[Manual] R语言quantstrat包使用手册

在阅读本文之前，你可能对以下扫盲贴有兴趣：
先来一段代码压压惊吧：
# _________________________________
# date: 2017-02-02
# version: 0.1
# backtesting-strategies learning
# 我们约定，注释中一个#表示一级标题；两个#表示二级标题
# 正文注释使用 # *** ￥%#……￥*@！***
# 你需要某些技术手段去翻墙，比如XXnet，好吧，其实没有人看这篇东西
# 因为要从google得到案例数据，yahoo已废
# 这个脚本完完全全是抄袭了《backtesting-strategies》中的前5章和第12章
# 你看，把这个脚本从头跑一遍就可以读掉了半本书
# 作者越往后写的文字越少，其实把代码全扒下来，剩下的东西也没什么营养了
# 我还会回来的
 
# ________________________________
# Chapter 1 Libraries
# *** 下面是你需要安装的包 ***
library(quantstrat)
library(data.table)
library(dplyr)
library(DT)
library(ggplot2)
library(htmltools)
library(htmlwidgets)
library(knitr)
library(lattice)
library(pander)
library(tidyr)
library(webshot)
# Chapter 2 Terminology
# *** BTO: Buy to Open (open long positions) ***
# *** BTC: Buy to close (close short positions) ***
# *** SL: Stop-limit order ***
# *** STO: Sell to open (open short positions) ***
# *** STC: Sell to close (close long positions) ***
# *** TS: Trailing-stop order ***
## 3.1 Settings and Variables
# *** 设定美帝的时区和货币种类 ***
Sys.setenv(TZ = "UTC")
currency('USD')
# *** 初始化起止日和账户资金 ***
init_date <- "2007-12-31"
start_date <- "2008-01-01"
end_date <- "2009-12-31"
init_equity <- 1e4 # $10,000
adjustment <- TRUE
## 3.2 Symbols
# *** 设定测试中所用的股票代码 ***
basic_symbols <- function() {
  symbols <- c(
    "IWM", # iShares Russell 2000 Index ETF
    "QQQ", # PowerShares QQQ TRust, Series 1 ETF
    "SPY" # SPDR S&P 500 ETF Trust
  )
}
enhanced_symbols <- function() {
  symbols <- c(
    basic_symbols(), 
    "TLT", # iShares Barclays 20+ Yr Treas. Bond ETF
    "XLB", # Materials Select Sector SPDR ETF
    "XLE", # Energy Select Sector SPDR ETF
    "XLF", # Financial Select Sector SPDR ETF
    "XLI", # Industrials Select Sector SPDR ETF
    "XLK", # Technology  Select Sector SPDR ETF
    "XLP", # Consumer Staples  Select Sector SPDR ETF
    "XLU", # Utilities  Select Sector SPDR ETF
    "XLV", # Health Care  Select Sector SPDR ETF
    "XLY" # Consumer Discretionary  Select Sector SPDR ETF
  )
}
global_symbols <- function() {
  symbols <- c(
    enhanced_symbols(), 
    "EFA", # iShares EAFE
    "EPP", # iShares Pacific Ex Japan
    "EWA", # iShares Australia
    "EWC", # iShares Canada
    "EWG", # iShares Germany
    "EWH", # iShares Hong Kong
    "EWJ", # iShares Japan
    "EWS", # iShares Singapore
    "EWT", # iShares Taiwan
    "EWU", # iShares UK
    "EWY", # iShares South Korea
    "EWZ", # iShares Brazil
    "EZU", # iShares MSCI EMU ETF
    "IGE", # iShares North American Natural Resources
    "IYR", # iShares U.S. Real Estate
    "IYZ", # iShares U.S. Telecom
    "LQD", # iShares Investment Grade Corporate Bonds
    "SHY" # iShares 42372 year TBonds
  )
}
## 3.3 checkBlotterUpdate()
# Guy Yollin, 2014
# http://www.r-programming.org/papers
checkBlotterUpdate <- function(port.st = portfolio.st, 
                               account.st = account.st, 
                               verbose = TRUE) {
  
  ok <- TRUE
  p <- getPortfolio(port.st)
  a <- getAccount(account.st)
  syms <- names(p$symbols)
  port.tot <- sum(
    sapply(
      syms, 
      FUN = function(x) eval(
        parse(
          text = paste("sum(p$symbols", 
                       x, 
                       "posPL.USD$Net.Trading.PL)", 
                       sep = "$")))))
  
  port.sum.tot <- sum(p$summary$Net.Trading.PL)
  
