以下是我正在使用quantstrat进行多时间帧策略的示例。这是做多时间框架策略的正确方法还是我做错了？我没有遇到任何其他在quantstrat演示或谷歌搜索中做多时间帧的例子。
为了保持策略部分简单（这不是某人会交易的策略）并将重点放在多时间框架方面，我将演示一个使用tick数据的简单策略和 5分钟OHLC数据。策略逻辑是当滴答数据超过5分钟数据的30周期SMA时买入，并在滴答数据低于同一个SMA时关闭仓位。
例如：如果策略是平的，则时间是13:02，并且之前观察到的5分钟数据的30周期SMA是90.55（对于12：55-结束13:00的时段），并且蜱数据从低于90.55到高于它（90.56）交叉，这是一个买入，当蜱数据再次低于它时，它退出该位置。
为了达到这个目的，我需要将tick数据和5分钟，30周期的SMA同时放入同一个对象中进行量子处理。我得到了5分钟的OHLC xts并计算了它的30周期SMA。然后我将它合并到刻度数据xts对象中，这将给我一个包含所有刻度数据的对象，然后每隔5分钟，我将获得最后一次观察到的5分钟，30周期SMA的行。
如果在13:00有30个周期的SMA值，则为5分12：55-13：00。由于下次更新SMA是5分钟后，我需要填充行直到观察到下一个值（在13:05），依此类推。
这是tick数据的head（我的刻度数据不包括毫秒，但我使用make.index.unique(clemtick)使行唯一：
head(clemtick)
Price Volume
2013-01-15 09:00:00 93.90 1
2013-01-15 09:00:00 93.89 1
2013-01-15 09:00:00 93.89 1
2013-01-15 09:00:00 93.88 2
2013-01-15 09:00:00 93.89 1
2013-01-15 09:00:00 93.89 2
这是1分钟数据的head（每分钟代表前一分钟的数据，例如时间戳09:01:00 ==数据从09:00:00 - 09:01:00）：
head(clemin)
Open High Low Close Volume
2013-01-15 09:01:00 93.90 94.04 93.87 93.97 1631
2013-01-15 09:02:00 93.97 93.98 93.90 93.91 522
2013-01-15 09:03:00 93.91 93.97 93.90 93.96 248
2013-01-15 09:04:00 93.95 93.98 93.93 93.95 138
2013-01-15 09:05:00 93.95 93.96 93.91 93.92 143
2013-01-15 09:06:00 93.93 93.97 93.91 93.91 729
将1分钟数据转换为5分钟数据：
cle5min <- to.minutes5(clemin)
clemin.Open clemin.High clemin.Low clemin.Close clemin.Volume
2013-01-15 09:04:00 93.90 94.04 93.87 93.95 2539
2013-01-15 09:09:00 93.95 93.97 93.81 93.89 2356
2013-01-15 09:14:00 93.90 94.05 93.86 93.89 4050
2013-01-15 09:19:00 93.90 94.03 93.84 94.00 2351
2013-01-15 09:24:00 93.99 94.21 93.97 94.18 3261
2013-01-15 09:29:00 94.18 94.26 94.18 94.19 1361
您会注意到第一个OHLC是09:04:00，这是由于to.minutes5函数的工作方式，discussed in this thread。基本上第一次印章09:04:00 == OHLC 4分钟的数据从09:00:00 - 09:04:00。从09:04:00 - 09:09:00 09:09:00时间戳是下一个完整的5分钟。理想情况下，我希望每个时间戳都是5,10,15等，但我还没有弄清楚如何做到这一点。
将5分钟数据的30 SMA转换为刻度数据
clemtick$sma30 <- SMA(cle5min$clemin.Close, 30)
这将使用SMA创建一个新列。 SMA需要30个周期来计算第一个值和 SMA只会出现每5分钟的时间戳（11：29：00,11：34：00,11：39 ......） 。它看起来像：
clemtick["2013-01-15 11:28:59::2013-01-15 11:29:00"]
Price Volume SMA30
2013-01-15 11:28:59 93.87 1 NA
2013-01-15 11:28:59 93.87 1 NA
2013-01-15 11:28:59 93.88 1 NA
2013-01-15 11:29:00 93.87 1 93.92633
2013-01-15 11:29:00 93.87 1 NA
2013-01-15 11:29:00 93.88 1 NA
2013-01-15 11:29:00 93.88 1 NA
现在我需要用重复值填充SMA30列。 SMA30在11:29:00的值是从11:24:00到11:29:00的OHLC。此值的下一次更新将在11:34:00之前完成，因此我需要将行填充到下一个值，因为这是策略在逐行处理时将引用的内容。
clemtick <- na.locf(clemtick)
现在，如果我再次查询该对象，
clemtick["2013-01-15 11:33:58::2013-01-15 11:34:01"]
Price Volume SMA30
2013-01-15 11:33:58 93.84 1 93.92633
2013-01-15 11:34:00 93.84 1 93.92267
2013-01-15 11:34:00 93.85 1 93.92267
2013-01-15 11:34:01 93.84 1 93.92267
现在我们在这里有最终对象正在运行策略：
require(quantstrat)
options("getSymbols.warning4.0"=FALSE)
rm(list=ls(.blotter), envir=.blotter)
Sys.setenv(TZ="UTC")
