Clustering and attention model based for intelligent trading
This work addresses foreign exchange trading risks for investors, but it appears incremental as it applies existing methods to a specific scenario without clear novelty.
The paper tackles foreign exchange rate forecasting by using historical data and technical indicators from 2005 to 2021 to build machine learning models for event-driven price prediction in oversold scenarios, but no concrete results or numbers are provided.
The foreign exchange market has taken an important role in the global financial market. While foreign exchange trading brings high-yield opportunities to investors, it also brings certain risks. Since the establishment of the foreign exchange market in the 20th century, foreign exchange rate forecasting has become a hot issue studied by scholars from all over the world. Due to the complexity and number of factors affecting the foreign exchange market, technical analysis cannot respond to administrative intervention or unexpected events. Our team chose several pairs of foreign currency historical data and derived technical indicators from 2005 to 2021 as the dataset and established different machine learning models for event-driven price prediction for oversold scenario.