knitr::opts_chunk$set(echo = TRUE)
##https://rpubs.com/kcin999/yahoo-finance
# Yahoo Finance is a powerful suite of information supported by Yahoo,
# which provides information such as:
#. Financial News
#. Stock and Market Data (Stock Quotes, Financial Reports, etc.)
# In R, there is quantmod, which also retrieves the information needed.
# It also provides a couple of charts and details that we can use
# in order to further help our analysis.
# install.packages("quantmod")
library(xts)
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(zoo)
library(quantmod)
## Loading required package: TTR
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
#These three indices are representative of the capitalization of companies
# listed on North American stock exchanges, but each group has shares and
# weightings with a specific method.
# The Dow Jones is a stock index made up of 30 companies with the largest
# market capitalization on the New York Stock Exchange (NYSE), with the
# exception of transportation and public services.
# The Nasdaq is a stock of securities prepared by electronic companies and
# based on various indices that it represents.
# Finally, the S&P 500 is an index with the 500 most representative
# companies on the New York Stock Exchange.
# https://cran.r-project.org/web/packages/quantmod/quantmod.pdf
# To collect information about Dow Jones Industrial Average (DJI).
# We only need to know their ticker, which is ‘^DJI’ on the Yahoo.Finance.
DJ=getSymbols('^DJI',
src='yahoo',
auto.assign=FALSE)
na.omit(DJ)
## DJI.Open DJI.High DJI.Low DJI.Close DJI.Volume DJI.Adjusted
## 2007-01-03 12459.54 12580.35 12404.82 12474.52 327200000 12474.52
## 2007-01-04 12473.16 12510.41 12403.86 12480.69 259060000 12480.69
## 2007-01-05 12480.05 12480.13 12365.41 12398.01 235220000 12398.01
## 2007-01-08 12392.01 12445.92 12337.37 12423.49 223500000 12423.49
## 2007-01-09 12424.77 12466.43 12369.17 12416.60 225190000 12416.60
## 2007-01-10 12417.00 12451.61 12355.63 12442.16 226570000 12442.16
## 2007-01-11 12442.96 12544.46 12442.96 12514.98 261720000 12514.98
## 2007-01-12 12514.66 12561.04 12489.66 12556.08 256530000 12556.08
## 2007-01-16 12555.84 12585.08 12538.93 12582.59 242720000 12582.59
## 2007-01-17 12571.46 12614.00 12550.55 12577.15 272720000 12577.15
## ...
## 2023-11-27 35376.44 35410.37 35280.57 35333.47 258340000 35333.47
## 2023-11-28 35332.13 35518.67 35307.73 35416.98 256210000 35416.98
## 2023-11-29 35436.80 35579.13 35405.89 35430.42 299330000 35430.42
## 2023-11-30 35596.57 35970.70 35592.22 35950.89 435540000 35950.89
## 2023-12-01 35914.45 36264.85 35914.45 36245.50 321840000 36245.50
## 2023-12-04 36089.38 36238.18 36029.69 36204.44 329450000 36204.44
## 2023-12-05 36135.65 36164.68 36010.85 36124.56 309250000 36124.56
## 2023-12-06 36183.73 36292.58 36024.25 36054.43 283240000 36054.43
## 2023-12-07 36124.17 36164.17 36021.95 36117.38 287280000 36117.38
## 2023-12-08 36084.82 36296.20 36061.64 36247.87 295180000 36247.87
## chartSeries in order to generate some visualizations.
# This code generates the trend of Dow Jones Industrial Average (DJI)
# price and volume over the last 6 months.
chartSeries(DJ,
name="Dow Jones Industrial Average (DJI)",
subset="last 6 months",
type = "auto", # same as "candlesticks"
theme=chartTheme("white"))
