library(xml2)
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library(tidyverse)
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library(tidyquant)
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## Loading required package: lubridate
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##
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## date, intersect, setdiff, union
## Loading required package: PerformanceAnalytics
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## first, last
##
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## legend
## Loading required package: quantmod
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## Loading required package: TTR
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## method from
## as.zoo.data.frame zoo
## Version 0.4-0 included new data defaults. See ?getSymbols.
## == Need to Learn tidyquant? =============================================================================================================
## Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
## </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
library(dygraphs)
## Warning: package 'dygraphs' was built under R version 3.6.3
library(reshape2)
## Warning: package 'reshape2' was built under R version 3.6.3
##
## Attaching package: 'reshape2'
## The following object is masked from 'package:tidyr':
##
## smiths
Ra <- c("AAPL", "GOOG", "NFLX", "XLK") %>%
tq_get(get = "stock.prices",
from = "2010-01-01",
to = "2020-04-09") %>%
group_by(symbol)
RaReturn<-Ra%>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
col_rename = "Ra")
options("scipen"=100, "digits"=4)
Ra
Ra<-Ra[,c(1,2,8)]
Ra_w<-dcast(melt(Ra, id.vars=c("date", "symbol")), date~symbol+variable)
dates<-seq(as.Date("2010-01-01"), length=2584,by="days" )
Ra_w<-xts(Ra_w[,c(2:5)], Ra_w$date)
dygraph(Ra_w)%>%dyLegend(show="always")
RaReturn
RaR_w<-dcast(melt(RaReturn, id.vars=c("date", "symbol")), date~symbol+variable)
dates<-seq(as.Date("2010-01-29"), length=124,by="months" )
RaR_w<-xts(RaR_w[,c(2:5)],RaR_w$date)
dygraph(RaR_w)%>%dyLegend(show="always")
RaReturn<-Ra%>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "yearly",
col_rename = "Ra")
options("scipen"=100, "digits"=4)
RaReturn
RaReturn %>%
ggplot(aes(x = date, y = Ra, fill = symbol)) +
geom_col() +
geom_hline(yintercept = 0, color = palette_light()[[1]]) +
scale_y_continuous(labels = scales::percent) +
labs(title = "Annual Returns",
subtitle = "Get annual returns quickly with tq_transmute!",
y = "Annual Returns", x = "") +
facet_wrap(~ symbol, ncol = 2, scales = "free_y") +
theme_tq() +
scale_fill_tq()
RaReturn_daily<-Ra%>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "daily",
col_rename = "Ra")
options("scipen"=100, "digits"=4)
RaReturn_daily
RaReturn_daily %>%
ggplot(aes(x = Ra, fill = symbol)) +
geom_density(alpha = 0.5) +
labs(title = " Charting the Daily Log Returns",
x = "daily Returns", y = "Density") +
theme_tq() +
scale_fill_tq() +
facet_wrap(~ symbol, ncol = 2)
RaReturn_max<-Ra%>%
tq_transmute(select = adjusted,
mutate_fun = apply.quarterly,
FUN=max,
col_rename = "max.adjusted")
RaReturn_min<-Ra%>%
tq_transmute(select = adjusted,
mutate_fun = apply.quarterly,
FUN=min,
col_rename = "min.adjusted")
options("scipen"=100, "digits"=4)
Min_max<-left_join(RaReturn_max,RaReturn_min )
