HW06
許循閒
2019年4月1日
con = gzcon(url('http://www.systematicportfolio.com/sit.gz', 'rb'))
source(con)
close(con)連結資料的網址
load.packages('quantmod')## Warning: package 'quantmod' was built under R version 3.5.3##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Warning: package 'TTR' was built under R version 3.5.3##
## Attaching package: 'TTR'## The following object is masked _by_ '.GlobalEnv':
##
## DVI## Version 0.4-0 included new data defaults. See ?getSymbols.bt.simple <- function(data, signal)
{
signal <- lag(signal,1)
signal <- na.locf(signal, na.rm = FALSE)
signal[is.na(signal)] = 0
ret <- ROC(Cl(data), type="discrete")
ret[1] = 0
bt <- list()
bt$ret <- ret * signal
bt$equity <- cumprod(1 + bt$ret)
return(bt)
}load.packages(載入套件)
資料為時間序列的形式
signal是買賣的指標向量
lag(signal,1) - 延遲一個時間序列資料
計算接近收益的回報
ROC()計算n個週期內序列的(變化率)變化
計算統計數據 bt <- list() - 將結果存成list
data <- getSymbols('SPY', src = 'yahoo', from = '2000-01-01', to = '2018-12-31', auto.assign = F)## 'getSymbols' currently uses auto.assign=TRUE by default, but will
## use auto.assign=FALSE in 0.5-0. You will still be able to use
## 'loadSymbols' to automatically load data. getOption("getSymbols.env")
## and getOption("getSymbols.auto.assign") will still be checked for
## alternate defaults.
##
## This message is shown once per session and may be disabled by setting
## options("getSymbols.warning4.0"=FALSE). See ?getSymbols for details.##
## WARNING: There have been significant changes to Yahoo Finance data.
## Please see the Warning section of '?getSymbols.yahoo' for details.
##
## This message is shown once per session and may be disabled by setting
## options("getSymbols.yahoo.warning"=FALSE).signal <- rep(1, nrow(data))
buy.hold <- bt.simple(data, signal)
buy.hold$equity<-as.xts(buy.hold$equity)
head(buy.hold$equity)## SPY.Close
## 2000-01-03 1.0000000
## 2000-01-04 0.9608939
## 2000-01-05 0.9626128
## 2000-01-06 0.9471422
## 2000-01-07 1.0021487
## 2000-01-10 1.0055866tail(buy.hold$equity)## SPY.Close
## 2018-12-20 1.699493
## 2018-12-21 1.655006
## 2018-12-24 1.611276
## 2018-12-26 1.692686
## 2018-12-27 1.705681
## 2018-12-28 1.703481buy.hold$ret<-as.xts(buy.hold$ret)
head(buy.hold$ret)## SPY.Close
## 2000-01-03 0.000000000
## 2000-01-04 -0.039106145
## 2000-01-05 0.001788909
## 2000-01-06 -0.016071429
## 2000-01-07 0.058076225
## 2000-01-10 0.003430532sma <- SMA(Cl(data), 200)
head(sma, 201)## SMA
## 2000-01-03 NA
## 2000-01-04 NA
## 2000-01-05 NA
## 2000-01-06 NA
## 2000-01-07 NA
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## 2000-10-16 144.7205
## 2000-10-17 144.6670signal <- ifelse(Cl(data) > sma, 1, 0)
head(signal, 201)## SPY.Close
## 2000-01-03 NA
## 2000-01-04 NA
## 2000-01-05 NA
## 2000-01-06 NA
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## 2000-10-16 0
