pennyPerSharequantstrat无法正常工作 [英] pennyPerShare quantstrat not working
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问题描述
当我使用时:
stratRank <- add.rule(stratRank, name="ruleSignal",
arguments=list(sigcol="EntryCond", sigval=TRUE, orderqty=max.size,
ordertype="market", orderside="long", pricemethod="market",
replace=FALSE,osFUN=osMaxPos,TxnFees=pennyPerShare), type="enter")
stratRank <- add.rule(stratRank, name="ruleSignal",
arguments=list(sigcol="ExitCond", sigval=TRUE, orderqty="all",
ordertype="market", orderside="long", pricemethod="market",
replace=FALSE,TxnFees=pennyPerShare), type="exit")
我得到一个错误:
Error in `[.xts`(ordersubset, , "Order.Status") : unsupported type
我认为可以添加pennyPerShare函数吗?难道我做错了什么?
I thought that pennyPerShare function is possible to add? Am I doing something wrong?
注意，that works，如果我使用静态交易费用:
Note, that works, if I use static transaction costs:
stratRank <- add.rule(stratRank, name="ruleSignal",
arguments=list(sigcol="EntryCond", sigval=TRUE, orderqty=max.size,
ordertype="market", orderside="long", pricemethod="market",
replace=FALSE,osFUN=osMaxPos,TxnFees=-5), type="enter")
stratRank <- add.rule(stratRank, name="ruleSignal",
arguments=list(sigcol="ExitCond", sigval=TRUE, orderqty="all",
ordertype="market", orderside="long", pricemethod="market",
replace=FALSE,TxnFees=-5), type="exit")
和session info:用于当前设置.非常感谢任何建议.最好的问候
And session info: for the currect set up. Greatful for any suggestions. Best Regards
sessionInfo()
R version 3.0.2 (2013-09-25)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=Swedish_Sweden.1252 LC_CTYPE=Swedish_Sweden.1252 LC_MONETARY=Swedish_Sweden.1252
[4] LC_NUMERIC=C LC_TIME=Swedish_Sweden.1252
attached base packages:
[1] graphics grDevices utils datasets stats methods base
other attached packages:
[1] quantstrat_0.8.2 foreach_1.4.1 blotter_0.9.1644 PerformanceAnalytics_1.1.0
[5] FinancialInstrument_1.1.9 quantmod_0.4-0 TTR_0.22-0.1 Defaults_1.1-1
[9] xts_0.9-7 zoo_1.7-10 ggplot2_0.9.3.1
loaded via a namespace (and not attached):
[1] codetools_0.2-8 colorspace_1.2-3 dichromat_2.0-0 digest_0.6.3 fBasics_3010.86
[6] fGarch_3010.82 grid_3.0.2 gtable_0.1.2 iterators_1.0.6 labeling_0.2
[11] lattice_0.20-23 MASS_7.3-29 munsell_0.4.2 plyr_1.8 proto_0.3-10
[16] rCharts_0.4.2 RColorBrewer_1.0-5 reshape2_1.2.2 RJSONIO_1.0-3 ROAuth_0.9.3
[21] scales_0.2.3 stabledist_0.6-6 stringr_0.6.2 timeDate_3010.98 timeSeries_3010.97
[26] tools_3.0.2 whisker_0.3-2 yaml_2.1.7
Code:
library(quantstrat)
library(PerformanceAnalytics)
ttz<-Sys.getenv('TZ')
#Sys.setenv(TZ='UTC')
Sys.setenv(TZ='GMT')
currency("USD")
symbols <- c("XLY", "XLP", "XLE", "AGG", "IVV")
stock(symbols, currency="USD")
# get data for the symbols
getSymbols(symbols, from="2005-01-01", to="2012-12-31")
# create an xts object of monthly adjusted close prices
symbols.close <- monthlyPrices(symbols)
# create an xts object of the symbol ranks
sym.rank <- applyRank(x=symbols.close, rankFun=ave3ROC, n=c(2, 4, 6))
# this is an important step in naming the columns, e.g. XLY.Rank
# the "Rank" column is used as the trade signal (similar to an indicator)
# in the qstratRank function
colnames(sym.rank) <- gsub(".Close", ".Rank", colnames(sym.rank))
# ensure the order of order symbols is equal to the order of columns
# in symbols.close
stopifnot(all.equal(gsub(".Close", "", colnames(symbols.close)), symbols))
# bind the rank column to the appropriate symbol market data
# loop through symbols, convert the data to monthly and cbind the data
# to the rank
for(i in 1:length(symbols)) {
x <- get(symbols[i])
x <- to.weekly(x,indexAt='lastof',drop.time=TRUE)
indexFormat(x) <- '%Y-%m-%d'
colnames(x) <- gsub("x",symbols[i],colnames(x))
x <- cbind(x, sym.rank[,i])
assign(symbols[i],x)
}
bt <- qstratRank(symbols=symbols, init.equity=100000, top.N=2,
max.size=1000, max.levels=1)
Functions:
qstratRank <- function(symbols, init.equity=100000, top.N=1,
