加速WMA（加权移动平均线）的计算

Question

我试图计算15天线条上的指数移动平均线，但希望看到15日线条EMA在每个（尾）日/线条上的“演变”。所以，这意味着我有15天的酒吧。当每天都有新的数据出现时，我想使用新的信息重新计算EMA。其实我有15天的酒吧，然后，每天过后，我新的15天酒吧开始增长，每个新酒吧应该用于EMA计算以及以前的整整15天的酒吧。

假设我们从2012-01-01开始（本例中为每个日历日的数据），在2012-01-15年底，我们有第一个完整的15天吧。我们可以在2012-03-01完成4个完整的15天的条形图后，开始计算4 bar EMA（EMA（x，n = 4））。在2012年3月2日结束时，我们将使用我们目前为止的信息，并在2012-03-02计算EMA，假设2012-03-02的OHLC是15天的酒吧进行中。所以我们在2012-03-02拿4个完整的酒吧和酒吧并且计算EMA（x，n = 4）。然后，我们再等一天，看看正在进行的新的15天工作条发生了什么（有关详细信息，请参阅下面的函数to.period.cumulative），并计算EMA的新值......并且在接下来的15天之后... ...请参阅功能EMA.cumulative以下详细...

下面请找到我能够想出到现在。这种表现对我来说是不可接受的，而且我对有限的R知识无法做得更快。

library(quantmod) 

do.call.rbind <- function(lst) { 
    while(length(lst) > 1) { 
     idxlst <- seq(from=1, to=length(lst), by=2) 

     lst <- lapply(idxlst, function(i) { 
        if(i==length(lst)) { return(lst[[i]]) } 

        return(rbind(lst[[i]], lst[[i+1]])) 
       }) 
    } 
    lst[[1]] 
} 

to.period.cumulative <- function(x, name=NULL, period="days", numPeriods=15) { 
    if(is.null(name)) 
     name <- deparse(substitute(x)) 

    cnames <- c("Open", "High", "Low", "Close") 
    if (has.Vo(x)) 
     cnames <- c(cnames, "Volume") 

    cnames <- paste(name, cnames, sep=".") 

    if (quantmod:::is.OHLCV(x)) { 
     x <- OHLCV(x) 
     out <- do.call.rbind( 
       lapply(split(x, f=period, k=numPeriods), 
         function(x) cbind(rep(first(x[,1]), NROW(x[,1])), 
           cummax(x[,2]), cummin(x[,3]), x[,4], cumsum(x[,5])))) 
    } else if (quantmod:::is.OHLC(x)) { 
     x <- OHLC(x) 
     out <- do.call.rbind( 
       lapply(split(x, f=period, k=numPeriods), 
         function(x) cbind(rep(first(x[,1]), NROW(x[,1])), 
           cummax(x[,2]), cummin(x[,3]), x[,4]))) 
    } else { 
     stop("Object does not have OHLC(V).") 
    } 

    colnames(out) <- cnames 

    return(out) 
} 

EMA.cumulative<-function(cumulativeBars, nEMA = 4, period="days", numPeriods=15) { 
    barsEndptCl <- Cl(cumulativeBars[endpoints(cumulativeBars, on=period,  k=numPeriods)]) 

    # TODO: This is sloooooooooooooooooow... 
    outEMA <- do.call.rbind(
      lapply(split(Cl(cumulativeBars), period), 
        function(x) { 
         previousFullBars <- barsEndptCl[index(barsEndptCl) < last(index(x)), ] 
         if (NROW(previousFullBars) >= (nEMA - 1)) { 
           last(EMA(last(rbind(previousFullBars, x), n=(nEMA + 1)), n=nEMA)) 
         } else { 
          xts(NA, order.by=index(x)) 
         } 
        })) 

    colnames(outEMA) <- paste("EMA", nEMA, sep="") 

    return(outEMA) 
} 

getSymbols("SPY", from="2010-01-01") 

SPY.cumulative <- to.period.cumulative(SPY, , name="SPY") 

system.time(
     SPY.EMA <- EMA.cumulative(SPY.cumulative) 
) 

在我的系统需要

user system elapsed 
    4.708 0.000 4.410 

可接受的执行时间将超过1秒，少...是否有可能实现这一目标使用纯的R？

这篇文章链接到Optimize moving averages calculation - is it possible?我没有收到答案。我现在能够创建一个可重现的例子，并更详细地解释我想加速的内容。我希望这个问题现在更有意义。

任何想法如何加快这一点高度赞赏。

Answer 1

我还没有找到一个令人满意的解决方案，我的问题使用R.所以我采用了旧的工具，c语言，结果比我预想的要好。感谢您使用Rcpp的这些优秀工具“推”我，内联等。令人惊叹。我想，只要我将来有性能需求，并且无法使用R来满足，我会将C添加到R，并且性能在那里。所以，请看下面我的代码和解决性能问题。

# How to speedup cumulative EMA calculation 
# 
############################################################################### 

library(quantmod) 
library(Rcpp) 
library(inline) 
library(rbenchmark) 

do.call.rbind <- function(lst) { 
    while(length(lst) > 1) { 
     idxlst <- seq(from=1, to=length(lst), by=2) 

     lst <- lapply(idxlst, function(i) { 
        if(i==length(lst)) { return(lst[[i]]) } 

        return(rbind(lst[[i]], lst[[i+1]])) 
       }) 
    } 
    lst[[1]] 
} 

to.period.cumulative <- function(x, name=NULL, period="days", numPeriods=15) { 
    if(is.null(name)) 
     name <- deparse(substitute(x)) 

    cnames <- c("Open", "High", "Low", "Close") 
    if (has.Vo(x)) 
     cnames <- c(cnames, "Volume") 

    cnames <- paste(name, cnames, sep=".") 

