将 quantstrat 中的时间范围从每天更改为每周
Changing time frame in quantstrat from daily to weekly
所以我只是在 quantstrat 中执行 EMA50 交叉策略，它工作正常，但我希望将时间范围从每天更改为每周。
我尝试将 stock() 函数存储为 to.weekly(SPY)，但他们不允许我这样做。我想稍后对多个股票尝试这个，所以它必须在投资组合中应用。
library(quantstrat)
rm(list=ls(.blotter), envir=.blotter)
strategy.st<-"firststrat"
portfolio.st<-"firststrat"
account.st<-"firststrat"
rm.strat(strategy.st)
#assignsymbol
getSymbols("SPY",auto.assign=TRUE,adjust=TRUE)
initdate<-"2009-01-01"
from<-"2010-01-01"
to<-"2016-11-01"
Sys.setenv(TZ="UTC")
currency("USD")
stock("SPY",currency="USD",multiplier=1)
tradesize<-10000
inieq<-100000
rm.strat(portfolio.st)
initPortf(portfolio.st,symbols="SPY",initDate=initdate,currency='USD')
initAcct(account.st,portfolios = portfolio.st,initDate = initdate,initEq = inieq,currency="USD")
initOrders(portfolio = portfolio.st,initDate = initdate)
strategy(strategy.st,store=TRUE)
add.indicator(strategy = strategy.st,name="EMA",arguments=list(x=quote(Cl(mktdata)),n=50),label="EMA50")
#if closing price goes over moving average 50 and TSi fference is less then 0.15, then long
#short when closing price touches below original closing price by x(depends on atr? previous lows?)
add.signal(strategy.st,name="sigCrossover",
arguments = list(columns=c("Close","EMA50"),
relationship="gt"),
label="crossentry"
)
add.signal(strategy.st,name="sigCrossover",
arguments = list(columns=c("Close","EMA50"),
relationship="lt"),
label="crossexit"
)
add.rule(strategy.st,name="ruleSignal",
arguments=list(sigcol = "crossentry",
sigval=TRUE,
orderqty=100,
ordertype="market",
orderside="long",
replace=FALSE,
prefer="Open",
path.dep=TRUE
),
type="enter"
)
add.rule(strategy.st,name="ruleSignal",
arguments=list(sigcol = "crossexit",
sigval=TRUE,
orderqty="all",
ordertype="market",
orderside="long",
replace=FALSE,
prefer="Open",
path.dep=TRUE
),
type="exit"
)
out <- applyStrategy(strategy = strategy.st, portfolios = portfolio.st)
.....
[1] "2016-01-04 00:00:00 SPY -100 @ 197.432029165538"
[1] "2016-02-23 00:00:00 SPY 100 @ 191.041013032617"
[1] "2016-02-24 00:00:00 SPY -100 @ 187.72241891553"
[1] "2016-02-26 00:00:00 SPY 100 @ 193.571820974787"
[1] "2016-03-01 00:00:00 SPY -100 @ 192.035603073637"
.....
