Search Results: automated-trading-with-r-quantitative-research-and-platform-development

Automated Trading with R

Quantitative Research and Platform Development

Author: Chris Conlan

Publisher: Apress

ISBN: 1484221788

Category: Computers

Page: 205

View: 6469

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-tester, strategy optimizer, and fully functional trading platform. The platform built in this book can serve as a complete replacement for commercially available platforms used by retail traders and small funds. Software components are strictly decoupled and easily scalable, providing opportunity to substitute any data source, trading algorithm, or brokerage. This book will: Provide a flexible alternative to common strategy automation frameworks, like Tradestation, Metatrader, and CQG, to small funds and retail traders Offer an understanding of the internal mechanisms of an automated trading system Standardize discussion and notation of real-world strategy optimization problems What You Will Learn Understand machine-learning criteria for statistical validity in the context of time-series Optimize strategies, generate real-time trading decisions, and minimize computation time while programming an automated strategy in R and using its package library Best simulate strategy performance in its specific use case to derive accurate performance estimates Understand critical real-world variables pertaining to portfolio management and performance assessment, including latency, drawdowns, varying trade size, portfolio growth, and penalization of unused capital Who This Book Is For Traders/practitioners at the retail or small fund level with at least an undergraduate background in finance or computer science; graduate level finance or data science students

Automated Trading with R

Quantitative Research and Platform Development

Author: Christopher Conlan

Publisher: Apress

ISBN: 9781484221778

Category: Computers

Page: 205

View: 8944

This book explains the broad topic of automated trading, starting with its mathematics and moving to its computation and execution. Readers will gain a unique insight into the mechanics and computational considerations taken in building a backtester, strategy optimizer, and fully functional trading platform. Automated Trading with R provides automated traders with all the tools they need to trade algorithmically with their existing brokerage, from data management, to strategy optimization, to order execution, using free and publically available data. If your brokerage’s API is supported, the source code is plug-and-play. The platform built in this book can serve as a complete replacement for commercially available platforms used by retail traders and small funds. Software components are strictly decoupled and easily scalable, providing opportunity to substitute any data source, trading algorithm, or brokerage. The book’s three objectives are: To provide a flexible alternative to common strategy automation frameworks, like Tradestation, Metatrader, and CQG, to small funds and retail traders. To offer an understanding the internal mechanisms of an automated trading system. To standardize discussion and notation of real-world strategy optimization problems. What you’ll learn Programming an automated strategy in R gives the trader access to R and its package library for optimizing strategies, generating real-time trading decisions, and minimizing computation time. How to best simulate strategy performance in their specific use case to derive accurate performance estimates. Important machine-learning criteria for statistical validity in the context of time-series. An understanding of critical real-world variables pertaining to portfolio management and performance assessment, including latency, drawdowns, varying trade size, portfolio growth, and penalization of unused capital. Who This Book Is For This book is for traders/practitioners at the retail or small fund level with at least an undergraduate background in finance or computer science. Graduate level finance or data science students.

Quantitative Trading with R

Understanding Mathematical and Computational Tools from a Quant’s Perspective

Author: Harry Georgakopoulos

Publisher: Springer

ISBN: 1137437472

Category: Business & Economics

Page: 272

View: 2829

Quantitative Finance with R offers a winning strategy for devising expertly-crafted and workable trading models using the R open source programming language, providing readers with a step-by-step approach to understanding complex quantitative finance problems and building functional computer code.

Building Trading Bots Using Java

Author: Shekhar Varshney

Publisher: Apress

ISBN: 1484225201

Category: Computers

Page: 281

View: 1790

Build an automated currency trading bot from scratch with java. In this book, you will learn about the nitty-gritty of automated trading and have a closer look at Java, the Spring Framework, event-driven programming, and other open source APIs, notably Google's Guava API. And of course, development will all be test-driven with unit testing coverage. The central theme of Building Trading Bots Using Java is to create a framework that can facilitate automated trading on most of the brokerage platforms, with minimum changes. At the end of the journey, you will have a working trading bot, with a sample implementation using the OANDA REST API, which is free to use. What You'll Learn Find out about trading bots Discover the details of tradeable instruments and apply bots to them Track and use market data events Place orders and trades Work with trade/order and account events Who This Book Is For Experienced programmers new to bots and other algorithmic trading and finance techniques.

