Deep Learning In Quantitative Trading
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Deep Learning In Quantitative Trading
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Author : Zihao Zhang
language : en
Publisher: Cambridge University Press
Release Date : 2025-10-31
Deep Learning In Quantitative Trading written by Zihao Zhang and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-31 with Business & Economics categories.
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.
Deep Learning For Quant Finance
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Author : Victor Trex
language : en
Publisher: HiTeX Press
Release Date : 2025-12-10
Deep Learning For Quant Finance written by Victor Trex and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-10 with Business & Economics categories.
"Deep Learning for Quant Finance: Transformers, LSTMs, and Reinforcement Learning" Deep learning is transforming quantitative finance, from intraday alpha generation to market making and derivatives hedging. This book is written for quantitative researchers, data scientists, and technically inclined practitioners who want to move beyond toy examples and build serious, production-grade models. Blending financial intuition with modern machine learning, it shows how to connect neural architectures directly to PnL, risk, and execution objectives in real markets. You will progress from mathematical and market microstructure foundations to a full deep learning stack tailored to financial time series. The book covers sequence models (RNNs, LSTMs, TCNs), attention and Transformers for irregular, high-frequency data, and reinforcement learning for trading, execution, and market making. Along the way, you will learn how to design finance-aware loss functions and evaluation metrics, manage walk-forward validation and leakage, and integrate predictive models into portfolio construction, risk management, and option pricing workflows. Assuming comfort with Python and basic probability, the text is self-contained in its treatment of the required math, optimization, and ML concepts. Throughout, it emphasizes robustness, MLOps, distribution shift, and explainability, culminating in end-to-end case studies. The result is a practical, rigorous guide to building deep learning systems that matter in a professional quantitative finance environment.
Python For Finance And Algorithmic Trading
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Author : Lucas INGLESE
language : fr
Publisher:
Release Date : 2021-09-25
Python For Finance And Algorithmic Trading written by Lucas INGLESE and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-25 with categories.
The financial sector is undergoing significant restructuring. Traders and portfolio managers are increasingly becoming financial data scientists. Banks, investment funds, and fintech are increasingly automating their investments by integrating machine learning and deep learning algorithms into their decision-making process. The book presents the benefits of portfolio management, statistics, and machine learning applied to live trading with MetaTrader 5. *Learn portfolio management technics and how to implement your optimization criterion *How to backtest a strategy using the most valuable metrics in trading *Import data from your broker to be as close as possible to the market *Learn statistical arbitrage through pair trading strategies *Generate market predictions using machine learning, deep learning, and time series analysis *Learn how to find the best take profit, stop loss, and leverage for your strategies *Combine trading strategies using portfolio management to increase the robustness of the strategies *Connect your Python algorithm to your MetaTrader 5 and run it with a demo or live trading account *Use all codes in the book for live trading or screener if you prefer manual trading
Deep Learning Approaches In Finance
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Author : Marcelo Sardelich Nascimento
language : en
Publisher:
Release Date : 2019
Deep Learning Approaches In Finance written by Marcelo Sardelich Nascimento and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Machine Learning In Finance
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Author : Matthew F. Dixon
language : en
Publisher: Springer Nature
Release Date : 2020-07-01
Machine Learning In Finance written by Matthew F. Dixon and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-01 with Business & Economics categories.
This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
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Author :
language : en
Publisher: Springer Nature
Release Date :
written by and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Artificial Intelligence And Machine Learning
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Author : Khalid S. Soliman
language : en
Publisher: Springer Nature
Release Date : 2024-06-28
Artificial Intelligence And Machine Learning written by Khalid S. Soliman and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-28 with Computers categories.
This book constitutes the revised selected papers of the 41st IBIMA International Conference on Artificial intelligence and Computer Science, IBIMA-AI 2023, which took place in Granada, Spain during June 26-27, 2023. The 30 full papers and 8 short papers included in this volume were carefully reviewed and selected from 58 submissions. The book showcases a diverse array of research papers spanning various disciplines within the realm of Artificial Intelligence, Machine Learning, Information Systems, Communications Technologies, Software Engineering, and Security and Privacy.
An Introduction To Machine Learning In Quantitative Finance
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Author : Hao Ni
language : en
Publisher: World Scientific
Release Date : 2021-04-07
An Introduction To Machine Learning In Quantitative Finance written by Hao Ni and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-07 with Business & Economics categories.
In today's world, we are increasingly exposed to the words 'machine learning' (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it.An Introduction to Machine Learning in Quantitative Finance aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authorsFeatured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data!
Deep Learning For Quantitative Finance
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Author : Vincent Bisette
language : en
Publisher: Independently Published
Release Date : 2025-08-25
Deep Learning For Quantitative Finance written by Vincent Bisette and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-25 with Business & Economics categories.
Reactive Publishing Deep Learning for Quantitative Finance is a cutting-edge guide that bridges advanced artificial intelligence with practical financial applications. Written for traders, analysts, data scientists, and students of quantitative finance, this book shows how to apply modern neural networks, transformers, and machine learning architectures to tackle today's most complex financial challenges. Inside, you'll learn how to: Build and train neural networks for time series forecasting, asset pricing, and volatility modeling. Apply transformer architectures to capture long-range dependencies in financial data. Combine deep reinforcement learning with trading systems for systematic alpha generation. Integrate risk management frameworks with AI-powered prediction models. Translate research-grade techniques into scalable, production-ready strategies. Unlike purely theoretical texts, this book emphasizes hands-on, practical implementation. With clear explanations, illustrative examples, and guidance on avoiding common pitfalls, it equips you with the tools to deploy deep learning effectively in live financial environments. Whether you're a quant professional seeking an edge, a data scientist entering finance, or a trader looking to expand your toolkit, this book provides the comprehensive foundation and advanced techniques you need to thrive in the age of AI-driven finance.
Utilizing Ai And Machine Learning In Financial Analysis
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Author : Darwish, Dina
language : en
Publisher: IGI Global
Release Date : 2025-01-21
Utilizing Ai And Machine Learning In Financial Analysis written by Darwish, Dina and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-21 with Business & Economics categories.
Machine learning models can imitate the cognitive process by assimilating knowledge from data and employing it to interpret and analyze information. Machine learning methods facilitate the comprehension of vast amounts of data and reveal significant patterns incorporated within it. This data is utilized to optimize financial business operations, facilitate well-informed judgements, and aid in predictive endeavors. Financial institutions utilize it to enhance pricing, minimize risks stemming from human error, mechanize repetitive duties, and comprehend client behavior. Utilizing AI and Machine Learning in Financial Analysis explores new trends in machine learning and artificial intelligence implementations in the financial sector. It examines techniques in financial analysis using intelligent technologies for improved business services. This book covers topics such as customer relations, predictive analytics, and fraud detection, and is a useful resource for computer engineers, security professionals, business owners, accountants, academicians, data scientists, and researchers.