Self-optimization algorithms for trading strategies include parameter optimization, evolutionary algorithms, heuristic methods, gradient-based techniques, machine learning, and simulation-based optimization. Each has unique pros and cons, tailored for different trading needs and market conditions. Python programs are an excellent tool to try ideas, create graphics quickly and confirm theoretical statements with historical trading data. Python allows to develop and adjust models agilely, ...
This is a unique research, the idea for which came to me while answering questions that arose during the discussion of one of my articles. I am hopeful that readers will appreciate the value and originality of this work. To carry out the experiment, we need to first initialize the coordinates of the agents forcibly outside the algorithm, using the coordinates of the global minimum, before measuring the fitness function at the first epoch. Such an experiment will ...
Adaboost, short for adaptive boosting is an ensemble machine learning model that attempts to build a strong classifier out of weak classifiers. more...
NASA's Curiosity rover has been exploring the Gale Crater on Mars since 2012. That's a long time to weather dust storms, drive along the base of a mountain and crawl over endless pointy rocks. The rover's aluminum wheels have taken a beating. New images show some dramatic gaping holes, but don't be alarmed. NASA has it under control. The photos are eye-opening. Curiosity has six wheels and some are worse off than others. The right-middle wheel looks rough, but "is still holding up well despite ...
Support Vector Regression (SVR) is a form of regression derived from Support Vector Machines. At its core, SVR uses kernel methods to map input data into higher-dimensional spaces, allowing for more complex relationships to be captured, which contrasts with dimensionality reduction. For this article though we are exploring strictly its loss function role when used with a multi-layer perceptron. A related but different form of regression we looked at in an earlier article was Gaussian Process ...
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