Professional Automated Trading

Theory and Practice

Author: Eugene A. Durenard

Publisher: John Wiley & Sons

ISBN: 1118419294

Category: Business & Economics

Page: 384

View: 7834

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.

Analyzing Financial Data and Implementing Financial Models Using R

Author: Clifford Ang

Publisher: Springer

ISBN: 3319140752

Category: Business & Economics

Page: 351

View: 7069

This book is a comprehensive introduction to financial modeling that teaches advanced undergraduate and graduate students in finance and economics how to use R to analyze financial data and implement financial models. This text will show students how to obtain publicly available data, manipulate such data, implement the models, and generate typical output expected for a particular analysis. This text aims to overcome several common obstacles in teaching financial modeling. First, most texts do not provide students with enough information to allow them to implement models from start to finish. In this book, we walk through each step in relatively more detail and show intermediate R output to help students make sure they are implementing the analyses correctly. Second, most books deal with sanitized or clean data that have been organized to suit a particular analysis. Consequently, many students do not know how to deal with real-world data or know how to apply simple data manipulation techniques to get the real-world data into a usable form. This book will expose students to the notion of data checking and make them aware of problems that exist when using real-world data. Third, most classes or texts use expensive commercial software or toolboxes. In this text, we use R to analyze financial data and implement models. R and the accompanying packages used in the text are freely available; therefore, any code or models we implement do not require any additional expenditure on the part of the student. Demonstrating rigorous techniques applied to real-world data, this text covers a wide spectrum of timely and practical issues in financial modeling, including return and risk measurement, portfolio management, options pricing, and fixed income analysis.

Mastering R for Quantitative Finance

Author: Edina Berlinger,Ferenc Illés,Milán Badics,Ádám Banai,Gergely Daróczi,Barbara Dömötör,Gergely Gabler,Dániel Havran,Péter Juhász,István Margitai,Balázs Márkus,Péter Medvegyev,Julia Molnár,Balázs Árpád Szűcs,Ágnes Tuza,Tamás Vadász,Kata Váradi,Ágnes Vidovics-Dancs

Publisher: Packt Publishing Ltd

ISBN: 1783552085

Category: Computers

Page: 362

View: 6417

This book is intended for those who want to learn how to use R's capabilities to build models in quantitative finance at a more advanced level. If you wish to perfectly take up the rhythm of the chapters, you need to be at an intermediate level in quantitative finance and you also need to have a reasonable knowledge of R.

Nonlinear Time Series Analysis with R

Author: Ray Huffaker,Marco Bittelli,Rodolfo Rosa

Publisher: Oxford University Press

ISBN: 0191085790

Category: Mathematics

Page: 312

View: 1331

Nonlinear Time Series Analysis with R provides a practical guide to emerging empirical techniques allowing practitioners to diagnose whether highly fluctuating and random appearing data are most likely driven by random or deterministic dynamic forces. It joins the chorus of voices recommending 'getting to know your data' as an essential preliminary evidentiary step in modelling. Time series are often highly fluctuating with a random appearance. Observed volatility is commonly attributed to exogenous random shocks to stable real-world systems. However, breakthroughs in nonlinear dynamics raise another possibility: highly complex dynamics can emerge endogenously from astoundingly parsimonious deterministic nonlinear models. Nonlinear Time Series Analysis (NLTS) is a collection of empirical tools designed to aid practitioners detect whether stochastic or deterministic dynamics most likely drive observed complexity. Practitioners become 'data detectives' accumulating hard empirical evidence supporting their modelling approach. This book is targeted to professionals and graduate students in engineering and the biophysical and social sciences. Its major objectives are to help non-mathematicians—with limited knowledge of nonlinear dynamics—to become operational in NLTS; and in this way to pave the way for NLTS to be adopted in the conventional empirical toolbox and core coursework of the targeted disciplines. Consistent with modern trends in university instruction, the book makes readers active learners with hands-on computer experiments in R code directing them through NLTS methods and helping them understand the underlying logic. The computer code is explained in detail so that readers can adjust it for use in their own work. The book also provides readers with an explicit framework—condensed from sound empirical practices recommended in the literature—that details a step-by-step procedure for applying NLTS in real-world data diagnostics.