  if(!isTRUE(all.equal(port.tot, port.sum.tot))) {
    ok <- FALSE
    if(verbose) print("portfolio P&L doesn't match sum of symbols P&L")
  }
  
  initEq <- as.numeric(first(a$summary$End.Eq))
  endEq <- as.numeric(last(a$summary$End.Eq))
  
  if(!isTRUE(all.equal(port.tot, endEq - initEq)) ) {
    ok <- FALSE
    if(verbose) print("portfolio P&L doesn't match account P&L")
  }
  
  if(sum(duplicated(index(p$summary)))) {
    ok <- FALSE
    if(verbose)print("duplicate timestamps in portfolio summary")
    
  }
  
  if(sum(duplicated(index(a$summary)))) {
    ok <- FALSE
    if(verbose) print("duplicate timestamps in account summary")
  }
  return(ok)
}
# *** checkBlotterUpdate是用于检查账户和资产组合，如果返回FALSE，需要检查还有哪些账户和资产组合还在R的工作空间里 ***
# 4 Get Symbols
print(basic_symbols())
symbols <- basic_symbols()
# *** 我需要翻墙google才能使用getSymbols这个函数 ***
# *** getSymbols函数的作用是返回各个symbol（股票）的数据，每个symbol是一个数据框 ***
getSymbols(Symbols = symbols, 
           src = "google", 
           index.class = "POSIXct",
           from = start_date, 
           to = end_date, 
           adjust = adjustment)
# *** 观察拉取的数据 ***
head(IWM) 
tail(IWM)
chartSeries(SPY)
# 5 Basic Strategy
## 5.1 Strategy Setup
# *** 将需要交易的symbol用stock绑定到某工作空间 ***
stock(symbols, 
      currency = "USD", 
      multiplier = 1)
# *** 命名你的资产组合、账户名称和策略名称，以免你贵人多忘事 ***
portfolio.st <- "Port.Luxor"
account.st <- "Acct.Luxor"
strategy.st <- "Strat.Luxor"
# *** 保守起见，将历史上可能遗留在工作空间的资产组合和账户全部销毁 ***
rm.strat(portfolio.st)
rm.strat(account.st)
# *** 初始化资产组合和账户 ***
initPortf(name = portfolio.st,
          symbols = symbols,
          initDate = init_date)
initAcct(name = account.st,
         portfolios = portfolio.st,
         initDate = init_date,
         initEq = init_equity)
initOrders(portfolio = portfolio.st,
           symbols = symbols,
           initDate = init_date)
strategy(strategy.st, store = TRUE)
## 5.2 Add Indicators
# *** 我们以双均线策略为例继续我们的练习 ***
# *** 短期均线SMA(10)上穿长期均线SMA(30)表明短期内会上涨，应买入 ***
# *** 短期均线SMA(10)下穿长期均线SMA(30)表明短期内会下跌，应卖出 ***
# *** add.indicator是一个给我们策略添加指标的函数 ***
# *** 具体地理解就是给每个股票的数据框增加一列指标 *** 
# *** 比如SMA10,是最近10期价格的平均 ***
# *** name的输入应当是一个函数的名称，arguments是这个函数的输入参数 ***
# *** SMA这个函数来自TTR包 ***
# *** label标明了在数据集里增加的新的一列指标的名字 ***
# *** 可以看到最重要最复杂的是arguments ***
# *** 如果你想引用数据集中某一列，赋值给x,请参考 x=IWM$Close ***
# *** mktdata 是一个特殊的数据集，存储了我们的signal和indicator *** 
# *** 当apply你的策略时，你可以看到最后一个symbol的mktdata ***
# *** Cl 是close的缩写，暂时可以忽略它***
add.indicator(strategy = strategy.st,
              name = "SMA",
              arguments = list(x = quote(Cl(mktdata)), 
                               n = 10),
              label = "nFast")
add.indicator(strategy = strategy.st, 
              name = "SMA", 
              arguments = list(x = quote(Cl(mktdata)), 
                               n = 30), 
              label = "nSlow")
## 5.3 Add Signals
# *** 同上的解释 ***
add.signal(strategy = strategy.st,
           name="sigCrossover",
           arguments = list(columns = c("nFast", "nSlow"),
                            relationship = "gte"),
           label = "long")
add.signal(strategy = strategy.st,
           name="sigCrossover",
           arguments = list(columns = c("nFast", "nSlow"),
                            relationship = "lt"),
           label = "short")
## 5.4 Add Rules
# *** 同上的解释 ***
add.rule(strategy = strategy.st,
         name = "ruleSignal",
         arguments = list(sigcol = "long",
                          sigval = TRUE,
                          orderqty = 100,
                          ordertype = "stoplimit",
                          orderside = "long", 
                          threshold = 0.0005,
                          prefer = "High", 
                          TxnFees = -10, 
                          replace = FALSE),
         type = "enter",
         label = "EnterLONG")
add.rule(strategy.st,
         name = "ruleSignal",
