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16

Wavelets are just one form of "basis decomposition". Wavelets in particular decompose in both frequency and time and thus are more useful than fourier or other purely-frequency based decompositions. There are other time-freq decompositions (for instance the HHT) which should be explored as well. Decomposition of a price series is useful in understanding ...


9

You need to investigate how to differentiate interpolation methods versus extrapolation methods. It's easy to build a model that repeats the past (just about any interpolation scheme will do the trick). The problem is, that model is typically worthless when it comes to extrapolating into the future. When you hear/see the word "cycles", a red flag should ...


6

Ehler's website has a technical papers section where there are papers available for free download, with code, so you can try things out for yourself. I personally have taken some of his ideas and combined them with other reading, forums etc. on the net and think that applying DSP to trading shows great promise and is definitely worthy investigation. If you ...


4

Cycle analysis and signal processing might be useful for seasonal patterns but without knowing more about the performance of such an approach to trading I would not consider a degree in signal processing for just trading. Would you be happy applying what you learn on standard engineering type problem because that may be what you'll be stuck doing if it ...


4

You need to define the parameters over which you are searching (i.e. # of bands, slope of trendline, some function relating slopes to trendline, etc.). Then you can use your favorite optimizer to identify which parameters satisfy your P&L objective. Of course, your approach is a surefire way to lose money since this curve-fitted model will not ...


4

The DSP techniques you're referring to are great for repeating signals, but not suitable for random signals (like price movements). While there are some techniques suitable for picking weak signals out of large noise environments (GPS is one that comes to mind), those techniques depend on knowing what the signal looks like, and if you knew what the signal ...


1

You have to mention more details as in what are your assumptions when you fit the digital filter. Also whether is it linear or not. If not then what non-linearities have you taken into account. The answer depends on that. Because you can either look at it as a form of non-stationarity or a different process altogether. Also how much time behind are you ...



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