## To collect information about NASDAQ Composite (^IXIC).
# We only need to know their ticker, which is ‘^IXIC’ Yahoo.Finance.
ND=getSymbols("^IXIC",
src='yahoo',
auto.assign=FALSE)
na.omit(ND)
## IXIC.Open IXIC.High IXIC.Low IXIC.Close IXIC.Volume IXIC.Adjusted
## 2007-01-03 2429.72 2454.62 2394.66 2423.16 2435280000 2423.16
## 2007-01-04 2423.82 2460.51 2413.75 2453.43 2104210000 2453.43
## 2007-01-05 2445.07 2445.07 2420.59 2434.25 2060360000 2434.25
## 2007-01-08 2435.25 2445.63 2421.13 2438.20 1905620000 2438.20
## 2007-01-09 2443.26 2449.87 2423.56 2443.83 2144160000 2443.83
## 2007-01-10 2434.04 2461.34 2427.90 2459.33 2274210000 2459.33
## 2007-01-11 2464.89 2489.46 2463.39 2484.85 2436270000 2484.85
## 2007-01-12 2481.72 2502.82 2481.54 2502.82 2175810000 2502.82
## 2007-01-16 2504.56 2508.93 2493.92 2497.78 2139550000 2497.78
## 2007-01-17 2487.89 2496.93 2475.83 2479.42 2273880000 2479.42
## ...
## 2023-11-27 14239.31 14305.71 14205.24 14241.02 4205880000 14241.02
## 2023-11-28 14224.63 14303.66 14195.72 14281.76 4474400000 14281.76
## 2023-11-29 14367.11 14423.22 14247.89 14258.49 4890220000 14258.49
## 2023-11-30 14265.05 14289.20 14127.05 14226.22 5678070000 14226.22
## 2023-12-01 14181.35 14311.95 14135.00 14305.03 5607000000 14305.03
## 2023-12-04 14168.66 14197.52 14058.52 14185.49 6270020000 14185.49
## 2023-12-05 14126.40 14285.66 14121.76 14229.91 6179120000 14229.91
## 2023-12-06 14325.62 14327.63 14138.51 14146.71 5167030000 14146.71
## 2023-12-07 14230.43 14353.11 14220.87 14339.99 5694310000 14339.99
## 2023-12-08 14279.46 14416.92 14264.96 14403.97 5430840000 14403.97
chartSeries(ND,
name="NASDAQ Composite (^IXIC)",
subset="last 6 months",
type = "matchsticks",
theme=chartTheme("white"))
## To collect information about S&P 500 (^GSPC).
# We only need to know their ticker, which is ‘^GSPC’ Yahoo.Finance.
SP=getSymbols("^GSPC",
src='yahoo',
auto.assign=FALSE)
na.omit(SP)
## GSPC.Open GSPC.High GSPC.Low GSPC.Close GSPC.Volume GSPC.Adjusted
## 2007-01-03 1418.03 1429.42 1407.86 1416.60 3429160000 1416.60
## 2007-01-04 1416.60 1421.84 1408.43 1418.34 3004460000 1418.34
## 2007-01-05 1418.34 1418.34 1405.75 1409.71 2919400000 1409.71
## 2007-01-08 1409.26 1414.98 1403.97 1412.84 2763340000 1412.84
## 2007-01-09 1412.84 1415.61 1405.42 1412.11 3038380000 1412.11
## 2007-01-10 1408.70 1415.99 1405.32 1414.85 2764660000 1414.85
## 2007-01-11 1414.84 1427.12 1414.84 1423.82 2857870000 1423.82
## 2007-01-12 1423.82 1431.23 1422.58 1430.73 2686480000 1430.73
## 2007-01-16 1430.73 1433.93 1428.62 1431.90 2599530000 1431.90
## 2007-01-17 1431.77 1435.27 1428.57 1430.62 2690270000 1430.62
## ...
## 2023-11-27 4554.86 4560.52 4546.32 4550.43 3403990000 4550.43
## 2023-11-28 4545.55 4568.14 4540.51 4554.89 3586240000 4554.89
## 2023-11-29 4571.84 4587.64 4547.15 4550.58 4418760000 4550.58
## 2023-11-30 4554.87 4569.89 4537.24 4567.80 5399300000 4567.80
## 2023-12-01 4559.43 4599.39 4554.71 4594.63 4397120000 4594.63
## 2023-12-04 4564.37 4572.37 4546.72 4569.78 4369910000 4569.78
## 2023-12-05 4557.25 4578.56 4551.68 4567.18 3909950000 4567.18
## 2023-12-06 4586.23 4590.74 4546.50 4549.34 4245680000 4549.34
## 2023-12-07 4568.84 4590.92 4565.22 4585.59 3818880000 4585.59
## 2023-12-08 4576.20 4609.23 4574.06 4604.37 3707010000 4604.37
chartSeries(SP,
name="S&P 500 (^GSPC)",
subset="last 6 months",
type = "candlesticks",
theme=chartTheme("white"))
# In order to visualize the 3 indices together we are going to build a
# Matrix of visualizacion
M=matrix(c(1,1,2,2,3,3),nrow=6,ncol=1)
layout(M)
layout.show(n=3)
par(bty="n")
# Then we plot each of the Indices' column of the Closed Price Adjusted
plot(x=DJ, # Draw first time series