## Joining, by = c("symbol", "date")
Min_max
Min_max %>%
ggplot(aes(x = date, color = symbol)) +
geom_segment(aes(xend = date, y = min.adjusted, yend = max.adjusted),
size = 1) +
geom_point(aes(y = max.adjusted), size = 2) +
geom_point(aes(y = min.adjusted), size = 2) +
facet_wrap(~ symbol, ncol = 2, scale = "free_y") +
labs(title = " Min/Max Price By Quarter",
y = "Stock Price", color = "") +
theme_tq() +
scale_color_tq() +
scale_y_continuous(labels = scales::dollar) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.title.x = element_blank())
Ra <- c("MA", "V") %>%
tq_get(get = "stock.prices",
from = "2015-01-01",
to = "2020-04-08") %>%
group_by(symbol)
Ra_pairs <- Ra %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "daily",
type = "log",
col_rename = "returns") %>%
spread(key = symbol, value = returns)
Ra_pairs
Ra_pairs %>%
ggplot(aes(x = MA, y = V)) +
geom_point(color = palette_light()[[1]], alpha = 0.5) +
geom_smooth(method = "lm") +
labs(title = "Visualizing Returns Relationship of Stock Pairs") +
theme_tq()
## `geom_smooth()` using formula 'y ~ x'
lm(MA ~ V, data = Ra_pairs) %>%
summary()
##
## Call:
## lm(formula = MA ~ V, data = Ra_pairs)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03712 -0.00355 -0.00008 0.00381 0.05234
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000153 0.000204 0.75 0.45
## V 0.959891 0.012861 74.63 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0074 on 1323 degrees of freedom
## Multiple R-squared: 0.808, Adjusted R-squared: 0.808
## F-statistic: 5.57e+03 on 1 and 1323 DF, p-value: <0.0000000000000002
regr_fun <- function(data) {
coef(lm(MA ~ V, data = timetk::tk_tbl(data, silent = TRUE)))
}
stock_pairs <- Ra_pairs %>%
tq_mutate(mutate_fun = rollapply,
width = 90,
FUN = regr_fun,
by.column = FALSE,
col_rename = c("coef.0", "coef.1"))
stock_pairs
stock_pairs %>%
ggplot(aes(x = date, y = coef.1)) +
geom_line(size = 1, color = palette_light()[[1]]) +
geom_hline(yintercept = 0.9598, size = 1, color = palette_light()[[2]]) +
labs(title = "MA ~ V: Visualizing Rolling Regression Coefficient", x = "") +
theme_tq()
## Warning: Removed 89 row(s) containing missing values (geom_path).
Ra %>%
tq_transmute(adjusted,
periodReturn,
period = "daily",
type = "log",
col_rename = "returns") %>%
mutate(wealth.index = 100 * cumprod(1 + returns)) %>%
ggplot(aes(x = date, y = wealth.index, color = symbol)) +
geom_line(size = 1) +
labs(title = "MA and V: Stock Prices") +
theme_tq() +
scale_color_tq()
RaReturn<-Ra%>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "yearly",
col_rename = "Ra")
options("scipen"=100, "digits"=4)
RaReturn
Ra%>%
group_by(symbol) %>%
tq_transmute(adjusted, periodReturn, period = "daily") %>%
tq_transmute(daily.returns, Return.clean, alpha = 0.05) %>%
tq_transmute(daily.returns, Return.excess, Rf = 0.03 / 252)
GS<-getQuote("GS", from="2020-04-01", what = yahooQF(c("Market Capitalization", "Earnings/Share",
"P/E Ratio", "Book Value", "EBITDA", "52-week Low","52-week High" )))
#RaRb_capm %>% select(symbol, Alpha, Beta)
quandl_api_key("feCffvprJPn9gRsdAGCK")
#quandl_search(query = "Oil", database_code = "NSE", per_page = 3)
#AAPL<-Quandl.datatable('SHARADAR/SF1', dimension='MRY', ticker='AAPL')
#Quandl.datatable('SHARADAR/SF1', calendardate.gte='2013-12-31', ticker='AAPL')
tiingo_api_key('f5c376557f134a9a1f3bf02576124ba45b7be6d2')
