Element contents
Series: Elements in Quantitative Finance
Deep Learning in Quantitative Trading
Published online by Cambridge University Press: 03 October 2025
Summary
This Element provides a comprehensive guide to deep learning in quantitative trading, merging foundational theory with hands-on applications. It is organized into two parts. The first part introduces the fundamentals of financial time-series and supervised learning, exploring various network architectures, from feedforward to state-of-the-art. To ensure robustness and mitigate overfitting on complex real-world data, a complete workflow is presented, from initial data analysis to cross-validation techniques tailored to financial data. Building on this, the second part applies deep learning methods to a range of financial tasks. The authors demonstrate how deep learning models can enhance both time-series and cross-sectional momentum trading strategies, generate predictive signals, and be formulated as an end-to-end framework for portfolio optimization. Applications include a mixture of data from daily data to high-frequency microstructure data for a variety of asset classes. Throughout, they include illustrative code examples and provide a dedicated GitHub repository with detailed implementations.
Keywords
Information
- Type
- Element
- Information
- Series: Elements in Quantitative FinanceOnline ISBN: 9781009707091Publisher: Cambridge University PressPrint publication: 30 October 2025
References
Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th acm sigkdd international conference on knowledge discovery & data mining (pp. 2623–2631).CrossRefGoogle Scholar
Almgren, R., & Chriss, N. (2001). Optimal execution of portfolio transactions. Journal of Risk, 3, 5–40.10.21314/JOR.2001.041CrossRefGoogle Scholar
Atkins, A., Niranjan, M., & Gerding, E. (2018). Financial news predicts stock market volatility better than close price. The Journal of Finance and Data Science, 4(2), 120–137.CrossRefGoogle Scholar
Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems with Applications, 36(3), 5932–5941.10.1016/j.eswa.2008.07.006CrossRefGoogle Scholar
Bachelier, L. (1900). Théorie de la spéculation. In Annales scientifiques de l’école normale supérieure (Vol. 17, pp. 21–86). ElsevierGoogle Scholar
Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.Google Scholar
Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one, 12(7), e0180944.CrossRefGoogle ScholarPubMed
Beck, M., Pöppel, K., Spanring, M., et al. (2024). xlstm: Extended long short-term memory. arXiv preprint arXiv:2405.04517.Google Scholar
Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157–166.CrossRefGoogle ScholarPubMed
Bertsimas, D., & Lo, A. W. (1998). Optimal control of execution costs. Journal of financial markets, 1(1), 1–50.CrossRefGoogle Scholar
Blondel, M., Teboul, O., Berthet, Q., & Djolonga, J. (2020). Fast differentiable sorting and ranking. In Daumé, Hal & Singh, Aarti (eds), International conference on machine learning (pp. 950–959). PMLRGoogle Scholar
Borovykh, A., Bohte, S., & Oosterlee, C. W. (2017). Conditional time series forecasting with convolutional neural networks. arXiv preprint arXiv:1703.04691.Google Scholar
Boureau, Y., Ponce, J., & LeCun, Y. (2010). A theoretical analysis of feature pooling in vision algorithms. In Proceedings of international conference on machine learning (icml’10) (Vol. 28, p. 3).Google Scholar
Briola, A., Bartolucci, S., & Aste, T. (2024). Deep limit order book forecasting. arXiv preprint arXiv:2403.09267.Google Scholar
Briola, A., Turiel, J., & Aste, T. (2020). Deep learning modeling of limit order book: A comparative perspective. arXiv preprint arXiv:2007.07319.Google Scholar
Cesa, M. (2017). A brief history of quantitative finance. Probability, Uncertainty and Quantitative Risk, 2(1), 1–16.CrossRefGoogle Scholar