max.size=1000, max.levels=1) {
# The qstratRank function uses the quantstrat framework to backtest a
# ranking or relative strength strategy
#
# args
# symbols : character vector of symbols
# init.equity : initial equity
# top.N : trade the top N ranked assets
# max.size : maximum position size
# max.levels : maximum levels to scale in a trade
# max.size and max.levels are passed to addPosLimit
#
# return value
# returns a list: end.eq, returns, book, stats
# remove variables
suppressWarnings(rm("order_book.Rank", pos=.strategy))
suppressWarnings(rm("account.Rank", "portfolio.Rank", pos=.blotter))
suppressWarnings(rm("account.st", "port.st", "stock.str", "stratRank",
"initDate", "initEq", 'start_t', 'end_t'))
# set initial variables
initDate <- "1900-01-01"
initEq <- init.equity
port.st <- "Rank"
account.st <- "Rank"
# trade the top "N" ranked symbols
N <- top.N
# initialize quantstrat objects
initPortf(port.st, symbols=symbols, initDate=initDate)
initAcct(account.st, portfolios=port.st, initDate=initDate,initEq=initEq)
initOrders(portfolio=port.st, initDate=initDate)
# initialize a strategy object
stratRank <- strategy("Rank")
# there are two signals
# the first signal is when Rank is less than or equal to N
# (i.e. trades the #1 ranked symbol if N=1)
stratRank <- add.signal(strategy=stratRank, name="sigThreshold",
arguments=list(threshold=N, column="Rank",
relationship="lte", cross=FALSE),
label="Rank.lte.N")
# the second signal is when Rank is greter than or equal to N
# (i.e. trades the #1 ranked symbol if N=1)
stratRank <- add.signal(strategy=stratRank, name="sigThreshold",
arguments=list(threshold=N, column="Rank",
relationship="gt", cross=FALSE),
label="Rank.gt.N")
# add buy rule
stratRank <- add.rule(strategy=stratRank, name='ruleSignal',
arguments = list(sigcol="Rank.lte.N", sigval=TRUE,
orderqty=max.size, ordertype='market',
orderside='long', pricemethod='market',
replace=FALSE, osFUN=osMaxPos,TxnFees=pennyPerShare),
type='enter', path.dep=TRUE)
# add exit rule
stratRank <- add.rule(strategy = stratRank, name='ruleSignal',
arguments = list(sigcol="Rank.gt.N", sigval=TRUE,
orderqty='all', ordertype='market',
orderside='long', pricemethod='market',
replace=FALSE,TxnFees=pennyPerShare),
type='exit', path.dep=TRUE)
#set max position size and levels
for(symbol in symbols){ addPosLimit(port.st, symbol, initDate, max.size, max.levels) }
print("setup completed")
# apply the strategy to the portfolio
start_t <- Sys.time()
out <- try(applyStrategy(strategy=stratRank, portfolios=port.st))
end_t <- Sys.time()
print(end_t-start_t)
# update Portfolio
start_t <- Sys.time()
updatePortf(Portfolio=port.st, Dates=paste('::', as.Date(Sys.time()), sep=''))
end_t <- Sys.time()
print("trade blotter portfolio update:")
print(end_t - start_t)
# update account
updateAcct(account.st)
# update ending equity
updateEndEq(account.st)
# get ending equity
eq <- getEndEq(account.st, Sys.Date()) + initEq
# view order book to confirm trades
order.book <- getOrderBook(port.st)
# get trade statistics
stats <- tradeStats(port.st)
# portfolio returns
ret1 <- PortfReturns(port.st)
ret1$total <- rowSums(ret1, na.rm=TRUE)
return(list(end.eq=eq, returns=ret1, book=order.book, stats=stats))
}
##### monthlyAd function #####
monthlyAd <- function(x){
# Converts daily data to monthly and returns only the monthly close
# Note: only used with Yahoo Finance data so far
# Thanks to Joshua Ulrich for the Monthly Ad function
#
# args:
# x = daily price data from Yahoo Finance
#
# Returns:
# xts object with the monthly adjusted close prices
sym <- sub("\\..*$", "", names(x)[1])
Cl(to.weekly(x, indexAt = 'lastof', drop.time = TRUE, name = sym)) ##ändrade från to.monthly och till Close from Adjusted
}
##### monthlyReturns function #####
monthlyReturns <- function(symbols) {
