ANLY515_HW1_Code_and_Plots.pdf

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Feb 20, 2024

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ANLY515HW1 01/29/2024 Plot the data for each security to check for missing data 1. Citi (Financials) data.C <- read.csv ( "C.csv" , header = TRUE ) head (data.C) ## Date Open High Low Close Adj.Close Volume ## 1 2011-01-03 47.8 49.0 47.8 49.0 43.09877 65798900 ## 2 2011-01-04 49.1 49.4 48.3 49.0 43.09877 58340430 ## 3 2011-01-05 48.8 50.0 48.8 49.7 43.71446 66673580 ## 4 2011-01-06 50.0 50.5 49.3 49.5 43.53856 71529750 ## 5 2011-01-07 49.6 50.0 48.4 49.4 43.45059 68069720 ## 6 2011-01-10 49.2 49.3 48.7 49.1 43.18671 46153210 class (data.C) ## [1] "data.frame" date <- as.Date (data.C $ Date, format = "%Y-%m-%d" ) head (date) ## [1] "2011-01-03" "2011-01-04" "2011-01-05" "2011-01-06" "2011-01-07" ## [6] "2011-01-10" data.C <- cbind (date, data.C[, -1 ]) data.C <- data.C[ order (data.C $ date), ] class (data.C) ## [1] "data.frame" library (xts) ## Warning: package ’xts’ was built under R version 4.0.3 ## Loading required package: zoo ## Warning: package ’zoo’ was built under R version 4.0.3 ## ## Attaching package: ’zoo’ 1

## The following objects are masked from ’package:base’: ## ## as.Date, as.Date.numeric data.C <- xts (data.C[, 2 : 7 ], order.by = data.C[, 1 ]) class (data.C) ## [1] "xts" "zoo" names (data.C) ## [1] "Open" "High" "Low" "Close" "Adj.Close" "Volume" names (data.C) <- paste ( c ( "C.Open" , "C.High" , "C.Low" , "C.Close" , "C.Adjusted" , "C.Volume" )) head (data.C) ## C.Open C.High C.Low C.Close C.Adjusted C.Volume ## 2011-01-03 47.8 49.0 47.8 49.0 43.09877 65798900 ## 2011-01-04 49.1 49.4 48.3 49.0 43.09877 58340430 ## 2011-01-05 48.8 50.0 48.8 49.7 43.71446 66673580 ## 2011-01-06 50.0 50.5 49.3 49.5 43.53856 71529750 ## 2011-01-07 49.6 50.0 48.4 49.4 43.45059 68069720 ## 2011-01-10 49.2 49.3 48.7 49.1 43.18671 46153210 plot (data.C $ C.Close) data.C$C.Close 2011−01−03 / 2015−12−31 2

Jan 03 2011 Jan 03 2012 Jan 02 2013 Jan 02 2014 Jan 02 2015 Dec 31 2015 dim (data.C) ## [1] 1258 6 summary (data.C) ## Index C.Open C.High C.Low ## Min. :2011-01-03 Min. :22.56 Min. :24.10 Min. :21.40 ## 1st Qu.:2012-04-02 1st Qu.:36.70 1st Qu.:37.16 1st Qu.:36.25 ## Median :2013-07-04 Median :47.88 Median :48.25 Median :47.4 7 ## Mean :2013-07-02 Mean :44.29 Mean :44.74 Mean :43.80 ## 3rd Qu.:2014-10-01 3rd Qu.:51.65 3rd Qu.:52.11 3rd Qu.:51.12 ## Max. :2015-12-31 Max. :60.29 Max. :60.95 ## C.Close C.Adjusted C.Volume ## Min. :23.11 Min. :20.34 Min. : 4671200 ## 1st Qu.:36.71 1st Qu.:32.33 1st Qu.: 17777825 ## Median :47.81 Median :42.20 Median : 26960950 ## Mean :44.27 Mean :39.05 Mean : 31295855 ## 3rd Qu.:51.65 3rd Qu.:45.59 3rd Qu.: 39677100 ## Max. :60.34 Max. :53.33 Max. :180960580 Max. :59.73 2. Blackrock (Financials) 3 30 40 50 60 30 40 50 60

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data.BLK <- read.csv ( "BLK.csv" , header = TRUE ) head (data.BLK) ## Date Open High Low Close Adj.Close Volume ## 1 2011-01-03 191.52 195.00 189.53 190.19 145.7494 1085200 ## 2 2011-01-04 191.26 191.50 188.71 190.04 145.6343 794400 ## 3 2011-01-05 190.03 192.84 189.25 192.00 147.1364 925300 ## 4 2011-01-06 192.68 192.79 188.36 189.93 145.5501 727300 ## 5 2011-01-07 191.52 191.52 185.46 188.36 144.3469 886200 ## 6 2011-01-10 188.11 191.54 187.43 191.17 146.5003 898300 class (data.BLK) ## [1] "data.frame" date <- as.Date (data.BLK $ Date, format = "%Y-%m-%d" ) head (date) ## [1] "2011-01-03" "2011-01-04" "2011-01-05" "2011-01-06" "2011-01-07" ## [6] "2011-01-10" data.BLK <- cbind (date, data.BLK[, -1 ]) data.BLK <- data.BLK[ order (data.BLK $ date), ] class (data.BLK) ## [1] "data.frame" library (xts) data.BLK <- xts (data.BLK[, 2 : 7 ], order.by = data.BLK[, 1 ]) class (data.BLK) ## [1] "xts" "zoo" names (data.BLK) ## [1] "Open" "High" "Low" "Close" "Adj.Close" "Volume" names (data.BLK) <- paste ( c ( "BLK.Open" , "BLK.High" , "BLK.Low" , "BLK.Close" , "BLK.Adjusted" , "BLK.Volume" )) head (data.BLK) ## BLK.Open BLK.High BLK.Low BLK.Close BLK.Adjusted BLK.Volume ## 2011-01-03 191.52 195.00 189.53 190.19 145.7494 1085200 ## 2011-01-04 191.26 191.50 188.71 190.04 145.6343 794400 ## 2011-01-05 190.03 192.84 189.25 192.00 147.1364 925300 ## 2011-01-06 192.68 192.79 188.36 189.93 145.5501 727300 4

