Sep

28

FFT (Fast Fourier Transform) constructs a “best cyclic approximation to the data” that can be constructed with N cycles. And you get to pick the N value. Better yet is that the output is a smoothed representation of the raw data without any lags. Wow, no lags! However, FFT assumes that the cyclic behavior is repetitive from the beginning of time to the end of time. That’s great for fitting data, but not generally reliable for forecasting markets. Also, every time you add or drop a datapoint, a subsequent cyclic approximation will have different values over the entire period.

Suggestion: FFT is fine for seasonally adjusting past macroeconomic data, but your expected value of using it for trading will be negative.


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