In short, this
is a new feature that allows us to optimize strategies across noise
adjusted data series as opposed to the traditional method of
optimization which only optimizes across the single historical price
series.
The problem we face is the historical data is merely only one possible path of what *could* have happened. We need to prepare ourselves for the probable future not the certain past. In
order to do this, we can generate synthetic price series that have
altered amounts of noise/volatility than the actual historical data.
This provides us with a rough sample of some alternate realities and
potentially what can happen going forward. This is the exact type of
data that can help us build more robust strategies that can succeed
across whatever the market throws at us – which is our end goal in all
of this, right?
Let’s look at a Noise Test Parameter Optimization (NTO) case study to show exactly how it works…
I have built a strategy from 2004 to 2016 that does quite well. The strategy’s performance over this period is shown below…
Now, if we
right click on the strategy and select optimize, we can generate a
sensitivity graph that shows how our strategy performs as we alter some
parameters. This is done on the original historical price data with no
noise adjusted data sample added (yet). We simply retrade different
variations of parameter settings on the single historical price data and
plot the respective performances. This is how most platforms allow you
to optimize parameters and I want to show how misleading it can be to
traders. The rule I’ve optimized had original parameter values of X = 9
and Y = 4 (black arrow). The sensitivity graph is shown below. Each plot
consists of three points: parameter 1, parameter 2 and the resulting
profit.
Build Alpha:
We can see the original parameters are near a sensitive area on the
surface where performance degrades in the surrounding areas. Performance
drops pretty hard near our original strategy’s parameters which means
slight alterations to the future price data’s characteristics can
degrade our strategy’s performance quite a bit. Not what we want at all
and, as we all know, there will be alterations to future price’s
characteristics! How many times has a backtest not matched live results?
Perhaps more robust parameter selection can help
The more
robust selection using the typical simple optimization method on the
historical data shows we should probably pick a parameter more near X = 8
and Y = 8 (pictured arrow below). This is the traditional method taught
in textbooks, trading blogs, etc. We optimize on the single historical
data then find a flat/non-peaked area close to our original parameters
and use those new parameters.
However, if we run BuildAlpha’s
Noise Test Optimization with up to 50% noise alterations and 50 data
samples (green box below), we see a much different picture. What this
does is, instead of optimizing on one historical path we now optimize
across the one historical path AND 50 noise altered data series. The
sensitivity graph shows a much different picture when optimized across
the 51 data series. We are less concerned with the total profit and loss
but rather the shape of the surface…
Originally Posted: http://buildalpha.com/noise-test-parameter-optimization
Originally Posted: http://buildalpha.com/noise-test-parameter-optimization
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