mlsanityData to 14 Oct 2026

Quantformer: from attention to profit with a quantitative transformer trading strategy

arXiv 2404.00424 ↗First posted 30 Mar 2024q-fin.MF cs.AI cs.CEcited by 6

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Abstract (arXiv)

In traditional quantitative trading practice, navigating the complicated and dynamic financial market presents a persistent challenge. Fully capturing various market variables, including long-term information, as well as essential signals that may lead to profit remains a difficult task for learning algorithms. In order to tackle this challenge, this paper introduces quantformer, an enhanced neural network architecture based on transformer, to build investment factors. By transfer learning from sentiment analysis, quantformer not only exploits its original inherent advantages in capturing long-range dependencies and modeling complex data relationships, but is also able to solve tasks with numerical inputs and accurately forecast future returns over a given period. This work collects more than 5,000,000 rolling data of 4,601 stocks in the Chinese capital market from 2010 to 2023. The results of this study demonstrate the model's superior performance in predicting stock trends compared with other 100-factor-based quantitative strategies. Notably, the model's innovative use of transformer-like model to establish factors, in conjunction with market sentiment information, has been shown to enhance the accuracy of trading signals significantly, thereby offering promising implications for the future of quantitative trading strategies.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 39 of its 91 references that are in the map. First posted on arXiv 30 Mar 2024.

Builds on

39 of 91 references.

References · 39

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  4. 2020Language Models are Few-Shot Learnerscited by 32,419
  5. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  6. 2023GPT-4 Technical Reportcited by 18,396
  7. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  8. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  9. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  10. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  11. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  12. 2020Reformer: The Efficient Transformercited by 1,668
27 more
  1. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  2. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
  3. 2023BloombergGPT: A Large Language Model for Financecited by 939
  4. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 931
  5. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 607
  6. 2017Self-Normalizing Neural Networkscited by 598
  7. 2015An Introduction to Convolutional Neural Networkscited by 514
  8. 2020A Transformer-based Framework for Multivariate Time Series Representation Learningcited by 453
  9. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 391
  10. 2019FinBERT: Financial Sentiment Analysis with Pre-trained Language Modelscited by 385
  11. 2020Transformer Hawkes Processcited by 235
  12. 2022On Embeddings for Numerical Features in Tabular Deep Learningcited by 186
  13. 2019The Evolved Transformercited by 180
  14. 2020FinBERT: A Pretrained Language Model for Financial Communicationscited by 161
  15. 2018Temporal Relational Ranking for Stock Predictioncited by 102
  16. 2022AirFormer: Predicting Nationwide Air Quality in China with Transformerscited by 73
  17. 2022Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Versioncited by 59
  18. 2021Gated Transformer Networks for Multivariate Time Series Classificationcited by 58
  19. 2022Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecastingcited by 54
  20. 2020MQTransformer: Multi-Horizon Forecasts with Context Dependent and Feedback-Aware Attentioncited by 29
  21. 2020DA-Transformer: Distance-aware Transformercited by 23
  22. 2021SSDNet: State Space Decomposition Neural Network for Time Series Forecastingcited by 15
  23. 2023Financial Time Series Forecasting using CNN and Transformercited by 15
  24. 2021Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatilitycited by 10
  25. 2023Exploring the Advantages of Transformers for High-Frequency Tradingcited by 4
  26. 2023Stockformer: A Price-Volume Factor Stock Selection Model Based on Wavelet Transform and Multi-Task Self-Attention Networkscited by 2
  27. 2022Comparative Study of Machine Learning Models for Stock Price Predictioncited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025On Evaluating Loss Functions for Stock Ranking: An Empirical Analysis With Transformer Modelcited by 3
  2. 2025QTMRL: An Agent for Quantitative Trading Decision-Making Based on Multi-Indicator Guided Reinforcement Learningcited by 1
  3. 2026Patch-Level Tokenization with CNN Encoders and Attention for Improved Transformer Time-Series Forecastingcited by 1
  4. 2025Auditing Algorithmic Bias in Transformer-Based Tradingcited by 0
  5. 2025Adaptive Dueling Double Deep Q-networks in Uniswap V3 Replication and Extension with Mambacited by 0
  6. 2026QFCQT: A Chaotically Gated Quantformer Framework for Volatile Time-Series Forecastingcited by 0