‏349.00 ₪

Applications of Computational Intelligence in Data -Driven Trading

‏349.00 ₪
ISBN13
9781119550501
יצא לאור ב
New York
זמן אספקה
21 ימי עסקים
עמודים
256
פורמט
Hardback
תאריך יציאה לאור
25 בדצמ׳ 2019
An introduction to applications of computational intelligence in finance Applications of Computational Intelligence in Data-Driven Trading features modern educational content that is at the confluence between data-driven decision-making and computational intelligence. The book caters to trading and investment professionals interested in the new paradigm of data-driven decision-making, as well as to graduate students who desire to get more familiar with the emerging field of computational intelligence. Doloc introduces the reader to the new paradigm of Data-Intensive Computing and its applications in the world of trading and investing. The goal is to promote the use of computational intelligencetechniques, as the vehicle to augment human performance thorough automation and emulate human intelligence via innovation and discovery. Several case studies from the field of data-driven trading and investing are presented. The author's two decades of experience as a computational scientist and quantitative practitioner in the financial trading industry endow him with a unique perspective that he conveys to the reader. The financial trading industry is fertile ground for the adoption of advanced technologies, and Doloc walks the reader through two key areas: automation and innovation.
מידע נוסף
עמודים 256
פורמט Hardback
ISBN10 1119550505
יצא לאור ב New York
תאריך יציאה לאור 25 בדצמ׳ 2019
תוכן עניינים About the Author Acknowledgments About the Website Introduction Motivation Target audience Book structure Acknowledgements 1 The evolution of trading paradigms 1.1 Infrastructure-related paradigms in Trading 1.1.1 The open outcry trading 1.1.2 Advances in communication technology 1.1.3 The Digital revolution in the financial markets 1.1.4 The High Frequency Trading paradigm 1.1.5 Blockchain and the decentralization of markets 1.2 Decision-Making paradigms in trading 1.2.1 Discretionary trading 1.2.2 Algorithmic trading 1.3 The new paradigm of Data-Driven Trading References 2 The role of Data in Trading and Investing 2.1 The Data-driven decision-making paradigm 2.2 The Data economy is fulling the future 2.3 Defining Data and its utility 2.4 The journey from Data to Intelligence 2.5 The utility of Data in Trading and Investing 2.5.1 The use of Big Data analytics to feed financial models 2.5.2 The use of real-time analytics 2.5.3 The use of Machine Learning 2.5.4 Automated Risk Management 2.5.5 Data management 2.5.6 Consumer analytic 2.5.7 Fraud detection 2.6 The Alternative data and its use in Trading and Investing References 3 Artificial Intelligence - between myth and reality 3.1 Introduction 3.2 A brief history of AI 3.2.1 Early history 3.2.2 The modern AI era 3.2.3 Important milestones in the development of AI 3.2.4 Projections for the immediate future 3.2.5 Meta-Learning - an exciting new development 3.3 The meaning of the term "AI" - a critical view 3.4 On the applicability of "AI" to Finance 3.4.1 Data stationarity 3.4.2 Data quality 3.4.3 Data dimensionality 3.5 Perspectives and future directions References 4 Computational Intelligence: A principled approach for the era of Data exploration 4.1 Introduction to Computational Intelligence 4.1.1 Defining Intelligence 4.1.2 What is Computational Intelligence? 4.1.3 Mapping the field of study 4.1.4 Problems vs. tools 4.1.5 Current challenges 4.1.6 The future of Computational Intelligence 4.1.7 Examples in Finance 4.2 The PAC theory 4.2.1 The Probably Approximately Correct framework 4.2.2 Why AI is a very lofty goal to achieve? 