基于协整关系的配对交易比之前的基于距离和相关性要更先进一些。收益上确实也体现出来了。

library(tseries)

adf.test(xom)

adf.test(cvx)

model <- lm(xom ~ cvx + 0)

summary(model)

adf.test(as.ts(model$residuals))

bp <- get_symbol_cl("BP")

model <- lm(xom ~ bp + 0)

adf.test(as.ts(model$residuals))

plot(cbind(xom,bp * model$coefficients),

col = c("black", "red"),

main = "xom & model$coefficients")

plot(as.xts(model$residuals, index(xom)),

main = "model$residuals")

plot(signal, main = "交易信号")

roll_me <- rollapply(model$residuals,14,mean)

roll_std <- rollapply(model$residuals,14,sd)

n <- 1

roll_ub <- roll_me + n * roll_std

roll_lb <- roll_me - n * roll_std

signal <- NULL

signal <- ifelse(model$residuals>roll_ub,-1,

ifelse(model$residuals roll_me,-1,

ifelse(lagsignal==1 & model$residuals < roll_me,1,0))

# 计算收益

spread_return <- ret_xom - lag(hedge_ratio) * ret_cvx

cost <- 0

trade_return <- spread_return * lag(signal) - cost

names(trade_return) <- "TradStrat_PT#3"

# 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 = "StdDev")

SharpeRatio.annualized(trade_return, Rf = 0)

> # 交易效果和绩效指标

> summary(as.vector(trade_return))

Min. 1st Qu. Median Mean 3rd Qu. Max. NA's

-0.036732 0.000000 0.000000 0.000192 0.000520 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#3

Cumulative Return 0.3088741

> maxDrawdown(trade_return)

[1] 0.1295894

> StdDev(trade_return)

[,1]

StdDev 0.004902459

> StdDev.annualized(trade_return)

TradStrat_PT#3

Annualized Standard Deviation 0.07782412

> VaR(trade_return)

TradStrat_PT#3

VaR -0.006463428

> SharpeRatio(trade_return, Rf = 0, FUN = "StdDev")

TradStrat_PT#3

StdDev Sharpe (Rf=0%, p=95%): 0.03917563

> SharpeRatio.annualized(trade_return, Rf = 0)

TradStrat_PT#3

Annualized Sharpe Ratio (Rf=0%) 0.5964265

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