MACD簡單策略（R語言代碼）

在《R的極客理想》中看到一個MACD的簡單策略。以爲還行，記下來之後優化用。我添加了一些註釋幫助你們理解證券交易和R語言代碼。

安裝須要的包

一次都裝上，備用

> pkgs <- c("quantmod","zoo","xts","TTR","PerformanceAnalytics","ggplot2")
> install.packages(pkgs)

  There is a binary version available but the source version is later:
    binary source needs_compilation
zoo  1.8-6  1.8-7              TRUE

  Binaries will be installed
……
trying URL 'https://mirrors.tongji.edu.cn/CRAN/bin/windows/contrib/3.6/ggplot2_3.2.1.zip'
Content type 'application/zip' length 3974945 bytes (3.8 MB)
downloaded 3.8 MB

package ‘quantmod’ successfully unpacked and MD5 sums checked
package ‘zoo’ successfully unpacked and MD5 sums checked
package ‘xts’ successfully unpacked and MD5 sums checked
package ‘TTR’ successfully unpacked and MD5 sums checked
package ‘PerformanceAnalytics’ successfully unpacked and MD5 sums checked
package ‘ggplot2’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\chang\AppData\Local\Temp\RtmpygTUIm\downloaded_packages
>

下載股價數據

載入quantmod包，數據源是Yahoo，固然外國網站，你懂的，有時候不是能用的。下載全球五大指數數據。

指數名稱	英文簡稱
標普500	GSPC
日經225	N225
恆生指數	HSI
富時新加坡指STI數	STI
上證指數	SSE

> library(quantmod)
載入須要的程輯包：xts
載入須要的程輯包：zoo

載入程輯包：‘zoo’

The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric

Registered S3 method overwritten by 'xts':
  method     from
  as.zoo.xts zoo 
載入須要的程輯包：TTR
Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 
Version 0.4-0 included new data defaults. See ?getSymbols.
> options(stringsAsFactors = FALSE)
> # 指定股票代碼，從yahoo下載
> # 開始日期，自動判斷最近交易日，不用本身費心
> symbols <- c("^GSPC","^N225","^HSI","^STI","000001.SS")
> suppressWarnings(getSymbols(symbols,src = "yahoo",from="2012-01-01"))
> 
‘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.

[1] "^GSPC"     "^N225"     "^HSI"      "^STI"      "000001.SS"
> head(`000001.SS`)
           000001.SS.Open 000001.SS.High 000001.SS.Low 000001.SS.Close 000001.SS.Volume 000001.SS.Adjusted
2012-01-04       2211.995       2217.520      2168.644        2169.390            49200           2169.390
2012-01-05       2160.897       2183.404      2145.556        2148.452            58800           2148.452
2012-01-06       2148.147       2164.322      2132.626        2163.395            50600           2163.395
2012-01-09       2164.741       2226.218      2148.453        2225.890            76800           2225.890
2012-01-10       2221.832       2288.628      2218.275        2285.744           109600           2285.744
2012-01-11       2282.913       2290.644      2265.185        2276.046            84400           2276.046

處理股價數據

> # 準備用收益率替代指數價格，方便對比比較
> df <- merge(GSPC$GSPC.Adjusted,N225$N225.Adjusted,HSI$HSI.Adjusted,STI$STI.Adjusted,`000001.SS`$`000001.SS.Adjusted`)
> names(df) <- c("GSPC","N225","HSI","STI","SSE")
> head(df)
              GSPC    N225      HSI     STI      SSE
2012-01-03 1277.06      NA 18877.41 2688.36       NA
2012-01-04 1277.30 8560.11 18727.31 2711.02 2169.390
2012-01-05 1281.06 8488.71 18813.41 2713.02 2148.452
2012-01-06 1277.81 8390.35 18593.06 2715.59 2163.395
2012-01-09 1280.70      NA 18865.72 2691.28 2225.890
2012-01-10 1292.08 8422.26 19004.28 2719.83 2285.744
> g <- ggplot(aes(x = Index,y = Value,color = Series),data = fortify(df,melt = TRUE)) + geom_line(size = 1) + scale_y_continuous(breaks = seq(1000,30000,4000)) + ggtitle("Gloabel Index")
> g

