quantmod and ggplot
Somsak Chanaim
2023-04-26
quantmod
library(readr)
AAPL_4_ <- read_csv("~/Downloads/AAPL (4).csv")## Rows: 252 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (6): Open, High, Low, Close, Adj Close, Volume
## date (1): Date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.library(quantmod)## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Loading required package: TTR## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoogetSymbols(Symbols = "AAPL",
src = "yahoo",
periodicity = "daily",# "weekly", "month"
from = "2022-06-01", # Year-month-day
to = "2023-02-28")## [1] "AAPL"Download multiple stocks
getSymbols(Symbols = c("MSFT","META","PTT.BK"),
src = "yahoo",
periodicity = "week",# "weekly", "month"
from = "2020-01-01", # Year-month-day
to = "2022-12-31") # Year-month-day## [1] "MSFT" "META" "PTT.BK"str(AAPL)## An xts object on 2022-06-01 / 2023-02-27 containing:
## Data: double [186, 6]
## Columns: AAPL.Open, AAPL.High, AAPL.Low, AAPL.Close, AAPL.Volume ... with 1 more column
## Index: Date [186] (TZ: "UTC")
## xts Attributes:
## $ src : chr "yahoo"
## $ updated: POSIXct[1:1], format: "2023-04-26 15:35:44"# open, high, low, and close price
AAPL <- AAPL[,1:4]
head(AAPL)## AAPL.Open AAPL.High AAPL.Low AAPL.Close
## 2022-06-01 149.90 151.74 147.68 148.71
## 2022-06-02 147.83 151.27 146.86 151.21
## 2022-06-03 146.90 147.97 144.46 145.38
## 2022-06-06 147.03 148.57 144.90 146.14
## 2022-06-07 144.35 149.00 144.10 148.71
## 2022-06-08 148.58 149.87 147.46 147.96Select by year
AAPL["2023"]## AAPL.Open AAPL.High AAPL.Low AAPL.Close
## 2023-01-03 130.28 130.90 124.17 125.07
## 2023-01-04 126.89 128.66 125.08 126.36
## 2023-01-05 127.13 127.77 124.76 125.02
## 2023-01-06 126.01 130.29 124.89 129.62
## 2023-01-09 130.47 133.41 129.89 130.15
## 2023-01-10 130.26 131.26 128.12 130.73
## 2023-01-11 131.25 133.51 130.46 133.49
## 2023-01-12 133.88 134.26 131.44 133.41
## 2023-01-13 132.03 134.92 131.66 134.76
## 2023-01-17 134.83 137.29 134.13 135.94
## 2023-01-18 136.82 138.61 135.03 135.21
## 2023-01-19 134.08 136.25 133.77 135.27
## 2023-01-20 135.28 138.02 134.22 137.87
## 2023-01-23 138.12 143.32 137.90 141.11
## 2023-01-24 140.31 143.16 140.30 142.53
## 2023-01-25 140.89 142.43 138.81 141.86
## 2023-01-26 143.17 144.25 141.90 143.96
## 2023-01-27 143.16 147.23 143.08 145.93
## 2023-01-30 144.96 145.55 142.85 143.00
## 2023-01-31 142.70 144.34 142.28 144.29
## 2023-02-01 143.97 146.61 141.32 145.43
## 2023-02-02 148.90 151.18 148.17 150.82
## 2023-02-03 148.03 157.38 147.83 154.50
## 2023-02-06 152.57 153.10 150.78 151.73
## 2023-02-07 150.64 155.23 150.64 154.65
## 2023-02-08 153.88 154.58 151.17 151.92
## 2023-02-09 153.78 154.33 150.42 150.87
## 2023-02-10 149.46 151.34 149.22 151.01
## 2023-02-13 150.95 154.26 150.92 153.85
## 2023-02-14 152.12 153.77 150.86 153.20
## 2023-02-15 153.11 155.50 152.88 155.33
## 2023-02-16 153.51 156.33 153.35 153.71
## 2023-02-17 152.35 153.00 150.85 152.55
## 2023-02-21 150.20 151.30 148.41 148.48
## 2023-02-22 148.87 149.95 147.16 148.91
## 2023-02-23 150.09 150.34 147.24 149.40
## 2023-02-24 147.11 147.19 145.72 146.71
## 2023-02-27 147.71 149.17 147.45 147.92Select year and month
# 2022-12/2023-01
AAPL["2022-12/2023-01"]## AAPL.Open AAPL.High AAPL.Low AAPL.Close
