将输出对象存储到R中不同变量的数据框中

Question

下面是我的股票回溯测试项目的脚本。我写了一个测试2 SMA交叉的回归买入/卖出策略。现在，我想自动化x和y的输入值，并为不同的结果生成一个表。 X值从（1:30）变化以及Y值从（1:30）变化并输出到Excel，就像数据帧文件一样，从1到30列反对。我尝试了不同的方法，但仍然无法进入最终R中的数据帧

library(quantmod)
library(lubridate)
library(xlsx)
stock0<-getSymbols("^HSI",src="yahoo",from="1988-01-01",auto.assign=F)
stock0 <- to.weekly(stock0)


x<-1
y<-30


stock1<-na.locf(stock0)
stock1$SMA1<-SMA(Cl(stock1),n=x)
stock1$SMA30<-SMA(Cl(stock1),n=y)

stock1$SMACheck<-ifelse(stock1$SMA1>stock1$SMA30,1,0)
stock1$SMA_CrossOverUp<-ifelse(diff(stock1$SMACheck)==1,1,0)
stock1$SMA_CrossOverDown<-ifelse(diff(stock1$SMACheck)==-1,-1,0)


stock1<-stock1[index(stock1)>="1998-01-01",]

stock1_df<-data.frame(index(stock1),coredata(stock1))

colnames(stock1_df)<-c("Date","Open","High","Low","Close","Volume","Adj","SMA1","SMA30","SMACheck","SMACheck_up","SMACheck_down")




sum(stock1_df$SMACheck_up==1 & index(stock1)>="2010-01-01",na.rm=T)

stock1_df$Date[stock1_df$SMACheck_up==1 & index(stock1)>="2010-01-01"]

sum(stock1_df$SMACheck_down==-1 & index(stock1)>="2010-01-01",na.rm=T)

stock1_df$Date[stock1_df$SMACheck_down==-1 & index(stock1)>="2010-01-01"]


#To generate the transcation according to the strategy

transaction_dates<-function(stock2,Buy,Sell)
{
Date_buy<-c()
Date_sell<-c()
hold<-F
stock2[["Hold"]]<-hold
for(i in 1:nrow(stock2)) {
  if(hold == T) {
    stock2[["Hold"]][i]<-T
    if(stock2[[Sell]][i] == -1) {
      #stock2[["Hold"]][i]<-T
      hold<-F
    }
  } else {
    if(stock2[[Buy]][i] == 1) {
      hold<-T
      stock2[["Hold"]][i]<-T
    }
  }
}


stock2[["Enter"]]<-c(0,ifelse(diff(stock2[["Hold"]])==1,1,0))
stock2[["Exit"]]<-c(ifelse(diff(stock2[["Hold"]])==-1,-1,0),0)

Buy_date <- stock2[["Date"]][stock2[["Enter"]] == 1]
Sell_date <- stock2[["Date"]][stock2[["Exit"]] == -1]

if (length(Sell_date)<length(Buy_date)){
  #Sell_date[length(Sell_date)+1]<-tail(stock2[["Date"]],n=2)[1]
  Buy_date<-Buy_date[1:length(Buy_date)-1]

}

return(list(DatesBuy=Buy_date,DatesSell=Sell_date))
}

#transaction dates generate:
stock1_df <- na.locf(stock1_df)

transactionDates<-transaction_dates(stock1_df,"SMACheck_up","SMACheck_down")


num_transaction1<-length(transactionDates[[1]])

Open_price<-function(df,x) {
  df[which(df[["Date"]]==x)+1,][["Open"]]
}

transactions_date<-function(df,x) {
  df[which(df[["Date"]]==x)+1,][["Date"]]
}

transactions_generate<-function(df,num_transaction)
{
price_buy<-sapply(1:num_transaction,function(x) {Open_price(df,transactionDates[[1]][x])})
price_sell<-sapply(1:num_transaction,function(x) {Open_price(df,transactionDates[[2]][x])})
Dates_buy<-as.Date(sapply(1:num_transaction,function(x) {transactions_date(df,transactionDates[[1]][x])}))
Dates_sell<-as.Date(sapply(1:num_transaction,function(x) {transactions_date(df,transactionDates[[2]][x])}))


