BackTest_New.R

# Backtest, buy and hold
# Signal Format (InnerCode, Date, Weight) 
# 每天的目标持仓，实际上并不是每天的，中间可能会有空档
# 每天都要检查是否有信号，如果持仓权重有变化，则就是一个调仓日

##### 说明 #####
# 1、对于PR债，前收盘价已经相应调整过，所以计算当日收益没有问题，但由于会提前兑付本金，造成相应债券金额下降，此回测对此不作考虑
# 2、假设没有最小购买份额，可以按需要的资金全部买入债券

# 20170901新增说明
# 当期目标持仓和上一期发生变化才调仓，调仓时考虑买入卖出的交易额情况，决定是否能够成交以及实际成交的量，注意
# 仅在调仓日做调整，若调仓日无法卖出或买入，后续的时间不再进行补充的交易操作

# 20170907更新说明
# 方法二，相对方法一，在Total_Position的细节上的观察更方便
# 按照真实情况逐步处理，将几种不同类型的交易分开以此处理
# 先考察是否有非目标持仓的券，若有，则先处理这些券，再看是否调仓日，若是则进行调仓，否则不调仓，仅处理非目标持仓券。

# 20170921更新说明
# BondFuture.R构造出国债期货主力合约行情及久期，用主力合约进行利率风险的对冲
# 需要导入主力合约行情数据FutMainQuote.csv
# 总收益还是未引入期货前的收益，即不包括国债期货对冲收益


#### load libraries ####
library(dplyr)
library(lubridate)
library(readr)
library(DBI)
library(rJava)
library(RJDBC)
library(zoo)
library(ggplot2)
library(reshape2)

#### set path ####
jdbc_path <- "C:/Users/shuorui.zhang/Documents/R_WORKING_DIR/jdbc driver/sqljdbc_6.0/enu/jre8/sqljdbc42.jar"
load_path <- "D:/DATA_NEW/"
signal_path <- "D:/BackTest_Framework/SignalPool_Bonds/"

#### DB connection ####
drv <- JDBC("com.microsoft.sqlserver.jdbc.SQLServerDriver", jdbc_path, identifier.quote = "`")    
conn <- dbConnect(drv,"jdbc:sqlserver://192.168.66.12",databaseName="ZSR","shuorui.zhang","Xyinv1704")

#### set parameters ####
StartDate_Backtest <- ymd('2015-04-22')
EndDate_Backtest <- ymd('2017-05-19') 
Interval_Backtest <- 'day' #调仓周期，日调仓
TRCost <- 0 #交易成本设置为0，暂时不考虑交易成本
Cash_Initial <- 100000000 #初始资金设定
Trade_Ratio <- 0.95 #资金当中的可投资百分比，即每次调仓时，用于投资的资金比例
Buy_Proportion <- 0.2 #买入时买入额占当天成交额的最大比例
Sell_Proportion <- 0.2 #卖出时卖出额占当天成交额的最大比例
Weight_eps <- 1e-6 #计算Weight和0的差距的临界值
FutType <- 'TT' #选择对冲的国债期货品种，TT是十年期，TF是五年期
Margin <- ifelse(FutType=='TT', 0.02, 0.01) #国债期货的保证金比例，暂时没用

#### set signal info ####
Signal_Series <- 'Contrarian'
Signal_Name <- 'Contrarian_AvgDiff_1pct'

#### load functions ####
source('functions.R', encoding = 'UTF-8')

#### load data ####
load(paste0(load_path,"/QT_TradingDayNew_DB.RData"))
FutMainQuote <- read_csv('FutMainQuote.csv') %>% 
  data.frame() %>% 
  filter(ContractType == FutType) %>%
  arrange(TradingDay) %>%
  mutate(PrevDuration = dplyr::lag(Duration)) %>%
  mutate(CumReturn = cumprod(1+DailyReturn)/(1+DailyReturn[1]) - 1) %>% #国债期货的累计收益
  select(TradingDay,ContractCode,DailyReturn,CumReturn,PrevDuration) 

