Deep Learning in Quantitative Trading (Record no. 756977)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02256nam a2200217 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781009707114 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | English |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 332.6 |
| Item number | ZHA/D |
| 084 ## - OTHER CLASSIFICATION NUMBER | |
| Source of Number | Colon Classification |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Zhang, Zihao |
| 245 ## - TITLE STATEMENT | |
| Title | Deep Learning in Quantitative Trading |
| Statement of responsibility, etc | /By Zihao Zhang and Stefan Zohren |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | UK: |
| Name of publisher | Cambridge University Press, |
| Year of publication | 2025. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 174p. |
| 500 ## - GENERAL NOTE | |
| General note | 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 |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Part I: Foundations. Fundamentals of financial time-series<br/>Supervised learning and canonical networks<br/>The model training workflow<br/>Part II: Applications. Enhancing classical quantitative trading strategies<br/>Deep learning for risk management and portfolio optimization<br/>Applications to market microstructure and high-frequency data<br/>Conclusions<br/>Acronyms<br/>Appendix A: Different asset classes<br/>Appendix B: Access to market data<br/>Appendix C: Investment performance metrics<br/>Appendix D: Code scripts |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Artificial intelligence/ Financial applications- Deep learning (Machine learning) /Economic aspects |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Zohren, Stefan |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Book |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home Library | Current Location | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Accession Number | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Non-fiction | Dept. of Economics | Dept. of Economics | Processing Center | 23/02/2026 | MBC/0763/2025,13/02/2026 | 2222.00 | 332.6 ZHA/D | ECN16986 | 23/02/2026 | Book |