Chen, J.- F., Chen, W.- L., Huang, C.- P., Huang, S.- H., & Chen, A.- P. (2016). Financial time-series data analysis using deep convolutional neural networks. In Cloud computing and big data (ccbd), 2016 7th international conference on (pp. 87–92).Google Scholar
Cho, K., Van Merriënboer, B., Gulcehre, C., et al. (2014). Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.Google Scholar
Cont, R., Cucuringu, M., Kochems, J., & Prenzel, F. (2023). Limit order book simulation with generative adversarial networks. SSRN 4512356.CrossRefGoogle Scholar
Cuturi, M., Teboul, O., & Vert, J.- P. (2019). Differentiable ranking and sorting using optimal transport. Advances in Neural Information Processing Systems, 32.Google Scholar
Devlin, J., Chang, M.- W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.Google Scholar
Di Persio, L., & Honchar, O. (2016). Artificial neural networks architectures for stock price prediction: Comparisons and applications. International Journal of Circuits, Systems and Signal Processing, 10, 403–413.Google Scholar
Dixon, M. (2018). Sequence classification of the limit order book using recurrent neural networks. Journal of Computational Science, 24, 277–286.10.1016/j.jocs.2017.08.018CrossRefGoogle Scholar
Doering, J., Fairbank, M., & Markose, S. (2017). Convolutional neural networks applied to high-frequency market microstructure forecasting. In Computer science and electronic engineering (ceec), 2017 (pp. 31–36).CrossRefGoogle Scholar
Du, K., Xing, F., Mao, R., & Cambria, E. (2024). Financial sentiment analysis: Techniques and applications. ACM Computing Surveys, 56(9), 1–42.10.1145/3649451CrossRefGoogle Scholar
Ekmekcioğlu, Ö., & Pınar, M. Ç. (2023). Graph neural networks for deep portfolio optimization. Neural Computing and Applications, 35(28), 20663–20674.CrossRefGoogle Scholar
Fischer, T., & Krauss, C. (2017). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669.10.1016/j.ejor.2017.11.054CrossRefGoogle Scholar
Frazier, P. I. (2018). Bayesian optimization. In Recent advances in optimization and modeling of contemporary problems (pp. 255–278). Informs.10.1287/educ.2018.0188CrossRefGoogle Scholar
Gatheral, J. (2010). No-dynamic-arbitrage and market impact. Quantitative finance, 10(7), 749–759.10.1080/14697680903373692CrossRefGoogle Scholar
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. (www.deeplearningbook.org)Google Scholar
Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.Google Scholar
Grover, A., Wang, E., Zweig, A., & Ermon, S. (2018). Stochastic optimization of sorting networks via continuous relaxations. In International conference on learning representations.Google Scholar
Gu, A., & Dao, T. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752.Google Scholar
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 770–778).Google Scholar
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.CrossRefGoogle ScholarPubMed
Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359–366.10.1016/0893-6080(89)90020-8CrossRefGoogle Scholar
Hwang, Y., Kong, Y., Lee, Y., & Zohren, S. (2025). Decision-informed neural networks with large language model integration for portfolio optimization.Google Scholar
Jin, M., Wang, S., Ma, L., et al. (2023). Time-LLM: Time series forecasting by reprogramming large language models. arXiv preprint arXiv:2310.01728.Google Scholar
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, Transactions of the ASME, 82(1), 35–45.10.1115/1.3662552CrossRefGoogle Scholar
Kingma, D. P., & Welling, M. (2013). Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114.Google Scholar
Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.Google Scholar
Kong, Y., Nie, Y., Dong, X., et al. (2024). Large language models for financial and investment management: Applications and benchmarks. Journal of Portfolio Management, 51(2) 162–210.Google Scholar