# The function takes a character vector of symbols loaded into
# the environment and returns an xts object of simple returns
# Currently this is only for prepping monthly data
# symbols : character vector of symbols
ROC(x = monthlyPrices(symbols), n = 1, type = "discrete", na.pad = TRUE)
}
##### monthlyPrices function #####
monthlyPrices <- function(symbols) {
# The function takes a character vector of symbols loaded into
# the environment and returns an xts object of Adjusted close prices
# Currently this is only for prepping monthly data
# symbols : character vector of symbols
# list.sym : list of symbols with market data
list.sym <- list()
for(i in 1:length(symbols)) {
list.sym[[symbols[i]]] <- get(symbols[i])
}
do.call(merge, lapply(list.sym, monthlyAd))
}
# RankningFunktioner ------------------------------------------------------
# Functions for ways to rank assets based on rate of change
# TODO - add functions to rank based on other factors
# The functions defined below depend on functions in the xts and TTR packages
# library(TTR)
##### applyRank #####
applyRank <- function(x, rankFun, ...) {
# symbols : character vector of symbols
# rankFun : function that returns the rank
# rankFun should be ave3ROC, weightAve3ROC, strengthROC, strengthAve3ROC,
# etfReplayRank, or strengthSMA.
# x : xts object of prices
# ... : arguments to rankFun
FUN <- match.fun(rankFun)
FUN(x, ...)
}
##### symbolRank #####
symbolRank <- function(symbols, rank.obj) {
# loop through symbols
# convert the market data to monthly periodicity
# cbind the appropriate column from rank.obj to the market data
# makes the assumption that the order symbols and rank.obj are equal
# symbols : character vector of symbols
# rank.obj : xts object of ranks of each symbol
for(i in 1:length(symbols)) {
x <- get(symbols[i])
x <- to.monthly(x,indexAt='lastof',drop.time=TRUE)
indexFormat(x) <- '%Y-%m-%d'
colnames(x) <- gsub("x", symbols[i], colnames(x))
x <- cbind(x, rank.obj[,i])
assign(symbols[i],x)
}
}
##### rowRank #####
rowRank <- function(x){
# Computes the rank of an xts object of ranking factors
# ranking factors are the factors that are ranked (i.e. asset returns)
#
# x : xts object of ranking factors
#
# Returns an xts object with ranks
# For ranking asset returns, the asset with the greatest return
# receives a rank of 1
as.xts(t(apply(-x, 1, rank, na.last = "keep")))
}
#Use the supplied TTR::ROC function for a straight ROC computation
##### ave3ROC #####
ave3ROC <- function(x, n=c(1, 3, 6)){
# Computes the average rate of change based on averaging 3 periods
#
# x : xts object of prices
# n : vector of periods to use n = (period1, period2, period3)
# ave : xts object of asset rate of change by averaging 3 periods
roc1 <- ROC(x, n = n[1], type = "discrete")
roc2 <- ROC(x, n = n[2], type = "discrete")
roc3 <- ROC(x, n = n[3], type = "discrete")
ave <- (roc1 + roc2 + roc3)/3
rowRank(ave)
}
推荐答案
我终于有一点时间来研究这个问题.问题是TxnFees必须是包含函数名称的字符串，而不是实际的函数本身.我将在addOrder的文档中对此进行更清晰的说明.
I finally had a little bit of time to look into this. The problem is that TxnFees needs to be a character string containing the name of a function not an actual function itself. I'm going to make this more clear in the documentation for addOrder.
同时，如果我更改以下几行，您的示例将对我有用:
In the meantime, your example works for me if I change the lines below:
# add buy rule
stratRank <- add.rule(strategy=stratRank, name='ruleSignal',
arguments = list(sigcol="Rank.lte.N", sigval=TRUE,
orderqty=max.size, ordertype='market',
orderside='long', pricemethod='market',
replace=FALSE, osFUN=osMaxPos,TxnFees="pennyPerShare"),
type='enter', path.dep=TRUE)
# add exit rule
stratRank <- add.rule(strategy = stratRank, name='ruleSignal',
arguments = list(sigcol="Rank.gt.N", sigval=TRUE,
orderqty='all', ordertype='market',
orderside='long', pricemethod='market',
replace=FALSE,TxnFees="pennyPerShare"),
type='exit', path.dep=TRUE)
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