On 04/06/2016 07:58 PM, Joshua Ulrich wrote:> On Tue, Apr 5, 2016 at 9:17 PM, James Hirschorn
> <james.hirschorn at hotmail.com> wrote:
>> OpCl works on xts objects but not on quantmod.OHLC objects. Is this a
bug?
>>
> Thanks for the minimal, reproducible example.
>
> Looks like a bug. There's no as.quantmod.OHLC.xts method, so the zoo
> method is dispatched. Calling Op() or Cl() on this zoo-based object
> results in a vector (since zoo will drop dimensions, like a matrix or
> data.frame), and you can't set column names on a vector.
>
> I'm not sure whether it makes more sense to check for dims in all the
> combination transformations (consisting of combined Op, Hi, Lo, Cl) or
> to create a as.quantmod.OHLC.xts method.
>
> Can you provide some details about your use case?
At this stage, my use case is making some custom indicators. I've not
used quantmod much in the past, but I just assumed that quantmod.OHLC
was the class I should be using with quantmod.
Some details: The starting point was tick data, for example
# n seconds of tick data
n <- 600
tick.data.timestamp <- as.POSIXct("2016-04-06 00:00:00", tz =
'GMT') + 1:n
set.seed(1)
tick.data <- xts(cbind(Price = runif(n, 0, 1),
Volume = sample(1:100, replace = T, n)),
tick.data.timestamp)
Then aggregating to minute OHLC data as quantmod.OHLC:
minute.data <- as.quantmod.OHLC(to.minutes(tick.data),
c("Open","High","Low","Close","Volume"),
name = 'Sym')
or alternatively as xts:
minute.data.xts <- as.xts(minute.data)
OpCl is naturally useful for indicators, since it shows whether we have
a red or green candlestick. xts is working fine for my indicators for
now, but I don't know if not using quantmod.OHLC will be a problem for
backtesting.
There are other differences I noticed too. For example, the Lag function
(maybe a different bug?):
# OK
Lag(minute.data)
# error
Lag(minute.data.xts)
And lag shifts in the opposite direction!
> lag(minute.data)[1:4]
Sym.Open Sym.High Sym.Low Sym.Close Sym.Volume
2016-04-06 00:00:59 0.4068302 0.9926841 0.01307758 0.44628435 3133
2016-04-06 00:01:59 0.6401010 0.9918386 0.03554058 0.60530345 2896
2016-04-06 00:02:59 0.9030816 0.9614099 0.04646089 0.42962441 3323
2016-04-06 00:03:59 0.4527201 0.9815635 0.02778712 0.05043966 2657
> lag(minute.data.xts)[1:4]
Sym.Open Sym.High Sym.Low Sym.Close Sym.Volume
2016-04-06 00:00:59 NA NA NA NA NA
2016-04-06 00:01:59 0.2655087 0.9919061 0.01339033 0.6620051 3136
2016-04-06 00:02:59 0.4068302 0.9926841 0.01307758 0.4462843 3133
2016-04-06 00:03:59 0.6401010 0.9918386 0.03554058 0.6053034 2896
