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Author: Howard B. Bandy Publisher: ISBN: 9780979183829 Category : Expert systems (Computer science) Languages : en Pages : 400
Book Description
"This book, (MSTP) is intended to be an introduction to techniques that can be used to model the performance and risk of trading systems. MSTP is a sequel to [the author's] earlier book, Quantitative Trading Systems (QTS). QTS discusses the design, testing, and validation of trading systems. Although it illustrates examples using the AmiBroker trading system development platform, the concepts it discusses are universal. MSTP uses analogies from gambling to illustrate the effects of uncertainty and to build easily understood simulation models using Monte Carlo simulation."--Adapted from author/ publisher's preface and Introduction.
Author: Howard B. Bandy Publisher: ISBN: 9780979183829 Category : Expert systems (Computer science) Languages : en Pages : 400
Book Description
"This book, (MSTP) is intended to be an introduction to techniques that can be used to model the performance and risk of trading systems. MSTP is a sequel to [the author's] earlier book, Quantitative Trading Systems (QTS). QTS discusses the design, testing, and validation of trading systems. Although it illustrates examples using the AmiBroker trading system development platform, the concepts it discusses are universal. MSTP uses analogies from gambling to illustrate the effects of uncertainty and to build easily understood simulation models using Monte Carlo simulation."--Adapted from author/ publisher's preface and Introduction.
Author: John R. Wolberg Publisher: John Wiley & Sons ISBN: Category : Business & Economics Languages : de Pages : 264
Book Description
With the proliferation of computer programs to predict market direction, professional traders and sophisticated individual investors have increasingly turned to mathematical modeling to develop predictive systems. Kernel regression is a popular data modeling technique that can yield useful results fast. Provides data modeling methodology used to develop trading systems. * Shows how to design, test, and measure the significance of results John R. Wolberg (Haifa, Israel) is professor of mechanical engineering at the Haifa Institute in Israel. He does research and consulting in data modeling in the financial services area.
Author: Valerii Salov Publisher: John Wiley & Sons ISBN: 0470112212 Category : Business & Economics Languages : en Pages : 266
Book Description
"Mr. Salov has taken one of my favorite creations – Perfect Profit – and provided an expanded description of his interpretation of it and put it in your hands with the included software. Like I said fifteen years ago, Perfect Profit is an important tool for the trading system developer. See for yourself." —Robert Pardo, President, Pardo Capital Limited "A very in-depth reference for programmers that should serve well into the future. The code herein lends itself well to other syntactically similar programming languages such as Java, PHP, and C#." —Ralph Vince The goal of trading is to make money, and for many, profits are the best way to measure that success. Author Valerii Salov knows how to calculate potential profit, and in Modeling Maximum Trading Profits with C++, he outlines an original and thought-provoking approach to trading that will help you do the same. This detailed guide will show you how to effectively calculate the potential profit in a market under conditions of variable transaction costs, and provide you with the tools needed to compute those values from real prices. You'll be introduced to new notions of s-function, s-matrix, s-interval, and polarities of s-intervals, and discover how they can be used to build the r- and l-algorithms as well as the first and second profit and loss reserve algorithms. Optimal money management techniques are also illustrated throughout the book, so you can make the most informed trading decisions possible. Filled with in-depth insight and expert advice, Modeling Maximum Trading Profits with C++ contains a comprehensive overview of trading, money management, and C++. A companion website is also included to help you test the concepts described throughout the book before you attempt to use them in real-world situations.
Author: Stefan Jansen Publisher: Packt Publishing Ltd ISBN: 1839216786 Category : Business & Economics Languages : en Pages : 822
Book Description
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: Robert Kissell Publisher: Academic Press ISBN: 0124016936 Category : Business & Economics Languages : en Pages : 492
Book Description
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.
Author: Robert M. Barnes Publisher: Irwin Professional Publishing ISBN: 9780786310982 Category : Business & Economics Languages : en Pages : 165
Book Description
Addressed not to merchants of grain, textiles, spices, or useful goods, but to buyers and sellers of stocks and bonds. A Wall Street program developer explains the factors important for developing and analyzing a trading system. Among them are isolating such components as measuring and controlling risk, candidate selection, the timing of buying and selling, and portfolio management; the necessity for a valid simulation system; and generating new price scenarios. Annotation copyrighted by Book News, Inc., Portland, OR
Author: Perry J. Kaufman Publisher: John Wiley & Sons ISBN: 1118043561 Category : Business & Economics Languages : en Pages : 1232
Book Description
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: Kevin J. Davey Publisher: John Wiley & Sons ISBN: 1118778987 Category : Business & Economics Languages : en Pages : 294
Book Description
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: Timothy Masters Publisher: Apress ISBN: 1484241738 Category : Computers Languages : en Pages : 325
Book Description
Build, test, and tune financial, insurance or other market trading systems using C++ algorithms and statistics. You’ve had an idea and have done some preliminary experiments, and it looks promising. Where do you go from here? Well, this book discusses and dissects this case study approach. Seemingly good backtest performance isn't enough to justify trading real money. You need to perform rigorous statistical tests of the system's validity. Then, if basic tests confirm the quality of your idea, you need to tune your system, not just for best performance, but also for robust behavior in the face of inevitable market changes. Next, you need to quantify its expected future behavior, assessing how bad its real-life performance might actually be, and whether you can live with that. Finally, you need to find its theoretical performance limits so you know if its actual trades conform to this theoretical expectation, enabling you to dump the system if it does not live up to expectations. This book does not contain any sure-fire, guaranteed-riches trading systems. Those are a dime a dozen... But if you have a trading system, this book will provide you with a set of tools that will help you evaluate the potential value of your system, tweak it to improve its profitability, and monitor its on-going performance to detect deterioration before it fails catastrophically. Any serious market trader would do well to employ the methods described in this book. What You Will Learn See how the 'spaghetti-on-the-wall' approach to trading system development can be done legitimatelyDetect overfitting early in developmentEstimate the probability that your system's backtest results could have been due to just good luckRegularize a predictive model so it automatically selects an optimal subset of indicator candidatesRapidly find the global optimum for any type of parameterized trading systemAssess the ruggedness of your trading system against market changesEnhance the stationarity and information content of your proprietary indicatorsNest one layer of walkforward analysis inside another layer to account for selection bias in complex trading systemsCompute a lower bound on your system's mean future performanceBound expected periodic returns to detect on-going system deterioration before it becomes severeEstimate the probability of catastrophic drawdown Who This Book Is For Experienced C++ programmers, developers, and software engineers. Prior experience with rigorous statistical procedures to evaluate and maximize the quality of systems is recommended as well.
Author: Howard B. Bandy
Author: Howard B. Bandy
Author: John R. Wolberg
Author: Valerii Salov
Author: Stefan Jansen
Author: Robert Kissell
Author: Robert M. Barnes
Author: Perry J. Kaufman
Author: Howard Bandy
Author: Kevin J. Davey
Author: Timothy Masters