         arguments = list(sigcol = "short",
                          sigval = TRUE,
                          orderqty = -100,
                          ordertype = "stoplimit",
                          threshold = -0.005, 
                          orderside = "short", 
                          replace = FALSE, 
                          TxnFees = -10, 
                          prefer = "Low"),
         type = "enter",
         label = "EnterSHORT")
add.rule(strategy.st, 
         name = "ruleSignal", 
         arguments = list(sigcol = "short", 
                          sigval = TRUE, 
                          orderside = "long", 
                          ordertype = "market", 
                          orderqty = "all", 
                          TxnFees = -10, 
                          replace = TRUE), 
         type = "exit", 
         label = "Exit2SHORT")
add.rule(strategy.st, 
         name = "ruleSignal", 
         arguments = list(sigcol = "long", 
                          sigval = TRUE, 
                          orderside = "short", 
                          ordertype = "market", 
                          orderqty = "all", 
                          TxnFees = -10, 
                          replace = TRUE), 
         type = "exit", 
         label = "Exit2LONG")
# 5.5 Apply Strategy
# *** 执行你的策略，得到回测结果 ***
cwd <- getwd()
setwd("./_data/")
results_file <- paste("results", strategy.st, "RData", sep = ".")
if( file.exists(results_file) ) {
  load(results_file)
} else {
  results <- applyStrategy(strategy.st, portfolios = portfolio.st)
  updatePortf(portfolio.st)
  updateAcct(account.st)
  updateEndEq(account.st)
  if(checkBlotterUpdate(portfolio.st, account.st, verbose = TRUE)) {
    save(list = "results", file = results_file)
    save.strategy(strategy.st)
  }
}
setwd(cwd)
# Chapter 12 Analyzing Results
## 12.1 Apply Strategy
# *** 这一小节实际上和前面重复了，可以不执行 ***
checkBlotterUpdate(portfolio.st, account.st, verbose = TRUE)
updatePortf(portfolio.st)
updateAcct(account.st)
updateEndEq(account.st)
## 12.2 Chart Positions
# *** 画出仓位走势图 ***
for(symbol in symbols) {
  chart.Posn(portfolio.st, Symbol = symbol, 
             TA = "add_SMA(n = 10, col = 4); add_SMA(n = 30, col = 2)")
}
## 12.3 Trade Statistics
tstats <- tradeStats(portfolio.st)
kable(t(tstats))
### 12.3.1 Trade Related
tab.trades <- tstats %>% 
  mutate(Trades = Num.Trades, 
         Win.Percent = Percent.Positive, 
         Loss.Percent = Percent.Negative, 
         WL.Ratio = Percent.Positive/Percent.Negative) %>% 
  select(Trades, Win.Percent, Loss.Percent, WL.Ratio)
# 我自己加上去的，不然下一句报错the table must have a header (column names)
rownames(tab.trades)=c('stock1','stock2','stock3') 
kable(t(tab.trades))
### 12.3.2 Profit Related
tab.profit <- tstats %>% 
  select(Net.Trading.PL, Gross.Profits, Gross.Losses, Profit.Factor)
kable(t(tab.profit))
### 12.3.3 Averages
tab.wins <- tstats %>% 
  select(Avg.Trade.PL, Avg.Win.Trade, Avg.Losing.Trade, Avg.WinLoss.Ratio)
kable(t(tab.wins))
### 12.3.4 Performance Summary
rets <- PortfReturns(Account = account.st)
rownames(rets) <- NULL
charts.PerformanceSummary(rets, colorset = bluefocus)
### 12.3.5 Per Trade Statistics
for(symbol in symbols) {
  pts <- perTradeStats(portfolio.st, Symbol = symbol)
  kable(pts, booktabs = TRUE, caption = symbol)
}
kable(pts)
### 12.3.6 Performance Statistics
tab.perf <- table.Arbitrary(rets,
                            metrics=c(
                              "Return.cumulative",
                              "Return.annualized",
                              "SharpeRatio.annualized",
                              "CalmarRatio"),
                            metricsNames=c(
                              "Cumulative Return",
                              "Annualized Return",
                              "Annualized Sharpe Ratio",
