Addition/subtraction are also much simpler/cheaper than they would be in an entirely logarithmic model. If floats were just 2^x with some 64 bit fixed point x, it's not clear to me how to do addition efficiently.
The "200 basis point spread" comes from the difference between the very low or non-existent interest paid on the cash in brokerage accounts and the rates the brokerage can earn by lending that money out basically risk-free. If you keep 10% of your assets in cash, a 200 bp interest spread becomes effectively a 20 bp management fee on your assets.
The problem being solved essentially is: you have two binary strings, and you want to offset one of them so that they match up the best. For each offset, you're taking a dot product of one sequence with the offset version of the other. This is the same as computing the convolution of the two sequences together (https://en.wikipedia.org/wiki/Convolution). Computing this naively would be O(n^2) (doing linear work for each possible offset).
One property of the Fourier transform is that convolution in the time domain corresponds to element-wise multiplication in the frequency domain (https://en.wikipedia.org/wiki/Convolution_theorem), so you can compute the convolution efficiently by taking the FFT of both series, doing element-wise multiplication, and then taking the inverse FFT of the result.