2011−01−03 / 2015−12−31 ## 2011-01-07 191.52 191.52 185.46 188.36 144.3469 886200 ## 2011-01-10 188.11 191.54 187.43 191.17 146.5003 898300 plot (data.BLK $ BLK.Close) data.BLK$BLK.Close Jan 03 2011 Jan 03 2012 Jan 02 2013 Jan 02 2014 Jan 02 2015 Dec 31 2015 dim (data.BLK) ## [1] 1258 6 summary (data.BLK) ## Index BLK.Open BLK.High BLK.Low ## Min. :2011-01-03 Min. :140.1 Min. :145.4 Min. :137.0 ## 1st Qu.:2012-04-02 1st Qu.:190.5 1st Qu.:191.9 1st Qu.:188.7 ## Median :2013-07-04 Median :269.2 Median :271.8 Median :266.7 ## Mean :2013-07-02 Mean :260.3 Mean :262.7 Mean :257.7 ## Max. Max. 5 150 200 250 300 350 150 200 250 300 350

## 3rd Qu.:2014-10-01 3rd Qu.:319.6 3rd Qu.:321.6 3rd Qu.:316.5 ## Max. :2015-12-31 Max. :380.5 Max. :382.8 Max. :377.8 ## BLK.Close BLK.Adjusted BLK.Volume ## Min. :141.8 Min. :111.1 Min. : 123500 ## 1st Qu.:190.0 1st Qu.:149.7 1st Qu.: 494750 ## Median :269.8 Median :223.1 Median : 659550 ## Mean :260.3 Mean :216.5 Mean : 752086 ## 3rd Qu.:318.8 3rd Qu.:272.2 3rd Qu.: 854975 :380.3 Max. :326.5 :22888000 3. Starbucks (Consumer Discretionary) data.SBUX <- read.csv ( "SBUX.csv" , header = TRUE ) head (data.SBUX) ## Date Open High Low Close Adj.Close Volume ## 1 2011-01-03 16.245 16.710 16.230 16.625 14.08596 12764600 ## 2 2011-01-04 16.625 16.645 16.220 16.240 13.75975 13306000 ## 3 2011-01-05 16.130 16.420 16.125 16.175 13.70468 11501800 ## 4 2011-01-06 16.185 16.250 15.895 15.980 13.53946 13253400 ## 5 2011-01-07 16.020 16.430 15.930 16.390 13.88684 19791400 ## 6 2011-01- 10 16.240 16.475 16.065 16.385 13.88261 15540600 class (data.SBUX) ## [1] "data.frame" date <- as.Date (data.SBUX $ Date, format = "%Y-%m-%d" ) head (date) ## [1] "2011-01-03" "2011-01-04" "2011-01-05" "2011-01-06" "2011-01-07" ## [6] "2011-01-10" data.SBUX <- cbind (date, data.SBUX[, -1 ]) data.SBUX <- data.SBUX[ order (data.SBUX $ date), ] class (data.SBUX) ## [1] "data.frame" library (xts) data.SBUX <- xts (data.SBUX[, 2 : 7 ], order.by = data.SBUX[, 1 ]) class (data.SBUX) ## [1] "xts" "zoo" 6

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2011−01−03 / 2015−12−31 names (data.SBUX) ## [1] "Open" "High" "Low" "Close" "Adj.Close" "Volume" names (data.SBUX) <- paste ( c ( "SBUX.Open" , "SBUX.High" , "SBUX.Low" , "SBUX.Close" , "SBUX.Adjusted" , "SBUX.Volume" )) head (data.SBUX) ## SBUX.Open SBUX.High SBUX.Low SBUX.Close SBUX.Adjusted SBUX.Volume ## 2011-01-03 16.245 16.710 16.230 16.625 14.08596 127646 00 ## 2011-01-04 16.625 16.645 16.220 16.240 13.75975 133060 00 ## 2011-01-05 16.130 16.420 16.125 16.175 13.70468 115018 00 ## 2011-01-06 16.185 16.250 15.895 15.980 13.53946 132534 00 ## 2011-01-07 16.020 16.430 15.930 16.390 13.88684 197914 00 ## 2011-01-10 16.240 16.475 16.065 16.385 13.88261 155406 00 plot (data.SBUX $ SBUX.Close) data.SBUX$SBUX.Close ## Max. Max. 7 20 30 40 50 60 20 30 40 50 60

