date.time, final.date.time, date.time.format="%Y.%m.%d %H:%M", period="H", path = fxpro.data.path) {
# This function reads one single Forex .csv file in a given time interval
# and returns a matrix containing raw data
# Input 1: commodity: A charachter strings containing the name of the currency-pair
# or commodity. Example: commodity = "EURUSD"
# Input 2: start.date.time : A charachter string containing the start date of the interval
# Input 3: final.date.time : A charachter string containing the final date of the interval
# Input 4: date.time.format: A charachter string containing the format of the dates given for inputs 4&5
# Input 5: period : A charachter string containing the period of prices
# "H": Hourly (Default), "D": Daily, "M": Minute, "5M": 5 minutes and etc.
# Input 6: path : A charachter string containing the path name where the files are located
# Output : A Matrix (n X 6) containing the forex data
# where n is the number of data rows found in the given
# interval. The first column contains date.time of each row in POSIXlt format.
# The next columns contain in order: open, high, low, close, vol
#
if (commodity == ""){
print("read.forex.data Error: Empty commodity given")
return(o)
}
# concatenates the path , commodity name, period and file extension to make the full filename
period.str=switch(period, "M"='1',"M5"='5',"M15"='15',"H"='60',"H4"='240',"D"='1440',"W"='10080')
filename = paste(path, "/", commodity, period.str,".csv",sep="")
raw.data = read.csv(filename, as.is=TRUE)
# Extract the dates
dates=raw.data[,1]
times=raw.data[,2]
# and convert them to POSIXlt
date.times.str = paste(dates,times)
date.times = strptime(date.times.str, format = date.time.format)
start = strptime(start.date.time, format = date.time.format)
final = strptime(final.date.time, format = date.time.format)
if (is.na(start) | is.na(final)) {
print("read.forex.data Error: Given date-times not in the given format")
return(0)
}
index = which(date.times >= start & date.times <= final)
R= cbind(date.times[index],raw.data[index,3:7])
rownames(R)=date.times.str[index]
colnames(R)=c("time","open","high","low","close","volume")
return(R)
}
read.forex.files <- function (commodities, start.date.time, final.date.time, date.time.format="%Y.%m.%d %H:%M", period="H", path= fxpro.data.path, figures=gen.fig.default(length(commodities))) {
