I made a pine-script v4 program. My objective is to make relative value. The program is calculating and drawing a curve which determine the spread in % between two shares (here Apple and Microsoft) with the same start point. The problem is that the result is wrong. This is my code:
//#version=4
study("Apple-Microsoft", overlay=false)
Apple=(security("AAPL","D",close)/security("AAPL","D",2020-01-01-09-30)-1)*100
Microsoft=(security("MSFT","D",close)/security("MSFT","D",2020-01-01-09-30)-1)*100
spread=Apple-Microsoft
plot(spread)
Thanks for your answers.
Related
I have two - likely simple - questions that are bothering me, both related to quadratic programming:
1). There are two "standard" forms of the objective function I have found, differing by multiplication of negative 1.
In the R package quadprog, the objective function to be minimized is given as −dTb+12bTDb and in Matlab the objective is given as dTb+12bTDb. How can these be the same? It seems that one has been multiplied through by a negative 1 (which as I understand it would change from a min problem to a max problem.
2). Related to the first question, in the case of using quadprog for minimizing least squares, in order to get the objective function to match the standard form, it is necessary to multiply the objective by a positive 2. Does multiplication by a positive number not change the solution?
EDIT: I had the wrong sign for the Matlab objective function.
Function f(b)=dTb is a linear function thus it is both convex and concave. From optimization standpoint it means you can maximize or minimize it. Nevertheless minimizer of −dTb+12bTDb will be different from dTb+12bTDb, because there is additional quadratic term. Matlab implementation will find the one with plus sign. So if you are using different optimization software you will need to change d→−d to get the same result.
The function −dTb+12bTDb where D is symmetric and convex and thus has unique minimum. In general that is called standard quadratic programming form, but that doesn't really matter. The other function dTb−12bTDb is concave function which has unique maximum. It is easy to show that for, say, bounded function f(x) from above the following holds:
argmaxxf=argminx−f
Using the identity above value b∗1 where −dTb+12bTDb achieves minimum is the same as the value b∗2 which achieves maximum at dTb−12bTDb, that is b∗1=b∗2.
Programmatically it doesn't matter if you are minimizing −dTb+12bTDb or maximizing the other one. These are implementation-dependent details.
No it does not. ∀α>0 if x∗=argmaxxf(x), then x∗=argmaxxαf(x). This can be showed by contradiction.
So I was reading a Document about Displacement Mappings and Surface Blendings and came across this equation which is supposed to be a Alpha-Blending equation:
while v1,...,vn are supposed to be the value vector and w1,....,wn the weight vector (is how the document describes it).
To tell what my interpretation of this equation is, is that considering n being the number of surfaces we are trying to blend together the value vectors are supposed to represent as the name says the value (probably color related?) of each surface and the weight vector basically describing the value preference of each surface (so the higher the weight value the more we would see the color of that one surface after the blend). The multiplication and division part is something what i do not fully understand (just interpreting it as the 'it just works like that' part of the equation)
I couldn't find any similar equation anywhere so far so I figured out that either I didn't search deep enough or I am not understanding something that is supposed to be very obvious and I wanted to make sure that fully understand this equation for further read in the document which bases on this idea.
I download the following graph-cut code:
https://github.com/shaibagon/GCMex
I compiled the mex files, and ran it for pre-defined image in the code (which is rgb image)
I wanna optimize the image segmentation results,
I have probability map of the image, which its dimension is (width,height, 5). Five probability distribution over the image dimension are stacked together. each relates to one the classes.
My problem is which parts of code should according to the probability image.
I want to define Data and Smoothing terms based on my application.
My question is:
1) Has someone refined the code according to the defining different energy function (I wanna change Unary and pair-wise formulation).
2) I have a stack of 3D images. I wanna define 6-neighborhood system, 4 neighbors in current slice and the other two from two adjacent slices. In which function and part of code can I do the refinements?
Thanks
I am trying to smooth a curve like x=x(t) with t as time and x as plane vector; I have done some search work and simply known some names like Moving Average and Savitzky–Golay filter.
My problem is that the curve is ongoing and we can only get the position of the end point on the curve in each similar interval. However, the position we get is slightly different from the real position or in other words that it has a noise. If using the method like moving average to reduce the noise, like x'[t]=(x[t+2]+x[t+1]+x[t]+x[t-1]+x[t-2])/5, I won't get the refined point x'[t] which might be more close to the point x[t] until I get the point x[t+2].
Is there any method that when get the newest end point x[t], I get the refined point x'[t] which is close to x[t] using only previous points serial?
I am working on a program that needs to fit numerous cosine waves in order to determine one of the parameters for the function. The equation that I am using is y = y_0 + Acos((4*pi*L)/x + pi) where L is the value that I am trying to obtain from the best fit line.
I know that it is possible to do this correctly by hand for each set of data, but what is the best way to automate this process? I am currently reading in the data from text files, and running a loop with the initial paramiters changing until I have an array of paramater values that have an amplitude similar to the data, then I check the percent difference between points on the center peak and two end peaks to try to pick the best one. It in consistently picking lower values than what I get when fitting by hand (almost exactly one phase off). So is there a way to improve this method, or another method that works better?
Edit: My LabVIEW version has a cos fitting VI which is what I am using, the problem is when I try to automate the fitting by changing the initial parameters using a loop, I cant figure out how to get the program to pick the same best fit line as a human would pick.
Why not just use a Fast Fourier Transform? This should be way faster than fitting a cosine. In the result vector of complex numbers look for the largest peak of in the totals. You're given frequency (position in the FFT result vector), amplitude and phase.
You can evaluate the goodness of the fit by computing the difference between fitting curve and your data. A VI does this in the "Advanced curve fitting" palette. Then all you have to do is pick up the best fit.