运行我的 quantstrat 代码时，我收到一些警告，例如其中之一是：
Warning messages:
1: In match.names(columns, colnames(data)) :
all columns not located in Close SMA50 for AGG.Open AGG.High AGG.Low AGG.Close AGG.Volume AGG.Adjusted AGG.Rank SMA.SMA50 Cl.gt.SMA50
我的代码是：
library(quantstrat)
library(PerformanceAnalytics)
Sys.setenv(TZ="UTC")
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=1,
max.size=1000, max.levels=1)
qstratRank功能是：
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.indicator(strategy=stratRank, name="SMA",
arguments=list(x = quote(Cl(mktdata)), n=50), label="SMA50")
stratRank <- add.signal(stratRank, name="sigComparison",
arguments=list(columns=c("Close", "SMA50"), relationship="gt"), label="Cl.gt.SMA50")
stratRank <- add.signal(stratRank, name="sigComparison",
arguments=list(columns=c("Close", "SMA50"), relationship="lt"), label="Cl.lt.SMA50")
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="Cl.lt.SMA50", sigval=TRUE,
orderqty=max.size, ordertype='market',
orderside='long', pricemethod='market',
replace=FALSE, osFUN=osMaxPos),
type='enter', path.dep=TRUE)
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),
type='enter', path.dep=TRUE)
# add exit rule
# stratRank <- add.rule(strategy=stratRank, name='ruleSignal',
# arguments = list(sigcol="Cl.lt.SMA50", sigval=TRUE,
# orderqty="all", ordertype='market',
# orderside='long', pricemethod='market',
# replace=FALSE, osFUN=osMaxPos),
# type='exit', path.dep=TRUE)
#
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),
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$total <- rowSums(ret1, na.rm=TRUE)
return(list(end.eq=eq, returns=ret1, book=order.book, stats=stats))
}
我使用的功能是：
##### monthlyAd function #####
monthlyAd <- function(x){
# Converts daily data to monthly and returns only the monthly close
# Note: only used with Yahoo Finance data so far
# Thanks to Joshua Ulrich for the Monthly Ad function
#
# args:
# x = daily price data from Yahoo Finance
#
# Returns:
# xts object with the monthly adjusted close prices
sym <- sub("\\..*$", "", names(x)[1])
Cl(to.weekly(x, indexAt = 'lastof', drop.time = TRUE, name = sym))
}
##### 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))
}
# 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)
}
所以问题似乎出在SMA上，有什么想法吗？最好的祝福