Aggregate irregular intra-day data
Intraday data can be huge, with hundreds of thousands of observations per day, millions per month, and hundreds of millions per year. These datasets often need to be aggregated before you can work with them.
You learned how to aggregate daily data in the Introduction to xts and zoo course. This exercise will use to.period()
to aggregate intraday data to an OHLC series. You often need to specify both period
and k
arguments to aggregate intraday data.
The intraday_xts
object contains one trading day of random data.
This exercise is part of the course
Importing and Managing Financial Data in R
Exercise instructions
- Use
to.period()
to convertintraday_xts
into a 5-second price series calledxts_5sec
. - Use
to.period()
to convertintraday_xts
into a 10-minute price series calledxts_10min
. - Use
to.period()
to convertintraday_xts
into a 1-hour price series calledxts_1hour
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Convert raw prices to 5-second prices
xts_5sec <- to.period(intraday_xts, period = "___", k = ___)
# Convert raw prices to 10-minute prices
xts_10min <-
# Convert raw prices to 1-hour prices
xts_1hour <- to.period(___, period = "___", k = ___)