Jan 03 2011 Jan 03 2012 Jan 02 2013 Jan 02 2014 Jan 02 2015 Dec 31 2015 dim (data.SBUX) ## [1] 1258 6 summary (data.SBUX) ## Index SBUX.Open SBUX.High SBUX.Low ## Min. :2011-01-03 Min. :15.77 Min. :15.87 Min. :15.38 ## 1st Qu.:2012-04-02 1st Qu.:24.20 1st Qu.:24.39 1st Qu.:24.00 ## Median :2013-07-04 Median :33.49 Median :33.92 Median :33.2 0 ## Mean :2013-07-02 Mean :33.87 Mean :34.17 Mean :33.55 ## 3rd Qu.:2014-10-01 3rd Qu.:39.56 3rd Qu.:39.79 3rd Qu.:39.26 ## Max. :2015-12-31 Max. :63.69 Max. :64.00 ## SBUX.Close SBUX.Adjusted SBUX.Volume ## Min. :15.77 Min. :13.36 Min. : 2204300 ## 1st Qu.:24.20 1st Qu.:20.91 1st Qu.: 7526825 ## Median :33.60 Median :29.53 Median : 9999700 ## Mean :33.87 Mean :29.98 Mean :11647537 ## 3rd Qu.:39.56 3rd Qu.:35.14 3rd Qu.:13912800 :63.51 Max. :57.56 :72206200 Max. :62.97 4. Nike (Consumer Discretionary) data.NKE <- read.csv ( "NKE.csv" , header = TRUE ) head (data.NKE) ## Date Open High Low Close Adj.Close Volume ## 1 2011-01-03 21.4575 21.6450 21.3150 21.5225 19.05092 8566400 ## 2 2011-01-04 21.4000 21.4375 20.9375 20.9925 18.58179 13797600 ## 3 2011-01-05 20.9125 21.2075 20.8775 21.1300 18.70350 11598800 ## 4 2011-01-06 21.1125 21.1250 20.8900 20.9400 18.53532 8057200 ## 5 2011-01-07 20.9250 20.9875 20.8175 20.8825 18.48442 8174400 ## 6 2011-01-10 20.8125 21.1225 20.7650 21.0425 18.62605 8914400 class (data.NKE) ## [1] "data.frame" date <- as.Date (data.NKE $ Date, format = "%Y-%m-%d" ) head (date) ## [1] "2011-01-03" "2011-01-04" "2011-01-05" "2011-01-06" "2011-01-07" ## [6] "2011-01-10" 8

2011−01−03 / 2015−12−31 data.NKE <- cbind (date, data.NKE[, -1 ]) data.NKE <- data.NKE[ order (data.NKE $ date), ] class (data.NKE) ## [1] "data.frame" library (xts) data.NKE <- xts (data.NKE[, 2 : 7 ], order.by = data.NKE[, 1 ]) class (data.NKE) ## [1] "xts" "zoo" names (data.NKE) ## [1] "Open" "High" "Low" "Close" "Adj.Close" "Volume" names (data.NKE) <- paste ( c ( "NKE.Open" , "NKE.High" , "NKE.Low" , "NKE.Close" , "NKE.Adjusted" , "NKE.Volume" )) head (data.NKE) ## NKE.Open NKE.High NKE.Low NKE.Close NKE.Adjusted NKE.Volume ## 2011-01-03 21.4575 21.6450 21.3150 21.5225 19.05092 85664 00 ## 2011-01-04 21.4000 21.4375 20.9375 20.9925 18.58179 137976 00 ## 2011-01-05 20.9125 21.2075 20.8775 21.1300 18.70350 115988 00 ## 2011-01-06 21.1125 21.1250 20.8900 20.9400 18.53532 80572 00 ## 2011-01-07 20.9250 20.9875 20.8175 20.8825 18.48442 81744 00 ## 2011-01-10 20.8125 21.1225 20.7650 21.0425 18.62605 89144 00 plot (data.NKE $ NKE.Close) ## Max. Max. 9

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data.NKE$NKE.Close Jan 03 2011 Jan 03 2012 Jan 02 2013 Jan 02 2014 Jan 02 2015 Dec 31 2015 dim (data.NKE) ## [1] 1258 6 summary (data.NKE) ## Index NKE.Open NKE.High NKE.Low ## Min. :2011-01-03 Min. :18.80 Min. :19.15 Min. :17.36 ## 1st Qu.:2012-04-02 1st Qu.:24.04 1st Qu.:24.29 1st Qu.:23.85 ## Median :2013-07-04 Median :31.75 Median :31.94 Median :31.5 6 ## Mean :2013-07-02 Mean :34.98 Mean :35.27 Mean :34.69 ## 3rd Qu.:2014-10-01 3rd Qu.:43.46 3rd Qu.:43.98 3rd Qu.:43.19 ## Max. :2015-12-31 Max. :68.12 Max. :68.19 ## NKE.Close NKE.Adjusted NKE.Volume ## Min. :18.86 Min. :16.76 Min. : 2439600 ## 1st Qu.:24.08 1st Qu.:21.70 1st Qu.: 5995850 ## Median :31.71 Median :29.09 Median : 7691600 ## Mean :34.99 Mean :32.27 Mean : 9038119 ## 3rd Qu.:43.57 3rd Qu.:40.63 3rd Qu.:10177200 :67.17 Max. :63.33 :86339600 Max. :66.67 10 20 30 40 50 60 20 30 40 50 60

