Bøger af Peng Liu

Build and implement trading strategies using Python. This book will introduce you to the fundamental concepts of quantitative trading and shows how to use Python and popular libraries to build trading models and strategies from scratch. It covers practical trading strategies coupled with step-by-step implementations that touch upon a wide range of topics, including data analysis and visualization, algorithmic trading, backtesting, risk management, optimization, and machine learning, all coupled with practical examples in Python.Part one of Quantitative Trading Strategies with Python covers the fundamentals of trading strategies, including an introduction to quantitative trading, the electronic market, risk and return, and forward and futures contracts. Part two introduces common trading strategies, including trend-following, momentum trading, and evaluation process via backtesting. Part three covers more advanced topics, including statistical arbitrage using hypothesistesting, optimizing trading parameters using Bayesian optimization, and generating trading signals using a machine learning approach. Whether you're an experienced trader looking to automate your trading strategies or a beginner interested in learning quantitative trading, this book will be a valuable resource. Written in a clear and concise style that makes complex topics easy to understand, and chock full of examples and exercises to help reinforce the key concepts, yoüll come away from it with a firm understanding of core trading strategies and how to use Python to implement them.What You Will LearnMaster the fundamental concepts of quantitative tradingUse Python and its popular libraries to build trading models and strategies from scratchPerform data analysis and visualization, algorithmic trading, backtesting, risk management, optimization, and machine learning for trading strategies using PythonUtilize common trading strategies such as trend-following, momentum trading, and pairs tradingEvaluate different quantitative trading strategies by applying the relevant performance measures and statistics in a scientific manner during backtestingWho This Book Is ForAspiring quantitative traders and analysts, data scientists interested in finance, and researchers or students studying quantitative finance, financial engineering, or related fields.

This book covers the essential theory and implementation of popular Bayesian optimization techniques in an intuitive and well-illustrated manner. The techniques covered in this book will enable you to better tune the hyperparemeters of your machine learning models and learn sample-efficient approaches to global optimization.The book begins by introducing different Bayesian Optimization (BO) techniques, covering both commonly used tools and advanced topics. It follows a ¿develop from scratch¿ method using Python, and gradually builds up to more advanced libraries such as BoTorch, an open-source project introduced by Facebook recently. Along the way, yoüll see practical implementations of this important discipline along with thorough coverage and straightforward explanations of essential theories. This book intends to bridge the gap between researchers and practitioners, providing both with a comprehensive, easy-to-digest, and useful reference guide. After completingthis book, you will have a firm grasp of Bayesian optimization techniques, which yoüll be able to put into practice in your own machine learning models.What You Will LearnApply Bayesian Optimization to build better machine learning modelsUnderstand and research existing and new Bayesian Optimization techniquesLeverage high-performance libraries such as BoTorch, which offer you the ability to dig into and edit the inner workingDig into the inner workings of common optimization algorithms used to guide the search process in Bayesian optimizationWho This Book Is ForBeginner to intermediate level professionals in machine learning, analytics or other roles relevant in data science.

Information security concerns the confidentiality, integrity, and availability of information processed by a computer system. With an emphasis on prevention, traditional information security research has focused little on the ability to survive successful attacks, which can seriously impair the integrity and availability of a system. Trusted Recovery And Defensive Information Warfare uses database trusted recovery, as an example, to illustrate the principles of trusted recovery in defensive information warfare. Traditional database recovery mechanisms do not address trusted recovery, except for complete rollbacks, which undo the work of benign transactions as well as malicious ones, and compensating transactions, whose utility depends on application semantics. Database trusted recovery faces a set of unique challenges. In particular, trusted database recovery is complicated mainly by (a) the presence of benign transactions that depend, directly or indirectly on malicious transactions; and (b) the requirement by many mission-critical database applications that trusted recovery should be done on-the-fly without blocking the execution of new user transactions. Trusted Recovery And Defensive Information Warfare proposes a new model and a set of innovative algorithms for database trusted recovery. Both read-write dependency based and semantics based trusted recovery algorithms are proposed. Both static and dynamic database trusted recovery algorithms are proposed. These algorithms can typically save a lot of work by innocent users and can satisfy a variety of attack recovery requirements of real world database applications. Trusted Recovery And Defensive Information Warfare is suitable as a secondary text for a graduate level course in computer science, and as a reference for researchers and practitioners in information security.