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Author: Brindha Priyadarshini Jeyaraman Publisher: BPB Publications ISBN: 935551977X Category : Computers Languages : en Pages : 458
Book Description
Observe, optimize, and transform in finance KEY FEATURES ● Learn observability basics in finance. ● Monitor financial data with logs and alerts and improve data security. ● Identify the key metrics for financial oversight. ● Use new tech for financial observability. DESCRIPTION This book explains the role of observability in the finance sector, showing how new technologies can help monitor and manage financial systems more effectively. It outlines the use of real-time data monitoring, Machine Learning, and cloud computing to enhance the efficiency of financial operations and ensure they meet regulatory standards. The chapters guide you through the process of setting up systems to track financial activities accurately, analyze market trends, and predict future challenges to keep operations secure and competitive. It offers clear explanations of how these technologies can help finance professionals make better decisions and manage risks proactively. Designed for finance professionals looking to update their technical skills, this book provides practical guidance on adopting modern observability tools and practices. It will help you understand how to apply these technologies to increase transparency and strengthen the resilience of financial operations in a constantly evolving industry. WHAT YOU WILL LEARN ● Implement effective data monitoring strategies in finance. ● Use Machine Learning to enhance financial risk assessment. ● Develop robust compliance frameworks using observability tools. ● Apply real-time analytics for quicker financial decision-making. ● Integrate predictive analytics for forward-looking financial insights. ● Understand and deploy distributed tracing for financial operations. WHO THIS BOOK IS FOR This book is ideal for financial professionals seeking to deepen their understanding of observability. It is also suitable for IT specialists in finance who wish to advance their skills in modern observability tools and practices. TABLE OF CONTENTS 1. Introduction 2. The Fundamentals of Observability 3. Monitoring and Logging for Financial Data 4. Tracing and Correlation in Finance 5. Metrics and Key Performance Indicators for Finance 6. Real-time Monitoring and Alerting in Finance 7. Observability for Algorithmic Trading and Market Data 8. Compliance and Regulatory Considerations 9. Advanced Techniques: Machine Learning and Predictive Analytics 10. Organizational Culture and Collaboration 11. Case Studies and Best Practices Observability 12. The Future of Observability in Finance 13. The Horizon of Financial Observability
Author: Brindha Priyadarshini Jeyaraman Publisher: BPB Publications ISBN: 935551977X Category : Computers Languages : en Pages : 458
Book Description
Observe, optimize, and transform in finance KEY FEATURES ● Learn observability basics in finance. ● Monitor financial data with logs and alerts and improve data security. ● Identify the key metrics for financial oversight. ● Use new tech for financial observability. DESCRIPTION This book explains the role of observability in the finance sector, showing how new technologies can help monitor and manage financial systems more effectively. It outlines the use of real-time data monitoring, Machine Learning, and cloud computing to enhance the efficiency of financial operations and ensure they meet regulatory standards. The chapters guide you through the process of setting up systems to track financial activities accurately, analyze market trends, and predict future challenges to keep operations secure and competitive. It offers clear explanations of how these technologies can help finance professionals make better decisions and manage risks proactively. Designed for finance professionals looking to update their technical skills, this book provides practical guidance on adopting modern observability tools and practices. It will help you understand how to apply these technologies to increase transparency and strengthen the resilience of financial operations in a constantly evolving industry. WHAT YOU WILL LEARN ● Implement effective data monitoring strategies in finance. ● Use Machine Learning to enhance financial risk assessment. ● Develop robust compliance frameworks using observability tools. ● Apply real-time analytics for quicker financial decision-making. ● Integrate predictive analytics for forward-looking financial insights. ● Understand and deploy distributed tracing for financial operations. WHO THIS BOOK IS FOR This book is ideal for financial professionals seeking to deepen their understanding of observability. It is also suitable for IT specialists in finance who wish to advance their skills in modern observability tools and practices. TABLE OF CONTENTS 1. Introduction 2. The Fundamentals of Observability 3. Monitoring and Logging for Financial Data 4. Tracing and Correlation in Finance 5. Metrics and Key Performance Indicators for Finance 6. Real-time Monitoring and Alerting in Finance 7. Observability for Algorithmic Trading and Market Data 8. Compliance and Regulatory Considerations 9. Advanced Techniques: Machine Learning and Predictive Analytics 10. Organizational Culture and Collaboration 11. Case Studies and Best Practices Observability 12. The Future of Observability in Finance 13. The Horizon of Financial Observability
Author: Nicholas Ryder Publisher: Routledge ISBN: 1317311736 Category : Law Languages : en Pages : 392
Book Description
This book offers a commentary on the responses to white collar crime since the financial crisis. The book brings together experts from academia and practice to analyse the legal and policy responses that have been put in place following the 2008 financial crisis. The book looks at a range of topics including: the low priority and resources allocated to fraud; EU regulatory efforts to fight financial crime; protecting whistleblowers in the financial industry; the criminality of the rogue trader; the evolution of financial crime in cryptocurrencies; and the levying of financial penalties against banks and corporations by the US Department of Justice and Securities and Exchange Commission.