# This function reads Forex data from a list of .csv files in a given time interval
# and returns a matrix of figures (current prices,lagged prices or volumes)
# and their equivalent future figures (candle lengths or prices)
# Input 1: path : A charachter string containing the path name where the files are located
# Input 2: commodities: A vector of charachter strings containing the names of the currency-pairs
# or commodities. Example: commodities=c("EURUSD", "AUDUSD", "GOLD")
# Input 3: period : A charachter string containing the period of prices
# "H": Hourly (Default), "D": Daily, "M": Minute, "5M": 5 minutes and etc.
# Input 4: start.date.time : A charachter string containing the start date of the interval
# Input 5: final.date.time : A charachter string containing the final date of the interval
# Input 6: date.time.format: A charachter string containing the format of the dates given for inputs 4&5
# Input 7: figures: A data frame of three iso-length vectors of chars and numbers
# containing the specifications of figures. The influence of these vectors will
# be applied in order:
# vector 1: figures$commindex: a vector of numbers specifying which commodity
# should be considered as the figure.
# vector 2: figures$column: a vector of char strings specifying which columns of the
# commodity should be read. Options are: ("open","high","low","close","volume")
# vector 3: figures$lag: a vector of signed integer numbers specifying the
# lag. Considered backward for negative and forward for positive lags
# vector 4: figures$action: a vector of char strings specifying what to replace the element:
# "shift": each row is replaced by its lag
# "delta": each row is replaced by the difference between that row and its lag.
# "average": each row is replaced by the moving average among that row and all "lag" rows before or after that.
# vector 5: figures$func: A vector of char containing the functions to
# be applied on the dtata for each figure
# The supported function names are:
# "IDTY" (default): identity function
# "SQR", "SQRT", "CUBE" , "CNT", "NRM","STDRD", "LOG", "EXPNT", "INV", "MA"
# vector 6: figures$gain: A vector of real numbers. All elements of the figure
# will be multiplied by this number. The value is 1.0 by thefault.
# rownames(figures): A vector of char containing labels of the figures
# by default,they contain a key string as a name generated by function gen.fig.default()
# for eample: "Figure 1"
# Example: If figures$commindex[1] = 4,
# figures$function[1] = "IDTY"
# figures$column[1] = "open"
# figures$lag[1] = 0
# and commodities[4]="EURUSD",
# the first figure is the current open prices of "EURUSD" with no lag
# Output : A Matrix (n X m) containing the figures in the given interval
# where n is the number of data rows found common in all given commodities in the given
# interval and m is total number of figures.
# Note: Predictor figures (lag 0 and negative) are usually arranged in the first columns
# and future figures (lag positive) are the next.
m = length(commodities)
if (m == 0){
print("read.forex.files Error: Empty list of commodities given")
return(o)
}
# Read all data and find common dates
raw.data.list=c()
for (i in 1:m){
R = read.forex.data(commodities[i], start.date.time, final.date.time, date.time.format, period=period, path=path)
if (i==1){common.date.times = R[,1]}
else {common.date.times = intersect(common.date.times, R[,1])}
raw.data.list[[i]]=R
}
X=c()
p = dim(figures)
# delete rows corresponding to non-sharing dates from all data and extract the figures
for (i in 1:p[1]){
comm.index = figures$commindex[i]
R = raw.data.list[[comm.index]]
index = which(R[,1] %in% common.date.times)
R = R[index,]
col = switch(figures$column[i], "open"=2,"high"=3,"low"=4,"close"=5,"volume"=6)
X = cbind(X, R[,col])
}
# Assign date-times as row labels
rownames(X)=rownames(R)
# Assign column labels (figures' titles)
# colnames(X)=paste(commodities[figures$commindex],".",figures$column,".lag:",figures$lag,figures$action,figures$func,".gain:",figures$gain,sep="")
# Applying lags and diffs
for (i in 1:p[1]){
if (figures$lag[i] > 0){
switch(figures$action[i],
"delta" = {X = column.delta.up(X, i,figures$lag[i], keep.rows = TRUE)},
"shift" = {X = column.shift.up(X, i,figures$lag[i], keep.rows = TRUE)},
"average" = {X = column.average.up(X, i,figures$lag[i], keep.rows = TRUE)})
} else if (figures$lag[i] < 0){
switch (figures$action[i],
"delta" = {X = column.delta.down(X, i, - figures$lag[i], keep.rows = TRUE)},
"shift" = {X = column.shift.down(X, i, - figures$lag[i], keep.rows = TRUE)},
"average" = {X = column.average.down(X, i, - figures$lag[i], keep.rows = TRUE)})
} else{ # This means the lag is 0
if (figures$action[i]=="delta"){
X[,i] = 0.0
}
}
}
# Removing empty top and bottom rows
n=dim(X)[1]
keep=c(-1:min(figures$lag), (-n + max(figures$lag)-1):-n)
X = X[keep,]
# Applying functions and gains
for (i in 1:p[1]){
switch(figures$func[i],
"SQR" = {X[,i]=X[,i]^2},
"SQRT" = {X[,i]=sqrt(X[,i])},
"CUBE" = {X[,i]=X[,i]^3},
"CNT" = {X[,i]=vector.centralize(X[,i])},
"STDRD" = {X[,i]=scale(X[,i], scale=TRUE)},
"NRM" = {X[,i]=vector.normalize(X[,i])},
"LOG" = {X[,i]=log(X[,i])},
"EXPNT" = {X[,i]=exp(X[,i])},
"INV" = {X[,i]=1.0/X[,i]},{}
)
X[,i]=figures$gain[i]*X[,i]
}
return(X)
}
prepare.environment <- function(currency_pair, from_date = "2000.01.01 00:00", until_date = "2014.04.01 00:00", period = "D", dt_format = "%Y.%m.%d %H:%M"){
D = read.forex.data(currency_pair, from_date, until_date, date.time.format=dt_format, period=period, path = fxpro.data.path)
vt = new("VIRTUAL.TRADER", data = as.data.frame(D))
if (currency_pair %in% c("EURJPY", "USDJPY", "GBPJPY", "AUDJPY", "NZDJPY", "CADJPY", "CHFJPY")){
vt$pip = 0.01
}
if (currency_pair %in% c("GOLD")){
vt$pip = 0.1
}
if (currency_pair %in% c("SILVER")){
vt$pip = 0.01
vt$spread = 5.0
}
if (currency_pair %in% c("#HPQ", "#INTC", "#MSFT","#XOM")){
vt$pip = 0.01
vt$spread = 4.0
}
if (currency_pair %in% c("AMP")){
vt$pip = 0.001
vt$spread = 0.0
}
return(vt)
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.