Traders that gravitate towards quantitative strategies are typically nerds.
I don’t mean that in a negative way, because I consider myself one as well. However, nerds have a tendency to speak and write using far more complex words and sentences than actually necessary.
Because of that tendency, entry-level explanations can often be confusing for beginners to understand.
This comes across especially well on the topic of walk forward optimization. Most of the articles about the topic are very complex and involve some high level math. This can be extremely discouraging for someone just looking into systematic trading strategies.
VBO Systems posted a very helpful case study using walk forward optimization this week. They started by briefly explaining the three main steps of their walk forward optimization process:
- Define in-sample and out-of-sample periods
- Define a robust parameters area
- Execute the walk forward
That’s simple enough. Next, they specified the system and data that they would be using for the case study:
For this test we will use the FDAX and a volatility breakout (VBO) intraday trading system.
We will use NinjaTrader and CQG historical 1-minute data, assuming 3 points of slippage for each R/T trade to cover trading frictions.
The first step in their process was to identify the in-sample and out-of-sample periods. Here is how they explained it:
We will choose as in-sample 1/1/2001 to 12/31/2009 for system design and in-sample optimization and 1/1/2010 to 12/31/2012 as out-of-sample period to evaluate the in-sample optimization robustness and execute the walk forward. We will then use a 3:1 ratio for the WFO (walk forward optimization):
- Optimize 2007 to 2009 and verify performance out-of-sample in 2010
- Optimize 2008 to 2010 and verify performance out-of-sample in 2011
- Optimize 2009 to 2011 and verify performance out-of-sample in 2012
The next step is to define the parameters that they are looking to optimize. Here are the three that they listed:
- Lookback period of the fast average
- Lookback period of the slow average
- Volatility filter
So far, this has been a pretty simple process, and VBO Systems does a great job of keeping their explanations simple. In order to define the robust area for each of these parameters, the article uses a 3D chart to identify the moving average lookback periods that perform reasonably well over the course of the in-sample data. The same process is applied on a standard chart to get the volatility filter parameters.
The final step is to perform the walk forward using the identified data parameters on the defined data periods. Basically, they just see which moving averages and volatility filter would have worked best on each of the in-sample data periods, and then test those parameters on the out-of-sample data periods to see if the returns are in-line with expectations.
The result of this case study is that each of the walk forward optimization periods produce similar returns to the overall system returns for the entire in-sample data set. This gives reassurances that the strategy has a certain level of robustness.