New article Continuous Walk-Forward Optimization (Part 4): Optimization Manager (Auto Optimizer) has been published:
The main purpose of the article is to describe the mechanism of working with our application and its capabilities. Thus the article can be treated as an instruction on how to use the application. It covers all possible pitfalls and specifics of the application usage.
To proceed with the analysis of the created program we first need to define the purpose of this project. We decided to use a scientific approach in trading and started creating clearly programmed trading algorithms (no matter whether we deal with indicator-based robots or those applying fuzzy logic and neural networks — all of them are programmed algorithms that perform specific tasks). Therefore, the approach to the selection of optimization results should also be formalized. In other words, if during refuse to apply randomness in the trading process, then the process of preparation for trading should also be automated. Otherwise, we can select the results that we like randomly, which is closer to intuition than to the system trading. This idea is the first motive that encouraged me to create this application. The next one is the possibility to test algorithms by optimizing them — by using the Continuous Walk-Forward Optimization shown in the below figure.
Continuous walk-forward optimization alternates between historical (yellow) and forward (green) optimization passes at a given time interval. Suppose you have a 10-year history. We determine that the optimization period should consist of an interval equal to 1 year, and a forward interval of 1 quarter (or 3 months). As a result, we have an interval equal to 1.25 years (1 year + 1 quarter) for one optimization pass + a forward test. In the figure, each line characterizes this time interval.
Author: Andrey Azatskiy