Author: Leslie Pack Kaelbling Publisher: Springer Science & Business Media ISBN: 0792397053 Category : Computers Languages : en Pages : 286
Book Description
Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).
Author: Matthew F. Dixon Publisher: Springer Nature ISBN: 3030410684 Category : Business & Economics Languages : en Pages : 565
Book Description
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.
Author: Jerome Yen Publisher: Springer Nature ISBN: 9464631988 Category : Computers Languages : en Pages : 1595
Book Description
This is an open access book.With the rapid development of modern economy and Internet technology, the traditional financial industry has to develop Internet finance to provide better services and meet the needs of the times. It is against this background that the blockchain, relying on its special advantages (collective maintenance, reliable databases, and decentralization), provides the reliability to solve the credit risk of Internet finance, has an impact on institutions, trust mechanisms, risk control, etc. in the Internet finance industry, and has derived more new application scenarios, thus paving the way for the development of finance in the Internet era. Applying blockchain technology to the financial field can promote data information sharing, improve value transmission efficiency, and enhance database security. The financial market based on the decentralized system of blockchain technology can reduce the operating costs of financial institutions, improve economic efficiency, and solve problems such as information asymmetry. The new financial business model of "blockchain+finance" is conducive to improving the Internet credit reporting system, preventing and controlling Internet financial risks, and further realizing "financial disintermediation". At present, in China's financial field, blockchain technology has been applied and innovated in supply chain finance, cross-border payment, trade finance, asset securitization and other scenarios. To promote the exchange and development of blockchain, information technology and financial experts and scholars. The 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023) will be held in Hangzhou from February 17 to 19, 2023. This conference mainly focuses on the latest research on "blockchain, information technology and smart finance". This conference brings together experts, scholars, researchers and relevant practitioners in this field from all over the world to share research results, discuss hot issues, and provide participants with cutting-edge scientific and technological information, so that you can timely understand the development trends of the industry and master the latest technologies, broaden research horizons and promote academic progress.
Author: Kwong S. Leung Publisher: Springer ISBN: 3540444912 Category : Computers Languages : en Pages : 576
Book Description
X Table of Contents Table of Contents XI XII Table of Contents Table of Contents XIII XIV Table of Contents Table of Contents XV XVI Table of Contents K.S. Leung, L.-W. Chan, and H. Meng (Eds.): IDEAL 2000, LNCS 1983, pp. 3›8, 2000. Springer-Verlag Berlin Heidelberg 2000 4 J. Sinkkonen and S. Kaski Clustering by Similarity in an Auxiliary Space 5 6 J. Sinkkonen and S. Kaski Clustering by Similarity in an Auxiliary Space 7 0.6 1.5 0.4 1 0.2 0.5 0 0 10 100 1000 10000 10 100 1000 Mutual information (bits) Mutual information (bits) 8 J. Sinkkonen and S. Kaski 20 10 0 0.1 0.3 0.5 0.7 Mutual information (mbits) Analyses on the Generalised Lotto-Type Competitive Learning Andrew Luk St B&P Neural Investments Pty Limited, Australia Abstract, In generalised lotto-type competitive learning algorithm more than one winner exist. The winners are divided into a number of tiers (or divisions), with each tier being rewarded differently. All the losers are penalised (which can be equally or differently). In order to study the various properties of the generalised lotto-type competitive learning, a set of equations, which governs its operations, is formulated. This is then used to analyse the stability and other dynamic properties of the generalised lotto-type competitive learning.
