Products related to Algorithms:
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Market Risk Analysis, Quantitative Methods in Finance
Written by leading market risk academic, Professor Carol Alexander, Quantitative Methods in Finance forms part one of the Market Risk Analysis four volume set.Starting from the basics, this book helps readers to take the first step towards becoming a properly qualified financial risk manager and asset manager, roles that are currently in huge demand.Accessible to intelligent readers with a moderate understanding of mathematics at high school level or to anyone with a university degree in mathematics, physics or engineering, no prior knowledge of finance is necessary.Instead the emphasis is on understanding ideas rather than on mathematical rigour, meaning that this book offers a fast-track introduction to financial analysis for readers with some quantitative background, highlighting those areas of mathematics that are particularly relevant to solving problems in financial risk management and asset management.Unique to this book is a focus on both continuous and discrete time finance so that Quantitative Methods in Finance is not only about the application of mathematics to finance; it also explains, in very pedagogical terms, how the continuous time and discrete time finance disciplines meet, providing a comprehensive, highly accessible guide which will provide readers with the tools to start applying their knowledge immediately. All together, the Market Risk Analysis four volume set illustrates virtually every concept or formula with a practical, numerical example or a longer, empirical case study.Across all four volumes there are approximately 300 numerical and empirical examples, 400 graphs and figures and 30 case studies many of which are contained in interactive Excel spreadsheets available from the accompanying CD-ROM.Empirical examples and case studies specific to this volume include: Principal component analysis of European equity indices;Calibration of Student t distribution by maximum likelihood;Orthogonal regression and estimation of equity factor models;Simulations of geometric Brownian motion, and of correlated Student t variables;Pricing European and American options with binomial trees, and European options with the Black-Scholes-Merton formula;Cubic spline fitting of yields curves and implied volatilities;Solution of Markowitz problem with no short sales and other constraints;Calculation of risk adjusted performance metrics including generalised Sharpe ratio, omega and kappa indices.
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A Complete Guide to the Futures Market : Technical Analysis, Trading Systems, Fundamental Analysis, Options, Spreads, and Trading Principles
The essential futures market reference guide A Complete Guide to the Futures Market is the comprehensive resource for futures traders and analysts.Spanning everything from technical analysis, trading systems, and fundamental analysis to options, spreads, and practical trading principles, A Complete Guide is required reading for any trader or investor who wants to successfully navigate the futures market. Clear, concise, and to the point, this fully revised and updated second edition provides a solid foundation in futures market basics, details key analysis and forecasting techniques, explores advanced trading concepts, and illustrates the practical application of these ideas with hundreds of market examples.A Complete Guide to the Futures Market: Details different trading and analytical approaches, including chart analysis, technical indicators and trading systems, regression analysis, and fundamental market models. Separates misleading market myths from reality. Gives step-by-step instruction for developing and testing original trading ideas and systems. Illustrates a wide range of option strategies, and explains the trading implications of each. Details a wealth of practical trading guidelines and market insights from a recognized trading authority. Trading futures without a firm grasp of this market’s realities and nuances is a recipe for losing money.A Complete Guide to the Futures Market offers serious traders and investors the tools to keep themselves on the right side of the ledger.
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Adaptive Control : Algorithms, Analysis and Applications
Adaptive Control (second edition) shows how a desired level of system performance can be maintained automatically and in real time, even when process or disturbance parameters are unknown and variable.It is a coherent exposition of the many aspects of this field, setting out the problems to be addressed and moving on to solutions, their practical significance and their application.Discrete-time aspects of adaptive control are emphasized to reflect the importance of digital computers in the application of the ideas presented.The second edition is thoroughly revised to throw light on recent developments in theory and applications with new chapters on: multimodel adaptive control with switching, direct and indirect adaptive regulation and adaptive feedforward disturbance compensation.Many algorithms are newly presented in MATLAB® m-file format to facilitate their employment in real systems.Classroom-tested slides for instructors to use in teaching this material are also now provided.All of this supplementary electronic material can be downloaded from fill in URL.The core material is also up-dated and re-edited to keep its perspective in line with modern ideas and more closely to associate algorithms with their applications giving the reader a solid grounding in: synthesis and analysis of parameter adaptation algorithms, recursive plant model identification in open and closed loop, robust digital control for adaptive control; • robust parameter adaptation algorithms, practical considerations and applications, including flexible transmission systems, active vibration control and broadband disturbance rejection and a supplementary introduction on hot dip galvanizing and a phosphate drying furnace.Control researchers and applied mathematicians will find Adaptive Control of significant and enduring interest and its use of example and application will appeal to practitioners working with unknown- and variable-parameter plant.Praise for the first edition:…well written, interesting and easy to follow, so that it constitutes a valuable addition to the monographies in adaptive control for discrete-time linear systems… suitable (at least in part) for use in graduate courses in adaptive control.
