Discussing the article: "Defining your Edge (Part 6): Harnessing Fourier Transform and a Spiking Neural Network in an Expert Advisor"
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Check out the new article: Defining your Edge (Part 6): Harnessing Fourier Transform and a Spiking Neural Network in an Expert Advisor.
Article revisits Trading Robot that merged Discrete Fourier Transform with Leaky Integrate-and-Fire Spiking Neural Network. Evaluation is made over the seven operating modes with different symbols, timeframes, and test windows. We use two-thirds of the test window to optimize while the one-third does a forward walk run. This study tries to detail parameter interactions, input scaling, gating effects, and outlines practical checks that include rolling windows, frozen inputs, and others. The goal remains identifying settings worth further testing or paper trading.
We resume our series on Defining your Edge in the markets by introducing a variant to the article-format we have presented so far. In the first five parts we introduced algorithm and network pairings. That article-format was about discovering and testing novel algorithms and neural-networks that are not mainstream; for this article we are "doing the opposite" of this exploration by looking at how to possibly exploit algorithm and neural-network pairings that have already been introduced.
Therefore, this 6th part, returns to the algorithm-network pairing that started this series. This earlier article united a Discrete Fourier Transform with a small Leaky Integrate-and-Fire Spiking Neural Network. The Fourier computation searched price, MACD and RSI for recurring structure. The Neural Network served as an extra layer that let directional stimulation (or votes) accumulate across completed bars before a signal was launched.
This time we want to examine in what way the same EA behaves when its seven operating modes are applied to different settings. This could be taken as a key step in the adoption and use of this model although there are different approaches to our own that could be taken to accomplish the same result. In our testing reports presented below, we change up the symbol, timeframe, and test-window. The symbol EURUSD is used twice due to its liquidity but again this method is not cast in stone as the reader can always change things up.
Every report presented below is a forward-run check. On average, two thirds of the test window supplied the optimization sample with the final third staying reserved for the forward walk. The chosen inputs are then run through this later, one-third segment without any extra adjustments, as a forward walk. From the testing reports, five were able to finish profitably while two were in the red. Both of these are discussed below, since we take the loss to mark where our input settings could be unfit which could be more pragmatic than picking another symbol or test window where we get "good results".
Author: Stephen Njuki