This work deals with the issue of problematic market price prediction in the context of crowd behavior. "Intelligent Trading Systems" describes technical analysis methods used to predict price movements.

With the exponential growth of program trading in the global financial industry, quantum finance and its underlying technologies have become one of the hottest topics in the fintech community. Numerous financial institutions and fund houses around the world require computer professionals with a basic understanding of quantum finance to develop intelligent financial systems. This book presents a selection of the author’s past 15 years’ R&D work and practical implementation of the Quantum Finance Forecast System – which integrates quantum field theory and related AI technologies to design and develop intelligent global financial forecast and quantum trading systems. The book consists of two parts: Part I discusses the basic concepts and theories of quantum finance and related AI technologies, including quantum field theory, quantum price fields, quantum price level modelling and quantum entanglement to predict major financial events. Part II then examines the current, ongoing R&D projects on the application of quantum finance technologies in intelligent real-time financial prediction and quantum trading systems. This book is both a textbook for undergraduate & masters level quantum finance, AI and fintech courses and a valuable resource for researchers and data scientists working in the field of quantum finance and intelligent financial systems. It is also of interest to professional traders/ quants & independent investors who would like to grasp the basic concepts and theory of quantum finance, and more importantly how to adopt this fascinating technology to implement intelligent financial forecast and quantum trading systems. For system implementation, the interactive quantum finance programming labs listed on the Quantum Finance Forecast Centre official site (QFFC.org) enable readers to learn how to use quantum finance technologies presented in the book.

The new edition of the definitive reference to trading systems—expanded and thoroughly updated. Professional and individual traders haverelied on Trading Systems and Methods for over three decades. Acclaimed trading systems expert Perry Kaufman provides complete, authoritative information on proven indicators, programs, systems, and algorithms. Now in its sixth edition, this respected book continues to provide readers with the knowledge required to develop or select the trading programs best suited for their needs. In-depth discussions of basic mathematical and statistical concepts instruct readers on how much data to use, how to create an index, how to determine probabilities, and how best to test your ideas. These technical tools and indicators help readers identify trends, momentum, and patterns, while an analytical framework enables comparisons of systematic methods and techniques. This updated, fully-revised edition offers new examples using stocks, ETFs and futures, and provides expanded coverage of arbitrage, high frequency trading, and sophisticated risk management models. More programs and strategies have been added, such as Artificial Intelligence techniques and Game Theory approaches to trading. Offering a complete array of practical, user-ready tools, this invaluable resource: Offers comprehensive revisions and additional mathematical and statistical tools, trading systems, and examples of current market situations Explains basic mathematical and statistical concepts with accompanying code Includes new Excel spreadsheets with genetic algorithms, TradeStation code, MetaStock code, and more Provides access to a companion website packed with supplemental materials Trading Systems and Methods is an indispensable reference on trading systems, as well as system design and methods for professional and individual active traders, money managers, trading systems developers.

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.

In Designing Stock Market Trading Systems Bruce Vanstone and Tobias Hahn guide you through their tried and tested methodology for building rule-based stock market trading systems using both fundamental and technical data. This book shows the steps required to design and test a trading system until a trading edge is found, how to use artificial neural networks and soft computing to discover an edge and exploit it fully. Learn how to build trading systems with greater insight and dependability than ever before Most trading systems today fail to incorporate data from existing research into their operation. This is where Vanstone and Hahn's methodology is unique. Designed to integrate the best of past research on the workings of financial markets into the building of new trading systems, this synthesis helps produce stock market trading systems with unrivalled depth and accuracy. This book therefore includes a detailed review of key academic research, showing how to test existing research, how to take advantage of it by developing it into a rule-based trading system, and how to improve it with artificial intelligence techniques. The ideas and methods described in this book have been tried and tested in the heat of the market. They have been used by hedge funds to build their trading systems. Now you can use them too.

The Intellectual Trader is the first book any trader should read when thinking about trading stocks, bonds, currencies, or commodities. Written by thirty-year Wall Street veteran David Hoffman, the book explores the entire gamut of trading using clear, easy to follow stories and analogies. In this book, you will learn the fundamentals of trading, how to develop trading ideas, how to operate in the markets profitably and manage your risk like a professional. You will understand the psychological skills needed to master your trading. Later, you will learn the leading qualitative and quantitative tactics of successful traders, leaving you emotionally and intellectually prepared to trade profitably. The author lays out what is wrong with the trading systems promoted by so many authors and breaks the many myths coming from easy money trading books. Take your trading beyond chat rooms, Reddit, and Robinhood, into the realm where the most successful traders thrive in all market conditions. If you have read other books about trading, you will wish you had read The Intelligent Trader first. This book is genuinely the prequel to every other book about trading. Sure, to become a classic on the subject.

Turn insight into profit with guru guidance toward successful algorithmic trading A Guide to Creating a Successful Algorithmic Trading Strategy provides the latest strategies from an industry guru to show you how to build your own system from the ground up. If you're looking to develop a successful career in algorithmic trading, this book has you covered from idea to execution as you learn to develop a trader's insight and turn it into profitable strategy. You'll discover your trading personality and use it as a jumping-off point to create the ideal algo system that works the way you work, so you can achieve your goals faster. Coverage includes learning to recognize opportunities and identify a sound premise, and detailed discussion on seasonal patterns, interest rate-based trends, volatility, weekly and monthly patterns, the 3-day cycle, and much more—with an emphasis on trading as the best teacher. By actually making trades, you concentrate your attention on the market, absorb the effects on your money, and quickly resolve problems that impact profits. Algorithmic trading began as a "ridiculous" concept in the 1970s, then became an "unfair advantage" as it evolved into the lynchpin of a successful trading strategy. This book gives you the background you need to effectively reap the benefits of this important trading method. Navigate confusing markets Find the right trades and make them Build a successful algo trading system Turn insights into profitable strategies Algorithmic trading strategies are everywhere, but they're not all equally valuable. It's far too easy to fall for something that worked brilliantly in the past, but with little hope of working in the future. A Guide to Creating a Successful Algorithmic Trading Strategy shows you how to choose the best, leave the rest, and make more money from your trades.