Can the Fourier transform be applied to moving averages with Python?

In summary, the Fourier transform can be applied to moving averages in Python to analyze frequency components in time series data. By transforming the moving average into the frequency domain, one can identify patterns and trends that may not be apparent in the time domain. Python libraries such as NumPy and SciPy facilitate this process, allowing for efficient computation and visualization of the Fourier transform results.
  • #1
herchell
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I would like to compare and backtest these signals by applying Fourier transform to the signals received from moving averages. I would be very pleased if you could share your opinions and suggestions on this issue.
 
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  • #2
Welcome to PF.

Can you say more about "these signals"? What are their characteristics? And when you do a moving average on a signal, that is basically a lowpass digital filter that you are applying. Do you really want a FT (or FFT) of this lowpass filtered signal, or do you want to FT the original signal?
 
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  • #3
berkeman said:
Welcome to PF.

Can you say more about "these signals"? What are their characteristics? And when you do a moving average on a signal, that is basically a lowpass digital filter that you are applying. Do you really want a FT (or FFT) of this lowpass filtered signal, or do you want to FT the original signal?
I want the fourier transform of the original signal. Wouldn't that be more effective for analysis?
 
  • #4
herchell said:
these signals
herchell said:
the original signal
herchell said:
the signals received from moving averages

:welcome:

Perhaps you could be a bit more specific about what exactly you have and what you want to do. Now we have to guess how to help you .

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