Kong, Y., Wang, Z., Nie, Y., et al. (2024). Unlocking the power of lstm for long term time series forecasting. arXiv preprint arXiv:2408.10006.Google Scholar
Korangi, K., Mues, C., & Bravo, C. (2024). Large-scale time-varying portfolio optimisation using graph attention networks. arXiv preprint arXiv:2407.15532.Google Scholar
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.Google Scholar
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90.CrossRefGoogle Scholar
Lim, B., Arık, S. Ö., Loeff, N., & Pfister, T. (2021). Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting, 37(4), 1748–1764.10.1016/j.ijforecast.2021.03.012CrossRefGoogle Scholar
Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: A survey. Philosophical Transactions of the Royal Society A, 379(2194), 20200209.10.1098/rsta.2020.0209CrossRefGoogle ScholarPubMed
Lim, B., Zohren, S., & Roberts, S. (2019). Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science, 1(4), 19–38.10.3905/jfds.2019.1.015CrossRefGoogle Scholar
Liu, T.- Y. (2009). Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval, 3(3), 225–331.10.1561/1500000016CrossRefGoogle Scholar
Liu, Y., Hu, T., Zhang, H., et al. (2023). itransformer: Inverted transformers are effective for time series forecasting. arXiv preprint arXiv:2310.06625.Google Scholar
Luong, M.- T., Pham, H., & Manning, C. D. (2015). Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025.Google Scholar
Maas, A. L., Hannun, A. Y., Ng, A. Y., et al. (2013). Rectifier nonlinearities improve neural network acoustic models. In Proc. icml (Vol. 30, p. 3).Google Scholar
Mhaskar, H. N., & Micchelli, C. A. (1993). How to choose an activation function. Advances in neural information processing systems, 6.Google Scholar
Moreno-Pino, F., & Zohren, S. (2024). Deepvol: Volatility forecasting from high-frequency data with dilated causal convolutions. Quantitative Finance, 24(8), 1105–1127.10.1080/14697688.2024.2387222CrossRefGoogle ScholarPubMed
Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228–250.10.1016/j.jfineco.2011.11.003CrossRefGoogle Scholar
Nagy, P., Calliess, J.- P., & Zohren, S. (2023). Asynchronous deep double dueling q-learning for trading-signal execution in limit order book markets. Frontiers in Artificial Intelligence, 6 1151003.10.3389/frai.2023.1151003CrossRefGoogle ScholarPubMed
Nagy, P., Frey, S., Sapora, S., et al. (2023). Generative AI for end-to-end limit order book modelling: A token-level autoregressive generative model of message flow using a deep state space network. In Proceedings of the fourth ACM international conference on AI in finance (pp. 91–99).10.1145/3604237.3626898CrossRefGoogle Scholar
Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted Boltzmann machines. In Icml (pp. 807–814).Google Scholar
Nelson, D. M., Pereira, A. C., & de Oliveira, R. A. (2017). Stock market’s price movement prediction with LSTM neural networks. In Neural networks (ijcnn), 2017 international joint conference on (pp. 1419–1426).CrossRefGoogle Scholar
Nie, Y., Nguyen, N. H., Sinthong, P., & Kalagnanam, J. (2022). A time series is worth 64 words: Long-term forecasting with transformers. arXiv preprint arXiv:2211.14730.Google Scholar
Obizhaeva, A. A., & Wang, J. (2013). Optimal trading strategy and supply/demand dynamics. Journal of Financial markets, 16(1), 1–32.10.1016/j.finmar.2012.09.001CrossRefGoogle Scholar
Ogryczak, W., & Tamir, A. (2003). Minimizing the sum of the k largest functions in linear time. Information Processing Letters, 85(3), 117–122.10.1016/S0020-0190(02)00370-8CrossRefGoogle Scholar
Poh, D., Lim, B., Zohren, S., & Roberts, S. (2021a). Building cross-sectional systematic strategies by learning to rank. The Journal of Financial Data Science, 3(2), 70–86.10.3905/jfds.2021.1.060CrossRefGoogle Scholar
Poh, D., Lim, B., Zohren, S., & Roberts, S. (2021b). Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention. arXiv preprint arXiv:2105.10019.10.3905/jfds.2022.1.099CrossRefGoogle Scholar
