r - 如何在 R 中使循环图动态/交互？

Question

我正在努力使我的平均方差图看起来更好看（交互式/动态）.. 根本不知道我应该如何使用 ggplot2 和 plotly 来做到这一点。有什么建议么。也许我必须在 ggplot2 中再自学一下。

我的一些数据作为 xts

tibble::tribble(
                  ~ACKB.BR........AGS.BR.......ABI.BR.....BPOST.BR.......COFB.BR.......COLR.BR........ELI.BR,
    "2016-05-31  0.0432320403  0.1163399079  0.053191613 -0.028769626  0.0432545905  0.0620524081  0.0400235221",
    "2016-06-30 -0.0548885132 -0.1477101053  0.032937977 -0.033980511 -0.0342467176 -0.0716291734  0.1089806345",
    "2016-07-29 -0.0176950484 -0.0287235657 -0.019557753  0.023596136  0.0557920959  0.0063540308 -0.0527623383",
    "2016-08-31 -0.0120092484  0.0290745587 -0.036860294 -0.026680880 -0.0111956891 -0.0137302145 -0.0502465026",
    "2016-09-30  0.1028518066  0.0482726448  0.049977505  0.056359657  0.0027172996  0.0260046039  0.0048596241",
    "2016-10-31  0.0729122009  0.0249498882 -0.103344822  0.006228007 -0.0356819002 -0.0080024626  0.0364915528",
    "2016-11-30 -0.0225206988  0.0583020592 -0.046570962 -0.125232125 -0.0238874811 -0.0261411446 -0.0119829618",
    "2016-12-30  0.0679063410  0.0680107330  0.025601801  0.115251765  0.0427061999 -0.0142601756  0.0665450090",
    "2017-01-31 -0.0454203118  0.0523796581 -0.043759445 -0.005777730 -0.0441785974 -0.0366983525 -0.0707458226",
    "2017-02-28  0.0348929722 -0.0944921026  0.073842995  0.048278881  0.0091478942 -0.0223056037  0.0484081773",
    "2017-03-31  0.1287357277  0.0223213145 -0.003389741 -0.061194075  0.0219465402  0.0395301722  0.0213821311",
    "2017-04-28  0.0196876401  0.0262007769  0.004859115 -0.000681393  0.0373482316  0.0244459733 -0.0149676863",
    "2017-05-31  0.0235088085  0.0124479040  0.024161724 -0.004406561  0.0178612484  0.0447553540  0.1019064399",
    "2017-06-30 -0.0359379769 -0.0190569449 -0.068753066 -0.024013036  0.0000000000 -0.0635468744 -0.0479345700",
    "2017-07-31  0.0451436662  0.0795519618  0.052631785  0.095102979 -0.0032512916  0.0272087957 -0.0003027797",
    "2017-08-31 -0.0759162533  0.0248260844 -0.022397032  0.013177824  0.0102515785 -0.0148798099  0.0086798688",
    "2017-09-29  0.0506373915  0.0192256218  0.017885894  0.072494444  0.0023062834 -0.0462686102 -0.0196117981",
    "2017-10-31 -0.0087630367  0.0474096143  0.037018793 -0.037176948  0.0027611720  0.0132687797  0.0165340157",
    "2017-11-30 -0.0020401675 -0.0086445708 -0.064593948  0.070617443 -0.0055071379  0.0132087334 -0.0080321909",
    "2017-12-29 -0.0109029249 -0.0138065578 -0.036918286  0.020654406  0.0129211362 -0.0253988436 -0.0303642955",
    "2018-01-31  0.0341027565  0.0448238921 -0.021368033  0.054953659 -0.0123007273  0.0279057607  0.0334027717",
    "2018-02-28 -0.0119920598  0.0143395007 -0.040158094  0.041075369 -0.0461255171 -0.0053846741  0.0484849227",
    "2018-03-29 -0.0411329003 -0.0275783610  0.020576232 -0.341463287  0.0193424249  0.0133092651 -0.0211945344",
    "2018-04-30  0.0534458189  0.0593423208 -0.050930820 -0.010348756  0.0436433040  0.0391807731  0.0314960709",
    "2018-05-31 -0.0107008859  0.0234594147 -0.030222404 -0.153300956  0.0144044738 -0.0259211946  0.0670915291",