很明顯，直接的價格沒法比較，因此咱們都轉化爲收益率。順便算一下年收益率。

> library(PerformanceAnalytics)

載入程輯包：‘PerformanceAnalytics’

The following object is masked from ‘package:graphics’:

    legend

> ret_df <- Return.calculate(df,method="discrete")
> chart.CumReturns(ret_df,legend.loc = "topleft",main="Cumulative Daily Returns for Gloable Index")
> Return.annualized(ret_df)
                       GSPC      N225        HSI        STI       SSE
Annualized Return 0.1157303 0.1510661 0.05739009 0.03016343 0.0374123
>

指數	年收益率
GSPC	0.1157303
N225	0.1510661
HSI	0.05739009
STI	0.03016343
SSE	0.0374123

MCAD策略

這個策略很簡單，就是一根30日簡單均線的趨勢跟蹤策略。

># MACD模型
>MACD <- function(dt,n=30){
+ names(dt) <- c('close')
+ #均線
+ dat <- na.locf(dt) #na.locf刪掉數據裏面的全部NA
+ dat$ma <- SMA(dat$close,n)
+ #買賣信號
+ sig_buy <- which(dat$ma-dat$close>0)
+ sig_sell <- which(dat$ma-dat$close<0)
+ sig_buy <- sig_buy[which(diff(sig_buy)>1)]
+ sig_sell <- sig_sell[which(diff(sig_sell)>1)]
+ if(first(sig_sell)<first(sig_buy)) sig_sell <- sig_sell[-1]
+ if(last(sig_sell)<last(sig_buy)) sig_buy <- sig_buy[-length(sig_buy)]
+ trade_dat <- do.call(rbind.data.frame,apply(cbind(sig_buy,sig_sell), 1, function(row){
+ dt[row[1]:row[2],]
+     }))
+ ret_trade <- Return.calculate(trade_dat,method="discrete")
+ return(ret_trade)
+ }
> #用MACD計算一遍歷史數據，計算每日收益
> macd_ret <- lapply(df,function(col) MACD(col,30))
> #算年收益率
> t(do.call(rbind.data.frame,lapply(macd_ret, Return.annualized)))
           GSPC      N225       HSI        STI       SSE
close 0.2082891 0.1823697 0.1879596 0.03251933 0.1429962
>

指數	指數年收益率	MACD策略年收益率
GSPC	0.1157303	0.2082891
N225	0.1510661	0.1823697
HSI	0.05739009	0.1879596
STI	0.03016343	0.03251933
SSE	0.0374123	0.1429962

改善明顯啊 。

從2012年到2020年，竟然美股達到了年化20%的收益率。巴菲特啊。日本和香港也能18%。A股票也很爭氣，14%至關漂亮啊。

補充說明

• na.locf刪掉數據裏面的全部NA，查看詳情。幾個指數的交易時間不同，因此裏面一堆NA
• SMA在R的TTR包裏面的簡單均線，也就是平日K線裏面的MA，EMA（Exponential moving average）指數平均，WMA（Weighted moving average）加權移動均線，查看詳情
• diff() 向量計算對應值的差別，案例
• first()和last() 取第一個值，取最後一個值，案例
• lapply函數看這篇 R點石成金三人組：apply,lapply,sapply
• Return.calculate 計算回報率，查看詳情
• quantmod能夠看使用quantmod包獲取股票信息並作簡單數據分析

感謝閱讀，歡迎關注和留言> 感謝閱讀，歡迎關注和留言 量化投資與期貨外匯散仙，基金保險水平也拿的出手