Financial Analytics with R

Author: Mark J. Bennett,Dirk L. Hugen

Publisher: Cambridge University Press

ISBN: 1107150752

Category: Business & Economics

Page: 390

View: 887

Financial Analytics with R sharpens readers' skills in time-series, forecasting, portfolio selection, covariance clustering, prediction, and derivative securities.

Introduction to R for Quantitative Finance

Author: Gergely Daróczi,Michael Puhle,Edina Berlinger,Péter Csóka,Daniel Havran,Márton Michaletzky,Zsolt Tulassay,Kata Váradi,Agnes Vidovics-Dancs

Publisher: Packt Publishing Ltd

ISBN: 1783280948

Category: Computers

Page: 164

View: 8685

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.

Handbook of Multi-Commodity Markets and Products

Structuring, Trading and Risk Management

Author: Andrea Roncoroni,Gianluca Fusai,Mark Cummins

Publisher: John Wiley & Sons

ISBN: 0470661836

Category: Business & Economics

Page: 1064

View: 2347

The comprehensive guide to working more effectively within the multi-commodity market. The Handbook of Multi-Commodity Markets and Products is the definitive desktop reference for traders, structurers, and risk managers who wish to broaden their knowledge base. This non-technical yet sophisticated manual covers everything the professional needs to become acquainted with the structure, function, rules, and practices across a wide spectrum of commodity markets. Contributions from a global team of renowned industry experts provide real-world examples for each market, along with tools for analyzing, pricing, and risk managing deals. The discussion focuses on convergence, including arbitrage valuation, econometric modeling, market structure analysis, contract engineering, and risk, while simulated scenarios help readers understand the practical application of the methods and models presented. Gradual deregulation and the resulting increase in diversity and activity have driven the evolution of the traditionally segmented market toward integration, raising important questions about opportunity identification and analysis in multi-commodity deals. This book helps professionals navigate the shift, providing in-depth information and practical advice. Structure and manage both simple and sophisticated multi-commodity deals Exploit pay-off profiles and trading strategies with a diversified set of commodity prices Develop more accurate forecasting models by considering additional metrics Price energy products and other commodities in segmented markets with an eye toward specific structural features As one of the only markets strong enough to boom during the credit crunch, the commodities markets are growing rapidly. Combined with increasing convergence, this transition presents potentially valuable opportunities for the development of a robust multi-commodity portfolio. For the professional seeking deeper understanding and a more effective strategy, the Handbook of Multi-Commodity Markets and Products offers complete information and expert guidance.

The Blender Python API

Precision 3D Modeling and Add-on Development

Author: Chris Conlan

Publisher: Apress

ISBN: 1484228022

Category: Computers

Page: 138

View: 7473

Understand Blender's Python API to allow for precision 3D modeling and add-on development. Follow detailed guidance on how to create precise geometries, complex texture mappings, optimized renderings, and much more. This book is a detailed, user-friendly guide to understanding and using Blender’s Python API for programmers and 3D artists. Blender is a popular open source 3D modeling software used in advertising, animation, data visualization, physics simulation, photorealistic rendering, and more. Programmers can produce extremely complex and precise models that would be impossible to replicate by hand, while artists enjoy numerous new community-built add-ons. The Blender Python API is an unparalleled programmable visualization environment. Using the API is made difficult due to its complex object hierarchy and vast documentation. Understanding the Blender Python API clearly explains the interface. You will become familiar with data structures and low-level concepts in both modeling and rendering with special attention given to optimizing procedurally generated models. In addition, the book: Discusses modules of the API as analogs to human input modes in Blender Reviews low-level and data-level manipulation of 3D objects in Blender Python Details how to deploy and extend projects with external libraries Provides organized utilities of novel and mature API abstractions for general use in add-on development What You’ll Learn Generate 3D data visualizations in Blender to better understand multivariate data and mathematical patterns. Create precision object models in Blender of architectural models, procedurally generated landscapes, atomic models, etc. Develop and distribute a Blender add-on, with special consideration given to careful development practices Pick apart Blender’s 3D viewport and Python source code to learn about API behaviors Develop a practical knowledge of 3D modeling and rendering concepts Have a practical reference to an already powerful and vast API Who This Book Is For Python programmers with an interest in data science, game development, procedural generation, and open-source programming as well as programmers of all types with a need to generate precise 3D models. Also for 3D artists with an interest in programming or with programming experience and Blender artists regardless of programming experience.

Finding Alphas

A Quantitative Approach to Building Trading Strategies

Author: Igor Tulchinsky

Publisher: John Wiley & Sons

ISBN: 1119057892

Category: Business & Economics

Page: 272

View: 2386

Design more successful trading systems with this practical guide to identifying alphas Finding Alphas seeks to teach you how to do one thing and do it well: design alphas. Written by experienced practitioners from WorldQuant, including its founder and CEO Igor Tulchinsky, this book provides detailed insight into the alchemic art of generating trading signals, and gives you access to the tools you need to practice and explore. Equally applicable across regions, this practical guide provides you with methods for uncovering the hidden signals in your data. A collection of essays provides diverse viewpoints to show the similarities, as well as unique approaches, to alpha design, covering a wide variety of topics, ranging from abstract theory to concrete technical aspects. You'll learn the dos and don'ts of information research, fundamental analysis, statistical arbitrage, alpha diversity, and more, and then delve into more advanced areas and more complex designs. The companion website,, features alpha examples with formulas and explanations. Further, this book also provides practical guidance for using WorldQuant's online simulation tool WebSim® to get hands-on practice in alpha design. Alpha is an algorithm which trades financial securities. This book shows you the ins and outs of alpha design, with key insight from experienced practitioners. Learn the seven habits of highly effective quants Understand the key technical aspects of alpha design Use WebSim® to experiment and create more successful alphas Finding Alphas is the detailed, informative guide you need to start designing robust, successful alphas.

Machine Trading

Deploying Computer Algorithms to Conquer the Markets

Author: Ernest P. Chan

Publisher: John Wiley & Sons

ISBN: 1119219604

Category: Business & Economics

Page: 264

View: 9850

Dive into algo trading with step-by-step tutorials and expert insight Machine Trading is a practical guide to building your algorithmic trading business. Written by a recognized trader with major institution expertise, this book provides step-by-step instruction on quantitative trading and the latest technologies available even outside the Wall Street sphere. You'll discover the latest platforms that are becoming increasingly easy to use, gain access to new markets, and learn new quantitative strategies that are applicable to stocks, options, futures, currencies, and even bitcoins. The companion website provides downloadable software codes, and you'll learn to design your own proprietary tools using MATLAB. The author's experiences provide deep insight into both the business and human side of systematic trading and money management, and his evolution from proprietary trader to fund manager contains valuable lessons for investors at any level. Algorithmic trading is booming, and the theories, tools, technologies, and the markets themselves are evolving at a rapid pace. This book gets you up to speed, and walks you through the process of developing your own proprietary trading operation using the latest tools. Utilize the newer, easier algorithmic trading platforms Access markets previously unavailable to systematic traders Adopt new strategies for a variety of instruments Gain expert perspective into the human side of trading The strength of algorithmic trading is its versatility. It can be used in any strategy, including market-making, inter-market spreading, arbitrage, or pure speculation; decision-making and implementation can be augmented at any stage, or may operate completely automatically. Traders looking to step up their strategy need look no further than Machine Trading for clear instruction and expert solutions.

Python for Finance

Analyze Big Financial Data

Author: Yves Hilpisch

Publisher: "O'Reilly Media, Inc."

ISBN: 1491945389

Category: Computers

Page: 606

View: 9642

The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include: Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies

The Science of Algorithmic Trading and Portfolio Management

Author: Robert Kissell

Publisher: Academic Press

ISBN: 0124016936

Category: Business & Economics

Page: 496

View: 8124

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.

Quantitative Trading

How to Build Your Own Algorithmic Trading Business

Author: Ernest P. Chan

Publisher: N.A

ISBN: 9781119203377

Category: Investment analysis

Page: 181

View: 2517

"While institutional traders continue to implement quantitative (or algorithmic) trading, many independent traders have wondered if they can still challenge powerful industry professionals at their own game? The answer is "yes," and in Quantitative Trading, Dr. Ernest Chan, a respected independent trader and consultant, will show you how. Whether you're an independent "retail" trader looking to start your own quantitative trading business or an individual who aspires to work as a quantitative trader at a major financial institution, this practical guide contains the information you need to succeed"--Resource description page.