## 2000-10-17 0sma.cross <- bt.simple(data, signal)
names(sma.cross)## [1] "ret" "equity"tail(sma.cross$equity)## SPY.Close
## 2018-12-20 2.09504
## 2018-12-21 2.09504
## 2018-12-24 2.09504
## 2018-12-26 2.09504
## 2018-12-27 2.09504
## 2018-12-28 2.09504測試bt.simple函數
data <- getSymbols(‘SPY’, src = ‘yahoo’, from = ‘2000-01-01’, to = ‘2018-12-31’, auto.assign = F) - 加載雅虎財經的歷史價格
載入買入並持有的資訊
sma函數 - 表示移動平均線對近期數值可賦予更高的權重
head(sma, 201) 看201筆資料
MA cross - 移動平均線
Cl - 得到的收盤價
signal <- ifelse(Cl(data) > sma, 1, 0) - 如果價格大於移動意味著“買”
head/tail - 前/後幾筆資料(沒有指定為6筆)
dates <- '2000::2018'
buy.hold.equity <- buy.hold$equity[dates] / as.double(buy.hold$equity[dates][1])
sma.cross.equity <- sma.cross$equity[dates] / as.double(sma.cross$equity[dates][1])
chartSeries(buy.hold.equity, TA = c(addTA(sma.cross.equity, on=1, col='red')),
theme ='white', yrange = range(buy.hold.equity, sma.cross.equity) )
創一個圖表顯示2000年-2009年的策略性能
chartSeries()圖表工具，用於創建標準財務圖表，給出類似對象的時間序列
buy.hold.equity - 買進持有的權益
addTA - 設置顏色並把技術指標加入圖表中
yrange - y 的範圍
library(magrittr)
strategy.sma<-merge(buy.hold.equity, sma.cross.equity) %>%
set_colnames(c("BH", "SMA"))
head(strategy.sma,30)## BH SMA
## 2000-01-03 1.0000000 1
## 2000-01-04 0.9608939 1
## 2000-01-05 0.9626128 1
## 2000-01-06 0.9471422 1
## 2000-01-07 1.0021487 1
## 2000-01-10 1.0055866 1
## 2000-01-11 0.9935539 1
## 2000-01-12 0.9836700 1
## 2000-01-13 0.9969918 1
## 2000-01-14 1.0105283 1
## 2000-01-18 1.0025784 1
## 2000-01-19 1.0107434 1
## 2000-01-20 0.9952729 1
## 2000-01-21 0.9931242 1
## 2000-01-24 0.9649760 1
## 2000-01-25 0.9759347 1
## 2000-01-26 0.9681994 1
## 2000-01-27 0.9643318 1
## 2000-01-28 0.9342501 1
## 2000-01-31 0.9596046 1
## 2000-02-01 0.9690589 1
## 2000-02-02 0.9699183 1
## 2000-02-03 0.9845294 1
## 2000-02-04 0.9804466 1
## 2000-02-07 0.9789428 1
## 2000-02-08 0.9922647 1
## 2000-02-09 0.9714221 1
## 2000-02-10 0.9733563 1
## 2000-02-11 0.9535883 1
## 2000-02-14 0.9591749 1tail(strategy.sma)## BH SMA
## 2018-12-20 1.699493 2.09504
## 2018-12-21 1.655006 2.09504
## 2018-12-24 1.611276 2.09504
## 2018-12-26 1.692686 2.09504
## 2018-12-27 1.705681 2.09504
## 2018-12-28 1.703481 2.09504library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.5.3strategy.sma.df<-fortify(strategy.sma, melt=TRUE)
head(strategy.sma.df)## Index Series Value
## 1 2000-01-03 BH 1.0000000
## 2 2000-01-04 BH 0.9608939
## 3 2000-01-05 BH 0.9626128
## 4 2000-01-06 BH 0.9471422
## 5 2000-01-07 BH 1.0021487
## 6 2000-01-10 BH 1.0055866p<-ggplot(strategy.sma.df, aes(x = Index, y = Value))+
geom_line(aes(color = Series), size = 0.5) +
scale_x_date(date_labels = "%Y/%m")
p
執行magrittr
讀出買進持有的資訊
用ggplot2繪圖
執行ggplot2
用geom設定圖表資訊
fill - 改變條形圖的填充色
load.packages('quantmod')
tickers <- spl('SPY')
data1 <- new.env()