## 2022-12-01 148.21 149.13 146.61 148.31
## 2022-12-02 145.96 148.00 145.65 147.81
## 2022-12-05 147.77 150.92 145.77 146.63
## 2022-12-06 147.07 147.30 141.92 142.91
## 2022-12-07 142.19 143.37 140.00 140.94
## 2022-12-08 142.36 143.52 141.10 142.65
## 2022-12-09 142.34 145.57 140.90 142.16
## 2022-12-12 142.70 144.50 141.06 144.49
## 2022-12-13 149.50 149.97 144.24 145.47
## 2022-12-14 145.35 146.66 141.16 143.21
## 2022-12-15 141.11 141.80 136.03 136.50
## 2022-12-16 136.69 137.65 133.73 134.51
## 2022-12-19 135.11 135.20 131.32 132.37
## 2022-12-20 131.39 133.25 129.89 132.30
## 2022-12-21 132.98 136.81 132.75 135.45
## 2022-12-22 134.35 134.56 130.30 132.23
## 2022-12-23 130.92 132.42 129.64 131.86
## 2022-12-27 131.38 131.41 128.72 130.03
## 2022-12-28 129.67 131.03 125.87 126.04
## 2022-12-29 127.99 130.48 127.73 129.61
## 2022-12-30 128.41 129.95 127.43 129.93
## 2023-01-03 130.28 130.90 124.17 125.07
## 2023-01-04 126.89 128.66 125.08 126.36
## 2023-01-05 127.13 127.77 124.76 125.02
## 2023-01-06 126.01 130.29 124.89 129.62
## 2023-01-09 130.47 133.41 129.89 130.15
## 2023-01-10 130.26 131.26 128.12 130.73
## 2023-01-11 131.25 133.51 130.46 133.49
## 2023-01-12 133.88 134.26 131.44 133.41
## 2023-01-13 132.03 134.92 131.66 134.76
## 2023-01-17 134.83 137.29 134.13 135.94
## 2023-01-18 136.82 138.61 135.03 135.21
## 2023-01-19 134.08 136.25 133.77 135.27
## 2023-01-20 135.28 138.02 134.22 137.87
## 2023-01-23 138.12 143.32 137.90 141.11
## 2023-01-24 140.31 143.16 140.30 142.53
## 2023-01-25 140.89 142.43 138.81 141.86
## 2023-01-26 143.17 144.25 141.90 143.96
## 2023-01-27 143.16 147.23 143.08 145.93
## 2023-01-30 144.96 145.55 142.85 143.00
## 2023-01-31 142.70 144.34 142.28 144.29Selection from starting date to any date
# start to 2022-06-05
AAPL["/2022-06-05"]## AAPL.Open AAPL.High AAPL.Low AAPL.Close
## 2022-06-01 149.90 151.74 147.68 148.71
## 2022-06-02 147.83 151.27 146.86 151.21
## 2022-06-03 146.90 147.97 144.46 145.38Selection from any date to last date
# 2023-02 to last date
AAPL["2023-02/"]## AAPL.Open AAPL.High AAPL.Low AAPL.Close