2011−01−03 / 2015−12−31 5. Netflix (Technology) data.NFLX <- read.csv ( "NFLX.csv" , header = TRUE ) head (data.NFLX) ## Date Open High Low Close Adj.Close Volume ## 1 2011-01-03 25.00000 25.83857 24.78571 25.48714 25.48714 39956000 ## 2 2011-01-04 25.90714 26.41429 25.47000 25.91000 25.91000 44065700 ## 3 2011-01-05 25.87857 26.11286 25.53000 25.67571 25.67571 31799600 ## 4 2011-01-06 25.16857 25.67000 25.09286 25.42714 25.42714 31991400 ## 5 2011-01-07 25.52429 25.79286 25.19714 25.61429 25.61429 22155000 ## 6 2011-01- 10 25.67143 26.85000 25.59714 26.84000 26.84000 43674400 class (data.NFLX) ## [1] "data.frame" date <- as.Date (data.NFLX $ Date, format = "%Y-%m-%d" ) head (date) ## [1] "2011-01-03" "2011-01-04" "2011-01-05" "2011-01-06" "2011-01-07" ## [6] "2011-01-10" data.NFLX <- cbind (date, data.NFLX[, -1 ]) data.NFLX <- data.NFLX[ order (data.NFLX $ date), ] class (data.NFLX) ## [1] "data.frame" library (xts) data.NFLX <- xts (data.NFLX[, 2 : 7 ], order.by = data.NFLX[, 1 ]) class (data.NFLX) ## [1] "xts" "zoo" names (data.NFLX) ## [1] "Open" "High" "Low" "Close" "Adj.Close" "Volume" names (data.NFLX) <- paste ( c ( "NFLX.Open" , "NFLX.High" , "NFLX.Low" , "NFLX.Close" , "NFLX.Adjusted" , "NFLX.Volume" )) head (data.NFLX) ## NFLX.Open NFLX.High NFLX.Low NFLX.Close NFLX.Adjusted NFLX.Volume ## 2011-01-03 25.00000 25.83857 24.78571 25.48714 25.48714 399560 00 ## Max. Max. 11

## 2011-01-04 25.90714 26.41429 25.47000 25.91000 25.91000 440657 00 ## 2011-01-05 25.87857 26.11286 25.53000 25.67571 25.67571 317996 00 ## 2011-01-06 25.16857 25.67000 25.09286 25.42714 25.42714 319914 00 ## 2011-01-07 25.52429 25.79286 25.19714 25.61429 25.61429 221550 00 ## 2011-01-10 25.67143 26.85000 25.59714 26.84000 26.84000 436744 00 12

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2011−01−03 / 2015−12−31 plot (data.NFLX $ NFLX.Close) data.NFLX$NFLX.Close Jan 03 2011 Jan 03 2012 Jan 02 2013 Jan 02 2014 Jan 02 2015 Dec 31 2015 dim (data.NFLX) ## [1] 1258 6 summary (data.NFLX) ## Index NFLX.Open NFLX.High NFLX.Low ## Min. :2011-01-03 Min. : 7.713 Min. : 7.926 Min. : 7.544 ## 1st Qu.:2012-04-02 1st Qu.: 17.487 1st Qu.: 17.750 1st Qu.: 16.911 ## Median :2013-07-04 Median : 37.449 Median : 38.154 Median : 36.959 ## Mean :2013-07-02 Mean : 44.831 Mean : 45.597 Mean : 44.055 ## 3rd Qu.:2014-10-01 3rd Qu.: 61.532 3rd Qu.: 62.638 3rd Qu.: 61.071 ## Max. :2015-12-31 Max. :131.190 Max. :133.270 ## NFLX.Close NFLX.Adjusted NFLX.Volume ## Min. : 7.686 Min. : 7.686 Min. : 3531300 Max. :126.390 ## Max. 13 20 40 60 80 100 120 20 40 60 80 100 120

## 1st Qu.: 17.409 1st Qu.: 17.409 1st Qu.: 15593550 ## Median : 37.648 Median : 37.648 Median : 22808800 ## Mean : 44.855 Mean : 44.855 Mean : 30124902 ## 3rd Qu.: 61.936 3rd Qu.: 61.936 3rd Qu.: 34767425 :130.930 Max. :130.930Max. :315541800 6. Apple (Technology) data.AAPL <- read.csv ( "AAPL.csv" , header = TRUE ) head (data.AAPL) ## Date Open High Low Close Adj.Close Volume ## 1 2011-01-03 11.63000 11.79500 11.60143 11.77036 10.13856 445138400 ## 2 2011-01-04 11.87286 11.87500 11.71964 11.83179 10.19147 309080800 ## 3 2011-01-05 11.76964 11.94071 11.76786 11.92857 10.27484 255519600 ## 4 2011-01-06 11.95429 11.97321 11.88929 11.91893 10.26653 300428800 ## 5 2011-01-07 11.92821 12.01250 11.85357 12.00429 10.34005 311931200 ## 6 2011-01- 10 12.10107 12.25821 12.04179 12.23036 10.53478 448560000 class (data.AAPL) ## [1] "data.frame" date <- as.Date (data.AAPL $ Date, format = "%Y-%m-%d" ) head (date) ## [1] "2011-01-03" "2011-01-04" "2011-01-05" "2011-01-06" "2011-01-07" ## [6] "2011-01-10" data.AAPL <- cbind (date, data.AAPL[, -1 ]) data.AAPL <- data.AAPL[ order (data.AAPL $ date), ] class (data.AAPL) ## [1] "data.frame" library (xts) data.AAPL <- xts (data.AAPL[, 2 : 7 ], order.by = data.AAPL[, 1 ]) class (data.AAPL) ## [1] "xts" "zoo" names (data.AAPL) ## [1] "Open" "High" "Low" "Close" "Adj.Close" "Volume" names (data.AAPL) <- paste ( c ( "AAPL.Open" , "AAPL.High" , "AAPL.Low" , "AAPL.Close" , "AAPL.Adjusted" , "AAPL.Volume" )) head (data.AAPL) 14

