Discussing the article: "Defining your Edge (Part 1): Using a Discrete Fourier Transform and a Spiking Neural Network in a Trading Robot"
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Check out the new article: Defining your Edge (Part 1): Using a Discrete Fourier Transform and a Spiking Neural Network in a Trading Robot.
In this article we make the case for pairing the Discrete Fourier Transform with a Spiking Neural Network in a Trading Robot. The Fourier Transform helps represent data as oscillations instead of its raw values. To govern how we interpret these cycles, we engage a Spiking Neural Network that unlike regular networks, uses time dependent electrical charges to accumulate potential and only "spike" when a target threshold is met. Combining these two engines allows us better control on the timing of discrete market movements, that in theory should give us entry signals with rigorous mathematical confirmation.
It can be argued that trading systems today suffer from data bloat or the over-reliance on correlated, lagging indicators. In order for one to develop an edge therefore, this research series introduces a diverse library of special algorithm-neural network pairings. We launch off with an unusual hybrid union: a Discrete Fourier Transform cycle decoder that we marry to a biological Leaky Integrate-and-Fire Spiking Neural Network. We are after isolating hidden market frequencies.
The Fourier-SNN Pairing
This pairing of the Fourier Transform and Spiking Neural Network (SNN) is intended to give us a non-correlated edge via spectral decomposition and a time based confirmation. While traditional indicators often lag from static lookbacks, the Fourier engine acts as a cycle decoder that dynamically scans past price or indicator data in order to separate the dominant frequency wave. This is thus able to transform metrics such as MACD and RSI into predictive wave functions that identify key turning points before they visually unfold.
The SNN rules execution timing by bringing time and pressure metrics to the mix. Rather than generating immediate noisy trade signals with static weights, the inputs are treated as synaptic stimulation that charges a digital membrane's potential energy. This potential energy leaks away when momentum stalls but accumulates when several market variables align over consecutive bars. So, a trade is only triggered when the accumulated "voltage" exceeds a strict threshold, thus generating an execution spike. This ensures that cyclical reversals are verified by sustained momentum before capital is used.
Author: Stephen Njuki