#### main ####
#rebalance date#
RebalanceDate_Backtest <- QT_TradingDayNew_DB %>% filter(SecuMarket==83, IfTradingDay==1) %>% 
  filter(TradingDate >= StartDate_Backtest, TradingDate <= EndDate_Backtest) %>% 
  arrange(TradingDate) %>% mutate(BuyDate = dplyr::lag(TradingDate)) %>% na.omit() %>% 
  select(BuyDate, SellDate = TradingDate)

#load Signal
Signal_Raw <- read_csv(file = paste0(signal_path, Signal_Series, '/', Signal_Name, '.csv')) %>%
  data.frame() %>% mutate(InnerCode=as.character(InnerCode))

#Signal_Raw <- read_csv('Signal_Contrarian_5pct.csv') %>% data.frame() %>% mutate(InnerCode=as.character(InnerCode))

#添加辅助信号，使得每一期都至少有一个信号，每天都要检查是否有信号，如果持仓权重有变化，则就是一个调仓日
Signal_Assist <- RebalanceDate_Backtest %>% select(BuyDate) %>% mutate(InnerCode = 'AS0000', Weight =0) %>% 
  select(InnerCode, Date=BuyDate, Weight)

Signal_Total <- rbind(Signal_Raw, Signal_Assist) %>% arrange(Date)

#得到Rebalance序列


#读入行情数据#
# BondQuote_Orig <- dbGetQuery(conn,paste0("SELECT t2.SecuCode,t.InnerCode,t2.SecuAbbr,t.TradingDay,t.PrevClosePrice,t.ClosePrice,t.OpenPrice
#                                          ,t.LowPrice,t.HighPrice,t.TurnoverValue,t.TurnoverVolume,t.TurnoverDeals,
#                                          t1.BondTypeLevel1Desc AS BondType, t1.BondNature, t4.IssuerNature,
#                                          t3.ValueCleanPrice,t3.VPADuration as Duration
#                                          FROM jydb.dbo.QT_DailyQuote t
#                                          LEFT JOIN jybond.dbo.Bond_Code t1
#                                          ON t.InnerCode=t1.InnerCode
#                                          LEFT JOIN jydb.dbo.SecuMain t2
#                                          ON t.InnerCode=t2.InnerCode
#                                          LEFT JOIN jybond.dbo.Bond_CSIValuation t3
#                                          ON t.InnerCode=t3.InnerCode AND t.TradingDay=t3.EndDate
#                                          LEFT JOIN jybond.dbo.Bond_BasicInfo t4
#                                          ON t.InnerCode=t4.InnerCode
#                                          WHERE
#                                          t2.SecuCategory IN ( 6, 7, 11, 28)
#                                          /*6国债,7金融债,11企业债券,28地方政府债;9可转债,29可交换公司债*/
#                                          AND t2.SecuMarket IN ( 83, 90 )
#                                          AND t.TradingDay>='",StartDate_Backtest,"' AND t.TradingDay<='",EndDate_Backtest,"'")) %>%
#   mutate(TradingDay=as.Date(TradingDay),SecuCode=as.character(SecuCode),InnerCode=as.character(InnerCode))

#裁剪行情数据，仅保留有信号的债券的行情，加速回测
BondQuote_Cut_All <- BondQuote_Orig %>% filter(InnerCode %in% distinct(Signal_Total,InnerCode)$InnerCode) %>%
  mutate(ReturnDaily=ClosePrice/PrevClosePrice-1) %>% 
  select(-OpenPrice,-LowPrice,-HighPrice,-TurnoverVolume,-TurnoverDeals,-PrevClosePrice,-ClosePrice,
         -BondType,-ValueCleanPrice,-BondNature,-IssuerNature) %>% #减少字段，方便代码编写及结果查看
  group_by(SecuCode) %>%
  arrange(SecuCode,TradingDay) %>%
  mutate(PrevDuration = dplyr::lag(Duration)) %>% #取前一天的修正久期
  ungroup