## 2023-02-01 143.97 146.61 141.32 145.43
## 2023-02-02 148.90 151.18 148.17 150.82
## 2023-02-03 148.03 157.38 147.83 154.50
## 2023-02-06 152.57 153.10 150.78 151.73
## 2023-02-07 150.64 155.23 150.64 154.65
## 2023-02-08 153.88 154.58 151.17 151.92
## 2023-02-09 153.78 154.33 150.42 150.87
## 2023-02-10 149.46 151.34 149.22 151.01
## 2023-02-13 150.95 154.26 150.92 153.85
## 2023-02-14 152.12 153.77 150.86 153.20
## 2023-02-15 153.11 155.50 152.88 155.33
## 2023-02-16 153.51 156.33 153.35 153.71
## 2023-02-17 152.35 153.00 150.85 152.55
## 2023-02-21 150.20 151.30 148.41 148.48
## 2023-02-22 148.87 149.95 147.16 148.91
## 2023-02-23 150.09 150.34 147.24 149.40
## 2023-02-24 147.11 147.19 145.72 146.71
## 2023-02-27 147.71 149.17 147.45 147.92Data <- AAPL["2023-02/"]Select the first n units
# day, week, month, quarter, year
first(AAPL,"1 year")## AAPL.Open AAPL.High AAPL.Low AAPL.Close
## 2022-06-01 149.90 151.74 147.68 148.71
## 2022-06-02 147.83 151.27 146.86 151.21
## 2022-06-03 146.90 147.97 144.46 145.38
## 2022-06-06 147.03 148.57 144.90 146.14
## 2022-06-07 144.35 149.00 144.10 148.71
## 2022-06-08 148.58 149.87 147.46 147.96
## 2022-06-09 147.08 147.95 142.53 142.64
## 2022-06-10 140.28 140.76 137.06 137.13
## 2022-06-13 132.87 135.20 131.44 131.88
## 2022-06-14 133.13 133.89 131.48 132.76
## ...
## 2022-12-16 136.69 137.65 133.73 134.51
## 2022-12-19 135.11 135.20 131.32 132.37
## 2022-12-20 131.39 133.25 129.89 132.30
## 2022-12-21 132.98 136.81 132.75 135.45
## 2022-12-22 134.35 134.56 130.30 132.23
## 2022-12-23 130.92 132.42 129.64 131.86
## 2022-12-27 131.38 131.41 128.72 130.03
## 2022-12-28 129.67 131.03 125.87 126.04
## 2022-12-29 127.99 130.48 127.73 129.61
## 2022-12-30 128.41 129.95 127.43 129.93Select the last n units
# day, week, month, quarter, year
last(AAPL,"1 week")## AAPL.Open AAPL.High AAPL.Low AAPL.Close
## 2023-02-27 147.71 149.17 147.45 147.92Daily price to weekly price
to.weekly(AAPL)## AAPL.Open AAPL.High AAPL.Low AAPL.Close
## 2022-06-03 149.90 151.74 144.46 145.38
## 2022-06-10 147.03 149.87 137.06 137.13
## 2022-06-17 132.87 137.34 129.04 131.56
## 2022-06-24 133.42 141.91 133.32 141.66
## 2022-07-01 142.70 143.49 133.77 138.93
## 2022-07-08 137.77 147.55 136.93 147.04
## 2022-07-15 145.67 150.86 142.12 150.17
## 2022-07-22 150.74 156.28 146.70 154.09
## 2022-07-29 154.01 163.63 150.80 162.51
## 2022-08-05 161.01 167.19 159.63 165.35
## 2022-08-12 166.37 172.17 163.25 172.10
## 2022-08-19 171.52 176.15 171.31 171.52
## 2022-08-26 169.69 171.05 163.56 163.62
## 2022-09-02 161.15 162.90 154.67 155.81
## 2022-09-09 156.47 157.82 152.68 157.37
## 2022-09-16 159.59 164.26 148.37 150.70
## 2022-09-23 149.31 158.74 148.56 150.43
## 2022-09-30 149.66 154.72 138.00 138.20
## 2022-10-07 138.21 147.54 137.69 140.09
## 2022-10-14 140.42 144.52 134.37 138.38
## 2022-10-21 141.07 147.85 140.27 147.27
## 2022-10-28 147.19 157.50 144.13 155.74
## 2022-11-04 153.16 155.45 134.38 138.38
## 2022-11-11 137.11 150.01 134.59 149.70
## 2022-11-18 148.97 153.59 146.15 151.29
## 2022-11-25 150.16 151.83 146.93 148.11
## 2022-12-02 145.14 149.13 140.35 147.81
## 2022-12-09 147.77 150.92 140.00 142.16
## 2022-12-16 142.70 149.97 133.73 