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Learning Quantitative Finance with R
Implement machine learning, time-series analysis, algorithmic trading and more
2017
EN
Get the most out of R to solve your real-world quantitative finance problems with easeKey Features\[\*\] Understand the basics of R and how they can be applied in various Quantitative Finance scenarios\[\*\] Learn various algorithmic trading techniques and ways to optimize them using the tools available in R.\[\*\] Contain different methods to manage risk and explore trading using Machine Learning.Book DescriptionThe role of ...
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2014
EN
NEW & IMPROVED SIGNALLING SYSTEM!Since the highly successful debutant of "Making Money with Binary Options Starter Kit", comes the NEW and IMPROVED binary options trading starter guide for FOREX binary trading.- New signal rules for trading forex binary options.- Easier and simpler steps to follow and start earning faster.- Improved with One-Click chart for signalling rules. No setup required.- More trade logs to share and learn. See how my trades pr...
1998
EN
Most books on data structures assume an imperative language like C or C++. However, data structures for these languages do not always translate well to functional languages such as Standard ML, Haskell, or Scheme. This book describes data structures from the point of view of functional languages, with examples, and presents design techniques so that programmers can develop their own functional data structures. It includes both classical data structures, such as red-black trees and binomial...
2014
EN
A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.
25 Recipes for Getting Started with R
Excerpts from the R Cookbook
2011
EN
R is a powerful tool for statistics and graphics, but getting started with this language can be frustrating. This short, concise book provides beginners with a selection of how-to recipes to solve simple problems with R. Each solution gives you just what you need to know to use R for basic statistics, graphics, and regression.You'll find recipes on reading data files, creating data frames, computing basic statistics, testing means and correlations, creating a scatter plot, performi...
2012
EN
Driven by concrete computational problems in quantitative finance, this book provides aspiring quant developers with the numerical techniques and programming skills they need. The authors start from scratch, so the reader does not need any previous experience of C++. Beginning with straightforward option pricing on binomial trees, the book gradually progresses towards more advanced topics, including nonlinear solvers, Monte Carlo techniques for path-dependent derivative securities, finite ...
R Data Science Essentials
R Data Science Essentials
2016
EN
Learn the essence of data science and visualization using R in no time at allKey Features\[\*\]Become a pro at making stunning visualizations and dashboards quickly and without hassle\[\*\]For better decision making in business, apply the R programming language with the help of useful statistical techniques.\[\*\]From seasoned authors comes a book that offers you a plethora of fast-paced techniques to detect and analyze data patterns
2013
EN
This book is a tutorial guide for new users that aims to help you understand the basics of and become accomplished with the use of R for quantitative finance.If you are looking to use R to solve problems in quantitative finance, then this book is for you. A basic knowledge of financial theory is assumed, but familiarity with R is not required. With a focus on using R to solve a wide range of issues, this book provides useful content for both the R beginner and more experience users.
Automated Trading with R
Quantitative Research and Platform Development
2016
EN
Learn to trade algorithmically with your existing brokerage, from data management, to strategy optimization, to order execution, using free and publicly available data. Connect to your brokerage’s API, and the source code is plug-and-play.Automated Trading with R explains automated trading, starting with its mathematics and moving to its computation and execution. You will gain a unique insight into the mechanics and computational considerations taken in building a back-te...
Practical Business Analytics Using SAS
A Hands-on Guide
2015
EN
Practical Business Analytics Using SAS: A Hands-on Guide shows SAS users and businesspeople how to analyze data effectively in real-life business scenarios.The book begins with an introduction to analytics, analytical tools, and SAS programming. The authors—both SAS, statistics, analytics, and big data experts—first show how SAS is used in business, and then how to get started programming in SAS by importing data and learning how to manipulate it. Besides illustrating SAS ...
Automated Option Trading
Create, Optimize, and Test Automated Trading Systems
2012
EN
The first and only book of its kind, Automated Options Trading describes a comprehensive, step-by-step process for creating automated options trading systems. Using the authors’ techniques, sophisticated traders can create powerful frameworks for the consistent, disciplined realization of well-defined, formalized, and carefully-tested trading strategies based on their specific requirements. Unlike other books on automated trading, this book focuses specifically on the unique requi...
Artificial Intelligence in Financial Markets
Cutting Edge Applications for Risk Management, Portfolio Optimization and Economics
- Series -
- Economics and Finance (R0)
2016
EN
As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making.This book presents the mos...