Cash <- NULL
Real_Position <- NULL
for(cursor in 1:nrow(RebalanceDate_Backtest)){
  print(cursor)
  TradeDate_ <- RebalanceDate_Backtest$BuyDate[cursor]
  # SellDate_ <- RebalanceDate_Backtest$SellDate[cursor] #不是必需
  
  BondQuote_Cut_ <- BondQuote_Cut_All %>% 
    select(-Duration,-PrevDuration) %>% 
    filter(TradingDay ==TradeDate_) 
  #裁剪行情数据，加速回测,若包含TradeDate、SellDate两天行情，关联时需要关联TradingDay
  
  if(cursor ==1){#第一期单独处理
    Real_Position[[cursor]] <- Signal_Total %>% filter(Date==TradeDate_) %>% 
      left_join(BondQuote_Cut_, by = c('InnerCode','Date'='TradingDay')) %>% #关联上当日行情计算购买量
      mutate(Weight_LT     = 0,
             Target_Asset  = Weight*Cash_Initial*Trade_Ratio, #目标买入金额
             Asset_BfTrade = 0, #盘前资产
             Asset_AtTrade = 0, #换仓时资产
             Trade_Orders  = Target_Asset, #目标交易单
             TurnoverValue = ifelse(is.na(TurnoverValue),0,TurnoverValue),
             Buy_Ceiling   = TurnoverValue*Buy_Proportion, #买入金额上限
             Actual_Buying = ifelse(Buy_Ceiling>Target_Asset,Target_Asset,Buy_Ceiling), #实际能买入的金额
             Asset_AfTrade = Actual_Buying #盘后资产
             ) %>% 
      select(Date,InnerCode,Weight,Target_Asset,Asset_BfTrade,Asset_AtTrade,Trade_Orders,
             Asset_AfTrade,everything()) 
    
    Cash[cursor] <- Cash_Initial - sum(Real_Position[[cursor]]$Actual_Buying) #购买债券后的剩余资金
    
  } else {#第二期开始
    
    Cash[cursor] <- Cash[cursor-1] #初始化本期现金
    
    Target_Position_ <- Signal_Total %>% filter(Date==TradeDate_) #目标持仓
    
    Prev_Position_ <- Real_Position[[cursor-1]] %>%
      filter(Asset_AfTrade>0) #去掉前一期中已经卖出的债券
    
    #前后两期持仓权重
    Position_Matrix <- rbind(distinct(Prev_Position_,InnerCode),distinct(Target_Position_,InnerCode)) %>% 
      distinct(InnerCode) %>% #前后两期所有债券
      #关联上前一期的持仓情况，仅需上一期的权重和资产
      filter(InnerCode != 'AS0000') %>% #'AS0000'单独简单处理，最后再合并回来，否则后面都要考虑到AS0000，要做很多ifelse的处理
      left_join(Prev_Position_[c('InnerCode','Weight','Asset_AfTrade')], by = 'InnerCode') %>% 
      rename(Weight_LT = Weight, 
             Asset_BfTrade = Asset_AfTrade) %>%
      left_join(Target_Position_[c('InnerCode','Weight')], by = 'InnerCode') %>%  #关联上当前一期的持仓权重
      mutate(Weight_LT=ifelse(is.na(Weight_LT),0,Weight_LT), 
             Weight=ifelse(is.na(Weight),0,Weight),
             Asset_BfTrade = ifelse(is.na(Asset_BfTrade),0,Asset_BfTrade)) %>% #NA->0
      mutate(Date = TradeDate_) %>%
      left_join(BondQuote_Cut_, by = c('InnerCode', 'Date' = 'TradingDay')) %>%
      mutate(IfDelisted    = ifelse(is.na(SecuCode),1,0), #是否退市，若当天关联不到，代表上一期退市
             Sell_Ceiling  = TurnoverValue*Sell_Proportion,
             Buy_Ceiling   = TurnoverValue*Buy_Proportion,
             Asset_AtTrade = Asset_BfTrade*(1+ReturnDaily)
             )
    