Poh, D., Lim, B., Zohren, S., & Roberts, S. (2021c). Enhancing cross-sectional currency strategies by ranking refinement with transformer-based architectures. arXiv preprint arXiv:2105.10019.Google Scholar
Poh, D., Roberts, S., & Zohren, S. (2022). Transfer ranking in finance: applications to cross-sectional momentum with data scarcity. arXiv preprint arXiv:2208.09968.Google Scholar
Prata, M., Masi, G., Berti, L., et al. (2024). Lob-based deep learning models for stock price trend prediction: A benchmark study. Artificial Intelligence Review, 57(5), 1–45.CrossRefGoogle Scholar
Pu, X. S., Roberts, S., Dong, X., & Zohren, S. (2023). Network momentum across asset classes. Stephen and Dong, Xiaowen and Zohren, Stefan, Network Momentum across Asset Classes (August 7, 2023).10.2139/ssrn.4540651CrossRefGoogle Scholar
Rahimikia, E., Zohren, S., & Poon, S.- H. (2021). Realised volatility forecasting: Machine learning via financial word embedding. arXiv preprint arXiv:2108.00480.Google Scholar
Reisenhofer, R., Bayer, X., & Hautsch, N. (2022). Harnet: A convolutional neural network for realized volatility forecasting. arXiv preprint arXiv:2205.07719.Google Scholar
Schnaubelt, M. (2022). Deep reinforcement learning for the optimal placement of cryptocurrency limit orders. European Journal of Operational Research, 296(3), 993–1006.10.1016/j.ejor.2021.04.050CrossRefGoogle Scholar
Selvin, S., Vinayakumar, R., Gopalakrishnan, E., Menon, V. K., & Soman, K. (2017). Stock price prediction using LSTM, RNNs and CNN-sliding window model. In Advances in computing, communications and informatics (icacci), 2017 international conference on (pp. 1643–1647).Google Scholar
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.Google Scholar
Sirignano, J., & Cont, R. (2018). Universal features of price formation in financial markets: Perspectives from deep learning. arXiv preprint arXiv:1803.06917.Google Scholar
Soleymani, F., & Paquet, E. (2021). Deep graph convolutional reinforcement learning for financial portfolio management–deeppocket. Expert Systems with Applications, 182, 115127.10.1016/j.eswa.2021.115127CrossRefGoogle Scholar
Sun, Q., Wei, X., & Yang, X. (2024). Graphsage with deep reinforcement learning for financial portfolio optimization. Expert Systems with Applications, 238, 122027.10.1016/j.eswa.2023.122027CrossRefGoogle Scholar
Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. In Advances in neural information processing systems (pp. 3104–3112).Google Scholar
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.Google Scholar
Theron, L., & Van Vuuren, G. (2018). The maximum diversification investment strategy: A portfolio performance comparison. Cogent Economics & Finance, 6(1), 1427533.10.1080/23322039.2018.1427533CrossRefGoogle Scholar
Tsantekidis, A., Passalis, N., Tefas, A., et al. (2017a). Forecasting stock prices from the limit order book using convolutional neural networks. In Business informatics (cbi), 2017 ieee 19th conference on (Vol. 1, pp. 7–12). IEEE.10.1109/CBI.2017.23CrossRefGoogle Scholar
Tsantekidis, A., Passalis, N., Tefas, A., et al. (2017b). Using deep learning to detect price change indications in financial markets. In Signal processing conference (eusipco), 2017 25th european (pp. 2511–2515).10.23919/EUSIPCO.2017.8081663CrossRefGoogle Scholar
Van Den Oord, A., Dieleman, S., Zen, H., et al. (2016). Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499, 12.Google Scholar
Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. Advances in neural information processing systems, 30.Google Scholar
Vergara, J. R., & Estévez, P. A. (2014). A review of feature selection methods based on mutual information. Neural Computing and Applications, 24, 175–186.CrossRefGoogle Scholar
Wan, X., Yang, J., Marinov, S., et al. (2021). Sentiment correlation in financial news networks and associated market movements. Scientific Reports, 11(1), 3062.10.1038/s41598-021-82338-6CrossRefGoogle ScholarPubMed
