# FFT (Fast Fourier Transform) of time series — promises and pitfalls towards trading

**Intelligent Trading**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Fig 1. FFT transformed time series (EBAY) reconstructed with first three and twenty harmonics, respectively.

I see quite a few traders interested in advanced signal processing techniques. It is often instructive to see why they may or may not be useful. The concept behind fourier analysis is that any periodic signal can be broken down into a taylor series or sum of suitably scaled sine and cosine waveforms (even a square wave!). The key requirement is that the signals are periodic, which means that they repeat forwards and backwards to plus and minus infinity. Anyone who deals with financial series knows they are aperiodic (meaning they do not repeat indefinitely). The FFT, or fast fourier transform is an algorithm that essentially uses convolution techniques to efficiently find the magnitude and location of the tones that make up the signal of interest. We can often play with the FFT spectrum, by adding and removing successive tones (which is akin to selectively filtering particular tones that make up the signal), in order to obtain a smoothed version of the underlying signal.

In the posted example, I showed the effect of reconstructing the transformed waveform using only the first three tones (and cutting off all others), where we see a low passed version of the signal. The second example includes the first 20 tones, which begins to match the signal more closely, but is a smoothed representation of the signal, which is often a nice representation to isolate smoothed signal component from high frequency noise. Notice the terms tones and harmonics are practically synonymous for purposes of this discussion (a harmonic is more specifically a multiple of the fundamental tone); both represent the spectral frequency components that sum up to make the total waveform. The major problem that I wanted to illustrate with this simple example (among many), is the problem of ‘wraparound effects.’ As I mentioned earlier, one of the requirements for properly applying a fourier transform is that the signal is periodic or repeating, since the basis functions (sines and cosines) that are convolved are infinitely repeating functions.

With that requirement, the reconstructed waveform tries its best to match the beginning and endpoints for periodic repetition. The result is severe problems at the endpoints (left and right), which are often the points we are most concerned about. So it often pays to be cautious when hearing about applications of higher level signal processing techniques. There are several other requirements and limitations to applying FFT techniques, among them: requirement of 2^n samples, fsample must be greater than or equal to twice the max bandwidth of sampled signal (nyquist criterion), limited spectral tone bin resolution; ignoring any of these issues can cause severe reconstruction errors.

**leave a comment**for the author, please follow the link and comment on their blog:

**Intelligent Trading**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.