    #单独处理AS0000，最后合并回去
    AS0000 <- data.frame(InnerCode='AS0000',
                         Date = TradeDate_,
                         Weight_LT = 0,
                         Weight = 0,
                         Asset_AtTrade = 0,
                         Asset_AfTrade = 0,
                         stringsAsFactors = F
                         )
    
    ##首先处理退市债券
    Delisted_List <- NULL
    if(Position_Matrix %>% filter(is.na(SecuCode)) %>% nrow()>0){#检查是否有退市债券，有的话进行相应处理
      Delisted_List <- Position_Matrix %>% filter(is.na(SecuCode)) %>% 
        mutate(
               Asset_AtTrade = Asset_BfTrade,
               Asset_AfTrade = 0,
               Target_Asset  = 0,
               Actual_Selling = Asset_BfTrade
               )
      
      Cash[cursor] <- Cash[cursor] + sum(Delisted_List$Asset_BfTrade) #债券T日退市，然后在T+1日获得的资金
      
      Position_Matrix <- Position_Matrix %>% filter(!is.na(SecuCode)) #去除掉退市债券
    }
    
    #计算当期交易时的总资产
    Curr_Asset_Total <- sum(Position_Matrix$Asset_AtTrade, na.rm = T) + Cash[cursor]
    
    ##每天先检查是否有不在目标持仓中的债券，若有则先处理（卖出）这些债券，然后再根据目标持仓是否有变动进行Rebalance交易
    Daily_DealList <- Position_Matrix %>% filter(Weight < Weight_eps) %>% #处理不在目标持仓中的债券
      mutate(Target_Asset = 0,
             Trade_Orders   = -Asset_AtTrade,
             LongShort = -1,
             Actual_Selling = ifelse(Sell_Ceiling>=-Trade_Orders,-Trade_Orders,Sell_Ceiling),
             Asset_AfTrade = Asset_AtTrade - Actual_Selling
             )
    
    Cash[cursor] <- Cash[cursor] + sum(Daily_DealList$Actual_Selling) #每日查仓，卖出非目标持仓的债券后的现金
    
    Position_Matrix <- Position_Matrix %>% filter(Weight >= Weight_eps)
    
    ## 处理完退市、不在目标持仓中的债券后，对目标持仓进行调仓
    if(sum(Position_Matrix$Weight_LT!=Position_Matrix$Weight)==0){
      #如果后一期持仓权重和前一期相同，则不调仓，只对已有持仓的价值进行重新评估
      #若Weight相等也进行调仓，则变成每天动态调仓
      Real_Position[[cursor]]<- Position_Matrix %>%
        mutate(Trade_Orders  = 0, #虽然Target_Asset可能有变化，但是不调仓，所以Trade_Orders和Actual_Buying都为0
               Actual_Buying = 0,
               Asset_AfTrade = Asset_AtTrade,
               #Target_Asset  = Weight*Curr_Asset_Total*Trade_Ratio) %>% #此处随未调仓，但是重新计算了Target_Asset，
               #所以会对后面计算目标持仓和真实持仓的偏差造成影响，还有一种是令Target_Asset=Asset_AfTrade
               Target_Asset  = Asset_AfTrade) %>%
        bind_rows(Daily_DealList, Delisted_List, AS0000) #合并上不在目标持仓中的券以及AS0000
      
    } else{#如果后一期持仓权重和前一期不同，则进行调仓
      #先计算调仓时的总资产，然后按照总资金按权重进行分配，得到目标持仓
      #最后根据目标持仓和原来持仓的差额进行先卖后买
      