    if (quantmod:::is.OHLCV(x)) { 
     x <- quantmod:::OHLCV(x) 
     out <- do.call.rbind( 
       lapply(split(x, f=period, k=numPeriods), 
         function(x) cbind(rep(first(x[,1]), NROW(x[,1])), 
           cummax(x[,2]), cummin(x[,3]), x[,4], cumsum(x[,5])))) 
    } else if (quantmod:::is.OHLC(x)) { 
     x <- OHLC(x) 
     out <- do.call.rbind( 
       lapply(split(x, f=period, k=numPeriods), 
         function(x) cbind(rep(first(x[,1]), NROW(x[,1])), 
           cummax(x[,2]), cummin(x[,3]), x[,4]))) 
    } else { 
     stop("Object does not have OHLC(V).") 
    } 

    colnames(out) <- cnames 

    return(out) 
} 

EMA.cumulative<-function(cumulativeBars, nEMA = 4, period="days", numPeriods=15) { 
    barsEndptCl <- Cl(cumulativeBars[endpoints(cumulativeBars, on=period, k=numPeriods)]) 

    # TODO: This is sloooooooooooooooooow... 
    outEMA <- do.call.rbind(
      lapply(split(Cl(cumulativeBars), period), 
        function(x) { 
         previousFullBars <- barsEndptCl[index(barsEndptCl) < last(index(x)), ] 
         if (NROW(previousFullBars) >= (nEMA - 1)) { 
           last(EMA(last(rbind(previousFullBars, x), n=(nEMA + 1)), n=nEMA)) 
         } else { 
          xts(NA, order.by=index(x)) 
         } 
        })) 

    colnames(outEMA) <- paste("EMA", nEMA, sep="") 

    return(outEMA) 
} 

EMA.c.c.code <- ' 
    /* Initalize loop and PROTECT counters */ 
    int i, P=0; 

    /* ensure that cumbars and fullbarsrep is double */ 
    if(TYPEOF(cumbars) != REALSXP) { 
     PROTECT(cumbars = coerceVector(cumbars, REALSXP)); P++; 
    } 

    /* Pointers to function arguments */ 
    double *d_cumbars = REAL(cumbars); 
    int i_nper = asInteger(nperiod); 
    int i_n = asInteger(n); 
    double d_ratio = asReal(ratio); 

    /* Input object length */ 
    int nr = nrows(cumbars); 

    /* Initalize result R object */ 
    SEXP result; 
    PROTECT(result = allocVector(REALSXP,nr)); P++; 
    double *d_result = REAL(result); 

    /* Find first non-NA input value */ 
    int beg = i_n*i_nper - 1; 
    d_result[beg] = 0; 
    for(i = 0; i <= beg; i++) { 
     /* Account for leading NAs in input */ 
     if(ISNA(d_cumbars[i])) { 
      d_result[i] = NA_REAL; 
      beg++; 
      d_result[beg] = 0; 
      continue; 
     } 
     /* Set leading NAs in output */ 
     if(i < beg) { 
      d_result[i] = NA_REAL; 
     } 
     /* Raw mean to start EMA - but only on full bars*/ 
     if ((i != 0) && (i%i_nper == (i_nper - 1))) { 
      d_result[beg] += d_cumbars[i]/i_n; 
     } 
    } 

    /* Loop over non-NA input values */ 
    int i_lookback = 0; 
    for(i = beg+1; i < nr; i++) { 
     i_lookback = i%i_nper; 

     if (i_lookback == 0) { 
      i_lookback = 1; 
     } 
     /*Previous result should be based only on full bars*/ 
     d_result[i] = d_cumbars[i] * d_ratio + d_result[i-i_lookback] * (1-d_ratio); 
    } 

    /* UNPROTECT R objects and return result */ 
    UNPROTECT(P); 
    return(result); 
' 

EMA.c.c <- cfunction(signature(cumbars="numeric", nperiod="numeric", n="numeric",  ratio="numeric"), EMA.c.c.code) 

EMA.cumulative.c<-function(cumulativeBars, nEMA = 4, period="days", numPeriods=15) { 
    ratio <- 2/(nEMA+1) 

    outEMA <- EMA.c.c(cumbars=Cl(cumulativeBars), nperiod=numPeriods, n=nEMA, ratio=ratio) 

    outEMA <- reclass(outEMA, Cl(cumulativeBars)) 

    colnames(outEMA) <- paste("EMA", nEMA, sep="") 

    return(outEMA) 
} 

getSymbols("SPY", from="2010-01-01") 

SPY.cumulative <- to.period.cumulative(SPY, name="SPY") 

system.time(
     SPY.EMA <- EMA.cumulative(SPY.cumulative) 
) 

system.time(
     SPY.EMA.c <- EMA.cumulative.c(SPY.cumulative) 
) 


res <- benchmark(EMA.cumulative(SPY.cumulative), EMA.cumulative.c(SPY.cumulative), 
     columns=c("test", "replications", "elapsed", "relative", "user.self", "sys.self"), 
     order="relative", 
     replications=10) 

print(res) 

编辑：为了让过我麻烦的性能改进的指示（我相信它可以变得更好，因为实际上我已经创建了双重的for循环）这里R是打印出来：

> print(res) 
           test replications elapsed relative user.self 
2 EMA.cumulative.c(SPY.cumulative)   10 0.026 1.000  0.024 
1 EMA.cumulative(SPY.cumulative)   10 57.732 2220.462 56.755 

因此，按照我的标准，SF类型的改善...