{"id":37837,"date":"2024-11-01T10:00:51","date_gmt":"2024-11-01T10:00:51","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=37837"},"modified":"2024-11-01T11:09:20","modified_gmt":"2024-11-01T11:09:20","slug":"automated-trading-using-deep-learning-and-machine-learning-anomaly-detection-using-autoencoder-detecting-abnormal-movements-in-price-data-for-risk-management","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/37837\/","title":{"rendered":"Automated trading using deep learning and machine learning, anomaly detection using Autoencoder Detecting abnormal movements in price data for risk management."},"content":{"rendered":"

<\/p>\n

The cryptocurrency market, like Bitcoin, poses significant risks to investors due to high volatility and uncertainty. To manage these risks, automated trading systems utilizing deep learning and machine learning techniques are gaining attention. In particular, Autoencoder has established itself as a useful tool for risk management by detecting anomalous movements in data. This article will explain the concept of Autoencoder, its theoretical background, an application example of outlier detection in Bitcoin price data, and how to integrate this into an automated trading system.<\/p>\n

1. What is an Autoencoder?<\/h2>\n

An Autoencoder is an unsupervised learning model that compresses and reconstructs input data. The input and output share the same structure, with a low-dimensional representation known as latent space in between. An Autoencoder is divided into two main components:<\/p>\n