      Real_Position[[cursor]]<- Position_Matrix %>% 
        mutate(Target_Asset = Weight*Curr_Asset_Total*Trade_Ratio) %>% #调仓时的目标持仓市值
        mutate(Trade_Orders = Target_Asset - Asset_AtTrade) %>% #实际需要交易的差额形成的交易单
        mutate(LongShort = ifelse(Trade_Orders>0,1,ifelse(Trade_Orders<0,-1,0))) %>% 
        mutate(Actual_Selling = ifelse(Sell_Ceiling>=abs(Trade_Orders),-Trade_Orders,Sell_Ceiling)*ifelse(LongShort==-1,1,0)) %>%#实际卖出额
        mutate(Cash_Remain = Curr_Asset_Total*(1-Trade_Ratio), 
               Cash_Usable = Cash[cursor] + sum(Actual_Selling) - Cash_Remain) %>% #卖出后可用的资金总额
        mutate(Cash_Usable = ifelse(Cash_Usable>=0,Cash_Usable,0)) %>%
        mutate(AnyLong = sum(LongShort>0),
               Buy_Weight = ifelse(AnyLong>0,ifelse(Trade_Orders>0,Trade_Orders,0)/sum(Trade_Orders[Trade_Orders>0]),0),
               Cash_AlloToBuy = Cash_Usable * Buy_Weight) %>%
        mutate(Actual_Buying = ifelse(Buy_Ceiling>Cash_AlloToBuy, Cash_AlloToBuy, Buy_Ceiling)) %>%
        mutate(Asset_AfTrade=Asset_AtTrade-Actual_Selling+Actual_Buying) %>% 
        bind_rows(Daily_DealList, Delisted_List, AS0000)
      
      Cash[cursor] <- Curr_Asset_Total - sum(Real_Position[[cursor]]$Asset_AfTrade, na.rm = T) #调仓日剩余现金
      
    }
  }
}

#合并每期的持仓
Total_Positions <- bind_rows(Real_Position) %>%
  select(Date,InnerCode,SecuCode,SecuAbbr,Weight_LT,Weight,Asset_BfTrade,Asset_AtTrade,Target_Asset,Trade_Orders,Actual_Selling,
         Cash_AlloToBuy,Actual_Buying,Asset_AfTrade,ReturnDaily,LongShort,Sell_Ceiling,Buy_Ceiling,
         everything()) %>%
  mutate(IfHold = ifelse(Asset_AfTrade>0 | InnerCode=='AS0000', 1, 0)) #是否是当期持仓的标志，若为0，则当期已全部卖出

#### 粗糙的一种对冲 ####
# 等持仓都出来后，按照每天交易后剩余的非目标持仓金额进行对冲，每天结算当日期货部分收益
# 这样买卖的时候每次总资产的计算会略有偏差，因为没有计算期货对冲部分的收益
# 但是相对的好处是比较容易计算，且容易设置是否对冲的开关
# 债券和国债期货的Duration计算时都用前一天的Duration

HedgeReturnAll <- Total_Positions %>% left_join(BondQuote_Cut_All[c('InnerCode','TradingDay','PrevDuration')],
                                  by = c('InnerCode','Date'='TradingDay')) %>%
  mutate(IfUnTarget = ifelse(Weight < Weight_eps, 1, 0)) %>%
  group_by(Date) %>%
  summarise(Untarget_Asset = sum(IfUnTarget*Asset_AfTrade, na.rm = T),
            Avg_Duration = weighted.mean(PrevDuration, Asset_AfTrade, na.rm =T),
            Avg_Duration = ifelse(is.nan(Avg_Duration),0,Avg_Duration )) %>%
  ungroup() %>%
  left_join(FutMainQuote, by = c('Date'='TradingDay')) %>%
  arrange(Date) %>%
  mutate(HedgeValue = Untarget_Asset*Avg_Duration/PrevDuration,
         PrevHedgeValue = dplyr::lag(HedgeValue),
         HedgeReturn = -PrevHedgeValue*DailyReturn,
         HedgeReturn = ifelse(is.na(HedgeReturn),0,HedgeReturn)
  ) %>%
  mutate(CumHedgeReturn = cumsum(HedgeReturn)) 


