Building Automated Trading Strategies
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| Author | : Benjamin Van Vliet |
| Publisher | : Elsevier |
| Total Pages | : 331 |
| Release | : 2007-03-07 |
| Genre | : Business & Economics |
| ISBN | : 0080476252 |
Over the next few years, the proprietary trading and hedge fund industries will migrate largely to automated trade selection and execution systems. Indeed, this is already happening. While several finance books provide C++ code for pricing derivatives and performing numerical calculations, none approaches the topic from a system design perspective. This book will be divided into two sections: programming techniques and automated trading system ( ATS ) technology and teach financial system design and development from the absolute ground up using Microsoft Visual C++.NET 2005. MS Visual C++.NET 2005 has been chosen as the implementation language primarily because most trading firms and large banks have developed and continue to develop their proprietary algorithms in ISO C++ and Visual C++.NET provides the greatest flexibility for incorporating these legacy algorithms into working systems. Furthermore, the .NET Framework and development environment provide the best libraries and tools for rapid development of trading systems. The first section of the book explains Visual C++.NET 2005 in detail and focuses on the required programming knowledge for automated trading system development, including object oriented design, delegates and events, enumerations, random number generation, timing and timer objects, and data management with STL.NET and .NET collections. Furthermore, since most legacy code and modeling code in the financial markets is done in ISO C++, this book looks in depth at several advanced topics relating to managed/unmanaged/COM memory management and interoperability. Further, this book provides dozens of examples illustrating the use of database connectivity with ADO.NET and an extensive treatment of SQL and FIX and XML/FIXML. Advanced programming topics such as threading, sockets, as well as using C++.NET to connect to Excel are also discussed at length and supported by examples. The second section of the book explains technological concerns and design concepts for automated trading systems. Specifically, chapters are devoted to handling real-time data feeds, managing orders in the exchange order book, position selection, and risk management. A .dll is included in the book that will emulate connection to a widely used industry API ( Trading Technologies, Inc.'s XTAPI ) and provide ways to test position and order management algorithms. Design patterns are presented for market taking systems based upon technical analysis as well as for market making systems using intermarket spreads. As all of the chapters revolve around computer programming for financial engineering and trading system development, this book will educate traders, financial engineers, quantitative analysts, students of quantitative finance and even experienced programmers on technological issues that revolve around development of financial applications in a Microsoft environment and the construction and implementation of real-time trading systems and tools. - Teaches financial system design and development from the ground up using Microsoft Visual C++.NET 2005 - Provides dozens of examples illustrating the programming approaches in the book - Chapters are supported by screenshots, equations, sample Excel spreadsheets, and programming code
| Author | : Kevin J. Davey |
| Publisher | : John Wiley & Sons |
| Total Pages | : 294 |
| Release | : 2014-07-21 |
| Genre | : Business & Economics |
| ISBN | : 1118778987 |
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.
| Author | : Ernie Chan |
| Publisher | : John Wiley & Sons |
| Total Pages | : 230 |
| Release | : 2013-05-28 |
| Genre | : Business & Economics |
| ISBN | : 1118460146 |
Praise for Algorithmic TRADING “Algorithmic Trading is an insightful book on quantitative trading written by a seasoned practitioner. What sets this book apart from many others in the space is the emphasis on real examples as opposed to just theory. Concepts are not only described, they are brought to life with actual trading strategies, which give the reader insight into how and why each strategy was developed, how it was implemented, and even how it was coded. This book is a valuable resource for anyone looking to create their own systematic trading strategies and those involved in manager selection, where the knowledge contained in this book will lead to a more informed and nuanced conversation with managers.” —DAREN SMITH, CFA, CAIA, FSA, Managing Director, Manager Selection & Portfolio Construction, University of Toronto Asset Management “Using an excellent selection of mean reversion and momentum strategies, Ernie explains the rationale behind each one, shows how to test it, how to improve it, and discusses implementation issues. His book is a careful, detailed exposition of the scientific method applied to strategy development. For serious retail traders, I know of no other book that provides this range of examples and level of detail. His discussions of how regime changes affect strategies, and of risk management, are invaluable bonuses.” —ROGER HUNTER, Mathematician and Algorithmic Trader
| Author | : Sergey Izraylevich Ph.D. |
| Publisher | : FT Press |
| Total Pages | : 302 |
| Release | : 2012-03-12 |
| Genre | : Business & Economics |
| ISBN | : 0132491907 |
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 requirements of options, reflecting philosophy, logic, quantitative tools, and valuation procedures that are completely different from those used in conventional automated trading algorithms. Every facet of the authors’ approach is optimized for options, including strategy development and optimization; capital allocation; risk management; performance measurement; back-testing and walk-forward analysis; and trade execution. The authors’ system reflects a continuous process of valuation, structuring and long-term management of investment portfolios (not just individual instruments), introducing systematic approaches for handling portfolios containing option combinations related to different underlying assets. With these techniques, it is finally possible to effectively automate options trading at the portfolio level. This book will be an indispensable resource for serious options traders working individually, in hedge funds, or in other institutions.
