重复识别日期/日期时间范围内的最小和最大数值

Question

I am completely new to R so this is proving too complex to handle for me right now, so any help is much appreciated.

I am analysing price action data for BTC. I have 1 minute candles from 2019-09-08 19:13:00 to 2022-03-15 00:22:00 with the variables of open, high, low, close price as well as volume in BTC & USD and trade count for each of those minutes. Data source is https://www.cryptodatadownload.com/data/binance/ for anyone interested.

I cleaned up & correctly formatted the data and now want to analyse when BTC price made a low & high for various date & time ranges, for example:

What time of day in 30 minute increments did BTC made a low for the week?

Here is what I believe I need to do: I need to tell R that 30 minutes is a range and identify the lowest & highest value for the "Low" and "High" variables within in as well as that a day is a range and within that the lowest & highest value for the "Low" and "High" variables as well as define a week as a range and within that the lowest & highest value for the "Low" and "High" variables. Then I'd need to mark these values, the best method I can think of would be creating a new variable and have it as a TRUE/FALSE column like so:

btcusdt_binance_fut_1min$pa.low.of.week.30min
btcusdt_binance_fut_1min$pa.high.of.week.30min

Every minute row that is within that 30min low and high will be marked TRUE and every other minute within that week will be marked FALSE.

I looked at lubridate's interval() function but as far as I know the problem is I'd need to define each year, month, week, day, 30mins interval individually with start and end time, which is obviously not feasible. I believe I run into the same problem with the subset() function.

Another option seems to be the seq() and seq.POSIXt() functions as well as the range() function, but I haven't found a way for it.

Here is all my code and I am using this data set: https://www.cryptodatadownload.com/cdd/BTCUSDT_Binance_futures_data_minute.csv

library(readr)
library(lubridate)
library(tidyverse)
library(plyr)
library(dplyr)


# IMPORT CSV FILE AS DATA SET

# Name data set & choose import file
# Skip = 1 for skipping first row of CSV
btcusdt_binance_fut_1min <-
  read.csv(
    file.choose(),
    skip = 1,
    header = T,
    sep = ","
  )


# CLEAN UP & REORGANISE DATA

# Remove unix & symbol column
btcusdt_binance_fut_1min$unix = NULL
btcusdt_binance_fut_1min$symbol = NULL

# Rename date column to datetime
colnames(btcusdt_binance_fut_1min)[colnames(btcusdt_binance_fut_1min) == "date"] <-
  "datetime"

# Convert datetime column to POSIXct format
btcusdt_binance_fut_1min$datetime <-
  as_datetime(btcusdt_binance_fut_1min$datetime, tz = "UTC")

# Create variable column for each time element
btcusdt_binance_fut_1min$year <-
  year(btcusdt_binance_fut_1min$datetime)
btcusdt_binance_fut_1min$month <-
  month(btcusdt_binance_fut_1min$datetime)
btcusdt_binance_fut_1min$week <-
  isoweek(btcusdt_binance_fut_1min$datetime)
btcusdt_binance_fut_1min$weekday <-
  wday(btcusdt_binance_fut_1min$datetime,
       label = TRUE,
       abbr = FALSE)
btcusdt_binance_fut_1min$hour <-
  hour(btcusdt_binance_fut_1min$datetime)
btcusdt_binance_fut_1min$minute <-
  minute(btcusdt_binance_fut_1min$datetime)

# Reorder columns
btcusdt_binance_fut_1min <-
  btcusdt_binance_fut_1min[, c(1, 9, 10, 11, 12, 13, 14, 4, 3, 2, 5, 6, 7, 8)]

Answer 1

Using data.table we can do the following:

btcusdt_binance_fut_1min <- data.table(datetime = seq.POSIXt(as.POSIXct("2022-01-01 0:00"), as.POSIXct("2022-01-01 2:59"), by = "1 min"))
btcusdt_binance_fut_1min[, group := format(as.POSIXct(cut(datetime, breaks = "30 min")), "%H:%M")]

the cut function will "floor" each datetime to it's nearest, smaller, half an hour. The format and as.POSIXct are just there to remove the date part to allow for easy comparing between dates for the same half hours, but if you prefer to keep it a datetime you can remove these functions.

After this the next steps are pretty straightforward:

btcusdt_binance_fut_1min[, .(High = max(High), Low = min(Low)), by=.(group)]