{"id":35611,"date":"2024-11-01T09:40:46","date_gmt":"2024-11-01T09:40:46","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=35611"},"modified":"2024-11-01T11:12:02","modified_gmt":"2024-11-01T11:12:02","slug":"machine-learning-and-deep-learning-algorithm-trading-long-short-trading-strategy-using-boosting","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/35611\/","title":{"rendered":"Machine Learning and Deep Learning Algorithm Trading, Long Short Trading Strategy Using Boosting"},"content":{"rendered":"

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Introduction<\/h2>\n

\n Recently, algorithmic trading utilizing machine learning (ML) and deep learning (DL) has rapidly increased in the financial markets. In this article, we will deeply explore how to establish a long\/short trading strategy using boosting techniques. The long\/short trading strategy involves simultaneously buying and selling two different assets to hedge market risk. Such strategies can be implemented more effectively through well-designed machine learning models.\n <\/p>\n

Basic Concepts of Machine Learning and Deep Learning<\/h2>\n

Machine Learning<\/h3>\n

\n Machine learning is a set of algorithms that learn and make predictions from data, combining statistics and computer science. The primary goal of machine learning is to identify patterns and make predictions based on input data provided by users. This enables a wide range of applications based on allowing machines to learn from data.\n <\/p>\n

Deep Learning<\/h3>\n

\n Deep learning is a subfield of machine learning that uses artificial neural networks to perform abstractions on high-dimensional data. Deep learning is highly effective at solving complex problems such as image recognition, natural language processing, and time series forecasting. In stock market prediction, deep learning can be a particularly powerful tool.\n <\/p>\n

Boosting Algorithms<\/h2>\n

\n Boosting is a technique that combines weak learners to create a strong learner. Boosting algorithms iteratively create weak learners and reduce errors, assigning weights to mispredicted data in each iteration. Representative boosting algorithms include AdaBoost, Gradient Boosting, XGBoost, and LightGBM.\n <\/p>\n

Principle of Boosting<\/h3>\n

\n Boosting operates through the following processes:\n <\/p>\n