Remove first two weeks of data in half hourly resolution

Peter,

You haven't published a reproducible example, and I'm not going to take
the time to write a complete example from scratch.

We use xts subsetting for this type of thing, so I suggest using xts for
your time series (this is always good advice for time series in R).

Here's a partial example to get you started.

#######################

#load some data from the PerformanceAnalytics package
data(edhec)

#check the range
range(index(edhec))

#add 14 days from the start
first(index(edhec))+14

#now assume that you have an object 'z' with  intraday data
range(z)

#check the range of Dates by forcing the index to Date type
range(as.Date(index(z)))

#add 114 days, as before
first(as.Date(index(z)))+14

# now subset by cutting off the first 14 calendar days
# from the start of the series
zs <- z[paste0(first(as.Date(index(z)))+14,'/')]

#check the range
range(as.Date(index(zs)))

##################

Regards,

Brian

--
Brian G. Peterson
http://braverock.com/brian/
Ph: 773-459-4973
IM: bgpbraverock


On Thu, 2016-03-31 at 11:50 +0100, Peter Neumaier wrote:

Hi all,

I am doing some analysis on monthly futures contracts from 2011-2016.
Each monthly contract goes for eight weeks, in half hourly resolution.

I'd like to remove first two weeks of eight weeks history for each monthly
contract.
My approach was to work out the start and end date and cut the 1st two weeks
data off, but problem is that the half hourly resolution is sometimes
incomplete(
i.e. a trading day goes from 7:00am - 4:00pm but sometimes starts at
7:30am).

Any suggestion on how to resolve this? Below a sample trading day in half
hourly:

                    NGFH6.Open NGFH6.High NGFH6.Low NGFH6.Close
NGFH6.Volume NGFH6.WAP NGFH6.hasGaps NGFH6.Count
2016-01-06 07:30:00     0.3395     0.3395    0.3375      0.3380
45   0.33811             0           5
2016-01-06 08:00:00     0.3400     0.3400    0.3387      0.3395
140   0.33928             0          12
2016-01-06 08:30:00     0.3395     0.3395    0.3379      0.3379
70   0.33884             0           5
2016-01-06 09:00:00     0.3379     0.3379    0.3379      0.3379
0   0.33790             0           0
2016-01-06 09:30:00     0.3379     0.3379    0.3379      0.3379
0   0.33790             0           0
2016-01-06 10:00:00     0.3375     0.3380    0.3373      0.3373
230   0.33738             0          14
2016-01-06 10:30:00     0.3376     0.3379    0.3376      0.3379
20   0.33775             0           2
2016-01-06 11:00:00     0.3370     0.3370    0.3370      0.3370
105   0.33700             0           5
2016-01-06 11:30:00     0.3366     0.3366    0.3365      0.3365
65   0.33658             0           4
2016-01-06 12:00:00     0.3370     0.3370    0.3370      0.3370
10   0.33700             0           1
2016-01-06 12:30:00     0.3372     0.3372    0.3361      0.3361
125   0.33686             0           9
2016-01-06 13:00:00     0.3360     0.3360    0.3357      0.3360
225   0.33585             0          17
2016-01-06 13:30:00     0.3357     0.3357    0.3355      0.3355
50   0.33560             0           5
2016-01-06 14:00:00     0.3350     0.3359    0.3350      0.3359
25   0.33554             0           2
2016-01-06 14:30:00     0.3359     0.3359    0.3359      0.3359
0   0.33590             0           0
2016-01-06 15:00:00     0.3352     0.3352    0.3348      0.3352
150   0.33492             0          15
2016-01-06 15:30:00     0.3352     0.3352    0.3334      0.3341
280   0.33364             0          24
2016-01-06 16:00:00     0.3341     0.3375    0.3341      0.3370
145   0.33543             0          17
2016-01-06 16:30:00     0.3380     0.3385    0.3380      0.3385
25   0.33830             0           3

Many Thanks
Peter

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