Corpus ID: 235828793

Clustering and attention model based for Intelligent Trading

  title={Clustering and attention model based for Intelligent Trading},
  author={Mimansa Rana and Nanxiang Mao and Ming Ao and Xiaohui Wu and Poning Liang and Matloob Khushi},
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… Expand


Foreign Exchange Currency Rate Prediction using a GRU-LSTM Hybrid Network
A new model that combines two powerful neural networks used for time series prediction: Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) for predicting the future closing prices of FOREX currencies proves itself as the least risky model among all. Expand
Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder-LSTM Techniques
  • Gunho Jung, Sun-Yong Choi
  • Computer Science
  • Complex.
  • 2021
It is established that FX volatility can be accurately predicted using a combination of deep learning models, and the proposed hybrid model, which is called the autoencoder-LSTM model, outperforms the traditional LSTM method. Expand
Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators
This work used a popular deep learning tool called “long short-term memory” (LSTM), which has been shown to be very effective in many time-series forecasting problems, to make direction predictions in Forex, and proposed hybrid model, which combines two separate LSTMs corresponding to these two data sets, was found to be quite successful in experiments using real data. Expand
Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating
The proposed methodology with the combination of purposefully picked features shows an improvement over the previous studies, and the model predicts the direction of 1% price changes on the 10th day with high confidence and with enough buffer to even build a robotic trading system. Expand
A stacked generalization system for automated FOREX portfolio trading
This work examines the efficacy and the feasibility of developing a stacked generalization system, intelligently combining the predictions of diverse machine learning models, and establishes a novel inferential framework that leads to significantly better trading performance than the considered benchmarks. Expand
Prediction Stock Price Based on Different Index Factors Using LSTM
This paper proposes a novel idea that average previous five days stock market information as a new value then use this value to predict, and use the predicted value as the average of the stock price information for the next five days. Expand
Stock Prediction using Deep Learning and Sentiment Analysis
  • Yichuan Xu, V. Keselj
  • Computer Science
  • 2019 IEEE International Conference on Big Data (Big Data)
  • 2019
The finance tweets that are posted from market closure to market open in the next day has more predictive power on next day stock movement and the weighted sentiment on max follower on StockTwits also outperforms other methods. Expand
Event-Driven LSTM For Forex Price Prediction
The majority of studies in the field of AI guided financial trading focus on applying machine learning algorithms to continuous historical price and technical analysis data. However, due to theExpand
Text Mining of Stocktwits Data for Predicting Stock Prices
FinALBERT, an ALBERT based model trained to handle financial domain text classification tasks by labelling Stocktwits text data based on stock price change is introduced, which can help analyse the historical data effectively and the mathematical function can be easily customised to predict stock movement. Expand
Wavelet Denoising and Attention-based RNN- ARIMA Model to Predict Forex Price
A novel approach that integrates the wavelet denoising, Attention-based Recurrent Neural Network (ARNN), and Autoregressive Integrated Moving Average (ARIMA) models is proposed, capable of modelling dynamic systems such as the forex market. Expand