2011−01−03 / 2015−12−31 ## AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Adjusted AAPL.Volume ## 2011-01-03 11.63000 11.79500 11.60143 11.77036 10.13856 4451384 00 ## 2011-01-04 11.87286 11.87500 11.71964 11.83179 10.19147 3090808 00 ## 2011-01-05 11.76964 11.94071 11.76786 11.92857 10.27484 2555196 00 ## 2011-01-06 11.95429 11.97321 11.88929 11.91893 10.26653 3004288 00 ## 2011-01-07 11.92821 12.01250 11.85357 12.00429 10.34005 3119312 00 ## 2011-01-10 12.10107 12.25821 12.04179 12.23036 10.53478 4485600 00 plot (data.AAPL $ AAPL.Close) data.AAPL$AAPL.Close Jan 03 2011 Jan 03 2012 Jan 02 2013 Jan 02 2014 Jan 02 2015 Dec 31 2015 dim (data.AAPL) ## [1] 1258 6 summary (data.AAPL) 15 15 20 25 30 15 20 25 30

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## Index AAPL.Open AAPL.High AAPL.Low ## Min. :2011-01-03 Min. :11.31 Min. :11.35 Min. :11.09 ## 1st Qu.:2012-04-02 1st Qu.:15.42 1st Qu.:15.55 1st Qu.:15.27 ## Median :2013-07-04 Median :19.69 Median :19.84 Median :19.4 1 ## Mean :2013-07-02 Mean :20.72 Mean :20.91 Mean :20.51 ## 3rd Qu.:2014-10-01 3rd Qu.:25.31 3rd Qu.:25.55 3rd Qu.:25.13 ## Max. :2015-12-31 Max. :33.62 Max. :33.63 ## AAPL.Close AAPL.Adjusted AAPL.Volume ## Min. :11.26 Min. : 9.70 Min. :5.219e+07 ## 1st Qu.:15.37 1st Qu.:13.48 1st Qu.:2.211e+08 ## Median :19.61 Median :17.33 Median :3.266e+08 ## Mean :20.71 Mean :18.44 Mean :3.771e+08 ## 3rd Qu.:25.31 3rd Qu.:22.93 3rd Qu.:4.722e+08 ## Max. :33.25 Max. :30.38 Max. :1.881e+09 Max. :32.85 16

Plot candlestick charts for one stock from each sector Apple wk <- data.AAPL data.weekly <- to.weekly (wk) data.weekly[ c ( 1 : 3 , nrow (data.weekly)), ] ## wk.Open wk.High wk.Low wk.Close wk.Volume wk.Adjusted ## 2011-01-07 11.63000 12.01250 11.60143 12.00429 1622098800 311931200 ## 2011-01-14 12.10107 12.44571 12.04179 12.44571 1800878800 308840000 ## 2011-01-21 11.76857 12.45000 11.64286 11.66857 4535801200 754401200 ## 2015-12-31 26.89750 27.35750 26.20500 26.31500 495046000 163649200 library (quantmod) ## Warning: package ’quantmod’ was built under R version 4.0.3 ## Loading required package: TTR ## Warning: package ’TTR’ was built under R version 4.0.3 ## Registered S3 method overwritten by ’quantmod’: ## method from ## as.zoo.data.frame zoo OHLC <- data.weekly[ - 1 , -6 ] AAPL.ohlc <- as.quantmod.OHLC (OHLC, col.names = c ( "Open" , "High" , "Low" , "Close" , "Volume" )) AAPL.ohlc[ c ( 1 : 3 , nrow (AAPL.ohlc)), ] ## OHLC.Open OHLC.High OHLC.Low OHLC.Close OHLC.Volume ## 2011-01-14 12.10107 12.44571 12.04179 12.44571 1800878800 ## 2011-01-21 11.76857 12.45000 11.64286 11.66857 4535801200 ## 2011-01-28 11.67393 12.34286 11.6685712.00357 2505510000 ## 2015-12-31 26.89750 27.35750 26.20500 26.31500 495046000 chartSeries (AAPL.ohlc, name = "AAPL OHLC" , theme = "white.mono" ) 17

AAPL OHLC [2011−01−14/2015−12−31] Jan 14 2011 Jul 06 2012 Jan 03 2014 Jul 02 2015 Citi wk1 <- data.C data.weekly1 <- to.weekly (wk1) data.weekly1[ c ( 1 : 3 , nrow (data.weekly1)), ] 18