symbols <- "clemtick"
currency('USD')
stock(symbols, currency="USD", multiplier=1)
account.st <- 0
strategy.st <- portfolio.st <- account.st <- "multi"
rm.strat(portfolio.st)
rm.strat(strategy.st)
initDate <- "1980-01-01"
tradeSize <- 1000
initEq <- tradeSize*length(symbols)
initPortf(portfolio.st, symbols=symbols, initDate=initDate, currency='USD')
initAcct(account.st, portfolios=portfolio.st,
initDate=initDate, currency='USD', initEq=initEq)
initOrders(portfolio.st, initDate=initDate)
strategy(strategy.st, store=TRUE)
add.signal(strategy.st, name="sigCrossover",
arguments=list(columns=c("Price", "sma30"), relationship="gt"),
label="golong")
add.signal(strategy.st, name="sigCrossover",
arguments=list(columns=c("Price", "sma30"), relationship="lt"),
label="exitlong")
#enter rule
add.rule(strategy.st, name="ruleSignal",
arguments=list(sigcol="golong",
sigval=TRUE,
ordertype="market",
orderside="long",
replace=TRUE,
prefer="Price",
orderqty=1),
type="enter", path.dep=TRUE, label="long")
#exit rule
add.rule(strategy.st, name = "ruleSignal",
arguments=list(sigcol="exitlong",
sigval=TRUE,
ordertype="market",
orderside="long",
replace=TRUE,
prefer="Price",
orderqty=-1),
type="exit", path.dep=TRUE, label="exitlong")
#apply strategy
t1 <- Sys.time()
out2 <- applyStrategy(strategy=strategy.st, portfolios=portfolio.st, debug=TRUE)
t2 <- Sys.time()
print(t2-t1)
head(mktdata)
nrow(mktdata)
总结一下这是做多时间框架策略的最佳方法吗？
答案 0 :(得分：2)
以下两种方法可将多时间范围指示符/信号合并到您的策略中。两者都只使用quantstrat样本数据开箱即用。
两者都遵循相同的策略（并给出相同的结果）：该策略在1分钟柱和SMA（10）上使用SMA（20） 在30分钟的柱上产生交易信号。当输入多头头寸时 SMA（20,1分钟柱）穿过SMA（10,30分钟柱）。退出很久 当SMA（20,1分钟柱）穿过SMA（10,30分钟柱）时的位置
方法1：在较低的时间频率内建立价格数据和指标
add.indicator调用的自定义函数。 （你不能进入更高的时间频率
比符号的原始市场数据）。
from <- "2002-10-20"
to <- "2002-10-24"
symbols <- "GBPUSD"
# Load 1 minute data stored in the quantstrat package
getSymbols.FI(Symbols = symbols,
dir=system.file('extdata',package='quantstrat'),
from=from,
to=to
)
currency(c('GBP', 'USD'))
exchange_rate('GBPUSD', tick_size=0.0001)
strategy.st <- "multiFrame"
portfolio.st <- "multiFrame"
account.st <- "multiFrame"
initEq <- 50000
rm.strat(strategy.st)
initPortf(portfolio.st, symbols = symbols)
initAcct(account.st, portfolios = portfolio.st, initEq = initEq)
initOrders(portfolio.st)
strategy(strategy.st, store = TRUE)
# Create an SMA on 20 1 minute bars:
add.indicator(strategy.st, name = "SMA",
arguments = list(x = quote(Cl(mktdata)),
n = 20),
label = "MA20")
# Define the function that add.indicator will use to create an SMA(10) on 30 minute bars:
ind30minMA <- function(x, n30min = 10) {
if (!is.OHLC(x))
stop("Must pass in OHLC data")
x.h <- to.period(x[, 1:4], period = "minutes", k = 30, indexAt = "endof")
#^ Ensure that the timestamp on the lower frequency data is at the END of the bar/candle, to avoid look forward bias.