#### 回测分析 ####

#合并资金曲线,并计算最大回撤
Total_Asset <- Total_Positions %>%  group_by(Date) %>% summarise(Asset_Securities = sum(Asset_AfTrade,na.rm=T)) %>% 
  select(Date,Asset_Securities) %>% ungroup %>% 
  arrange(Date) %>% cbind(.,Cash) %>% #关联上现金
  mutate(Asset_Total = Asset_Securities + Cash) %>% 
  mutate(MaxDD = maxDrawdown(Asset_Total/Asset_Total[1]-1)) %>% #计算最大回撤
  #计算当日收益
  mutate(prev_Asset = lag(Asset_Total), Term_Return = Asset_Total/prev_Asset -1, Term_Return=ifelse(is.na(Term_Return),0,Term_Return))%>%
  select(-prev_Asset)

##计算IR, benchmark=0
IR <- Total_Asset %>% arrange(Date) %>%
  summarise(Years = round(as.numeric(EndDate_Backtest - StartDate_Backtest, units='days')/365, 2),
            denominator = round(sqrt(250), 4),
            AnnualizedReturn = round((Asset_Total[n()]/Asset_Total[1])^(1/Years) - 1, 4),
            AnnualizedVol = round(sd(Term_Return)*denominator,4)) %>%
  mutate(IR = round(AnnualizedReturn/AnnualizedVol, 2))

##绘图
Graph_Total_Asset <- ggplot(data = Total_Asset)+
  geom_line(aes(x=Date, y= Asset_Total/Asset_Total[1]))+
  labs(title = 'Cumulative return',
       x = 'Time Line',
       y = 'Cumulative Return')

Graph_Securities_Ratio <- ggplot(data = Total_Asset) +
  geom_line(aes(x=Date,y=Asset_Securities/Asset_Total)) +
  labs(title = 'Holding Ratio',
       x = 'Date',
       y = 'Holding Ratio')
  

##目标持仓和真实持仓的偏差
#此处的目标持仓和后面的目标持仓的定义略有差别
Total_Miss <-Total_Positions %>% filter(InnerCode != 'AS0000') %>% 
  mutate(IfTarget=ifelse(Weight>0,1,0)) %>% #是否是目标持仓的标志,1是0否
  mutate(Diff=Asset_AfTrade-Target_Asset) %>% #偏差=真实-目标
  group_by(Date,IfTarget) %>%
  summarise(Total_Diff=sum(abs(Diff),na.rm=T)) %>%
  left_join(Total_Asset, by = 'Date') %>%
  mutate(MissRate=Total_Diff/Asset_Total) %>% #偏差绝对值占当期总资产的比重
  select(Date,IfTarget,Total_Diff,Asset_Securities,Cash,Asset_Total,MissRate) %>% ungroup

Miss_InSide <- Total_Miss %>% filter(IfTarget==1) %>% 
  select(Date,IfTarget,Total_Diff,MissRate) %>%
  right_join(Total_Asset, by = 'Date') %>%
  mutate(Total_Diff = ifelse(is.na(Total_Diff),0,Total_Diff),
         MissRate = ifelse(is.na(MissRate),0,MissRate),
         IfTarget = ifelse(is.na(IfTarget),1,IfTarget)
         )

Miss_OutSide <- Total_Miss %>% filter(IfTarget==0) %>% 
  select(Date,IfTarget,Total_Diff,MissRate) %>%
  right_join(Total_Asset, by = 'Date') %>%
  mutate(Total_Diff = ifelse(is.na(Total_Diff),0,Total_Diff),
         MissRate = ifelse(is.na(MissRate),0,MissRate),
         IfTarget = ifelse(is.na(IfTarget),0,IfTarget)
         )

#绘图
Graph_Position_Miss <- ggplot(data = Miss_InSide) +
  geom_line(aes(x=Date, y = MissRate,colour="In"), show.legend = TRUE) +
  geom_line(data = Miss_OutSide, aes(x=Date,y=MissRate, colour="Out"), show.legend = TRUE) +
  ylim(0, 1) +
  labs(title = 'Holdings Positions Bias(Real vs Target)',
       x = 'TimeLine',
       y = 'Holding Bias Rate')+
  theme(legend.position = 'right')+
  scale_colour_manual(name = 'Side' , values=c('In'='blue','Out'='red'))

