Processing time of backtests on a single computer

So I tried to see what effect trailing stop and stop loss rules have on the
processing time. For the same dataset, only SL took 5 mins while only
trailing SL took 10 mins, and trailing SL with normal SL took 13 mins. I
guess it is processing it as fast as it should be. Thanks for all the help.

2016-04-07 20:45 GMT+03:00 Joshua Ulrich <josh.m.ulrich at gmail.com
<javascript:;>>:

On Thu, Apr 7, 2016 at 11:30 AM, Jersey Fanatic
<jerseyfanatic1 at gmail.com <javascript:;>> wrote:

Thanks for the insight. I did not know variations in processing time of

20

minutes or so could happen between different parameter combinations.

I ran the strategy with the same dataset with random parameters without
Trailing SL, on single core and it took 5.15 minutes. The number of
transactions were 7800. The amount of processing time seems too much
compared to yours though. 5sec data of 3 years vs M5 data of just 1

year; 20

min vs 5 mins.

Again, the number of observations is not a good predictor of the
amount of time it will take.  You have 7800 transactions.  My shortest
(longest) run had 25 (1500) transactions.

Seems reasonable to me that a strategy producing nearly 8000
transactions takes about 5 minutes; that's about 25 transactions a
second.

2016-04-07 16:32 GMT+03:00 Joshua Ulrich <josh.m.ulrich at gmail.com

<javascript:;>>:

On Thu, Apr 7, 2016 at 8:10 AM, Jersey Fanatic <

jerseyfanatic1 at gmail.com <javascript:;>>

wrote:

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

<javascript:;>>:

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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questions

should go.

--
Joshua Ulrich  |  about.me/joshuaulrich
FOSS Trading  |  www.fosstrading.com
R/Finance 2016 | www.rinfinance.com

--
Joshua Ulrich  |  about.me/joshuaulrich
FOSS Trading  |  www.fosstrading.com
R/Finance 2016 | www.rinfinance.com

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