Disliked{quote} If you throw away high frequency data you are losing valuable information. There are techniques for working with non-equally spaced high frequency tick data. For example, the volatility computed this way is much more accurate than when you compute it starting from smoothed data. The timing between the ticks is also important (see ACD model and it's derivatives). This kind of data is again thrown away if you aggregate ticks into candles, since candles are regularly spaced and lose the timing information in the original data. Also of notice...Ignored
My comment pertained entirely to standard analytic methods using equally-spaced samples. You are talking about non-equally spaced analyses. I am talking about mid-to-low frequency analysis. You are talking about high frequency analysis. For you, a highpass filter would make more sense than a lowpass filter
I would be very curious to hear more about these methods you have mentioned; they sound fascinating.