getSymbols(tickers, src = 'yahoo', from = '2000-01-01', to = '2018-12-31', env = data1, auto.assign = T)## [1] "SPY"bt.prep(data1, align='keep.all')
names(data1)## [1] "prices" "dates" "weight" ".getSymbols"
## [5] "symbolnames" "execution.price" "SPY"head(data1$prices)## SPY
## 2000-01-03 145.4375
## 2000-01-04 139.7500
## 2000-01-05 140.0000
## 2000-01-06 137.7500
## 2000-01-07 145.7500
## 2000-01-10 146.2500tail(data1$prices)## SPY
## 2018-12-20 247.17
## 2018-12-21 240.70
## 2018-12-24 234.34
## 2018-12-26 246.18
## 2018-12-27 248.07
## 2018-12-28 247.75示例代碼使用回測來實現上述策略
load.packages(載入套件)
data1 <- new.env() - 資料在environment中
bt.prep函數 - 合併並對齊數據環境中的所有符號
data1$weight[] <- 1
buy.hold <- bt.run.share(data1, clean.signal=F, trade.summary = TRUE)## Latest weights :
## SPY
## 2018-12-28 100
##
## Performance summary :
## CAGR Best Worst
## 2.8 14.5 -9.8 buy.hold <- bt.run(data1)## Latest weights :
## SPY
## 2018-12-28 100
##
## Performance summary :
## CAGR Best Worst
## 2.8 14.5 -9.8 prices<-data1$prices
sma.1 <- bt.apply(data1, function(x) { SMA(Cl(x), 200) } )
head(sma.1, 201)## SPY
## 2000-01-03 NA
## 2000-01-04 NA
## 2000-01-05 NA
## 2000-01-06 NA
## 2000-01-07 NA
## 2000-01-10 NA
## 2000-01-11 NA
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## 2000-08-03 NA
## 2000-08-04 NA
## 2000-08-07 NA
## 2000-08-08 NA
## 2000-08-09 NA
## 2000-08-10 NA
## 2000-08-11 NA
## 2000-08-14 NA
## 2000-08-15 NA
## 2000-08-16 NA
## 2000-08-17 NA
## 2000-08-18 NA
## 2000-08-21 NA
## 2000-08-22 NA
## 2000-08-23 NA
## 2000-08-24 NA
## 2000-08-25 NA
## 2000-08-28 NA
## 2000-08-29 NA
## 2000-08-30 NA
## 2000-08-31 NA
## 2000-09-01 NA
## 2000-09-05 NA
## 2000-09-06 NA
## 2000-09-07 NA
## 2000-09-08 NA
## 2000-09-11 NA
## 2000-09-12 NA
## 2000-09-13 NA
## 2000-09-14 NA
## 2000-09-15 NA
## 2000-09-18 NA
## 2000-09-19 NA
## 2000-09-20 NA
## 2000-09-21 NA
## 2000-09-22 NA
## 2000-09-25 NA
## 2000-09-26 NA
## 2000-09-27 NA
## 2000-09-28 NA
## 2000-09-29 NA
## 2000-10-02 NA
## 2000-10-03 NA
## 2000-10-04 NA
## 2000-10-05 NA
## 2000-10-06 NA
## 2000-10-09 NA
## 2000-10-10 NA
## 2000-10-11 NA
## 2000-10-12 NA
## 2000-10-13 NA
## 2000-10-16 144.7205
## 2000-10-17 144.6670data1$weight[] <- NA
data1$weight[] <- iif(prices >= sma.1, 1, 0)
sma.1.cross <- bt.run(data1, trade.summary=T) ## Latest weights :
## SPY
## 2018-12-28 0
##
## Performance summary :
## CAGR Best Worst
## 4 4.4 -4.2 plotbt.custom.report(sma.1.cross, buy.hold)
models<-list("SMA"= sma.1.cross, "BH" = buy.hold)
strategy.performance.snapshoot(sma.1.cross, T) 
## NULLstrategy.performance.snapshoot(buy.hold, T) 
## NULLstrategy.performance.snapshoot(models, T)
## NULL代碼策略
bt.run計算數據$ weight matrix指定的策略的權益曲線
數據$Sweight矩陣將權重(信號)保持在開倉/平倉位置
bt.apply函數將用戶給定的函數應用於數據環境中的每個符號
data1$weight[] <- NA (更新權重matirx)
放入模型(model)進行分析