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## wk1.Open wk1.High wk1.Low wk1.Close wk1.Volume wk1.Adjusted ## 2011-01-07 47.80 50.50 47.80 49.40 330412380 680697 20 ## 2011-01-14 49.20 51.50 48.70 51.30 308851080 1036343 70 ## 2011-01-21 49.30 49.50 47.20 48.90 380564110 655077 70 ## 2015-12-31 52.57 53.22 51.75 51.75 38961900 112818 00 library (quantmod) OHLC1 <- data.weekly1[ - 1 , -6 ] C.ohlc <- as.quantmod.OHLC (OHLC1, col.names = c ( "Open" , "High" , "Low" , "Close" , "Volume" )) C.ohlc[ c ( 1 : 3 , nrow (C.ohlc)), ] ## OHLC1.Open OHLC1.High OHLC1.Low OHLC1.Close OHLC1.Volume ## 2011-01-14 49.20 51.50 48.70 51.30 3088510 80 ## 2011-01-21 49.30 49.50 47.20 48.90 3805641 10 ## 2011-01-28 49.10 49.20 47.20 47.20 2155085 40 ## 2015-12-31 52.57 53.22 51.75 51.75 389619 00 ## 2011-01-14 16.240 16.475 16.030 16.350 705610 00 ## 2011-01-21 16.365 16.860 16.235 16.600 704356 00 ## 2011-01-28 16.580 16.890 15.760 15.865 1272306 00 ## 2015-12-31 60.020 61.400 59.580 60.030 188493 00 chartSeries (C.ohlc, name = "C OHLC" , theme = "white.mono" ) C OHLC [2011−01−14/2015−12−31] 19

Jan 14 2011 Jul 06 2012 Jan 03 2014 Jul 02 2015 Starbucks wk2 <- data.SBUX data.weekly2 <- to.weekly (wk2) data.weekly2[ c ( 1 : 3 , nrow (data.weekly2)), ] 20

## wk2.Open wk2.High wk2.Low wk2.Close wk2.Volume wk2.Adjusted ## 2011-01-07 16.245 16.710 15.895 16.39 70617200 197914 00 ## 2011-01-14 16.240 16.475 16.030 16.35 70561000 112658 00 ## 2011-01-21 16.365 16.860 16.235 16.60 70435600 231298 00 ## 2015-12-31 60.020 61.400 59.580 60.03 18849300 49609 00 library (quantmod) OHLC2 <- data.weekly2[ - 1 , -6 ] SBUX.ohlc <- as.quantmod.OHLC (OHLC2, col.names = c ( "Open" , "High" , "Low" , "Close" , "Volume" )) SBUX.ohlc[ c ( 1 : 3 , nrow (SBUX.ohlc)), ] ## OHLC2.Open OHLC2.High OHLC2.Low OHLC2.Close OHLC2.Volume chartSeries (SBUX.ohlc, name = "SBUX OHLC" , theme = "white.mono" ) SBUX OHLC [2011−01−14/2015−12−31] Jan 14 2011 Jul 06 2012 Jan 03 2014 Jul 02 2015 Develop a plot comparing the capital gains by sector AAPL .2013 <- subset (data.AAPL[, 4 ], + index (data.AAPL) >= "2013-01-01" & + index (data.AAPL) <= "2013-12-31" ) 21

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NFLX .2013 <- subset (data.NFLX[, 4 ], + index (data.NFLX) >= "2013-01-01" & + index (data.NFLX) <= "2013-12-31" ) BLK .2013 <- subset (data.BLK[, 4 ], + index (data.BLK) >= "2013-01-01" & + index (data.BLK) <= "2013-12-31" ) C .2013 <- subset (data.C[, 4 ], + index (data.C) >= "2013-01-01" & + index (data.C) <= "2013-12-31" ) SBUX .2013 <- subset (data.SBUX[, 4 ], + index (data.SBUX) >= "2013-01-01" & + index (data.SBUX) <= "2013-12-31" ) NKE .2013 <- subset (data.NKE[, 4 ], + index (data.NKE) >= "2013-01-01" & + index (data.NKE) <= "2013-12-31" ) 22

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Close.Prices <- cbind (AAPL .2013 $ AAPL.Close, NFLX .2013 $ NFLX.Close, BLK .2013 $ BLK.Close, C .2013 $ C.Close, SBUX .2013 $ SBUX.Close, NKE .2013 $ NKE.Close) Close.Prices[ c ( 1 : 3 , nrow (Close.Prices)), ] ## AAPL.Close NFLX.Close BLK.Close C.Close SBUX.Close NKE.Close ## 2013- 01-02 19.60821 13.14429 212.77 41.25 27.500 25.9 20 ## 2013- 01-03 19.36071 13.79857 213.35 41.39 27.685 26.1 85 ## 2013- 01-04 18.82143 13.71143 218.03 42.43 27.845 26.4 40 ## 2013- 12-31 20.03643 52.59571 316.47 52.11 39.195 39.3 20 multi.df <- cbind ( index (Close.Prices), data.frame (Close.Prices)) names (multi.df) <- paste ( c ( "date" , "AAPL" , "NFLX" , "BLK" , "C" , "SBUX" , "NKE" )) rownames (multi.df) <- seq ( 1 , nrow (multi.df), 1 ) multi.df[ c ( 1 : 3 , nrow (multi.df)), ] ## date AAPL NFLX BLK C SBUX NKE ## 1 2013-01-02 19.60821 13.14429 212.77 41.25 27.500 25.920 ## 2 2013-01-03 19.36071 13.79857 213.35 41.39 27.685 26.185 ## 3 2013-01-04 18.82143 13.71143 218.03 42.43 27.845 26.440 ## 252 2013-12-31 20.03643 52.59571 316.47 52.11 39.195 39.320 multi.df $ Tech.idx <- (multi.df $ AAPL + multi.df $ NFLX) / (multi.df $ AAPL[ 1 ] + multi.df $ NFLX[ 1 ]) multi.df $ Financials.idx <- (multi.df $ C + multi.df $ BLK) / (multi.df $ C[ 1 ] + multi.df $ BLK[ 1 ]) multi.df $ Consumer.idx <- (multi.df $ SBUX + multi.df $ NKE) / (multi.df $ SBUX[ 1 ] + multi.df $ NKE[ 1 ]) options ( digits = 5 ) multi.df[ c ( 1 : 3 , nrow (multi.df)), ] ## date AAPL NFLX BLK C SBUX NKE Tech.idx Financials.idx ## 1 2013-01-02 19.608 13.144 212.77 41.25 27.500 25.920 1.00000 1.0000 ## 2 2013-01-03 19.361 13.799 213.35 41.39 27.685 26.185 1.01242 1.0028 ## 3 2013-01-04 18.821 13.711 218.03 42.43 27.845 26.440 0.99329 1.0254 ## 252 2013-12-31 20.036 52.596 316.47 52.11 39.195 39.320 2.21761 ## Consumer.idx ## 1 1.0000 ## 2 1.0084 ## 3 1.0162 ## 252 1.4698 1.4510 plot ( x = multi.df $ date, y = multi.df $ Tech.idx, type = "l" , xlab = "Date" , ylab = "Value of Investment ($)" , col = "red" , lty = 1 , lwd = 2 , main = "Value of $1 Investment in Tech, Financials, and Consumer Discretionary sectors January 2, 2013 - January 31, 2013" , cex.main = 0.8 ) 23

