Label Unbalance in High-frequency Trading

• URL: http://arxiv.org/abs/2503.09988v3
• Date: Fri, 21 Mar 2025 03:10:17 GMT
• Title: Label Unbalance in High-frequency Trading
• Authors: Zijian Zhao, Xuming Zhang, Jiayu Wen, Mingwen Liu, Xiaoteng Ma,
• Abstract summary: In financial trading, return prediction is one of the foundation for a successful trading system.<n>In this paper, we adopts rigurious end-to-end deep learning framework with comprehensive label imbalance adjustment methods.<n>We succeed in predicting in high-frequency return in the Chinese future market.
• Score: 6.957870828620278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In financial trading, return prediction is one of the foundation for a successful trading system. By the fast development of the deep learning in various areas such as graphical processing, natural language, it has also demonstrate significant edge in handling with financial data. While the success of the deep learning relies on huge amount of labeled sample, labeling each time/event as profitable or unprofitable, under the transaction cost, especially in the high-frequency trading world, suffers from serious label imbalance issue.In this paper, we adopts rigurious end-to-end deep learning framework with comprehensive label imbalance adjustment methods and succeed in predicting in high-frequency return in the Chinese future market. The code for our method is publicly available at https://github.com/RS2002/Label-Unbalance-in-High-Frequency-Trading .

Related papers

• Combining Deep Learning on Order Books with Reinforcement Learning for Profitable Trading [0.0]
  This work focuses on forecasting returns across multiple horizons using order flow and training three temporal-difference imbalance learning models for five financial instruments. The results prove potential but require further minimal modifications for consistently profitable trading to fully handle retail trading costs, slippage, and spread fluctuation.
  arXiv  Detail & Related papers  (2023-10-24T15:58:58Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading [5.75899596101548]
  We propose a Reinforcement Learning (RL) algorithm that trades based on signals from a learned predictive model. We test our algorithm on 20+ years of equity data from Bursa Malaysia.
  arXiv  Detail & Related papers  (2023-07-18T16:00:02Z)
• SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning [101.86916775218403]
  This paper revisits the popular pseudo-labeling methods via a unified sample weighting formulation. We propose SoftMatch to overcome the trade-off by maintaining both high quantity and high quality of pseudo-labels during training. In experiments, SoftMatch shows substantial improvements across a wide variety of benchmarks, including image, text, and imbalanced classification.
  arXiv  Detail & Related papers  (2023-01-26T03:53:25Z)
• Denoised Labels for Financial Time-Series Data via Self-Supervised Learning [5.743034166791607]
  This work takes inspiration from image classification in trading and success in self-supervised learning. We investigate the idea of applying computer vision techniques to financial time-series to reduce the noise exposure. Our results show that our denoised labels improve the performances of the downstream learning algorithm.
  arXiv  Detail & Related papers  (2021-12-19T12:54:20Z)
• FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance [22.808509136431645]
  Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in quantitative finance. In this paper, we present the first open-source framework textitFinRL as a full pipeline to help quantitative traders overcome the steep learning curve.
  arXiv  Detail & Related papers  (2021-11-07T00:34:32Z)
• Bilinear Input Normalization for Neural Networks in Financial Forecasting [101.89872650510074]
  We propose a novel data-driven normalization method for deep neural networks that handle high-frequency financial time-series. The proposed normalization scheme takes into account the bimodal characteristic of financial time-series. Our experiments, conducted with state-of-the-arts neural networks and high-frequency data, show significant improvements over other normalization techniques.
  arXiv  Detail & Related papers  (2021-09-01T07:52:03Z)
• Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling [11.430440350359993]
  We propose LARA, a novel price movement forecasting framework with two main components. LA-Attention extracts potentially profitable samples through masked attention scheme. RA-Labeling refines the noisy labels of potentially profitable samples. LARA significantly outperforms several machine learning based methods on the Qlib quantitative investment platform.
  arXiv  Detail & Related papers  (2021-07-26T05:52:42Z)
• Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces [64.23172847182109]
  We show that different negative sampling schemes implicitly trade-off performance on dominant versus rare labels. We provide a unified means to explicitly tackle both sampling bias, arising from working with a subset of all labels, and labeling bias, which is inherent to the data due to label imbalance.
  arXiv  Detail & Related papers  (2021-05-12T15:40:13Z)
• Boosting Semi-Supervised Face Recognition with Noise Robustness [54.342992887966616]
  This paper presents an effective solution to semi-supervised face recognition that is robust to the label noise aroused by the auto-labelling. We develop a semi-supervised face recognition solution, named Noise Robust Learning-Labelling (NRoLL), which is based on the robust training ability empowered by GN.
  arXiv  Detail & Related papers  (2021-05-10T14:43:11Z)
• Fairness Constraints in Semi-supervised Learning [56.48626493765908]
  We develop a framework for fair semi-supervised learning, which is formulated as an optimization problem. We theoretically analyze the source of discrimination in semi-supervised learning via bias, variance and noise decomposition. Our method is able to achieve fair semi-supervised learning, and reach a better trade-off between accuracy and fairness than fair supervised learning.
  arXiv  Detail & Related papers  (2020-09-14T04:25:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.