Learning Quantitative Finance with R. Implement machine learning, time-series analysis, algorithmic trading and more

Dr. Param Jeet, PRASHANT VATS

The role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in top-tier organizations and investment banks. This book is your go-to resource if you want to equip yourself with the skills required to tackle any real-world problem in quantitative finance using the popular R programming language.You'll start by getting an understanding of the basics of R and its relevance in the field of quantitative finance. Once you've built this foundation, we'll dive into the practicalities of building financialmodels in R. This will help you have a fair understanding of the topics as well as their implementation, as the authors have presented some use cases along with examples that are easy to understand and correlate.We'll also look at risk management and optimization techniques for algorithmic trading. Finally, the book will explain some advanced concepts, such as trading using machine learning, optimizations, exotic options, and hedging.By the end of this book, you will have a firm grasp of the techniques required to implement basic quantitative finance models in R.

Python 3 Object-Oriented Programming. Build robust and maintainable software with object-oriented design patterns in Python 3.8 - Third Edition

Dusty Phillips

Object-oriented programming (OOP) is a popular design paradigm in which data and behaviors are encapsulated in such a way that they can be manipulated together. This third edition of Python 3 Object-Oriented Programming fully explains classes, data encapsulation, and exceptions with an emphasis on when you can use each principle to develop well-designed software.Starting with a detailed analysis of object-oriented programming, you will use the Python programming language to clearly grasp key concepts from the object-oriented paradigm. You will learn how to create maintainable applications by studying higher level design patterns. The book will show you the complexities of string and file manipulation, and how Python distinguishes between binary and textual data. Not one, but two very powerful automated testing systems, unittest and pytest, will be introduced in this book. You'll get a comprehensive introduction to Python's concurrent programming ecosystem.By the end of the book, you will have thoroughly learned object-oriented principles using Python syntax and be able to create robust and reliable programs confidently.

Azure Networking Cookbook. Practical recipes to manage network traffic in Azure, optimize performance, and secure Azure resources

Mustafa Toroman

Microsoft provides organizations with an effective way of managing their network with Azure's networking services. No matter the size of your organization, Azure provides a way to highly reliable performance and secure connectivity with its networking services. The book starts with an introduction to the Azure networking like creating Azure virtual networks, designing address spaces and subnets. Then you will learn to create and manage network security groups, application security groups, and IP addresses in Azure. Gradually, we move on to various aspects like S2S, P2S, and Vnet2Vnet connections, DNS and routing, load balancers and traffic manager. This book will cover every aspect and function required to deliver practical recipes to help readers learn from basic cloud networking practices to planning, implementing, and securing their infrastructure network with Azure. Readers will not only be able to upscale their current environment but will also learn to monitor, diagnose, and ensure secure connectivity. After learning to deliver a robust environment readers will also gain meaningful insights from recipes on best practices.By the end of this book, readers will gain hands-on experience in providing cost-effective solutions that benefit organizations.

Learn React with TypeScript 3. Beginner's guide to modern React web development with TypeScript 3

Carl Rippon

React today is one of the most preferred choices for frontend development. Using React with TypeScript enhances development experience and offers a powerful combination to develop high performing web apps. In this book, you’ll learn how to create well structured and reusable react components that are easy to read and maintain by leveraging modern web development techniques. We will start with learning core TypeScript programming concepts before moving on to building reusable React components. You'll learn how to ensure all your components are type-safe by leveraging TypeScript's capabilities, including the latest on Project references, Tuples in rest parameters, and much more. You'll then be introduced to core features of React such as React Router, managing state with Redux and applying logic in lifecycle methods. Further on, you'll discover the latest features of React such as hooks and suspense which will enable you to create powerful function-based components. You'll get to grips with GraphQL web API using Apollo client to make your app more interactive. Finally, you'll learn how to write robust unit tests for React components using Jest. By the end of the book, you'll be well versed with all you need to develop fully featured web apps with React and TypeScript.

Practical Real-time Data Processing and Analytics. Distributed Computing and Event Processing using Apache Spark, Flink, Storm, and Kafka

Shilpi Saxena, Saurabh Gupta

With the rise of Big Data, there is an increasing need to process large amounts of data continuously, with a shorter turnaround time. Real-time data processing involves continuous input, processing and output of data, with the condition that the time required for processing is as short as possible.This book covers the majority of the existing and evolving open source technology stack for real-time processing and analytics. You will get to know about all the real-time solution aspects, from the source to the presentation to persistence. Through this practical book, you’ll be equipped with a clear understanding of how to solve challenges on your own.We’ll cover topics such as how to set up components, basic executions, integrations, advanced use cases, alerts, and monitoring. You’ll be exposed to the popular tools used in real-time processing today such as Apache Spark, Apache Flink, and Storm. Finally, you will put your knowledge to practical use by implementing all of the techniques in the form of a practical, real-world use case.By the end of this book, you will have a solid understanding of all the aspects of real-time data processing and analytics, and will know how to deploy the solutions in production environments in the best possible manner.

Deep Learning with Hadoop. Distributed Deep Learning with Large-Scale Data

Dipayan Dev

This book will teach you how to deploylarge-scale dataset in deep neural networks with Hadoop foroptimal performance.Starting with understanding what deeplearning is, and what the various modelsassociated with deep neural networks are, thisbook will then show you how to set up theHadoop environment for deep learning.In this book, you will also learn how toovercome the challenges that you facewhile implementing distributed deeplearning with large-scale unstructured datasets. The book willalso show you how you can implementand parallelize the widely used deep learning models such as Deep Belief Networks,Convolutional Neural Networks, Recurrent Neural Networks, Restricted Boltzmann machines and autoencoder using the popular deep learning library Deeplearning4j.Get in-depth mathematical explanationsand visual representations to helpyou understand the design and implementationsof Recurrent Neural network and Denoising Autoencoders withDeeplearning4j. To give you a morepractical perspective, the book will alsoteach you the implementation of large-scale video processing, image processing andnatural language processing on Hadoop.By the end of this book, you willknow how to deploy various deep neural networks indistributed systems using Hadoop.

Mastering PostGIS. Modern ways to create, analyze, and implement spatial data

Dominik Mikiewicz, Michal Mackiewicz, Tomasz Nycz

PostGIS is open source extension onf PostgreSQL object-relational database system that allows GIS objects to be stored and allows querying for information and location services. The aim of this book is to help you master the functionalities offered by PostGIS- from data creation, analysis and output, to ETL and live edits.The book begins with an overview of the key concepts related to spatial database systems and how it applies to Spatial RMDS. You will learn to load different formats into your Postgres instance, investigate the spatial nature of your raster data, and finally export it using built-in functionalities or 3th party tools for backup or representational purposes. Through the course of this book, you will be presented with many examples on how to interact with the database using JavaScript and Node.js. Sample web-based applications interacting with backend PostGIS will also be presented throughout the book, so you can get comfortable with the modern ways of consuming and modifying your spatial data.

Python Reinforcement Learning Projects. Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow

Sean Saito, Yang Wenzhuo, Rajalingappaa Shanmugamani

Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years.In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks.By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life.