# Tiingo Prices (Free alternative to Yahoo Finance!)
tq_get(c("AAPL", "GOOG"), get = "tiingo", from = "2010-01-01")
tq_get(c("AAPL", "GOOG"),
get = "tiingo.iex",
from = "2020-01-01",
to = "2020-01-15",
resample_frequency = "5min")
library(httr)
## Warning: package 'httr' was built under R version 3.6.3
library(jsonlite)
## Warning: package 'jsonlite' was built under R version 3.6.3
##
## Attaching package: 'jsonlite'
## The following object is masked from 'package:purrr':
##
## flatten
ticker <- "AAPL"
apiKey <- "fgOZWUlhmZvcwxOJ2PYqaKaNA3zNrVJ2"
url <- paste("https://simfin.com/api/v1/info/find-id/ticker/",ticker,"?api-key=",apiKey,sep="")
get_data <- GET(url)
data <- fromJSON(content(get_data, "text"),flatten = TRUE)
simfinID <- data[,c("simId")]
statement <- "pl"
ptype <- "TTM"
fyear <- ""
url <- paste("https://simfin.com/api/v1/companies/id/",simfinID,"/statements/standardised","?api-key=",apiKey,
"&stype=",statement,"&ptype=",ptype,"&fyear=",fyear,
sep="")
get_data <- GET(url)
data <- fromJSON(content(get_data, "text"),flatten = TRUE)
print(data)
## $calculationScheme
## period fyear restated sign
## 1 Q1 2020 1 1
## 2 FY 2019 1 1
## 3 Q1 2019 1 -1
##
## $calculated
## [1] TRUE
##
## $periodEndDate
## [1] "2019-12-31"
##
## $dataQualityCheck
## [1] TRUE
##
## $industryTemplate
## [1] "general"
##
## $values
## tid uid standardisedName parent_tid displayLevel
## 1 1 1 Revenue 4 0
## 2 3 0 Sales & Services Revenue 1 1
## 3 5 0 Financing Revenue 1 1
## 4 6 0 Other Revenue 1 1
## 5 2 2 Cost of revenue 4 0
## 6 7 0 Cost of Goods & Services 2 1
## 7 8 0 Cost of Financing Revenue 2 1
## 8 9 0 Cost of Other Revenue 2 1
## 9 4 4 Gross Profit 19 0
## 10 10 10 Other Operating Income 19 0
## 11 11 11 Operating Expenses 19 0
## 12 12 12 Selling, General & Administrative 11 1
## 13 13 0 Selling & Marketing 12 2
## 14 14 0 General & Administrative 12 2
## 15 15 15 Research & Development 11 1
## 16 16 16 Depreciation & Amortization 11 1
## 17 17 17 Provision For Doubtful Accounts 11 1
## 18 18 18 Other Operating Expense 11 1
## 19 19 19 Operating Income (Loss) 28 0
## 20 20 20 Non-Operating Income (Loss) 28 0
## 21 21 21 Interest Expense, net 20 1
## 22 22 0 Interest Expense 21 2
## 23 23 0 Interest Income 21 2
## 24 24 0 Other Investment Income (Loss) 20 1
## 25 25 0 Foreign Exchange Gain (Loss) 20 1
## 26 26 26 Income (Loss) from Affiliates 20 1
## 27 27 27 Other Non-Operating Income (Loss) 20 1
## 28 28 28 Pretax Income (Loss), Adjusted 43 0
## 29 29 29 Abnormal Gains (Losses) 43 0
## 30 30 0 Acquired In-Process R&D 29 1
## 31 31 31 Merger / Acquisition Expense 29 1
## 32 32 32 Abnormal Derivatives 29 1
## 33 33 33 Disposal of Assets 29 1
## 34 34 34 Early extinguishment of Debt 29 1
## 35 35 