Wang, J., Zhang, S., Xiao, Y., & Song, R. (2021). A review on graph neural network methods in financial applications. arXiv preprint arXiv:2111.15367.Google Scholar
Wang, Y., Wu, H., Dong, J., et al. (2024). Deep time series models: A comprehensive survey and benchmark. arXiv preprint arXiv:2407.13278.Google Scholar
Wood, K., Giegerich, S., Roberts, S., & Zohren, S. (2021). Trading with the momentum transformer: An intelligent and interpretable architecture. arXiv preprint arXiv:2112.08534.Google Scholar
Wood, K., Kessler, S., Roberts, S. J., & Zohren, S. (2023). Few-shot learning patterns in financial time-series for trend-following strategies. arXiv preprint arXiv:2310.10500.Google Scholar
Wood, K., Roberts, S., & Zohren, S. (2021). Slow momentum with fast reversion: A trading strategy using deep learning and changepoint detection. arXiv preprint arXiv:2105.13727.Google Scholar
Wu, H., Xu, J., Wang, J., & Long, M. (2021). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems, 34, 22419–22430.Google Scholar
Wu, Z., Pan, S., Chen, F., et al. (2020). A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1), 4–24.10.1109/TNNLS.2020.2978386CrossRefGoogle Scholar
Zhang, C., Pu, X., Cucuringu, M., & Dong, X. (2023). Graph neural networks for forecasting multivariate realized volatility with spillover effects. arXiv preprint arXiv:2308.01419.Google Scholar
Zhang, C., Pu, X., Cucuringu, M., & Dong, X. (2024). Graph-based methods for forecasting realized covariances. Journal of Financial Econometrics, nbae026.Google Scholar
Zhang, C., Zhang, Z., Cucuringu, M., & Zohren, S. (2021). A universal end-to-end approach to portfolio optimization via deep learning. arXiv preprint arXiv:2111.09170.Google Scholar
Zhang, X., Chowdhury, R. R., Gupta, R. K., & Shang, J. (2024). Large language models for time series: A survey. arXiv preprint arXiv:2402.01801.Google Scholar
Zhang, Y., & Yan, J. (2023). Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In The Eleventh International Conference on Learning Representations.Google Scholar
Zhang, Z., Lim, B., & Zohren, S. (2021). Deep learning for market by order data. Applied Mathematical Finance, 28(1), 79–95.10.1080/1350486X.2021.1967767CrossRefGoogle Scholar
Zhang, Z., & Zohren, S. (2021). Multi-horizon forecasting for limit order books: Novel deep learning approaches and hardware acceleration using intelligent processing units. arXiv preprint arXiv:2105.10430.Google Scholar
Zhang, Z., Zohren, S., & Roberts, S. (2019). Deep convolutional neural networks for limit order books. IEEE Transactions on Signal Processing, 67(11), 3001–3012.10.1109/TSP.2019.2907260CrossRefGoogle Scholar
Zhang, Z., Zohren, S., & Roberts, S. (2019a). Deeplob: Deep convolutional neural networks for limit order books. IEEE Transactions on Signal Processing, 67(11), 3001–3012.10.1109/TSP.2019.2907260CrossRefGoogle Scholar
Zhang, Z., Zohren, S., & Roberts, S. (2019b). Extending deep learning models for limit order books to quantile regression. Proceedings of Time Series Workshop of the 36th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019.Google Scholar
Zhang, Z., Zohren, S., & Roberts, S. (2020). Deep learning for portfolio optimization. The Journal of Financial Data Science, 2(4), 8–20.10.3905/jfds.2020.1.042CrossRefGoogle Scholar
Zhou, H., Zhang, S., Peng, J., et al. (2021). Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of aaai (pp. 11106–11115).Google Scholar
Zhou, Y.- T., & Chellappa, R. (1988). Computation of optical flow using a neural network. In Icnn (pp. 71–78).Google Scholar
Accessibility standard: Unknown
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
Accessibility compliance for the PDF of this Element is currently unknown
and may be updated in the future.
Online Resources for this publication available here.