                              "Calmar Ratio"))
kable(tab.perf)
### 12.3.7 Risk Statistics
tab.risk <- table.Arbitrary(rets,
                            metrics=c(
                              "StdDev.annualized",
                              "maxDrawdown",
                              "VaR",
                              "ES"),
                            metricsNames=c(
                              "Annualized StdDev",
                              "Max DrawDown",
                              "Value-at-Risk",
                              "Conditional VaR"))
kable(tab.risk)
### 12.3.8 Buy and Hold Performance
rm.strat("buyHold")
# initialize portfolio and account
initPortf("buyHold", "SPY", initDate = init_date)
initAcct("buyHold", portfolios = "buyHold",
         initDate = init_date, initEq = init_equity)
# place an entry order
CurrentDate <- time(getTxns(Portfolio = portfolio.st, Symbol = "SPY"))[2]
equity = getEndEq("buyHold", CurrentDate)
ClosePrice <- as.numeric(Cl(SPY[CurrentDate,]))
UnitSize = as.numeric(trunc(equity/ClosePrice))
addTxn("buyHold", Symbol = "SPY", TxnDate = CurrentDate, TxnPrice = ClosePrice,
       TxnQty = UnitSize, TxnFees = 0)
# place an exit order
LastDate <- last(time(SPY))
LastPrice <- as.numeric(Cl(SPY[LastDate,]))
addTxn("buyHold", Symbol = "SPY", TxnDate = LastDate, TxnPrice = LastPrice,
       TxnQty = -UnitSize , TxnFees = 0)
updatePortf(Portfolio = "buyHold")
updateAcct(name = "buyHold")
updateEndEq(Account = "buyHold")
chart.Posn("buyHold", Symbol = "SPY")
### 12.3.9 Strategy vs. Market
rets <- PortfReturns(Account = account.st)
rets.bh <- PortfReturns(Account = "buyHold")
returns <- cbind(rets, rets.bh)
charts.PerformanceSummary(returns, geometric = FALSE, wealth.index = TRUE, 
                          main = "Strategy vs. Market")
### 12.3.10 Risk/Return Scatterplot
chart.RiskReturnScatter(returns, Rf = 0, add.sharpe = c(1, 2), 
                        main = "Return vs. Risk", colorset = c("red", "blue"))
### 12.3.11 Relative Performance
for(n in 1:(ncol(returns) - 1)) {
  chart.RelativePerformance(returns[, n], returns[, ncol(returns)], 
                            colorset = c("red", "blue"), lwd = 2, 
                            legend.loc = "topleft")
}
### 12.3.12 Portfolio Summary
#' Error
pf <- getPortfolio(portfolio.st)
xyplot(pf$summary, type = "h", col = 4)
### 12.3.13 Order Book
ob <- getOrderBook(portfolio.st)
### 12.3.14 Maximum Adverse Excursion
for(symbol in symbols) {
  chart.ME(Portfolio = portfolio.st, Symbol = symbol, type = "MAE", 
           scale = "percent")
}
### 12.3.15 Maximum Favorable Excursion
for(symbol in symbols) {
  chart.ME(Portfolio = portfolio.st, Symbol = symbol, type = "MFE", 
           scale = "percent")
}
## 12.4 Account Summary
a <- getAccount(account.st)
xyplot(a$summary, type = "h", col = 4)
### 12.4.1 Equity Curve
equity <- a$summary$End.Eq
plot(equity, main = "Equity Curve")
### 12.4.2 Account Performance Summary
ret <- Return.calculate(equity, method = "log")
charts.PerformanceSummary(ret, colorset = bluefocus, 
                          main = "Strategy Performance")
### 12.4.3 Cumulative Returns
rets <- PortfReturns(Account = account.st)
chart.CumReturns(rets, colorset = rich10equal, legend.loc = "topleft", 
                 main="SPDR Cumulative Returns")
### 12.4.4 Distribution Analysis
chart.Boxplot(rets, main = "SPDR Returns", colorset= rich10equal)
### 12.4.5 Annualized Returns
(ar.tab <- table.AnnualizedReturns(rets))
### 12.4.6 Performance Scatter Plot
max.risk <- max(ar.tab["Annualized Std Dev",])
max.return <- max(ar.tab["Annualized Return",])
chart.RiskReturnScatter(rets,
                        main = "SPDR Performance", colorset = rich10equal,
                        xlim = c(0, max.risk * 1.1), ylim = c(0, max.return))
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