    "2018-06-29  0.0102739967 -0.0043779825  0.078284834 -0.106930729  0.0302964846  0.0747745422 -0.0166052199",
    "2018-07-31  0.0576271353  0.0610969412  0.001040290 -0.004434567  0.0606060415  0.0460405131  0.0000000000",
    "2018-08-31 -0.0153846788 -0.0276989887 -0.067559636 -0.031180335  0.0008928954  0.0015649542  0.0093807639",
    "2018-09-28 -0.0240886321  0.0388067200 -0.068367818  0.071264337 -0.0419268510 -0.0238412101  0.0315985832",
    "2018-10-31 -0.0720478594 -0.0453466239 -0.134006760 -0.040772548 -0.0158287377  0.0527180324 -0.0054053789",
    "2018-11-30  0.0201292384 -0.0355123537  0.050870530 -0.149142482  0.0179754741  0.0997660745  0.0670290131",
    "2018-12-31 -0.0711768283 -0.0783302055 -0.146954385 -0.198867957  0.0083643321  0.1027640659 -0.0101867786",
    "2019-01-31  0.0599392756  0.0325699370  0.154419281 -0.001249163  0.0645160679  0.0080334093  0.0960548384",
    "2019-02-28 -0.0085898159  0.0677675249  0.029125022  0.003126951 -0.0112553745 -0.0006376248 -0.0203443320",
    "2019-03-29 -0.0288807955 -0.0076160253  0.090590811  0.198877773  0.0367775922  0.0510367762 -0.0015973463",
    "2019-04-30  0.0661708934  0.0930231770  0.060192428  0.113884583 -0.0371621796 -0.0248862153 -0.0384000305",
    "2019-05-31 -0.0439328885 -0.0230452514 -0.072088104 -0.157229669  0.0156460790  0.0351695751  0.0642529474",
    "2019-06-28 -0.0213683551  0.0464743156  0.072028466 -0.052511991  0.0106194596 -0.2333132316  0.0532527874",
    "2019-07-31 -0.0007579959  0.0647561020  0.169964229  0.011276407  0.0455341777 -0.0750981396  0.0617283542",
    "2019-08-30 -0.0015175132  0.0006163674 -0.053475365  0.077342789  0.0586264548 -0.0127199573  0.0959303043",
    "2019-09-30  0.0592704818  0.0447638575  0.014153171  0.059458200  0.0221518816  0.1078789652 -0.0053050712",
    "2019-10-31 -0.0150645113  0.0149370992 -0.175932327  0.065267153  0.0263158540 -0.0087510304  0.0306666458",
    "2019-11-29 -0.0058266447  0.0549962004  0.008771515  0.047317049  0.0120663011 -0.0569822975 -0.0297541848",
    "2019-12-31  0.0234431811 -0.0330396368  0.011687795  0.019115109 -0.0238449917 -0.0110638295  0.0546666395",
    "2020-01-31  0.0343592845 -0.0552391719 -0.059001505 -0.126601913  0.0977099692 -0.0286144122  0.0998735577",
    "2020-02-28 -0.1038061697 -0.1585291682 -0.258550167 -0.176522977  0.0027815828 -0.0737542550  0.0356321989",
    "2020-03-31 -0.0833977294 -0.0947947016 -0.202247102 -0.132019348 -0.1733703177  0.1781444629 -0.0110988342",
    "2020-04-30  0.0185341634 -0.1334741454  0.034840554 -0.008553648  0.0654362147  0.1085853224  0.1784511944",
    "2020-05-29  0.0231595962 -0.0663621855  0.054803155 -0.055389347  0.0078740488 -0.0043939705  0.0228635185",
    "2020-06-30 -0.0590136819  0.0370487756  0.057072438  0.015228426  0.0000000000 -0.0994850816 -0.0852272743",
    "2020-07-31 -0.0635739110  0.0098287252  0.051407658 -0.083333333 -0.0114379246  0.0120481837 -0.0455486655",
    "2020-08-31  0.0926605096  0.1051805736  0.058217716  0.524545455  0.0148760579  0.0694108129 -0.0227765626")