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Mathematical Analysis of Machine Learning Algorithms
The mathematical theory of machine learning not only explains the current algorithms but can also motivate principled approaches for the future.This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications.Topics covered include the analysis of supervised learning algorithms in the iid setting, the analysis of neural networks (e.g. neural tangent kernel and mean-field analysis), and the analysis of machine learning algorithms in the sequential decision setting (e.g. online learning, bandit problems, and reinforcement learning).Students will learn the basic mathematical tools used in the theoretical analysis of these machine learning problems and how to apply them to the analysis of various concrete algorithms.This textbook is perfect for readers who have some background knowledge of basic machine learning methods, but want to gain sufficient technical knowledge to understand research papers in theoretical machine learning.
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What is options trading?
Options trading is a type of investing strategy that involves buying and selling options contracts on the stock market. An options contract gives the holder the right, but not the obligation, to buy or sell a specific asset at a predetermined price within a set timeframe. Options trading allows investors to speculate on the direction of a stock's price movement without actually owning the stock itself. It can be a high-risk, high-reward strategy that requires a good understanding of the market and careful risk management.
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What do algorithms achieve?
Algorithms achieve the ability to process and analyze large amounts of data quickly and efficiently. They help in making predictions, identifying patterns, and solving complex problems. Algorithms are used in various fields such as finance, healthcare, and technology to optimize processes and improve decision-making. Overall, algorithms play a crucial role in automating tasks, improving productivity, and driving innovation.
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What do algorithms calculate?
Algorithms are designed to calculate specific tasks or operations based on a set of instructions. They can be used to perform mathematical calculations, process data, analyze patterns, make decisions, and solve problems. In essence, algorithms are used to automate and streamline various processes by following a predefined sequence of steps to produce a desired outcome.
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What are the Instagram algorithms?
The Instagram algorithms are a set of complex calculations used by the platform to determine what content users see on their feed. These algorithms analyze user behavior, such as likes, comments, and shares, to prioritize content from accounts that users engage with the most. The algorithms also take into account the timeliness of posts, the relationship between users, and the type of content being shared. By using these algorithms, Instagram aims to show users the most relevant and engaging content on their feed.
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Options Trading : 7 Golden Beginners Strategies to Start Trading Options Like a PRO! Perfect Guide to Learn Basics & Tactics for Investing in Stocks, Futures, Binary & Bonds. Create Passive Income Fas
There are a lot of different investment opportunities that you can choose from. Some will entail more risk than others, but they can also entail higher profit potentials as well. But one option that many investors may not consider when they first get started in this market is options trading. This guidebook is going to take some time to explore options trading and how even a beginner can get started making money if they choose the right strategy. Some of the topics that we will discuss about options trading in this guidebook include: What is options trading?,Working with the bull put spread strategy,Working with the bear call spread strategy,The importance of the butterfly and condor strategies. ,Working with both the long straddle and the long strangle. ,The bear put spread strategy,Working with the bull call spread strategy,The ratio spreads and how they work as a strategy. ,The best ways to reduce your risks when you are working with options trading. , Options trading is a great choice when it comes to investing your money. You will be able to earn unlimited profits without actually having to own the security outright. And this type of investment can work no matter what kind of market conditions are present with a stock. When you are ready to get started with options trading, make sure to check out this guidebook to help you out!
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Analytics: Business Intelligence, Algorithms and Statistical Analysis
Analytics - Business Intelligence, Algorithms and Statistical AnalysisIn today's world, analysis has become an extremely important aspect to consider when you are thinking of starting any new line of business or even when it comes to purchasing a new house. The unfortunate fact is that not many people know what analysis is all about.They tend to assume that each of the elements - analysis, business intelligence, algorithms and statistical analysis - are different entities when in reality they are all interlinked.This book will help you gather a greater insight on all the elements. And you will gain an in-depth knowledge on each of these elements in different parts of the book; helping you to increase your knowledge and widen your horizons!
