Question

亲爱的名单，我正在尝试用R进行技术分析，使用包TTR，quantmod，zoo 我有每日黄金价格，数据看起来像：

> library(quantmod)
> library(timeSeries)
> gold <- read.csv("gold.csv")
> g <- as.xts(gold, dateFormat = "Date")
> g.c<-Cl(g)
> head(g)
            Open  High   Low Close
1999-01-08 292.2 293.3 291.2 292.0
1999-01-11 292.3 294.3 291.6 293.6
1999-01-12 292.2 292.5 288.0 289.3
1999-01-13 288.8 289.1 285.0 287.0
1999-01-14 287.4 287.4 285.0 287.4
1999-01-15 286.7 287.6 286.4 287.4
> first(g)
            Open  High   Low Close
1999-01-08 292.2 293.3 291.2   292
> last(g)
           Open   High    Low  Close
2010-10-20 1332 1346.5 1330.8 1343.6

我已经定义了每日返回产生的信号和随机信号指标（在这种情况下为donchian渠道）

那么命中率是

> x<-g.c
> timeseries.eval <- function(x,signal) {
+    returns <- returns(x)
+    hit.rate <- function(x,signal) {
+      rate<- length(which(signal*returns> 0))/length(x)
+      rate
+    }
+  round(data.frame(N=length(x),HitRate=hit.rate(x,signal)),3)
+ }
> timeseries.eval(x, sig.dc)
     N HitRate
1 3074   0.628

这给了我整个时期的结果，但我希望看到命中每年和特定时期的比例（比方说100天）我已经尝试了quantmod的函数apply.yearly()，但它没有用。而且，我也尝试了

> years <- unique(substr(g[,"Date"],1,4))
Error in dimnames(cd) <- list(as.character(index(x)), colnames(x)) : 
  'dimnames' applied to non-array

而

> j<-as.data.frame(g)
> years <- unique(substr(y,1,4))
> years
[1] "1999" "2000" "2001" "2002" "2003" "2004" "2005" "2006" "2007" "2008" "2009" "2010"

智能循环的任何想法都是有价值的（注意：有必要维护xts类以便从包中正确处理指标 TTR）

亚历

Answer 1

您可以使用apply.yearly执行此操作，但要按周期拆分的所有数据都必须位于一个对象中，因为apply.yearly仅拆分x 而不是 signal（或通过...传递的任何其他内容）。

library(quantmod)
getSymbols("GLD", from="2007-01-03", to="2011-01-28")

set.seed(21)
sig <- sign(runif(NROW(GLD)))

hit.rate <- function(returnSignal) {
  N <- NROW(na.omit(returnSignal))
  HitRate <- sum(returnSignal[,1]*returnSignal[,2]>0, na.rm=TRUE)/N
  cbind(N,HitRate)
}

hit.rate(merge(ROC(Cl(GLD)),sig))
#         N  HitRate
# [1,] 1026 0.539961
apply.yearly(merge(ROC(Cl(GLD)),sig), hit.rate)
#            GLD.Close       sig
# 2007-12-31       250 0.5560000
# 2008-12-31       253 0.5256917
# 2009-12-31       252 0.5277778
# 2010-12-31       252 0.5634921
# 2011-01-28        19 0.3684211

此外，您对TTR需要xts对象的“注意”不正确。 TTR函数在内部使用xts，这允许它们处理大多数时间序列类（xts，zoo，timeSeries，chron，它们，irts，fts等）以及data.frame，matrix和numeric / integer向量。如果对象对xts是可强制的，则TTR函数将返回给予它们的同一类的对象。

例如：

> str(ROC(Cl(GLD)))
An ‘xts’ object from 2007-01-03 to 2011-01-28 containing:
  Data: num [1:1027, 1] NA -0.01017 -0.0243 0.00514 0.0061 ...
 - attr(*, "dimnames")=List of 2
  ..$ : NULL
  ..$ : chr "GLD.Close"
  Indexed by objects of class: [Date] TZ: 
  xts Attributes:  
List of 2
 $ src    : chr "yahoo"
 $ updated: POSIXct[1:1], format: "2011-01-31 08:41:53"
> str(ROC(as.zoo(Cl(GLD))))
‘zoo’ series from 2007-01-03 to 2011-01-28
  Data: Named num [1:1027] NA -0.01017 -0.0243 0.00514 0.0061 ...
 - attr(*, "names")= chr [1:1027] "2007-01-03" "2007-01-04" "2007-01-05" "2007-01-08" ...
  Index: Class 'Date'  num [1:1027] 13516 13517 13518 13521 13522 ...
> str(ROC(as.timeSeries(Cl(GLD))))
Time Series:          
 Name:               object
Data Matrix:        
 Dimension:          1027 1
 Column Names:       GLD.Close
 Row Names:          2007-01-03  ...  2011-01-28
Positions:          
 Start:              2007-01-03
 End:                2011-01-28
With:               
 Format:             %Y-%m-%d
 FinCenter:          GMT
 Units:              GLD.Close
 Title:              Time Series Object
 Documentation:      Mon Jan 31 08:48:35 2011
> str(ROC(as.ts(Cl(GLD))))
 Time-Series [1:1027] from 1 to 1027: NA -0.01017 -0.0243 0.00514 0.0061 ...
> str(ROC(as.data.frame(Cl(GLD))))
'data.frame':   1027 obs. of  1 variable:
 $ GLD.Close: num  NA -0.01017 -0.0243 0.00514 0.0061 ...
> str(ROC(as.matrix(Cl(GLD))))
 num [1:1027, 1] NA -0.01017 -0.0243 0.00514 0.0061 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:1027] "2007-01-03" "2007-01-04" "2007-01-05" "2007-01-08" ...
  ..$ : chr "GLD.Close"
> str(ROC(as.numeric(Cl(GLD))))
 num [1:1027] NA -0.01017 -0.0243 0.00514 0.0061 ...

R：技术分析年度结果

1 个答案: