Stocks & Commodities V. 23:2 (56-59, 63): Fourier Transforms As An Aid To Decision-Making by Alok Srivastava
Interested in improving the performance of
your indicators? It may be worth your while to
gain an understanding of Fourier transforms,
frequency analysis, and inverse transformations.
Traditionally, technical analysis has been used to detect and interpret patterns in past security prices to
provide insight into future price movement. This, in turn, has lured researchers to try to beat the markets consistently, using a range of techniques varying from mathematics, physics, and economics to psychology. In this article, I will focus on the use of Fourier transforms together with technical analysis in making
The application of Fourier transforms has been well established in fields like digital signal processing (DSP), medical diagnostics, image processing, the media, and so on. The Fourier transform breaks up a signal from the time domain to a frequency domain,
characterizing signals and letting you see order where there appeared to be none. For this article I used the transform to convert raw data into useful information to aid in making trading decisions.
Many technical analysis indicators seem to work well for specific kinds of patterns. Unfortunately, there are no indicators that can take into account all of the possible
characteristics that a price series can exhibit. I will show you how to incorporate stock price volatility so your indicators can adapt accordingly.