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Whoosh Goes the Market : Algorithms, Automation, and Alienation
A vivid, fast-paced inside look at financial markets, the people who work on them, and how technology is changing their world (and ours). Markets are messy, and no one knows this better than traders who work tirelessly to predict what they will do next.In Whoosh Goes the Market, Daniel Scott Souleles takes us into the day-to-day experiences of a team at a large trading firm, revealing what it’s actually like to make and lose money on contemporary capital markets. The traders Souleles shadows have mostly moved out of the pits and now work with automated, glitch-prone computer systems.They remember the days of trading manually, and they are suspicious of algorithmically driven machine-learning systems.Openly musing about their own potential extinction, they spend their time expressing fear and frustration in profanity-laced language.With Souleles as our guide, we learn about everything from betting strategies to inflated valuations, trading swings, and market manipulation.This crash course in contemporary finance vividly reveals the existential anxiety at the evolving front lines of American capitalism.
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Navigation and Tracking in Space: Analysis and Algorithms
This book focuses on the navigation and tracking of artificial space objects, with emphasis on modelling the dynamics in a wide range of space missions, including: earth-orbiting satellite missions, launch and re-entry missions as well as interplanetary missions.The book guides you in designing suitable estimation algorithms for each type of mission.It also helps you in addressing non-linearity in designing navigation algorithms for space missions, and walks you through the process for choosing estimators for navigation and tracking of space vehicles. You’ll find specific details on earth-orbiting satellite tracking and navigation that helps you determine precise orbit, and will understand how to get navigation and tracking results using the Least Square Estimation and the Extended Kalman Filter (EKF) for simulated observations.You also learn how to address tracking performance of spacecraft in interplanetary trajectories that are affected by a diverse set of problems, such low signal power, intermittent observations, observations at low rate and delays.Techniques for designing navigation and tracking algorithms to address these problems are delineated.The book also provides in-depth coverage of multi-object tracking, relevant data association and estimation algorithms in the Situational Space Awareness context. MATLAB /Simulink based software is provided for simulation and simulated data set.This is an excellent reference and practical tool for professionals in the field of Guidance, Navigation and Control, along with researchers and advanced students in the field of space vehicle navigation, tracking, guidance and control.
Price: 138.00 £ | Shipping*: 0.00 £
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Which sorting algorithms are there?
There are several common sorting algorithms, including bubble sort, selection sort, insertion sort, merge sort, quick sort, and heap sort. Each algorithm has its own advantages and disadvantages in terms of time complexity, space complexity, and stability. The choice of sorting algorithm depends on the specific requirements of the problem at hand.
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Should one learn without algorithms?
Learning without algorithms is certainly possible, as there are many different ways to acquire knowledge and skills. However, algorithms can be valuable tools for organizing and processing information, so learning about them can be beneficial. Understanding algorithms can help individuals solve complex problems, improve decision-making processes, and enhance their overall problem-solving abilities. Therefore, while it is not necessary to learn algorithms, doing so can certainly be advantageous in many fields.
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What are simple algorithms in Java?
Simple algorithms in Java are step-by-step procedures for solving a specific problem or performing a specific task. These algorithms are typically written in Java programming language and are designed to be easy to understand and implement. Examples of simple algorithms in Java include sorting algorithms like bubble sort or insertion sort, searching algorithms like linear search or binary search, and mathematical algorithms like finding the factorial of a number or calculating the Fibonacci sequence. These algorithms are fundamental building blocks in computer science and are essential for solving a wide range of problems in software development.
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How do logarithmic sorting algorithms work?
Logarithmic sorting algorithms work by dividing the input data into smaller subgroups and recursively sorting these subgroups. One common example is the merge sort algorithm, which divides the input list into two halves, sorts each half separately, and then merges them back together in sorted order. By repeatedly dividing the data and merging the sorted subgroups, logarithmic sorting algorithms achieve a time complexity of O(n log n), making them efficient for large datasets.
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