基于相关性的投资中性交易模型-R语言

投资中性模型。可以让我们的投资组合的风险敞口极小。

# 滚动计算相关性

data <- cbind(ret_xom,ret_cvx)

correlation <- function(x){
  result <- cor(x[,1],x[,2],use = "na.or.complete")
  return(result)
}

corr <- rollapply(data,252,correlation,by.column=FALSE)

plot(corr)

# 滚动计算上下边界

hedge_ratio <- xom / cvx

roll_me <- rollapply(hedge_ratio,14,mean,na.rm=TRUE)
roll_std <- rollapply(hedge_ratio,14,sd,na.rm=TRUE)

# n是关键值

n <-1
roll_ub <- roll_me + n * roll_std
roll_lb <- roll_me - n * roll_std

# 交易信号

signal <- NULL
signal <- ifelse(hedge_ratio > roll_ub,-1,
                 ifelse(hedge_ratioroll_me,-1,
                 ifelse(lagsignal == 1 & hedge_ratio 

> # 交易效果和绩效指标
> summary(as.vector(trade_return))
     Min.   1st Qu.    Median      Mean 
-0.036732 -0.000437  0.000000 -0.000148 
  3rd Qu.      Max.      NA's 
 0.000290  0.031850        15 
> cumm_ret <- Return.cumulative(trade_return)
> annual_ret <- Return.annualized(trade_return)
> par(mfrow=c(1,1))
> charts.PerformanceSummary(trade_return,main="交易报告")
> tail(cumm_ret)
                  TradStrat_PT#2
Cumulative Return     -0.2141587
> maxDrawdown(trade_return)
[1] 0.274468
> StdDev(trade_return)
              [,1]
StdDev 0.005041639
> StdDev.annualized(trade_return)
                              TradStrat_PT#2
Annualized Standard Deviation     0.08003354
> VaR(trade_return)
    TradStrat_PT#2
VaR   -0.007856979
> SharpeRatio(trade_return,Rf=0,FUN="StdDev")
                              TradStrat_PT#2
StdDev Sharpe (Rf=0%, p=95%):    -0.02944995
> SharpeRatio.annualized(trade_return,Rf=0)
                                TradStrat_PT#2
Annualized Sharpe Ratio (Rf=0%)     -0.4974092
> 

收益真惨还有极大的优化空间。

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量化投资与期货外汇散仙，基金保险水平也拿的出手