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_ratio<roll_lb,1,0)) lagsignal <- lag(signal,1) signal ifelse(lagsignal="=" -1 & hedge_ratio>roll_me,-1,
                 ifelse(lagsignal == 1 &amp; hedge_ratio <roll_me,1,0)) # 计算收益 spread_return <- ret_xom - lag(hedge_ratio) * ret_cvx cost 0 trade_return lag(signal) names(trade_return)<- "tradstrat_pt#2" 交易效果和绩效指标 summary(as.vector(trade_return)) 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) maxdrawdown(trade_return) stddev(trade_return) stddev.annualized(trade_return) var(trade_return) sharperatio(trade_return,rf="0,FUN=&quot;StdDev&quot;)" sharperatio.annualized(trade_return,rf="0)" ``` ![file](http: imgow.oss-cn-beijing.aliyuncs.com image-1581145816970.png) ```r> # 交易效果和绩效指标
&gt; 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 
&gt; cumm_ret &lt;- Return.cumulative(trade_return)
&gt; annual_ret &lt;- Return.annualized(trade_return)
&gt; par(mfrow=c(1,1))
&gt; charts.PerformanceSummary(trade_return,main="交易报告")
&gt; tail(cumm_ret)
                  TradStrat_PT#2
Cumulative Return     -0.2141587
&gt; maxDrawdown(trade_return)
[1] 0.274468
&gt; StdDev(trade_return)
              [,1]
StdDev 0.005041639
&gt; StdDev.annualized(trade_return)
                              TradStrat_PT#2
Annualized Standard Deviation     0.08003354
&gt; VaR(trade_return)
    TradStrat_PT#2
VaR   -0.007856979
&gt; SharpeRatio(trade_return,Rf=0,FUN="StdDev")
                              TradStrat_PT#2
StdDev Sharpe (Rf=0%, p=95%):    -0.02944995
&gt; SharpeRatio.annualized(trade_return,Rf=0)
                                TradStrat_PT#2
Annualized Sharpe Ratio (Rf=0%)     -0.4974092
&gt; 

收益真惨🤣还有极大的优化空间。</roll_me,1,0))></roll_lb,1,0))>

来源：oschina

链接：https://my.oschina.net/changzw/blog/3164291