It's hard to overfit (model noise) on typical asset prices with a parabola and three cosine waves. If I used a few more cosines ... Now there is a fit that would have made Jean-Baptiste Joseph Fourier proud!
No. The models are more for timing by looking at predicted inflection points and not for predicting price targets.
And what's the use in trading of your calculations? Because to me that's the only thing that counts. Are you not always running behind the facts? Like this: But you impressed me with that math. @globalarbtrader , you can use this for your book too, just mention my name.
That post is a tounge-in-cheek comment to "Would you mind if I used this as an example of overfitting in my next book?" The graph is a combination of a least-squares parabola and inverse Fourier Transform to get a very overfitted curve. My original CORN post presents fitted curves that have a parabola plus three cosine waves. In my opinion, those models do not overfit the CORN prices because they visually don't follow the prices very closely. Perhaps globalarbtrader or someone else can explain how these simple curves overfit? I tried to explain the philosophy of this here and here. The generated models try to capture a recent trend (parabola) and oscillations around the trend (cosine waves). Since the functions depend only on time, they can be extrapolated to find possible inflection points for times of trade entry and exit.
A function like Code: y = 16.7131156921387 - 0.0153137510642409 * x + 0.000483304698718712 * x * x + 0.959539353847504 * cos(twopi / 63.4018692382697 * x + 3.40450406074524) + 0.770700573921204 * cos(twopi / 43.3602527495009 * x + 4.73570346832275) + 0.467519104480743 * cos(twopi / 34.7284220393245 * x + 6.08380746841431) ; has y as the predicted price, and x is the number of bars (days) from the beginning of the input data. Since you know what time is in the future (e.g., two weeks from now is always 14 days), this makes it possible to predict future prices and the time(s) of the next price swing(s). An image from this post illustrates this: The two rightmost inflection points in the detrended curve are in the future relative to when the input data ended (after the mean line in the graph ends). You can't do that with a filter like a moving average because it uses prices as inputs, and you don't know future prices until they happen. FYI, the 9th degree polynomial fit above was intended as a joke because higher-order polynomials are known for creating extreme values.