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lines ( x = multi.df $ date, y = multi.df $ Financials.idx, col = "blue" , lty = 2 , lwd = 1 ) lines ( x = multi.df $ date, y = multi.df $ Consumer.idx, col = "green" , lty = 2 , lwd = 1 ) abline ( h = 1 , lty = 1 , col = "black" ) legend ( "topleft" , c ( "Tech" , "Financials" , "Consumer" ), col = c ( "red" , "blue" , "green" ), lty = c ( 1 , 2 , 2 , 1 ), lwd = c ( 2 , 1 , 1 , 1 )) Value of $1 Investment in Tech, Financials, and Consumer Discretionary sectors January 2, 2013 − January 31, 2013 Date Calculate the Rolling 50-Day and 200-Day Average Price AAPL .2015 <- subset (data.AAPL[, 4 ], + index (data.AAPL) >= "2015-01-01" & + index (data.AAPL) <= "2015-12-31" ) NFLX .2015 <- subset (data.NFLX[, 4 ], + index (data.NFLX) >= "2015-01-01" & + index (data.NFLX) <= "2015-12-31" ) Close.Prices .2015 <- cbind (AAPL .2015 $ AAPL.Close, NFLX .2015 $ NFLX.Close) Close.Prices .2015 [ c ( 1 : 3 , nrow (Close.Prices)), ] ## AAPL.Close NFLX.Close ## 2015-01-02 27.332 49.849 ## 2015-01-05 26.562 47.311 24 Jan Mar May Jul Sep Nov Jan 1.0 1.2 1.4 1.6 1.8 2.0 2.2 Value of Investment ($) Tech Financials Consumer

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## 2015-01-06 26.565 46.501 ## 2015-12-31 26.315 114.380 Close.Prices .2015 $ Tech.Close <- Close.Prices .2015 $ AAPL.Close + Close.Prices .2015 $ NFLX Close.Prices .2015 $ sma50 <- rollmeanr (Close.Prices .2015 $ AAPL.Close + Close.Prices .2015 $ NFLX.Close, k = 50 ) Close.Prices .2015 $ sma200 <- rollmeanr (Close.Prices .2015 $ AAPL.Close + Close.Prices .2015 $ NFLX.Close, k = 200 ) Close.Prices .2015 [ c ( 1 : 5 , nrow (Close.Prices .2015 ))] ## AAPL.Close NFLX.Close Tech.Close sma50 sma200 ## 2015-01-02 27.332 49.849 77.181 NA NA ## 2015-01-05 26.562 47.311 73.874 NA NA ## 2015-01-06 26.565 46.501 73.066 NA NA ## 2015-01-07 26.938 46.743 73.680 NA NA ## 2015-01-08 27.973 47.780 75.752 NA NA ## 2015-12-31 26.315 114.380 140.695 145.01 130.02 y.range <- range (Close.Prices .2015 , na.rm = TRUE ) y.range ## [1] 25.78 160.69 Janua Tech Sector− Simple Moving Average January 1, 2015 − December 31, 2015 25 Jan Mar May Jul Sep Nov Jan 40 60 80 120 160 Price ($) Tech Sector Price Day Moving Average 50− 200−Day Moving Average par ( mfrow = c ( 1 , 1 )) plot ( x = index (Close.Prices .2015 ), xlab = "Date" , y = Close.Prices .2015 $ Tech.Close, ylim = y.range, ylab = "Price ($)" , type = "l" , main = "Tech Sector- Simple Moving Average cex.main = 0.9 ) lines ( x = index (Close.Prices .2015 ), y = Close.Prices .2015 $ sma50) lines ( x = index (Close.Prices .2015 ), y = Close.Prices .2015 $ sma200, lty = 2 ) legend ( "topleft" , c ( "Tech Sector Price" , "50-Day Moving Average" , "200-Day Moving Average" ), lty = c ( 1 , 1 , 2 ))