# If you need to know what symbol you are currently processing:
# symbol <- parent.frame(n = 2)$symbol
sma.h <- SMA(Cl(x.h), n = n30min)
r <- merge(sma.h, xts(, index(x)), fill= na.locf)
#^ Carry forward the last value, no lookforward bias introduced
r <- r[index(x)]
# if you don't return the same # of rows in the argument x, then quantstrat won't work correctly. So let's check the data is OK after the merge above:
stopifnot(NROW(r) == NROW(x))
r
}
add.indicator(strategy.st, name = "ind30minMA",
arguments = list(x = quote(mktdata),
n30min = 10),
label = "MA30minbar")
add.signal(strategy.st, name = "sigCrossover",
arguments = list(columns = c("SMA.MA20", "SMA.MA30minbar"),
relationship = "gt"),
label = "FastCrossUp")
add.signal(strategy.st, name = "sigCrossover",
arguments = list(columns = c("SMA.MA20", "SMA.MA30minbar"),
relationship = "lt"),
label = "FastCrossDn")
add.rule(strategy.st,name='ruleSignal',
arguments = list(sigcol="FastCrossUp",
sigval=TRUE,
orderqty= 100,
ordertype='market',
orderside='long',
threshold=NULL),
type='enter',
label='enterL',
storefun=FALSE
)
add.rule(strategy.st,name='ruleSignal',
arguments = list(sigcol="FastCrossDn",
sigval=TRUE,
orderqty='all',
ordertype='market',
orderside='long',
threshold=NULL,
orderset='default',
replace = TRUE),
type='exit',
label='exitL'
)
applyStrategy(strategy.st, portfolio.st)
tail(mktdata)
# Open High Low Close Volume SMA.MA20 SMA.MA30minbar FastCrossUp FastCrossDn
# 2002-10-24 17:54:00 1.5552 1.5552 1.5552 1.5552 0 1.555115 1.55467 NA NA
# 2002-10-24 17:55:00 1.5552 1.5552 1.5551 1.5551 0 1.555120 1.55467 NA NA
# 2002-10-24 17:56:00 1.5551 1.5551 1.5551 1.5551 0 1.555125 1.55467 NA NA
# 2002-10-24 17:57:00 1.5551 1.5551 1.5551 1.5551 0 1.555130 1.55467 NA NA
# 2002-10-24 17:58:00 1.5551 1.5551 1.5551 1.5551 0 1.555130 1.55467 NA NA
# 2002-10-24 17:59:00 1.5551 1.5551 1.5551 1.5551 0 1.555135 1.55478 NA NA
tx <- getTxns(portfolio.st, "GBPUSD")
# Record total PL earned. This number should be identical to the result from the second approach listed below:
sum(tx$Net.Txn.Realized.PL)
# -0.03
方法2：我们的想法是，我们已经在[symbol].d的全局命名空间中使用名称计算了每日市场数据（请参阅下面的内容）。显然，这些日常数据也可以从磁盘加载到内存中。我们在不同的时间频率上使用这些预先计算的数据集，而不是计算指标函数内的条形数据（例如在上面的indDailyMA中完成）：
这种方法可以说是更高级和更高效的内存，因为我们不计算聚合（例如，在使用tick数据时计算成本可能很高）。
library(quantstrat)
from <- "2002-10-20"
to <- "2002-10-24"
symbols <- "GBPUSD"
# Load 1 minute data stored in the quantstrat package
getSymbols.FI(Symbols = symbols,
dir=system.file('extdata',package='quantstrat'),
from=from,
to=to
)
currency(c('GBP', 'USD'))
exchange_rate('GBPUSD', tick_size=0.0001)
strategy.st <- "multiFrame"
portfolio.st <- "multiFrame"
account.st <- "multiFrame"
# Parameters:
initEq <- 50000
rm.strat(strategy.st)
initPortf(portfolio.st, symbols = symbols)
initAcct(account.st, portfolios = portfolio.st, initEq = initEq)
initOrders(portfolio.st)
