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Liquidity takers behavior representation through a contrastive learning approach
Published 9 Jun 2023 in q-fin.ST, cs.LG, and stat.ML | (2306.05987v2)
Abstract: Thanks to the access to the labeled orders on the CAC40 data from Euronext, we are able to analyze agents' behaviors in the market based on their placed orders. In this study, we construct a self-supervised learning model using triplet loss to effectively learn the representation of agent market orders. By acquiring this learned representation, various downstream tasks become feasible. In this work, we utilize the K-means clustering algorithm on the learned representation vectors of agent orders to identify distinct behavior types within each cluster.
- Jonathan Brogaard et al. High frequency trading and its impact on market quality. Northwestern University Kellogg School of Management Working Paper, 66, 2010.
- High-frequency trading and price discovery. The Review of Financial Studies, 27(8):2267–2306, 2014.
- Statistical predictions of trading strategies in electronic markets. Available at SSRN 4442770, 2023.
- A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597–1607. PMLR, 2020.
- Learning a similarity metric discriminatively, with application to face verification. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 539–546. IEEE, 2005.
- Analysis and modeling of client order flow in limit order markets. Quantitative Finance, pages 1–19, 2023.
- Limit order books. Quantitative Finance, 13(11):1709–1742, 2013.
- Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271–21284, 2020.
- Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pages 297–304. JMLR Workshop and Conference Proceedings, 2010.
- The diversity of high-frequency traders. Journal of Financial Markets, 16(4):741–770, 2013.
- The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer, 2009.
- Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729–9738, 2020.
- Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
- Stock trend prediction with multi-granularity data: A contrastive learning approach with adaptive fusion. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pages 700–709, 2021.
- Simulating and analyzing order book data: The queue-reactive model. Journal of the American Statistical Association, 110(509):107–122, 2015.
- Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2015.
- The flash crash: High-frequency trading in an electronic market. The Journal of Finance, 72(3):967–998, 2017.
- Stuart Lloyd. Least squares quantization in pcm. IEEE transactions on information theory, 28(2):129–137, 1982.
- J MacQueen. Classification and analysis of multivariate observations. In 5th Berkeley Symp. Math. Statist. Probability, pages 281–297, 1967.
- Self-supervised representation learning from 12-lead ecg data. Computers in biology and medicine, 141:105114, 2022.
- Contrastive representation learning for electroencephalogram classification. In Machine Learning for Health, pages 238–253. PMLR, 2020.
- Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018.
- Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019.
- Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 815–823, 2015.
- Universal features of price formation in financial markets: perspectives from deep learning. Quantitative Finance, 19(9):1449–1459, 2019.
- Justin A Sirignano. Deep learning for limit order books. Quantitative Finance, 19(4):549–570, 2019.
- Kihyuk Sohn. Improved deep metric learning with multi-class n-pair loss objective. Advances in neural information processing systems, 29, 2016.
- Sequence to sequence learning with neural networks. Advances in neural information processing systems, 27, 2014.
- Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(11), 2008.
- Conditional mutual information-based contrastive loss for financial time series forecasting. In Proceedings of the First ACM International Conference on AI in Finance, pages 1–7, 2020.
- Queue-reactive hawkes models for the order flow. arXiv e-prints, pages arXiv–1901, 2019.
- Barlow twins: Self-supervised learning via redundancy reduction. In International Conference on Machine Learning, pages 12310–12320. PMLR, 2021.
- Deep learning for market by order data. Applied Mathematical Finance, 28(1):79–95, 2021.
- Deeplob: Deep convolutional neural networks for limit order books. IEEE Transactions on Signal Processing, 67(11):3001–3012, 2019.
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