Improved LSTM-Based Stock Prediction Method Authors: Fang Hong¹ Han Xingyu² Xu Tao³ (1. Shanghai Second Polytechnic University, School of Humanities and Sciences 2. University College London, Department of Computer Science) Source: Journal of Anhui University (Natural Science Edition) 2019.43(6):36-42 Abstract In response to the common lag issue of Long Short-Term Memory (LSTM) networks in stock … Read more
Hello everyone, I am Orange Brother! Today, we will look at how to use the LSTM model for stock price prediction and achieve automated trading through the Alpaca API. This code is mainly divided into the following key parts: importing necessary libraries, data preparation, model construction, model training, model loading and evaluation, and automated trading … Read more
RNN is a popular model for processing time series data, demonstrating significant effectiveness in fields such as NLP and time series forecasting.As this article focuses on the practical application of RNN rather than theoretical knowledge, interested readers are encouraged to study RNN systematically. The following example is implemented using TensorFlow.Using TensorFlow to implement RNN or … Read more
Source: Time Series Research This article is approximately 2800 words long and is recommended for a 5-minute read. This article proposes a higher-order Transformer architecture specifically designed to handle multimodal stock data for predicting stock movements. For investors and traders, predicting stock movements in the financial market is crucial as it enables them to make … Read more
Editorial Department WeChat Official Account Keywords All Network Search Latest Ranking “Quantitative Investment”: Rank One “Quantization”: Rank One “Machine Learning”: Rank Four We will continue to strive To become a high-qualityFinancial and Technical public account Introduction to LSTM Networks LSTM Networks are a type of Recurrent Neural Network (RNN) first introduced by Sepp Hochreiter and … Read more
Recurrent Neural Networks (RNN) are designed based on the recursive nature of sequential data (such as language, speech, and time series) and are a type of feedback neural network that contains loops and self-repetitions, hence the name “recurrent”. They are specifically used to handle sequential data, such as generating text word by word or predicting … Read more