##每期目标持仓数量与实际持仓数量（债券种类）的差别
Real_Species<- Total_Positions %>% filter(IfHold==1) %>% group_by(Date) %>% summarise(N_Real=n()-1)
Target_Species <- Signal_Total %>% group_by(Date) %>% summarise(N_Tar=n()-1)
Species_Diff<- Real_Species %>% inner_join(Target_Species, by ='Date') %>% mutate(N_Diff = N_Real - N_Tar)

#绘图
Graph_Species_Diff <- ggplot(data=melt(Species_Diff, id=('Date'), variable.name='Group', value.name='N'))+
  geom_line(aes(x=Date,y=N,colour=Group))+
  labs(title = 'Holding Species Difference',
       x ='Date',
       y = 'Number')

##盈亏来源分析
#对目标券和非目标券进行分组
#非目标券定义：Weight_LT=0且Weight=0的券，其余的都为目标券

PnL <- Total_Positions %>% 
  mutate(IfTarget = ifelse(Weight_LT<Weight_eps & Weight<Weight_eps, 0, 1)) %>% #IfTarget=1,代表目标持仓的盈亏，其余代表非目标持仓的盈亏
  mutate(IfTarget = as.character(IfTarget)) %>%
  group_by(Date,IfTarget) %>%
  summarise(PnL = sum(Asset_AtTrade - Asset_BfTrade, na.rm = T),  #当期盈利
            Holdings = sum(Asset_AfTrade, na.rm = T))             #当期持仓
 
  # left_join(Total_Asset[c('Date','Asset_Total')], by = 'Date') %>%
  # mutate(PnL_Pct = PnL/Asset_Total)

Total_PnL <- PnL %>% dcast(Date~IfTarget, value.var='PnL') %>%
  rename(NonTarget_PnL=`0`,Target_PnL=`1`) %>%
  mutate(Target_PnL=ifelse(is.na(Target_PnL),0,Target_PnL),
         NonTarget_PnL=ifelse(is.na(NonTarget_PnL),0,NonTarget_PnL)) %>% 
  mutate(Total_Target_PnL=cumsum(Target_PnL),
         Total_NonTarget_PnL=cumsum(NonTarget_PnL)) 

Target_Position_Grouped <- PnL %>% dcast(Date~IfTarget, value.var='Holdings') %>%
  rename(NonTarget_Holdings=`0`,Target_Holdings=`1`) %>%
  mutate(Target_Holdings=ifelse(is.na(Target_Holdings),0,Target_Holdings),
         NonTarget_Holdings=ifelse(is.na(NonTarget_Holdings),0,NonTarget_Holdings))

#目标持仓比重  
Graph_Position_Ratio <- ggplot(melt(Target_Position_Grouped[c('Date','Target_Holdings','NonTarget_Holdings')] %>%
                                      rename(`1`=Target_Holdings,`0`=NonTarget_Holdings),
                                    id = 'Date', variable.name ='IfTarget', value.name = 'Holding') %>%
                                 mutate(IfTarget=as.character(IfTarget)))+
  geom_line(position='stack',aes(x=Date,y=Holding,colour=IfTarget))+
  #facet_grid(IfTarget~.)+
  labs(title = 'Total Holdings (Grouped)',
       x ='Date',
       y = 'Holdings')

#绘图
Graph_Daily_PnL <- ggplot(data=PnL)+
  geom_line(aes(x=Date,y=PnL,colour=IfTarget))+
  labs(title = 'Daily PnL',
       x ='Date',
       y = 'Profit')+
  facet_grid(IfTarget~.)