有什么办法可以将其更改为每周一次吗？
你走在正确的轨道上。如果您在 运行 宁 applyStrategy 之前执行 SPY <- to.weekly(SPY)，那么您将 运行 每周柱上的策略。请记住，quantstrat 使用符号的名称来查找数据，而不管基础数据的存储频率如何（在您的情况下，在名为 SPY 的对象中是每天还是每周）。
这是适合您的更通用的自动化方法，因为您想对证券投资组合执行此操作。为了提高可读性，我只是展示了您问题中需要修改为 运行 with >= 2 securities:
的代码部分
symbols <- c("SPY", "XLE")
getSymbols(symbols,auto.assign=TRUE,adjust=TRUE)
# Change to weekly frequency, using the same names for the symbols in the global environment (which is where you have assigned them in your getSymbols call)
lapply(symbols, function(x) assign(x = x, value = to.weekly(get(x, envir = globalenv()), name = x), envir = globalenv()))
initdate<-"2009-01-01"
from<-"2010-01-01"
to<-"2016-11-01"
Sys.setenv(TZ="UTC")
currency("USD")
# Use your symbols to construct the correct instrument types for your strategy. You are running on stocks only, so simply pass in the vector named `symbols` to stock:
stock(symbols,currency="USD",multiplier=1)
tradesize<-10000
inieq<-100000
rm.strat(portfolio.st)
# When initializing your portfolios, pass in the symbols you want the strategy to run on (the vector named `symbols` here):
initPortf(portfolio.st,symbols=symbols,initDate=initdate,currency='USD')
# ... run the rest of your original code
在 applyStrategy 成功 运行 之后，您可以检查 mktdata 是您想要的格式（每周）最近的交易品种 运行（XLE 本例中符号向量中的 ETF）。你应该看到这样的东西：
mktdata["2016-04/2016-05"]
# XLE.Open XLE.High XLE.Low XLE.Close XLE.Volume XLE.Adjusted EMA.EMA50 crossentry crossexit
# 2016-04-01 61.08612 61.83661 59.70363 60.29613 70527800 60.29613 64.16397 NA NA
# 2016-04-08 60.18750 61.92548 59.19014 61.58974 81670700 61.58973 64.06302 NA NA
# 2016-04-15 62.08348 63.73259 61.33299 62.74510 94225700 62.74510 64.01134 NA NA
# 2016-04-22 61.45149 66.44819 61.30337 66.20132 94215100 66.20131 64.09722 1 NA
# 2016-04-29 65.97419 67.95905 64.96695 66.65556 92445000 66.65556 64.19754 NA NA
# 2016-05-06 66.53706 66.81356 63.69309 64.45345 76727000 64.45345 64.20758 NA NA
# 2016-05-13 64.08809 65.90507 62.70560 64.17696 65436100 64.17696 64.20638 NA 1
# 2016-05-20 65.01632 66.07294 63.68321 65.33233 79205200 65.33233 64.25053 1 NA
# 2016-05-27 64.94720 67.07030 64.72008 66.29019 55806400 66.29018 64.33052 NA NA
所以我只是在 quantstrat 中执行 EMA50 交叉策略，它工作正常，但我希望将时间范围从每天更改为每周。
我尝试将 stock() 函数存储为 to.weekly(SPY)，但他们不允许我这样做。我想稍后对多个股票尝试这个，所以它必须在投资组合中应用。
library(quantstrat)
rm(list=ls(.blotter), envir=.blotter)
strategy.st<-"firststrat"
portfolio.st<-"firststrat"
account.st<-"firststrat"
rm.strat(strategy.st)
#assignsymbol
getSymbols("SPY",auto.assign=TRUE,adjust=TRUE)
initdate<-"2009-01-01"
from<-"2010-01-01"
to<-"2016-11-01"
Sys.setenv(TZ="UTC")
currency("USD")
stock("SPY",currency="USD",multiplier=1)
tradesize<-10000
inieq<-100000
rm.strat(portfolio.st)
initPortf(portfolio.st,symbols="SPY",initDate=initdate,currency='USD')
initAcct(account.st,portfolios = portfolio.st,initDate = initdate,initEq = inieq,currency="USD")
initOrders(portfolio = portfolio.st,initDate = initdate)
strategy(strategy.st,store=TRUE)
add.indicator(strategy = strategy.st,name="EMA",arguments=list(x=quote(Cl(mktdata)),n=50),label="EMA50")
#if closing price goes over moving average 50 and TSi fference is less then 0.15, then long
#short when closing price touches below original closing price by x(depends on atr? previous lows?)
add.signal(strategy.st,name="sigCrossover",
arguments = list(columns=c("Close","EMA50"),
relationship="gt"),
label="crossentry"
)
add.signal(strategy.st,name="sigCrossover",
arguments = list(columns=c("Close","EMA50"),
relationship="lt"),
label="crossexit"
)
add.rule(strategy.st,name="ruleSignal",
arguments=list(sigcol = "crossentry",
sigval=TRUE,
orderqty=100,
ordertype="market",
orderside="long",
replace=FALSE,
prefer="Open",
path.dep=TRUE
),
type="enter"
)
add.rule(strategy.st,name="ruleSignal",
arguments=list(sigcol = "crossexit",
sigval=TRUE,
orderqty="all",
ordertype="market",
orderside="long",
replace=FALSE,
prefer="Open",
path.dep=TRUE
),
type="exit"
)
out <- applyStrategy(strategy = strategy.st, portfolios = portfolio.st)
.....