4.2.3 Examples of Ecorithms in Finance 4.3 Technology drivers behind the ML surge 4.3.1 Data 4.3.2 Algorithms 4.3.3 Hardware Accelerators References 5 How to apply the principles of CI in Quantitative finance 5.1 The viability of Computational Intelligence 5.2 On the applicability of CI to Quantitative finance 5.3 A brief introduction to Reinforcement Learning 5.3.1 Defining the Agent 5.3.2 Model-based Markov Decision Process 5.3.3 Model-free Reinforcement Learning 5.4 Conclusions References 6 Case Study 1: Optimizing trade execution 6.1. Introduction to the problem 6.1.1 On Limit Orders and market Microstructure 6.1.2 Formulation of base-line strategies 6.1.3 A RL formulation of the Optimized Execution problem 6.2 Current State-of-the-Art in Optimized Trade execution 6.3 Implementation methodology 6.3.1 Simulating the interaction with the market Microstructure 6.3.2 Using Dynamic Programming to optimize trade execution 6.3.3 Using Reinforcement Learning to optimize trade execution 6.4 Empirical results 6.4.1 Application to Equities 6.4.2 Using "private" variables only 6.4.3 Using both "private" and "market" variables 6.4.4 Application to Futures 6.4.5 Another example 6.5 Conclusions and future directions References 7 Case Study 2: The Dynamics of the Limit Order Book 7.1. Introduction to the problem 7.1.1 The new Era of Prediction 7.1.2 New Challenges 7.1.3 High Frequency data 7.2 Current SOTA in the prediction of directional price movement in the LOB 7.3 Using SVM and RF classifiers for directional price forecast 7.4 Studying the dynamics of the LOB with RL 7.5 Studying the dynamics of the LOB with DNN 7.6 Studying the dynamics of the LOB with LSTM 7.7 Studying the dynamics of the LOB with CNN References 8 Case Study 3: Applying ML to Portfolio Management 8.1 Introduction to the problem 8.2 Current State-of-the-Art in Portfolio modelling 8.2.1 The classic approach 8.2.2 The ML approach 8.3 A "Deep Portfolio" approach to portfolio optimization 8.3.1 Auto-encoders 8.3.2 Methodology - the 4-step algorithm 8.3.3 Results 8.4 A Q-learning approach to the problem of portfolio optimization 8.4.1 Problem statement 8.4.2 Methodology 8.4.3 The Deep Q-learning algorithm 8.4.4 Results 8.5 A Deep RL approach to portfolio management 8.5.1 Methodology 8.5.2 Data 8.5.3 The RL setting: agent, environment and policy 8.5.4 The CNN implementation 8.5.5 The RNN and LSTM implementations 8.5.6 Results References 9 Case Study 4: Applying ML to Market Making 9.1 Introduction to the problem 9.2 Current State-of-the-Art in Market Making 9.3 Applications of Temporal-Difference RL in Market Making 9.3.1 Methodology 9.3.2 The Simulator 9.3.3 Market Making Agent specification 9.3.4 Empirical Results 9.4 Market Making in HFT using RL 9.4.1 Methodology 9.4.2 Experimental setting 9.4.3 Results and Conclusions 9.5 Other research studies References 10 Case Study 5: Applications of ML to Derivatives valuation 10.1 Introduction to the problem 10.2 Current State-of-the-Art in Derivatives valuation by applying ML 10.2.1 The beginnings 10.2.2 The last decade 10.3 Using Deep Learning for valuation of Derivatives 10.3.1 Implementation Methodology 10.3.2 Empirical Results 10.3.3 Conclusions and future directions 10.3.3 Other research studies 10.4 Using Reinforcement Learning for valuation of Derivatives References 11 Case Study 6: Using ML for Risk Management and Compliance 11.1 Introduction to the problem 11.2 Current State-of-the-Art for applications of ML to Risk Management and Compliance 11.2.1 Credit risk 11.2.2 Market risk 11.2.3 Operational risk 11.2.4 Regulatory Compliance risk 11.3 ML in Credit Risk modelling 11.3.1 Data 11.3.2 Models 11.3.3 Results 11.4 Deep Learning for Credit scoring 11.3.1 Credit risk 11.4.2 Deep Belief networks and RBM 11.4.3 Empirical Results 11.5 Using ML in Operational Risk and Market surveillance 11.5.1 Intro 11.5.2 A ML approach to market surveillance 11.5.3 Conclusions References 12 Conclusions and future directions 12.1 Concluding remarks 12.2 The Paradigm shift 12.3 De-noising the AI hype 12.4 The birth of a new Engineering discipline 12.5 Future directions References
זמן אספקה 21 ימי עסקים