transactions_df<-data.frame(DatesBuy=Dates_buy,DatesSell=Dates_sell,pricesBuy=price_buy,pricesSell=price_sell)
#transactions_df$return<-100*(transactions_df$pricesSell-transactions_df$pricesBuy)/transactions_df$pricesBuy
transactions_df$Stop_loss<-NA
return(transactions_df)
}

transaction_summary<-transactions_generate(stock1_df,num_transaction1)
transaction_summary$Return<-100*(transaction_summary$pricesSell-transaction_summary$pricesBuy)/transaction_summary$pricesBuy

result<-sum(transaction_summary$Return,na.rm=T)
result

Answer 1

我不会重写整个脚本，但这应该让你去。

首先我创建了一个SMA_crossover函数，它给出了向上和向下交叉的输出，并为快速sma [1]和慢速[25]给出了类似“SMA_CrossOverUp_1_25”的名称。接下来，您将获得x和y值的双循环for循环。完成后，我将所有内容都转换为具有正确名称的data.frame。通过交易生成运行这个我将留给你。

# data should be a ohlcv timeseries from quantmad
# fast_period is the fast SMA
# slow_period is the slow SMA
# function returns only the sma up and down crossovers. 
SMA_crossover <- function(data, fast_period, slow_period) {
Fast <- SMA(Cl(data), n = fast_period)
Slow <- SMA(Cl(data), n = slow_period)
SMACheck <- ifelse(Fast > Slow, 1, 0)

fast_var <- paste("SMA_CrossOverUp", fast_period, slow_period, sep = "_")
slow_var <- paste("SMA_CrossOverDown", fast_period, slow_period, sep = "_")

data$SMA_CrossOverUp <- ifelse(diff(SMACheck) == 1, 1, 0)
data$SMA_CrossOverDown <- ifelse(diff(SMACheck) == -1, -1, 0)

data <- setNames(data[, c("SMA_CrossOverUp", "SMA_CrossOverDown")], c(fast_var, slow_var))

return(data)
}


# adjust length of x and y to create check more crossovers.
# loops merge the crossover data to the input data
for(x in 1:5){
  for(y in 25:30)
  Weekly_data <- merge(Weekly_data, SMA_crossover(Weekly_data, x, y))
}


# transform into data.frame
stock1_df <- data.frame(index(Weekly_data), coredata(Weekly_data))
stock1_df <- setNames(stock1_df, c("Date", colnames(Weekly_data))) 

# remove NA's
stock1_df <- na.locf(stock1_df)

str(stock1_df)
'data.frame':   1572 obs. of  67 variables:
 $ Date                  : Date, format: "1988-08-05" "1988-08-12" "1988-08-19" "1988-08-26" ...
 $ stock0.Open           : num  2703 2659 2601 2564 2440 ...
 $ stock0.High           : num  2703 2659 2601 2564 2450 ...
 $ stock0.Low            : num  2671 2579 2571 2465 2433 ...
 $ stock0.Close          : num  2671 2601 2580 2465 2450 ...
 $ stock0.Volume         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ stock0.Adjusted       : num  2671 2601 2580 2465 2450 ...
 $ SMA_CrossOverUp_1_25  : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SMA_CrossOverDown_1_25: num  0 -1 0 0 0 0 0 0 0 0 ...
 $ SMA_CrossOverUp_1_26  : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SMA_CrossOverDown_1_26: num  0 0 -1 0 0 0 0 0 0 0 ...
 $ SMA_CrossOverUp_1_27  : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SMA_CrossOverDown_1_27: num  0 0 -1 0 0 0 0 0 0 0 ...
 $ SMA_CrossOverUp_1_28  : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SMA_CrossOverDown_1_28: num  0 0 -1 0 0 0 0 0 0 0 ...
 $ SMA_CrossOverUp_1_29  : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SMA_CrossOverDown_1_29: num  0 0 0 -1 0 0 0 0 0 0 ...
 $ SMA_CrossOverUp_1_30  : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SMA_CrossOverDown_1_30: num  0 0 0 -1 0 0 0 0 0 0 ...
 $ SMA_CrossOverUp_2_25  : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SMA_CrossOverDown_2_25: num  0 0 -1 0 0 0 0 0 0 0 ...