MATLAB: Simple linear regression slope calculation

linear regression slopetrading algorithm

i am new to using matlab i have a trading system written in another language which uses linear regression slope in the algorithm. i thing that is the formula used by metastock originally. now i am trying to port it into matlab. Matlab has lots of linear regression models formulas etc. but i cannot decide which formula to use to define it . suppose i have 10,000×1 rows of data how can i calculate the slope of linear regression line for last 50 rows.

edit: after much reading i am pretty sure the linearregression line formula in trading softwares means ,for last x points of data linearregression(x) is the solution/prediction of next datapoint using linear least squares method. Still i dont know how can i do this. I am very thankful for everyone trying to help. i hope this clarification will help. i am adding a calculated (by some trading software) price,linearregression and linearregressionslope data here.

Best Answer

You don’t give enough information to write specific code, but the easiest way to do a linear regression would be to use the polyfit (and polyval) functions:

coefs = polyfit(x, y, 1);

The slope will be ‘coefs(1)’.

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MATLAB: How to draw a best fit line based on pixels, on the image itself

Please post your image and annotate it to indicate which pixels the line should be drawn through and indicate if you already know their (x,y) or (row,column) locations. In the meantime, see my attached polyfit demo.

And explain what "the distribution" means to you. Is it the histogram (probability density function), or is it the spatial arrangement/layout of where the pixels are?

MATLAB: Fit a polynimial function on a image curve

How about this:

% Demo to find fitted curve.
% By Image Analyst
clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 22;
%--------------------------------------------------------------------------------------------------------


%    READ IN IMAGE
folder = pwd;
baseFileName = 'image.jpeg';
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
% Check if file exists.
if ~exist(fullFileName, 'file')
	% The file doesn't exist -- didn't find it there in that folder.
	% Check the entire search path (other folders) for the file by stripping off the folder.
	fullFileNameOnSearchPath = baseFileName; % No path this time.
	if ~exist(fullFileNameOnSearchPath, 'file')
		% Still didn't find it.  Alert user.
		errorMessage = sprintf('Error: %s does not exist in the search path folders.', fullFileName);
		uiwait(warndlg(errorMessage));
		return;
	end
end
grayImage = imread(fullFileName);
% Get the dimensions of the image.
% numberOfColorChannels should be = 1 for a gray scale image, and 3 for an RGB color image.
[rows, columns, numberOfColorChannels] = size(grayImage);
if numberOfColorChannels > 1
	% It's not really gray scale like we expected - it's color.
	% Use weighted sum of ALL channels to create a gray scale image.
	grayImage = rgb2gray(grayImage);
	% ALTERNATE METHOD: Convert it to gray scale by taking only the green channel,
	% which in a typical snapshot will be the least noisy channel.
	% grayImage = grayImage(:, :, 2); % Take green channel.
end
%--------------------------------------------------------------------------------------------------------
%    SEGMENTATION OF IMAGE
% Get a binary image
binaryImage = imbinarize(grayImage);
subplot(3, 1, 1);
imshow(binaryImage, []);
impixelinfo;
title('Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');
hFig = gcf;
hFig.WindowState = 'maximized'; % May not work in earlier versions of MATLAB.
drawnow;
%--------------------------------------------------------------------------------------------------------
% REMOVE BOTTOM ARC
for col = 1 : columns
	thisCol = binaryImage(:, col);
	% Remove largest blob.
	biggestBlob = bwareafilt(thisCol, 1);
	thisCol(biggestBlob) = false;
	binaryImage(:, col) = thisCol;
end
% Display it.

subplot(3, 1, 2);
imshow(binaryImage)
grid on;
title('Without bottom arc', 'FontSize', fontSize, 'Interpreter', 'None');
% Get (x,y) of what's left
[y, x] = find(binaryImage);
% Add a tiny bit of noise because sometimes there are multiple y for the same x.
x = x + 0.001 * rand(1, length(x));
% Sort them
[x, sortOrder] = sort(x, 'ascend');
y = y(sortOrder);
% Filter with movmean
ySmoothed = movmean(y, 5);
% Display it.
subplot(3, 1, 3);
imshow(binaryImage)
grid on;
title('With Fitted Curve in Red', 'FontSize', fontSize, 'Interpreter', 'None');
hold on
plot(x, ySmoothed, 'r-', 'LineWidth', 3);

Best Answer

Related Solutions

MATLAB: How to draw a best fit line based on pixels, on the image itself

MATLAB: Fit a polynimial function on a image curve

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