Clustering and attention model based for intelligent trading

TL;DR

This paper proposes a novel approach combining clustering and attention models to improve foreign exchange rate prediction, addressing market complexity and unexpected events with machine learning techniques.

Contribution

It introduces a new hybrid model integrating clustering and attention mechanisms for more accurate forex trading predictions.

Findings

Enhanced prediction accuracy over traditional models

Effective handling of oversold market scenarios

Utilization of extensive historical data from 2005 to 2021

Abstract

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.