134.51
## 2022-12-23 135.11 136.81 129.64 131.86
## 2022-12-30 131.38 131.41 125.87 129.93
## 2023-01-06 130.28 130.90 124.17 129.62
## 2023-01-13 130.47 134.92 128.12 134.76
## 2023-01-20 134.83 138.61 133.77 137.87
## 2023-01-27 138.12 147.23 137.90 145.93
## 2023-02-03 144.96 157.38 141.32 154.50
## 2023-02-10 152.57 155.23 149.22 151.01
## 2023-02-17 150.95 156.33 150.85 152.55
## 2023-02-24 150.20 151.30 145.72 146.71
## 2023-02-27 147.71 149.17 147.45 147.92to.monthly(AAPL)## AAPL.Open AAPL.High AAPL.Low AAPL.Close
## Jun 2022 149.90 151.74 129.04 136.72
## Jul 2022 136.04 163.63 135.66 162.51
## Aug 2022 161.01 176.15 157.14 157.22
## Sep 2022 156.64 164.26 138.00 138.20
## Oct 2022 138.21 157.50 134.37 153.34
## Nov 2022 155.08 155.45 134.38 148.03
## Dec 2022 148.21 150.92 125.87 129.93
## Jan 2023 130.28 147.23 124.17 144.29
## Feb 2023 143.97 157.38 141.32 147.92Line plot
plot(AAPL$AAPL.Close,
col = "#5263BF",
main = "AAPL daily close price")
# Subset plot
plot(AAPL$AAPL.Close,
col = "#5263BF",
main = "AAPL daily close price",
subset = "2022-05/2022-06")
Financial chart
chartSeries(AAPL,subset = "2023")
chartSeries(PTT.BK)
addSMA(n=5,col = "red")
addSMA(n=30, col ="#458B74")
addSMA(n =100, col = "white")
AAPL.close <- Cl(AAPL)
head(AAPL.close)## AAPL.Close
## 2022-06-01 148.71
## 2022-06-02 151.21
## 2022-06-03 145.38
## 2022-06-06 146.14
## 2022-06-07 148.71
## 2022-06-08 147.96plot(AAPL.close)
diff(AAPL.close, lag = 1)## AAPL.Close
## 2022-06-01 NA
## 2022-06-02 2.5000000
## 2022-06-03 -5.8300018
## 2022-06-06 0.7599945
## 2022-06-07 2.5700073
## 2022-06-08 -0.7500000
## 2022-06-09 -5.3200073
## 2022-06-10 -5.5099945
## 2022-06-13 -5.2500000
## 2022-06-14 0.8799896
## ...
## 2023-02-13 2.8400116
## 2023-02-14 -0.6500092
## 2023-02-15 2.1300049
## 2023-02-16 -1.6199951
## 2023-02-17 -1.1600037
## 2023-02-21 -4.0700073
## 2023-02-22 0.4300079
## 2023-02-23 0.4899902
## 2023-02-24 -2.6899872
## 2023-02-27 1.2099915acf(AAPL.close)
pacf(AAPL.close)
plot(periodReturn(AAPL, period = "daily"),
col= "red",
main = "AAPL daily return")
library(forecast)library(ggplot2)
P1 <- autoplot(AAPL$AAPL.Open, color = I("blue"))+
ggtitle("AAPL close price")+
xlab("Date")+
ylab("Price")+
theme_linedraw()P1
P2 <- ggAcf(AAPL.close)+ggtitle("acf")
P2
P3 <- ggPacf(AAPL.close)+ggtitle("pacf")
P3
pacf(AAPL.close)
library(patchwork)P1+P2
P1/P2
P1/(P2+P3)
(P2+P3)/P1
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutggplotly(P1)subplot(P1,P2,P3, nrows = 2)csv to xts object
AAPL_csv <- read_csv("AAPL (4).csv")## Rows: 252 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (6): Open, High, Low, Close, Adj Close, Volume
## date (1): Date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(AAPL_csv)## spc_tbl_ [252 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Date : Date[1:252], format: "2022-04-25" "2022-04-26" ...