Graph_Total_Asset_Target <- ggplot(melt(Total_PnL[c('Date','Total_Target_PnL','Total_NonTarget_PnL')] %>%
             rename(`1`=Total_Target_PnL,`0`=Total_NonTarget_PnL),
            id = 'Date', variable.name ='IfTarget', value.name = 'Profit') %>%
         mutate(IfTarget=as.character(IfTarget)))+
  geom_line(aes(x=Date,y=Profit/Cash_Initial,colour=IfTarget))+
  facet_grid(IfTarget~.)+
  labs(title = 'Cumulative Return (Grouped)',
       x ='Date',
       y = 'Profit')

# 此处引入期货有所不同
Graph_Total_Asset_Mixed <- ggplot(melt(Total_PnL[c('Date','Total_Target_PnL','Total_NonTarget_PnL')], id = 'Date', variable.name ='IfTarget', value.name = 'Profit'))+
  geom_line(aes(x=Date,y=Profit/Cash_Initial,colour=IfTarget),size=1)+
  geom_line(data=Total_Asset,aes(x=Date, y= (Asset_Total-Cash_Initial)/Cash_Initial,colour='Total'),size=1.5)+
  geom_line(data=HedgeReturnAll,aes(x=Date,y=(CumHedgeReturn/Cash_Initial),colour='Future'))+
  geom_line(data=FutMainQuote,aes(x=TradingDay,y=CumReturn,colour='FutureIndex'))+
  theme(legend.position = 'bottom')+
  labs(title = 'Cumulative Return (Mixed)',
       x ='Date',
       y = 'Cumulative Return')


####  output results   ####
Path_BacktestResult <- paste0('BacktestResult/', Signal_Series, '/', Signal_Name, "/", Sys.Date(), "_", format(Sys.time(), "%H%M%S"))
dir.create(Path_BacktestResult, recursive = T)

BackTest_Info <- paste0("Signal Series: ", Signal_Series, "\n",
                        "Signal: ", Signal_Name, "\n",
                        "Report Generation Time: ", Sys.time(), "\n",
                        "Start Date: ", StartDate_Backtest, "\n",
                        "End Date: ", EndDate_Backtest, "\n",
                        "Rebalance Interval: ", Interval_Backtest, "\n",
                        "Initial Cash: ", Cash_Initial, "\n",
                        "Trade Ratio: ", Trade_Ratio, "\n",
                        "Buy Proportion: ", Buy_Proportion, "\n",
                        "Sell Proportion: ", Sell_Proportion, "\n")

write(BackTest_Info, file = paste0(Path_BacktestResult, paste0("/BackTest_Info_",Interval_Backtest,".txt")))

write_csv(IR, path = paste0(Path_BacktestResult, '/IR.csv'))

write_csv(Total_Asset, path = paste0(Path_BacktestResult, '/Total_Asset.csv'))

write_excel_csv(Total_Positions, path = paste0(Path_BacktestResult, '/Total_Positions.csv'))

#收益曲线
png(filename = paste0(Path_BacktestResult,
                      '/Graph_Total_Asset.png'),
    width = 700, height = 400)
print(Graph_Total_Asset)
dev.off()

png(filename = paste0(Path_BacktestResult,
                      '/Graph_Securities_Ratio.png'),
    width = 700, height = 400)
print(Graph_Securities_Ratio)
dev.off()

#持仓偏差曲线
png(filename = paste0(Path_BacktestResult,
                      '/Graph_Position_Miss.png'),
    width = 700, height = 400)
print(Graph_Position_Miss)
dev.off()

#持仓品种偏差
png(filename = paste0(Path_BacktestResult,
                      '/Graph_Species_Diff.png'),
    width = 700, height = 400)
print(Graph_Species_Diff)
dev.off()

#目标持仓比例偏差
png(filename = paste0(Path_BacktestResult,
                      '/Graph_Position_Ratio.png'),
    width = 700, height = 400)
print(Graph_Position_Ratio)
dev.off()

#每期损益
png(filename = paste0(Path_BacktestResult,
                      '/Graph_Daily_PnL.png'),
    width = 700, height = 400)
print(Graph_Daily_PnL)
dev.off()

#收益曲线（分组）
png(filename = paste0(Path_BacktestResult,
                      '/Graph_Total_Asset_Target.png'),
    width = 700, height = 400)
print(Graph_Total_Asset_Target)
dev.off()

#收益曲线（混合）
png(filename = paste0(Path_BacktestResult,
                      '/Graph_Total_Asset_Mixed.png'),
    width = 700, height = 400)
print(Graph_Total_Asset_Mixed)
dev.off()