There are many issues to consider when analyzing and measuring cycles in financial markets. Unfortunately, it is easy to make incorrect spectral measurements resulting in inaccurate cycle projections either on wrong phase or length gathered from the spectrum plot.
This course explains the key elements of a Fourier-based spectrum analysis. We will focus on explaining the core background in layman terms and concentrate on the important aspects on “how to read a spectrum” plot. We will compare different spectrum analysis methods in regards to their performance of detecting exact cycle lengths (“frequency”) components. You will learn why the Goertzel algorithm outperforms classical Fourier transforms for the purpose of cycles detection in financial markets.
Understanding the basic computations involved in FFT-based or Goertzel-algorithm-based measurement, knowing how to apply proper scaling, correct non-integer interpolation, converting different units (frequency vs. time) and learning how to read spectrum plots are all critical to the success of cycle analysis and their related projection. Being equipped with this knowledge and using the tools discussed in this application note can bring you more success with your individual cycle analysis application.
Compared to an FFT, the Goertzel algorithm is simple and much more efficient for detecting cycles in data series related to financial markets. You will learn and understand why in this course.