Processing time of backtests on a single computer

10 years of daily data makes about 2500 data points. So extrapolating from
that to 58000 data points (assuming the relation is linear), it should take
Number of data points is not necessarily a good estimator for run time
even if the strategies are the same.  What matters more is the number
of timestamps/observations that must be evaluated.  That includes
signals, moving orders, processing fills, etc.
about 12.2 secs for a single run with my dataset. For 144 runs (total
number of parameter combinations), it should take about 30 mins. However, I
Again, the relationship is not linear.  Different parameter
combinations will produce differing amounts of signals, order
movement, fills, etc.

For example, I ran parameter optimization on ~3 years of 5-second
data.  Some parameter combinations took 1-2 minutes, some took >20
minutes.
ran apply.paramset() this morning (without trailing stops), it took 4.5
hours. And the code is the one that I sent earlier with Trailing stop rules
enabled=FALSE'd.

Did you run the macd demo code with single core? If you did some parallel
processing, did you use doSNOW package or something else? Maybe that is the
reason, I am not sure.

Would deleting trailing stop rules speed things up, instead of defining
them but setting enabled=FALSE?

2016-04-07 0:34 GMT+03:00 Brian G. Peterson <brian at braverock.com>:

On Wed, 2016-04-06 at 23:58 +0300, Jersey Fanatic wrote:
I will try running the same code without trailing stops and see what
effect
it has on the processing time. I will report back as soon as it is
finished.
Running the macd demo code over 10 years of daily data on my machine (no
trailing stops) takes 0.5262365 secs for a single run.

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Processing time of backtests on a single computer

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