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我正在基于吸墨纸在 R 中进行简单的回测，发现它非常非常慢。有没有办法加快速度？并行处理会有帮助吗？还是该软件包仍在开发中并且将进行改进？

在哪里可以查看blotter的源代码？

谢谢

整个系统：

library(quantmod)
library(TTR)
library(blotter) # r-forge revision 193
library(PerformanceAnalytics)

# Set initial values
initDate='2002-07-31'
endDate='2009-10-31'
initEq=100000

# Set currency and instruments
currency("USD")
stock("IEF",currency="USD",multiplier=1)
stock("SPY",currency="USD",multiplier=1)
stock("TLT",currency="USD",multiplier=1)

# Load data with quantmod
print("Loading data")
symbols = c("IEF", "SPY","TLT")
getSymbols(symbols, from=initDate, to=endDate, index.class=c("POSIXt","POSIXct"))

# Adjust prices for splits/dividends (thanks pg)
#IEF = adjustOHLC(IEF)
#SPY = adjustOHLC(SPY)

# Convert data to monthly frequency (to.weekly() needs drop.time=FALSE)
#IEF = to.monthly(IEF, indexAt='endof')
#SPY = to.monthly(SPY, indexAt='endof')
#TLT = to.monthly(TLT, indexAt='endof')

# Set up indicators with TTR
print("Setting up indicators")
IEF$SMA = SMA(Cl(IEF), 200)
SPY$SMA = SMA(Cl(SPY), 200)
TLT$SMA = SMA(Cl(TLT), 200)

# Set up a portfolio object and an account object in blotter
initPortf(name='default', symbols=symbols, initDate=initDate)
initAcct(name='default', portfolios='default', initDate=initDate, initEq=initEq)
verbose = FALSE

# Create trades
for( i in 200:NROW(SPY) ) {
  CurrentDate=time(SPY)[i]
  equity = getEndEq(Account='default', CurrentDate)

  for( symbol in symbols ) {
    sym = get(symbol)
    ClosePrice = as.numeric(Cl(sym[i,]))
    Posn = getPosQty(Portfolio='default', Symbol=symbol, Date=CurrentDate)
    UnitSize = as.numeric(trunc((equity/NROW(symbols))/ClosePrice))

    # Position Entry (assume fill at close)
    if( Posn == 0 ) {
      # No position, so test to initiate Long position
      if( Cl(sym[i,]) > sym[i,'SMA'] ) {
       # Store trade with blotter
        addTxn('default', Symbol=symbol, TxnDate=CurrentDate,
               TxnPrice=ClosePrice, TxnQty=UnitSize, TxnFees=0, verbose=verbose)
      }
    } else {
      # Have a position, so check exit
      if( Cl(sym[i,]) < sym[i,'SMA'] ) {
        # Store trade with blotter
        addTxn(Portfolio='default', Symbol=symbol, TxnDate=CurrentDate,
               TxnPrice=ClosePrice, TxnQty=-Posn, TxnFees=0, verbose=verbose)
      }
    }
  } # End symbols loop

  # Calculate P&L and resulting equity with blotter
  updatePortf(Portfolio='default', Dates=CurrentDate)
  updateAcct(name='default', Dates=CurrentDate)
  updateEndEq(Account='default', Dates=CurrentDate)

} # End dates loop

# Buy and Hold cumulative equity
buyhold = exp(cumsum( ( 0.5*ROC(Cl(IEF)) + 0.5*ROC(Cl(SPY)) )[-1] ))

# Final values
cat('Tactical Asset Allocation Return: ',(getEndEq(Account='default', Date=CurrentDate)-initEq)/initEq,'\n')
cat('Buy and Hold Return: ',tail(buyhold,1)-1,'\n')

# Plot Strategy Summary
png(filename="20091118_blotter_strategy.png", 720, 720)
#charts.PerformanceSummary(ROC(getAccount('default')$TOTAL$End.Eq)[-1],main="Tactical Asset Allocation")
charts.PerformanceSummary(ROC(getAccount('default')$summary$End.Eq)[-1],main="Tactical Asset Allocation")
dev.off()

# Plot Buy and Hold Summary
png(filename="20091118_blotter_buyhold.png", 720, 720)
charts.PerformanceSummary(ROC(buyhold)[-1],main="Buy & Hold")
dev.off()

我的系统：i7-2630 2.0GHz 4GB 内存

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1 回答 1

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您引用的博客文章中的代码（在评论中）使用月度数据。您的代码使用每日数据。在示例期间，天数是月数的 24 倍，因此预期您的代码花费 24 倍的时间是合理的。

正如博文所说，如果您只想测试交易系统，则应该使用 quantstrat。它甚至提供了另一个帖子的链接，向您展示如何使用 quantstrat 测试相同的策略。

于 2012-08-17T23:57:55.130 回答