y=DJ$DJI.Adjusted,
type = "l", # Lines
main="Dow Jones Industrial Average (DJI)",
col.main="red", # Color of the main title
xlab = "Year",
ylab = "Values",
las=2) # Axis labels Perpendicular to axis
plot(ND, # Draw second time series
y=ND$IXIC.Adjusted,
main="NASDAQ Composite (^IXIC)",
type = "l", # Lines
col = 3,
las=2) # Axis labels Perpendicular to axis
## Warning in merge.xts(temp_xts, xts_object, fill = na.locf, join = "right", :
## NAs introduced by coercion to integer range
plot(SP, # Draw third time series
y=SP$GSPC.Adjusted ,
main="S&P 500 (^GSPC)",
type = "l", # Lines
col = 4,
las=2) # Axis labels Perpendicular to axis
## Warning in merge.xts(temp_xts, xts_object, fill = na.locf, join = "right", :
## NAs introduced by coercion to integer range
# We take only the column of the Closed Price Adjusted of each index and
# Binded in one Data Frame
df=cbind(DJ$DJI.Adjusted,ND$IXIC.Adjusted,SP$GSPC.Adjusted)
nombre=c("DowJones","Nasdaq","SP500") # Standard and Poor's 500
names(df)=nombre
head(df)
## DowJones Nasdaq SP500
## 2007-01-03 12474.52 2423.16 1416.60
## 2007-01-04 12480.69 2453.43 1418.34
## 2007-01-05 12398.01 2434.25 1409.71
## 2007-01-08 12423.49 2438.20 1412.84
## 2007-01-09 12416.60 2443.83 1412.11
## 2007-01-10 12442.16 2459.33 1414.85
#install.packages("tidyquant")
library(tidyquant)
## Loading required package: lubridate
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
## Loading required package: PerformanceAnalytics
##
## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
##
## legend
#install.packages("plotly")
library(plotly)
## Loading required package: ggplot2
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
tickers = c("^DJI", "^IXIC", "^GSPC")
for (i in tickers){
getSymbols(i,
from = "2018-01-01",
to = "2019-12-31")}
stock <- data.frame(DJI$DJI.Adjusted,
IXIC$IXIC.Adjusted,
GSPC$GSPC.Adjusted)
stock$DJI.Adjusted=stock$DJI.Adjusted/stock$DJI.Adjusted[1]
stock$IXIC.Adjusted=stock$IXIC.Adjusted/stock$IXIC.Adjusted[1]
stock$GSPC.Adjusted=stock$GSPC.Adjusted/stock$GSPC.Adjusted[1]
stock <- data.frame(stock,rownames(stock))
colnames(stock) <- append(tickers,'Dates')
fig <- plot_ly(stock, type = 'scatter', mode = 'lines')%>%
add_trace(x = ~Dates, y = ~`^DJI`, name = 'DowJones')%>%
add_trace(x = ~Dates, y = ~`^IXIC`, name = 'Nasdaq')%>%
add_trace(x = ~Dates, y = ~`^GSPC`, name = 'S&P500')%>%
layout(title = 'USA Indices',legend=list(title=list(text='variable')),
xaxis = list(dtick = "M1", tickformat="%b<br>%Y"), width = 1000)
## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()
options(warn = -1)
fig <- fig %>%
layout(
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6')
fig
# Compare Values of the time series between DowJones, Nasdaq, and S&P500
plot(df,
main="DowJones, Nasdaq, S&P500 ",
xlab="Dates",
ylab="",
legend.loc="topleft")
library(psych)
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
C=cor(df)
corPlot(df,
main=" Debido a la alta correlacion solo se debe
usar uno para el modelo",
min.length = 2,
cex = 1.25,
pval = TRUE,
upper = FALSE,
colors=TRUE)
# create a waterfall plot from the table
#install.packages("ggplot2")
library(ggplot2)
#install.packages("waterfalls")
library(waterfalls)
# create company income statement
category <- c("DowJones","Nasdaq","S&P500")
wdf <- data.frame(category,df[4264])
waterfall(wdf, # create waterfall chart with total column
value=FALSE,
calc_total=TRUE,
total_axis_text = "Total",
total_rect_text_color="black",
total_rect_color="goldenrod1") +
scale_y_continuous(label=scales::dollar) +
labs(title = "USA Indices",
subtitle = "Years 2017-2023",
y="",
x="") +
theme_minimal()
