Fast Fourier Transform - Cycle Extraction - page 112
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Cycle analysis using the Goertzel algorithm
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Goetzel cycles
(look at Goertzel algorithm page in wikipedia)
Discrete Hartley transform
In 1942, Ralph Hartley proposed an analogue of the Fourier transform in his article "A More Symmetrical Fourier Analysis Applied to Transmission Problems".
Just like Fourier transform (FT), Hartley transform (HT) turns the original signal into a sum of trigonometric functions. But there is one significant difference between them. FT converts real values to complex numbers, while HT provides only real results. Because of this difference, the Hartley transform did not become popular - scientists and technicians did not see any advantages in it and continued to use the usual Fourier transform. In 1983, Ronald Bracewell presented a discrete version of the Hartley transform.
Do you need to make a fast Fourier transform or to solve a differential equation system? Do you perform a complex data analysis trying to gather all methods in one place as a source code? Then ALGLIB library of numerical methods is for you!
ALGLIB numerical analysis library in MQL5
Financial markets generate data with a huge amount of complex relationships. To analyze them, we need to use the most modern methods of applied mathematics. Successfully combining the high complexity of financial data with the simplicity and efficiency of analysis is a challenging task. ALGLIB is a high-performance library designed specifically for working with numerical methods and data analysis algorithms. It is a reliable assistant in the analysis of financial markets.
ALGLIB advantages
Why choose ALGLIB when working with financial data?
Here are the key benefits of the library:
Defining your Edge (Part 6): Harnessing Fourier Transform and a Spiking Neural Network in an Expert Advisor
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.