000			02256nam a2200217 4500
005			20260325150934.0
020			_a9781009707114
041			_aEnglish
082			_a332.6 _bZHA/D
084			_2Colon Classification
100			_aZhang, Zihao _912522
245			_a Deep Learning in Quantitative Trading _c/By Zihao Zhang and Stefan Zohren
250			_a1
260			_aUK: _bCambridge University Press, _c2025.
300			_a174p.
500			_aThis 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
505			_aPart I: Foundations. Fundamentals of financial time-series Supervised learning and canonical networks The model training workflow Part II: Applications. Enhancing classical quantitative trading strategies Deep learning for risk management and portfolio optimization Applications to market microstructure and high-frequency data Conclusions Acronyms Appendix A: Different asset classes Appendix B: Access to market data Appendix C: Investment performance metrics Appendix D: Code scripts
650			_aArtificial intelligence/ Financial applications- Deep learning (Machine learning) /Economic aspects _912523
700			_aZohren, Stefan _912524
942			_2ddc _cBK
999			_c756977 _d756977