plot_mv_frontier <- function(mv, levmax_TP = 2, new_plot = TRUE,
                             add_CAL = TRUE) {
    weights <- mv$weights
    mu <- mv$mu
    sigma <- mv$sigma
    stds <- sqrt(diag(sigma))
    stat_p <- mv$stats
    
    w <- seq(0, levmax_TP, length.out = 100)
    w_mv <- sapply(w, function(x) (1-x)*weights[,"MVP"] + x*weights[,"TP"])
    
    i <- seq_along(w)
    mu_mv <- sapply(i, function(x) t(mu) %*% w_mv[,x])
    sigma_mv <- sqrt(sapply(i, function(x) t(w_mv[,x]) %*% sigma %*% w_mv[,x]))
    
    max_std <- max(c(stds,sigma_mv)) * 1.1 #set axis to get a nice graph
    max_mu <- max(c(mu,mu_mv)) * 1.1
    
    if(new_plot) plot(x = c(0, sigma_mv, max_std),
                      y = c(-0.01, mu_mv, max_mu),
                      type = 'n', xlab = "return volatility",
                      ylab = "expected return")
    lines(x = sigma_mv, y = mu_mv, type = 'l', col = "blue")
    
    if(new_plot) points(x = stds, y = mu, col = "red")
    if(new_plot) text(x = stds, y = mu, labels = assets, 
                      pos = 4, offset = 0.5, cex = 0.75, col = "darkgreen")
    points(x = stat_p[2,], y = stat_p[1,],
           col = "black")
    text(x = stat_p[2,], y = stat_p[1,], labels = colnames(stat_p),
         col = "black", pos = 2, offset = 0.5, cex = 0.75)
    grid()
    if(add_CAL) abline(a = 0, b = stat_p[3,1], lty = "dashed", col = "grey") #takes as b the sharpe of the TP
}

plot_mv_frontier(mv)（mv 计算如下：mv <- computeMVstats(R)）

computeMVstats <- function(R) { # R = matrix-like object containing returns
    mu <- colMeans(R) #average returns
    Sigma <- cov(R) #covariance matrix
    SigmaInv <- solve(Sigma) #inverse function using solve
    ones <- rep(1, length(mu))
    # the MV constants:
    a <- as.vector(t(mu) %*% SigmaInv %*% mu)
    b <- as.vector(t(mu) %*% SigmaInv %*% ones)
    c <- as.vector(t(ones) %*% SigmaInv %*% ones)
    
    # the Tangency Portfolio:
    w_TP <- SigmaInv %*% mu / b #weights
    mu_TP <- a/b #average return
    var_TP <- a/(b^2) #variance
    sh_TP <- mu_TP/sqrt(var_TP) #Sharpe ratio
    
    # the Minimum Variance Portfolio:
    w_MVP <- SigmaInv %*% ones / c
    mu_MVP <- b/c
    var_MVP <- 1/c
    sh_MVP <- mu_MVP/sqrt(var_MVP)
    
    # join together the stats of both MV portfolios:
    mv_stat <- matrix(data = c(mu_TP, sqrt(var_TP), sh_TP,  
                               #expected return, vol 
                               #, sharpe tangency portfolio
                               mu_MVP, sqrt(var_MVP), sh_MVP), 
                      #exactly the same 
                      #for MVP
                      nrow = 3)
    rownames(mv_stat) <- c("mean", "std", "Sharpe") #row names
    colnames(mv_stat) <- c("TP", "MVP") #first column and second column
    print(mv_stat)
    # join the portfolio weights for both portfolios:
    mv_weights <- cbind(w_TP, w_MVP)
    colnames(mv_weights) <- c("TP", "MVP")
    rownames(mv_weights) <- names(R) #names of the assets
    
    #Prepare function output as a named list:which is the one big one
    MVstats <- list(assets = names(R),
                    mu = mu, sigma = Sigma, #expected returns assets, cov matrix
                    stats = mv_stat,  #statistics
                    weights = mv_weights, #weights
                    mv_const = c(a = a, b = b, c = c)) #also need the constants
    
    return(MVstats)
}

如您所见，这是一个没有任何交互的正常图，并且标签有时会重叠。

标签： rggplot2plotplotly