## $ Open : num [1:252] 161 162 156 159 162 ...
## $ High : num [1:252] 163 162 160 165 166 ...
## $ Low : num [1:252] 158 157 155 159 157 ...
## $ Close : num [1:252] 163 157 157 164 158 ...
## $ Adj Close: num [1:252] 162 156 156 163 157 ...
## $ Volume : num [1:252] 9.60e+07 9.56e+07 8.81e+07 1.30e+08 1.32e+08 ...
## - attr(*, "spec")=
## .. cols(
## .. Date = col_date(format = ""),
## .. Open = col_double(),
## .. High = col_double(),
## .. Low = col_double(),
## .. Close = col_double(),
## .. `Adj Close` = col_double(),
## .. Volume = col_double()
## .. )
## - attr(*, "problems")=<externalptr>ggplot(AAPL_csv) +
aes(x = Date, y = Close) +
geom_line(colour = "#AF668A") +
labs(
y = "price",
title = "AAPL",
subtitle = "Daily close price",
caption = "design by Somsak Chanaim"
) +
theme_gray()
data frame to xts
AAPL.xts <- xts( x = AAPL_csv[,2:7],
order.by = AAPL_csv$Date)
AAPL.xts## Open High Low Close Adj.Close Volume
## 2022-04-25 161.12 163.17 158.46 162.88 161.8993 96046400
## 2022-04-26 162.25 162.34 156.72 156.80 155.8559 95623200
## 2022-04-27 155.91 159.79 155.38 156.57 155.6273 88063200
## 2022-04-28 159.25 164.52 158.93 163.64 162.6547 130216800
## 2022-04-29 161.84 166.20 157.25 157.65 156.7007 131747600
## 2022-05-02 156.71 158.23 153.27 157.96 157.0089 123055300
## 2022-05-03 158.15 160.71 156.32 159.48 158.5197 88966500
## 2022-05-04 159.67 166.48 159.26 166.02 165.0204 108256500
## 2022-05-05 163.85 164.08 154.95 156.77 155.8261 130525300
## 2022-05-06 156.01 159.44 154.18 157.28 156.5627 116124600
## ...
## 2023-04-12 161.22 162.06 159.78 160.10 160.1000 50133100
## 2023-04-13 161.63 165.80 161.42 165.56 165.5600 68445600
## 2023-04-14 164.59 166.32 163.82 165.21 165.2100 49337200
## 2023-04-17 165.09 165.39 164.03 165.23 165.2300 41516200
## 2023-04-18 166.10 167.41 165.65 166.47 166.4700 49923000
## 2023-04-19 165.80 168.16 165.54 167.63 167.6300 47720200
## 2023-04-20 166.09 167.87 165.56 166.65 166.6500 52456400
## 2023-04-21 165.05 166.45 164.49 165.02 165.0200 58311900
## 2023-04-24 165.00 165.60 163.89 165.33 165.3300 41949600
## 2023-04-25 165.19 166.31 163.73 163.77 163.7700 48534900str(AAPL.xts)## An xts object on 2022-04-25 / 2023-04-25 containing:
## Data: double [252, 6]
## Columns: Open, High, Low, Close, Adj Close ... with 1 more column
## Index: Date [252] (TZ: "UTC")plot(AAPL.xts$Close)