Clustering and attention model based for intelligent trading
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 47 references that are in the map. First posted on arXiv 6 Jul 2021.
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9 of 47 references.
References · 9
- 2020Efficient Transformers: A Surveycited by 790
- 2019Time2Vec: Learning a Vector Representation of Timecited by 213
- 2016Long Short-Term Memory-Networks for Machine Readingcited by 161
- 2017Divide-and-Conquer Reinforcement Learningcited by 48
- 2021Text Mining of Stocktwits Data for Predicting Stock Pricescited by 7
- 2020Portfolio Optimization with 2D Relative-Attentional Gated Transformercited by 4
- 2020Wavelet Denoising and Attention-based RNN-ARIMA Model to Predict Forex Pricecited by 3
- 2020GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTradingcited by 3
- 2021Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Ratingcited by 3
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