Mini Quiz 1

Cryptocurrency Trends on the Finicial Markets

Questions:

1.Does the dataset show any obvious price peaks or crashes?

2.Over the past 100 days, what are the daily patterns in the open, high, low, and close prices of Bitcoin?

3.What is the maximum and minimum daily close price?

#install.packages("jsonlite")
library(jsonlite)

url <- "https://min-api.cryptocompare.com/data/v2/histoday?fsym=BTC&tsym=USD&limit=99"
btc_raw <- fromJSON(url)
str(btc_raw, max.level = 2)

## List of 6
##  $ Response  : chr "Success"
##  $ Message   : chr ""
##  $ HasWarning: logi FALSE
##  $ Type      : int 100
##  $ RateLimit : Named list()
##  $ Data      :List of 4
##   ..$ Aggregated: logi FALSE
##   ..$ TimeFrom  : int 1750291200
##   ..$ TimeTo    : int 1758844800
##   ..$ Data      :'data.frame':   100 obs. of  9 variables:

Extract

btc_df <- btc_raw$Data$Data
head(btc_df)

##         time     high       low     open volumefrom   volumeto    close
## 1 1750291200 105264.4 103951.44 104927.9    9905.95 1036540458 104699.7
## 2 1750377600 106559.4 102367.77 104699.7   17700.79 1845670588 103313.3
## 3 1750464000 104021.0 100925.53 103313.3   11217.17 1151399558 102165.7
## 4 1750550400 103430.1  98264.46 102165.7   30700.21 3086806070 101015.4
## 5 1750636800 106134.1  99685.07 101015.4   35198.88 3606601777 105423.7
## 6 1750723200 106358.8 104710.65 105423.7   19908.29 2099787247 106133.2
##   conversionType conversionSymbol
## 1         direct                 
## 2         direct                 
## 3         direct                 
## 4         direct                 
## 5         direct                 
## 6         direct

btc_df$time <- as.POSIXct(btc_df$time, origin="1970-01-01", tz="UTC")
btc_clean <- btc_df[, c("time", "open", "high", "low", "close", "volumefrom", "volumeto")]
names(btc_clean) <- c("Date", "Open", "High", "Low", "Close", "Volume_From", "Volume_To")


head(btc_clean)

##         Date     Open     High       Low    Close Volume_From  Volume_To
## 1 2025-06-19 104927.9 105264.4 103951.44 104699.7     9905.95 1036540458
## 2 2025-06-20 104699.7 106559.4 102367.77 103313.3    17700.79 1845670588
## 3 2025-06-21 103313.3 104021.0 100925.53 102165.7    11217.17 1151399558
## 4 2025-06-22 102165.7 103430.1  98264.46 101015.4    30700.21 3086806070
## 5 2025-06-23 101015.4 106134.1  99685.07 105423.7    35198.88 3606601777
## 6 2025-06-24 105423.7 106358.8 104710.65 106133.2    19908.29 2099787247

Maximum

max_close <- max(btc_clean$Close, na.rm = TRUE)
max_close

## [1] 123374.6

Minimum

min_close <- min(btc_clean$Close, na.rm = TRUE)
min_close

## [1] 101015.4

Average

mean_close <- mean(btc_clean$Close, na.rm = TRUE)
mean_close

## [1] 113487.7