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Date Bollinger BandsPlot AAPL <- subset (data.AAPL[, 4 ], + index (data.AAPL) >= "2011-01-01" ) NFLX <- subset (data.NFLX[, 4 ], + index (data.NFLX) >= "2011-01-01" ) C <- subset (data.C[, 4 ], + index (data.C) >= "2011-01-01" ) BLK <- subset (data.BLK[, 4 ], + index (data.BLK) >= "2011-01-01" ) SBUX <- subset (data.SBUX[, 4 ], + index (data.SBUX) >= "2011-01-01" ) NKE <- subset (data.NKE[, 4 ], + index (data.NKE) >= "2011-01-01" ) Portfolio <- cbind (AAPL $ AAPL.Close, NFLX $ NFLX.Close, BLK $ BLK.Close, C $ C.Close, SBUX $ SBUX.Close, NKE $ NKE.Close) Portfolio[ c ( 1 : 3 , nrow (Portfolio)), ] ## AAPL.Close NFLX.Close BLK.Close C.Close SBUX.Close NKE.Close ## 2011-01-03 11.770 25.487 190.19 49.00 16.625 21.5 22 ## 2011-01-04 11.832 25.910 190.04 49.00 16.240 20.9 92 ## 2011-01-05 11.929 25.676 192.00 49.70 16.175 21.1 30 ## 2015-12-31 26.315 114.380 340.52 51.75 60.030 62.5 00 Portfolio $ All <- AAPL $ AAPL.Close + NFLX $ NFLX.Close + BLK $ BLK.Close + C $ C.Close + SBUX $ SBUX.Close + NKE $ NKE.Close Portfolio[ c ( 1 : 3 , nrow (Portfolio)), ] ## AAPL.Close NFLX.Close BLK.Close C.Close SBUX.Close NKE.Close All ## 2011-01-03 11.770 25.487 190.19 49.00 16.625 21.522 314.60 ## 2011-01-04 11.832 25.910 190.04 49.00 16.240 20.992 314.01 ## 2011-01-05 11.929 25.676 192.00 49.70 16.175 21.130 316.61 ## 2015-12-31 26.315 114.380 340.52 51.75 60.030 62.500 655.49 Portfolio $ avg <- rollmeanr (Portfolio $ All, k = 20 ) Portfolio $ sd <- rollapply (Portfolio $ All, width = 20 , FUN = sd, fill = NA ) Portfolio[ c ( 1 : 3 , nrow (Portfolio)), ] ## AAPL.Close NFLX.Close BLK.Close C.Close SBUX.Close NKE.Close All ## 2011-01-03 11.77 0 25.487 190.19 49.00 16.625 21.522 314.60 ## 2011-01-04 11.83 2 25.910 190.04 49.00 16.240 20.992 314.01 ## 2011-01-05 11.92 25.676 192.00 49.70 16.175 21.130 26

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9 316.61 ## 2015-12-31 26.31 5 114.380 340.52 51.75 60.030 62.500 655.49 ## avg sd ## 2011-01-03 NA NA ## 2011-01-04 NA NA ## 2011-01-05 NA NA ## 2015-12-31 664.26 17.673 Portfolio $ sd2up <- Portfolio $ avg + 2 * Portfolio $ sd Portfolio $ sd2down <- Portfolio $ avg - 2 * Portfolio $ sd Portfolio[ c ( 1 : 3 , nrow (Portfolio)), ] ## AAPL.Close NFLX.Close BLK.Close C.Close SBUX.Close NKE.Close All ## 2011-01-03 11.77 0 25.487 190.19 49.00 16.625 21.522 314.60 ## 2011-01-04 11.83 2 25.910 190.04 49.00 16.240 20.992 314.01 ## 2011-01-05 11.92 9 25.676 192.00 49.70 16.175 21.130 316.61 ## 2015-12-31 26.31 5 114.380 340.52 51.75 60.030 62.500 655.49 ## avg sd sd2up sd2down ## 2011-01-03 NA NA NA NA January 1, 2011- December 31,2015" , 27 y.range <- range (Portfolio[, -3 ], na.rm = TRUE ) plot ( x = index (Portfolio), xlab = "Date" , y = Portfolio $ All, ylim = y.range, ylab = "Price ($)" , type = "l" , lwd = 3 , main = "Portfolio - Bollinger Bands (20 days, 2 deviations) cex.main = 0.9 ) lines ( x = index (Portfolio), y = Portfolio $ avg, lty = 2 ) lines ( x = index (Portfolio), y = Portfolio $ sd2up, col = "gray40" ) lines ( x = index (Portfolio), y = Portfolio $ sd2down, col = "gray40" ) legend ( "topleft" , c ( "Portfolio Price" , "20-Day Moving Average" , "Upper Band" , "Lower Band" ), lty = c ( 1 , 2 , 1 , 1 ), lwd = c ( 3 , 1 , 1 , 1 ), col = c ( "black" , "black" , "gray40" , "gray40" ))

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Portfolio − Bollinger Bands (20 days, 2 deviations) January 1, 2011− December 31,20 Date 28 2011 2012 2013 2014 2015 2016 0 200 400 600 Price ($) Portfolio Price 20−Day Moving Average Upper Band Lower Band

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ANLY515_HW1_Code_and_Plots.pdf

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