[1] "2016-01-04 00:00:00 SPY -100 @ 197.432029165538"
[1] "2016-02-23 00:00:00 SPY 100 @ 191.041013032617"
[1] "2016-02-24 00:00:00 SPY -100 @ 187.72241891553"
[1] "2016-02-26 00:00:00 SPY 100 @ 193.571820974787"
[1] "2016-03-01 00:00:00 SPY -100 @ 192.035603073637"
..... 有什么办法可以将其更改为每周一次吗？
你走在正确的轨道上。如果您在 运行 宁 applyStrategy 之前执行 SPY <- to.weekly(SPY)，那么您将 运行 每周柱上的策略。请记住，quantstrat 使用符号的名称来查找数据，而不管基础数据的存储频率如何（在您的情况下，在名为 SPY 的对象中是每天还是每周）。
这是适合您的更通用的自动化方法，因为您想对证券投资组合执行此操作。为了提高可读性，我只是展示了您问题中需要修改为 运行 with >= 2 securities:
的代码部分symbols <- c("SPY", "XLE")
getSymbols(symbols,auto.assign=TRUE,adjust=TRUE)
# Change to weekly frequency, using the same names for the symbols in the global environment (which is where you have assigned them in your getSymbols call)
lapply(symbols, function(x) assign(x = x, value = to.weekly(get(x, envir = globalenv()), name = x), envir = globalenv()))
initdate<-"2009-01-01"
from<-"2010-01-01"
to<-"2016-11-01"
Sys.setenv(TZ="UTC")
currency("USD")
# Use your symbols to construct the correct instrument types for your strategy. You are running on stocks only, so simply pass in the vector named `symbols` to stock:
stock(symbols,currency="USD",multiplier=1)
tradesize<-10000
inieq<-100000
rm.strat(portfolio.st)
# When initializing your portfolios, pass in the symbols you want the strategy to run on (the vector named `symbols` here):
initPortf(portfolio.st,symbols=symbols,initDate=initdate,currency='USD')
# ... run the rest of your original code
在 applyStrategy 成功 运行 之后，您可以检查 mktdata 是您想要的格式（每周）最近的交易品种 运行（XLE 本例中符号向量中的 ETF）。你应该看到这样的东西：
mktdata["2016-04/2016-05"]
# XLE.Open XLE.High XLE.Low XLE.Close XLE.Volume XLE.Adjusted EMA.EMA50 crossentry crossexit
# 2016-04-01 61.08612 61.83661 59.70363 60.29613 70527800 60.29613 64.16397 NA NA
# 2016-04-08 60.18750 61.92548 59.19014 61.58974 81670700 61.58973 64.06302 NA NA
# 2016-04-15 62.08348 63.73259 61.33299 62.74510 94225700 62.74510 64.01134 NA NA
# 2016-04-22 61.45149 66.44819 61.30337 66.20132 94215100 66.20131 64.09722 1 NA
# 2016-04-29 65.97419 67.95905 64.96695 66.65556 92445000 66.65556 64.19754 NA NA
# 2016-05-06 66.53706 66.81356 63.69309 64.45345 76727000 64.45345 64.20758 NA NA
# 2016-05-13 64.08809 65.90507 62.70560 64.17696 65436100 64.17696 64.20638 NA 1
# 2016-05-20 65.01632 66.07294 63.68321 65.33233 79205200 65.33233 64.25053 1 NA
# 2016-05-27 64.94720 67.07030 64.72008 66.29019 55806400 66.29018 64.33052 NA NA