pennyPerSharequantstrat无法正常工作

本文介绍了pennyPerSharequantstrat无法正常工作的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 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)