# Clustering and attention model based for intelligent trading

arXiv: https://arxiv.org/abs/2107.06782 · HTML: https://mlsanity.com/paper/2107.06782/

## 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.

## Builds on (most cited first, 9 of 9)

- [Efficient Transformers: A Survey](https://mlsanity.com/paper/2009.06732/) (2020)
- [Time2Vec: Learning a Vector Representation of Time](https://mlsanity.com/paper/1907.05321/) (2019)
- [Long Short-Term Memory-Networks for Machine Reading](https://mlsanity.com/paper/1601.06733/) (2016)
- [Divide-and-Conquer Reinforcement Learning](https://mlsanity.com/paper/1711.09874/) (2017)
- [Text Mining of Stocktwits Data for Predicting Stock Prices](https://mlsanity.com/paper/2103.16388/) (2021)
- [Portfolio Optimization with 2D Relative-Attentional Gated Transformer](https://mlsanity.com/paper/2101.03138/) (2020)
- [Wavelet Denoising and Attention-based RNN-ARIMA Model to Predict Forex Price](https://mlsanity.com/paper/2008.06841/) (2020)
- [GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading](https://mlsanity.com/paper/2008.09471/) (2020)
- [Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating](https://mlsanity.com/paper/2103.09106/) (2021)

## 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.