| Author | : Chris Conlan |
| Publisher | : Apress |
| Total Pages | : 217 |
| Release | : 2016-09-28 |
| Genre | : Computers |
| ISBN | : 1484221788 |
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
| Author | : Perry J. Kaufman |
| Publisher | : John Wiley & Sons |
| Total Pages | : 1232 |
| Release | : 2013-01-29 |
| Genre | : Business & Economics |
| ISBN | : 1118043561 |
The ultimate guide to trading systems, fully revised and updated For nearly thirty years, professional and individual traders have turned to Trading Systems and Methods for detailed information on indicators, programs, algorithms, and systems, and now this fully revised Fifth Edition updates coverage for today's markets. The definitive reference on trading systems, the book explains the tools and techniques of successful trading to help traders develop a program that meets their own unique needs. Presenting an analytical framework for comparing systematic methods and techniques, this new edition offers expanded coverage in nearly all areas, including trends, momentum, arbitrage, integration of fundamental statistics, and risk management. Comprehensive and in-depth, the book describes each technique and how it can be used to a trader's advantage, and shows similarities and variations that may serve as valuable alternatives. The book also walks readers through basic mathematical and statistical concepts of trading system design and methodology, such as how much data to use, how to create an index, risk measurements, and more. Packed with examples, this thoroughly revised and updated Fifth Edition covers more systems, more methods, and more risk analysis techniques than ever before. The ultimate guide to trading system design and methods, newly revised Includes expanded coverage of trading techniques, arbitrage, statistical tools, and risk management models Written by acclaimed expert Perry J. Kaufman Features spreadsheets and TradeStation programs for a more extensive and interactive learning experience Provides readers with access to a companion website loaded with supplemental materials Written by a global leader in the trading field, Trading Systems and Methods, Fifth Edition is the essential reference to trading system design and methods updated for a post-crisis trading environment.
| Author | : Yves Hilpisch |
| Publisher | : O'Reilly Media |
| Total Pages | : 380 |
| Release | : 2020-11-12 |
| Genre | : Computers |
| ISBN | : 1492053325 |
Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading. You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field. Set up a proper Python environment for algorithmic trading Learn how to retrieve financial data from public and proprietary data sources Explore vectorization for financial analytics with NumPy and pandas Master vectorized backtesting of different algorithmic trading strategies Generate market predictions by using machine learning and deep learning Tackle real-time processing of streaming data with socket programming tools Implement automated algorithmic trading strategies with the OANDA and FXCM trading platforms
| Author | : Eugene A. Durenard |
| Publisher | : John Wiley & Sons |
| Total Pages | : 388 |
| Release | : 2013-10-04 |
| Genre | : Business & Economics |
| ISBN | : 1118419294 |
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.
| Author | : Stefan Jansen |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 822 |
| Release | : 2020-07-31 |
| Genre | : Business & Economics |
| ISBN | : 1839216786 |
Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
| Author | : Edward Leshik |
| Publisher | : John Wiley & Sons |
| Total Pages | : 273 |
| Release | : 2011-09-19 |
| Genre | : Business & Economics |
| ISBN | : 1119975093 |
Interest in algorithmic trading is growing massively – it’s cheaper, faster and better to control than standard trading, it enables you to ‘pre-think’ the market, executing complex math in real time and take the required decisions based on the strategy defined. We are no longer limited by human ‘bandwidth’. The cost alone (estimated at 6 cents per share manual, 1 cent per share algorithmic) is a sufficient driver to power the growth of the industry. According to consultant firm, Aite Group LLC, high frequency trading firms alone account for 73% of all US equity trading volume, despite only representing approximately 2% of the total firms operating in the US markets. Algorithmic trading is becoming the industry lifeblood. But it is a secretive industry with few willing to share the secrets of their success. The book begins with a step-by-step guide to algorithmic trading, demystifying this complex subject and providing readers with a specific and usable algorithmic trading knowledge. It provides background information leading to more advanced work by outlining the current trading algorithms, the basics of their design, what they are, how they work, how they are used, their strengths, their weaknesses, where we are now and where we are going. The book then goes on to demonstrate a selection of detailed algorithms including their implementation in the markets. Using actual algorithms that have been used in live trading readers have access to real time trading functionality and can use the never before seen algorithms to trade their own accounts. The markets are complex adaptive systems exhibiting unpredictable behaviour. As the markets evolve algorithmic designers need to be constantly aware of any changes that may impact their work, so for the more adventurous reader there is also a section on how to design trading algorithms. All examples and algorithms are demonstrated in Excel on the accompanying CD ROM, including actual algorithmic examples which have been used in live trading.