strategy(strategy.st, store = TRUE)
GBPUSD <- GBPUSD[, colnames(GBPUSD) != "Volume"]
# Before running the backtest, create the lower frequency market data
GBPUSD.30m <- to.period(OHLC(GBPUSD), period = "minutes", k = 30, indexAt = "endof", name = "GBPUSD")
GBPUSD.1m.idx <- index(GBPUSD)
NROW(GBPUSD)
# 5276
# Add the lower frequency data indicators to the higher frequency data that will be processed in quantstrat. Fill forward the lower frequency moving average
GBPUSD <- merge(GBPUSD, setNames(SMA(Cl(GBPUSD.30m), n = 10), "SMA.MA30minbar"))
GBPUSD$SMA.MA30minbar <- na.locf(GBPUSD$SMA.MA30minbar)
# Note: Short hand for the above will the fill argument, which can be helpful in special cases where NAs only exist in the new data to be added:
# GBPUSD <- merge(GBPUSD, setNames(SMA(Cl(GBPUSD.30m), n = 10), "SMA.MA30minbar"), fill = na.locf)
NROW(GBPUSD)
# 5276
# After doing this merge, sometimes extra rows will appear beyond what GBPUSD (based on the original 1 min bar data)
GBPUSD <- GBPUSD[GBPUSD.1m.idx, ]
# Now GBPUSD, which will be the raw data used in applyStrategy, already contains the 30 min bar indicators.
add.indicator(strategy.st, name = "SMA",
arguments = list(x = quote(Cl(mktdata)),
n = 20),
label = "MA20")
add.signal(strategy.st, name = "sigCrossover",
arguments = list(columns = c("SMA.MA20", "SMA.MA30minbar"),
relationship = "gt"),
label = "FastCrossUp")
add.signal(strategy.st, name = "sigCrossover",
arguments = list(columns = c("SMA.MA20", "SMA.MA30minbar"),
relationship = "lt"),
label = "FastCrossDn")
add.rule(strategy.st,name='ruleSignal',
arguments = list(sigcol="FastCrossUp",
sigval=TRUE,
orderqty= 100,
ordertype='market',
orderside='long',
threshold=NULL),
type='enter',
label='enterL',
storefun=FALSE
)
add.rule(strategy.st,name='ruleSignal',
arguments = list(sigcol="FastCrossDn",
sigval=TRUE,
orderqty='all',
ordertype='market',
orderside='long',
threshold=NULL,
orderset='sysMACD',
replace = TRUE),
type='exit',
label='exitL'
)
applyStrategy(strategy.st, portfolio.st)
tail(mktdata)
# Open High Low Close SMA.MA30minbar SMA.MA20 FastCrossUp FastCrossDn
# 2002-10-24 17:54:00 1.5552 1.5552 1.5552 1.5552 1.55467 1.555115 NA NA
# 2002-10-24 17:55:00 1.5552 1.5552 1.5551 1.5551 1.55467 1.555120 NA NA
# 2002-10-24 17:56:00 1.5551 1.5551 1.5551 1.5551 1.55467 1.555125 NA NA
# 2002-10-24 17:57:00 1.5551 1.5551 1.5551 1.5551 1.55467 1.555130 NA NA
# 2002-10-24 17:58:00 1.5551 1.5551 1.5551 1.5551 1.55467 1.555130 NA NA
# 2002-10-24 17:59:00 1.5551 1.5551 1.5551 1.5551 1.55478 1.555135 NA NA
tx <- getTxns(portfolio.st, "GBPUSD")
sum(tx$Net.Txn.Realized.PL)
# -0.03
# Same result as the first approach, as we would expect
您可能还会发现有关此主题的其他参考资料很有用：
Generating indicators of different periodicity in quantstrat
http://r.789695.n4.nabble.com/R-Quantstrat-package-question-td3772989.html