library(pacman)
pacman::p_load(data.table, fixest, BatchGetSymbols, finreportr, ggplot2, lubridate, esquisse)
## Set parameters
first.date <- Sys.Date() - 2500
last.date <- Sys.Date()
freq.data <- "monthly"
tickers <- c("TSLA", "NIO", "PRPL", "AAPL", "SNAP", "MU", "AMD",
"NVDA", "TWTR")
## Get Stock Prices
stocks <- BatchGetSymbols(tickers = tickers,
first.date = first.date,
last.date = last.date,
freq.data = freq.data,
do.cache = FALSE,
thresh.bad.data = 0)
## Warning: `BatchGetSymbols()` was deprecated in BatchGetSymbols 2.6.4.
## ℹ Please use `yfR::yf_get()` instead.
## ℹ 2022-05-01: Package BatchGetSymbols will soon be replaced by yfR. More
## details about the change is available at github
## <<www.github.com/msperlin/yfR> You can install yfR by executing:
##
## remotes::install_github('msperlin/yfR')
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
##
## Running BatchGetSymbols for:
## tickers =TSLA, NIO, PRPL, AAPL, SNAP, MU, AMD, NVDA, TWTR
## Downloading data for benchmark ticker
## ^GSPC | yahoo (1|1)
## TSLA | yahoo (1|9) - Got 100% of valid prices | OK!
## NIO | yahoo (2|9) - Got 91% of valid prices | Nice!
## PRPL | yahoo (3|9) - Got 100% of valid prices | Well done!
## AAPL | yahoo (4|9) - Got 100% of valid prices | Got it!
## SNAP | yahoo (5|9) - Got 100% of valid prices | OK!
## MU | yahoo (6|9) - Got 100% of valid prices | Got it!
## AMD | yahoo (7|9) - Got 100% of valid prices | Good job!
## NVDA | yahoo (8|9) - Got 100% of valid prices | You got it!
## TWTR | yahoo (9|9) - Error in download..
head(stocks)
## $df.control
## # A tibble: 8 × 6
## ticker src download.status total.obs perc.benchmark.dates threshold.decision
## <chr> <chr> <chr> <int> <dbl> <chr>
## 1 TSLA yahoo OK 1724 1 KEEP
## 2 NIO yahoo OK 1562 0.906 KEEP
## 3 PRPL yahoo OK 1724 1 KEEP
## 4 AAPL yahoo OK 1724 1 KEEP
## 5 SNAP yahoo OK 1724 1 KEEP
## 6 MU yahoo OK 1724 1 KEEP
## 7 AMD yahoo OK 1724 1 KEEP
## 8 NVDA yahoo OK 1724 1 KEEP
##
## $df.tickers
## # A tibble: 656 × 10
## ticker ref.date volume price.open price.high price.low price.close
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2018-01-22 1282969200 44.3 44.9 41.2 41.9
## 2 AAPL 2018-02-01 3711577200 41.8 45.2 37.6 44.5
## 3 AAPL 2018-03-01 2854910800 44.6 45.9 41.2 41.9
## 4 AAPL 2018-04-02 2664617200 41.7 44.7 40.2 41.3
## 5 AAPL 2018-05-01 2483905200 41.6 47.6 41.3 46.7
## 6 AAPL 2018-06-01 2110498000 47.0 48.5 45.2 46.3
## 7 AAPL 2018-07-02 1574765600 46.0 49.0 45.9 47.6
## 8 AAPL 2018-08-01 2801275600 49.8 57.2 49.3 56.9
## 9 AAPL 2018-09-04 2715888000 57.1 57.4 53.8 56.4
## 10 AAPL 2018-10-01 3158994000 57.0 58.4 51.5 54.7
## # ℹ 646 more rows
## # ℹ 3 more variables: price.adjusted <dbl>, ret.adjusted.prices <dbl>,
## # ret.closing.prices <dbl>
## Verify Returns
stocks_DT <- stocks$df.tickers %>% setDT() %>% # Convert to data.table
.[order(ticker, ref.date)] # Order by ticker and date
library(ggplot2)
# Create the plot
ggplot(stocks_DT, aes(x = ref.date, y = price.close, color = ticker)) +
geom_line() +
facet_wrap(~ ticker, ncol = 3) +
labs(title = "Stock Prices Over Time",
x = "Date",
y = "Closing Price",
color = "Ticker") +
theme_minimal() +
theme(legend.position = "right")