Author: Eugene A. Durenard Publisher: John Wiley & Sons ISBN: 1118419294 Category : Business & Economics Languages : en Pages : 388
Book Description
An insider's view of how to develop and operate an automated proprietary trading network Reflecting author Eugene Durenard's extensive experience in this field, Professional Automated Trading offers valuable insights you won't find anywhere else. It reveals how a series of concepts and techniques coming from current research in artificial life and modern control theory can be applied to the design of effective trading systems that outperform the majority of published trading systems. It also skillfully provides you with essential information on the practical coding and implementation of a scalable systematic trading architecture. Based on years of practical experience in building successful research and infrastructure processes for purpose of trading at several frequencies, this book is designed to be a comprehensive guide for understanding the theory of design and the practice of implementation of an automated systematic trading process at an institutional scale. Discusses several classical strategies and covers the design of efficient simulation engines for back and forward testing Provides insights on effectively implementing a series of distributed processes that should form the core of a robust and fault-tolerant automated systematic trading architecture Addresses trade execution optimization by studying market-pressure models and minimization of costs via applications of execution algorithms Introduces a series of novel concepts from artificial life and modern control theory that enhance robustness of the systematic decision making—focusing on various aspects of adaptation and dynamic optimal model choice Engaging and informative, Proprietary Automated Trading covers the most important aspects of this endeavor and will put you in a better position to excel at it.
Author: Robert Kissell Publisher: Academic Press ISBN: 0124016936 Category : Business & Economics Languages : en Pages : 492
Book Description
The Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. Readers learn how to evaluate market impact models and assess performance across algorithms, traders, and brokers, and acquire the knowledge to implement electronic trading systems. This valuable book summarizes market structure, the formation of prices, and how different participants interact with one another, including bluffing, speculating, and gambling. Readers learn the underlying details and mathematics of customized trading algorithms, as well as advanced modeling techniques to improve profitability through algorithmic trading and appropriate risk management techniques. Portfolio management topics, including quant factors and black box models, are discussed, and an accompanying website includes examples, data sets supplementing exercises in the book, and large projects. - Prepares readers to evaluate market impact models and assess performance across algorithms, traders, and brokers. - Helps readers design systems to manage algorithmic risk and dark pool uncertainty. - Summarizes an algorithmic decision making framework to ensure consistency between investment objectives and trading objectives.
Author: Stephen Boyd Publisher: ISBN: 9781680833287 Category : Mathematics Languages : en Pages : 92
Book Description
This monograph collects in one place the basic definitions, a careful description of the model, and discussion of how convex optimization can be used in multi-period trading, all in a common notation and framework.
Author: Laura Graesser Publisher: Addison-Wesley Professional ISBN: 0135172489 Category : Computers Languages : en Pages : 629
Book Description
The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games—such as Go, Atari games, and DotA 2—to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelized synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designed Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Author: Brindha Priyadarshini Jeyaraman
Author: Brindha Priyadarshini Jeyaraman
Author: Nicholas Ryder
Author: Leslie Pack Kaelbling
Author: Matthew F. Dixon
Author: Jerome Yen
Author: Kwong S. Leung
Author: Eugene A. Durenard
Author: Robert Kissell
Author: Stephen Boyd
Author: Laura Graesser