Summary of Research on Optimizing Real-time Data Processing in High-frequency Trading Algorithms Using Machine Learning, by Yuxin Fan et al.
Research on Optimizing Real-Time Data Processing in High-Frequency Trading Algorithms using Machine Learning
by Yuxin Fan, Zhuohuan Hu, Lei Fu, Yu Cheng, Liyang Wang, Yuxiang Wang
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Finance (q-fin.CP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors aim to optimize real-time data processing for high-frequency trading (HFT) algorithms. They develop a dynamic feature selection mechanism that analyzes market data in real-time using clustering and feature weight analysis, automatically selecting the most relevant features. The system employs an adaptive feature extraction method, adjusting its feature set in response to changing data inputs. Lightweight neural networks with fast convolutional layers and pruning techniques are designed to minimize computational complexity and inference time. Unlike traditional deep learning models, this architecture reduces the number of parameters and processing speed while maintaining consistent performance across varying market conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary High-frequency trading is a critical part of financial markets where speed and accuracy matter. The goal of this research is to improve real-time data processing for high-frequency trading algorithms. The system they developed can analyze market data quickly and automatically select the most important features. This helps the algorithm make better decisions and increase revenue. The researchers also designed lightweight neural networks that process information faster while still being accurate. In the end, their model shows consistent performance across different market conditions. |
Keywords
» Artificial intelligence » Clustering » Deep learning » Feature extraction » Feature selection » Inference » Pruning