Learning Quantitative Finance with R

Author: Dr. Param Jeet,Prashant Vats

Publisher: Packt Publishing Ltd

ISBN: 1786465256

Category: Computers

Page: 284

View: 1405

Implement machine learning, time-series analysis, algorithmic trading and more About This Book 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. Who This Book Is For If you want to learn how to use R to build quantitative finance models with ease, this book is for you. Analysts who want to learn R to solve their quantitative finance problems will also find this book useful. Some understanding of the basic financial concepts will be useful, though prior knowledge of R is not required. What You Will Learn Get to know the basics of R and how to use it in the field of Quantitative Finance Understand data processing and model building using R Explore different types of analytical techniques such as statistical analysis, time-series analysis, predictive modeling, and econometric analysis Build and analyze quantitative finance models using real-world examples How real-life examples should be used to develop strategies Performance metrics to look into before deciding upon any model Deep dive into the vast world of machine-learning based trading Get to grips with algorithmic trading and different ways of optimizing it Learn about controlling risk parameters of financial instruments In Detail 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 financial models 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. Style and approach This book introduces you to the essentials of quantitative finance with the help of easy-to-understand, practical examples and use cases in R. Each chapter presents a specific financial concept in detail, backed with relevant theory and the implementation of a real-life example.

The Quants

How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It

Author: Scott Patterson

Publisher: Crown Business

ISBN: 9780307453396

Category: Business & Economics

Page: 352

View: 4400

With the immediacy of today’s NASDAQ close and the timeless power of a Greek tragedy, The Quants is at once a masterpiece of explanatory journalism, a gripping tale of ambition and hubris, and an ominous warning about Wall Street’s future. In March of 2006, four of the world’s richest men sipped champagne in an opulent New York hotel. They were preparing to compete in a poker tournament with million-dollar stakes, but those numbers meant nothing to them. They were accustomed to risking billions. On that night, these four men and their cohorts were the new kings of Wall Street. Muller, Griffin, Asness, and Weinstein were among the best and brightest of a new breed, the quants. Over the prior twenty years, this species of math whiz--technocrats who make billions not with gut calls or fundamental analysis but with formulas and high-speed computers--had usurped the testosterone-fueled, kill-or-be-killed risk-takers who’d long been the alpha males the world’s largest casino. The quants helped create a digitized money-trading machine that could shift billions around the globe with the click of a mouse. Few realized, though, that in creating this unprecedented machine, men like Muller, Griffin, Asness and Weinstein had sowed the seeds for history’s greatest financial disaster. Drawing on unprecedented access to these four number-crunching titans, The Quants tells the inside story of what they thought and felt in the days and weeks when they helplessly watched much of their net worth vaporize--and wondered just how their mind-bending formulas and genius-level IQ’s had led them so wrong, so fast.

Building Algorithmic Trading Systems

A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Trading

Author: Kevin Davey

Publisher: John Wiley & Sons

ISBN: 111877888X

Category: Business & Economics

Page: 288

View: 6333

Develop your own trading system with practical guidance and expert advice In Building Algorithmic Trading Systems: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Training, award-winning trader Kevin Davey shares his secrets for developing trading systems that generate triple-digit returns. With both explanation and demonstration, Davey guides you step-by-step through the entire process of generating and validating an idea, setting entry and exit points, testing systems, and implementing them in live trading. You'll find concrete rules for increasing or decreasing allocation to a system, and rules for when to abandon one. The companion website includes Davey's own Monte Carlo simulator and other tools that will enable you to automate and test your own trading ideas. A purely discretionary approach to trading generally breaks down over the long haul. With market data and statistics easily available, traders are increasingly opting to employ an automated or algorithmic trading system—enough that algorithmic trades now account for the bulk of stock trading volume. Building Algorithmic Trading Systems teaches you how to develop your own systems with an eye toward market fluctuations and the impermanence of even the most effective algorithm. Learn the systems that generated triple-digit returns in the World Cup Trading Championship Develop an algorithmic approach for any trading idea using off-the-shelf software or popular platforms Test your new system using historical and current market data Mine market data for statistical tendencies that may form the basis of a new system Market patterns change, and so do system results. Past performance isn't a guarantee of future success, so the key is to continually develop new systems and adjust established systems in response to evolving statistical tendencies. For individual traders looking for the next leap forward, Building Algorithmic Trading Systems provides expert guidance and practical advice.

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