35 Asset Write-Down 29 1
## 36 36 36 Impairment of Goodwill & Intangibles 29 1
## 37 37 37 Sale of Business 29 1
## 38 38 38 Legal Settlement 29 1
## 39 39 39 Restructuring Charges 29 1
## 40 40 0 Sale of and Unrealized Investments 29 1
## 41 41 0 Insurance Settlement 29 1
## 42 42 42 Other Abnormal Items 29 1
## 43 43 43 Pretax Income (Loss) 49 0
## 44 44 44 Income Tax (Expense) Benefit, net 49 0
## 45 45 45 Current Income Tax 44 1
## 46 46 46 Deferred Income Tax 44 1
## 47 47 47 Tax Allowance/Credit 44 1
## 48 48 48 Income (Loss) from Affiliates, net of taxes 49 0
## 49 49 49 Income (Loss) from Continuing Operations 53 0
## 50 50 50 Net Extraordinary Gains (Losses) 53 0
## 51 51 51 Discontinued Operations 50 1
## 52 52 52 XO & Accounting Charges & Other 50 1
## 53 53 53 Income (Loss) Including Minority Interest 55 0
## 54 54 54 Minority Interest 55 0
## 55 55 55 Net Income 58 0
## 56 56 56 Preferred Dividends 58 0
## 57 57 57 Other Adjustments 58 0
## 58 58 58 Net Income Available to Common Shareholders 0 0
## valueAssigned valueCalculated valueChosen checkPossible
## 1 267683000000 0 267683000000 FALSE
## 2 NA 0 NA FALSE
## 3 NA 0 NA FALSE
## 4 NA 0 NA FALSE
## 5 -166105000000 0 -166105000000 FALSE
## 6 NA 0 NA FALSE
## 7 NA 0 NA FALSE
## 8 NA 0 NA FALSE
## 9 101578000000 101578000000 101578000000 TRUE
## 10 NA 0 NA FALSE
## 11 NA -35425000000 -35425000000 FALSE
## 12 -18659000000 0 -18659000000 FALSE
## 13 NA 0 NA FALSE
## 14 NA 0 NA FALSE
## 15 -16766000000 0 -16766000000 FALSE
## 16 NA 0 NA FALSE
## 17 NA 0 NA FALSE
## 18 NA 0 NA FALSE
## 19 66153000000 66153000000 66153000000 TRUE
## 20 NA 1596000000 1596000000 FALSE
## 21 NA 0 NA FALSE
## 22 NA 0 NA FALSE
## 23 NA 0 NA FALSE
## 24 NA 0 NA FALSE
## 25 NA 0 NA FALSE
## 26 NA 0 NA FALSE
## 27 1596000000 0 1596000000 FALSE
## 28 NA 67749000000 67749000000 FALSE
## 29 NA 0 NA FALSE
## 30 NA 0 NA FALSE
## 31 NA 0 NA FALSE
## 32 NA 0 NA FALSE
## 33 NA 0 NA FALSE
## 34 NA 0 NA FALSE
## 35 NA 0 NA FALSE
## 36 NA 0 NA FALSE
## 37 NA 0 NA FALSE
## 38 NA 0 NA FALSE
## 39 NA 0 NA FALSE
## 40 NA 0 NA FALSE
## 41 NA 0 NA FALSE
## 42 NA 0 NA FALSE
## 43 67749000000 67749000000 67749000000 TRUE
## 44 -10222000000 0 -10222000000 FALSE
## 45 NA 0 NA FALSE
## 46 NA 0 NA FALSE
## 47 NA 0 NA FALSE
## 48 NA 0 NA FALSE
## 49 NA 57527000000 57527000000 FALSE
## 50 NA 0 NA FALSE
## 51 NA 0 NA FALSE
## 52 NA 0 NA FALSE
## 53 NA 57527000000 57527000000 FALSE
## 54 NA 0 NA FALSE
## 55 57527000000 57527000000 57527000000 TRUE
## 56 NA 0 NA FALSE
## 57 NA 0 NA FALSE
## 58 NA 57527000000 57527000000 FALSE
#https://www.macrotrends.net/stocks/charts/CCL/carnival/pe-ratio
#https://www.macrotrends.net/stocks/charts/CCL/carnival/pe-ratio
#tq_performance_fun_options