Systematic Trading

This is not just another book with yet another trading system. This is a complete guide to developing your own systems to help you make and execute trading and investing decisions. It is intended for everyone who wishes to systematise their financial decision making, either completely or to some degree. Author Robert Carver draws on financial theory, his experience managing systematic hedge fund strategies and his own in-depth research to explain why systematic trading makes sense and demonstrates how it can be done safely and profitably. Every aspect, from creating trading rules to position sizing, is thoroughly explained. The framework described here can be used with all assets, including equities, bonds, forex and commodities. There is no magic formula that will guarantee success, but cutting out simple mistakes will improve your performance. You'll learn how to avoid common pitfalls such as over-complicating your strategy, being too optimistic about likely returns, taking excessive risks and trading too frequently. Important features include: - The theory behind systematic trading: why and when it works, and when it doesn't. - Simple and effective ways to design effective strategies. - A complete position management framework which can be adapted for your needs. - How fully systematic traders can create or adapt trading rules to forecast prices. - Making discretionary trading decisions within a systematic framework for position management. - Why traditional long only investors should use systems to ensure proper diversification, and avoid costly and unnecessary portfolio churn. - Adapting strategies depending on the cost of trading and how much capital is being used. - Practical examples from UK, US and international markets showing how the framework can be used. Systematic Trading is detailed, comprehensive and full of practical advice. It provides a unique new approach to system development and a must for anyone considering using systems to make some, or all, of their investment decisions.

With the right broker, and just a few hundred dollars or pounds, anyone can become a leveraged trader. The products and tools needed are accessible to all: FX, a margin account, CFDs, spread-bets and futures. But this level playing field comes with great risks. Trading with leverage is inherently dangerous. With leverage, losses and costs – the two great killers for traders – are magnified. This does not mean leverage must be avoided altogether, but it does mean that it needs to be used safely. In Leveraged Trading, Robert Carver shows you how to do exactly that, by using a trading system. A trading system can be employed to tackle those twin dangers of serious losses and high costs. The trading systems introduced in this book are simple and carefully designed to use the correct amount of leverage and trade at a suitable frequency. Robert shows how to trade a simple Starter System on its own, on a single instrument and with a single rule for opening positions. He then moves on to show how the Starter System can be adapted, as you gain experience and confidence. The system can be diversified into multiple instruments and new trading rules can be added. For those who wish to go further still, advice on making more complex improvements is included: how to develop your own trading systems, and how to combine a system with your own human judgement, using an approach Robert calls Semi-Automatic Trading. For those trading with leverage, looking for a way to take a controlled approach and manage risk, a properly designed trading system is the answer. Pick up Leveraged Trading and learn how.

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.

Sophisticated options traders need systematic, reliable approaches for identifying the best option combinations, underlying assets, and strategies. This book makes these approaches available for the first time. Leading-edge traders and researchers Sergey Izraylevich and Vadim Tsudikman treat the option market as a whole: an unlimited set of trading variants composed of all option combinations that can be constructed at any specific time moment (using all possible strategies and underlying assets). They introduce a system that permits thorough analysis and comparison of many option combinations in terms of both expected profitability and potential risk. For the first time, they formalize and classify more than a dozen criteria intended to select preferable trading alternatives from a vast quantity of potential opportunities, and show how to apply multiple valuation criteria concurrently to select the best possible trades. By applying these principles consistently, traders can systematically identify subtle price distortions using proven statistical parameters. They can gain a clear and consistent advantage over competing traders, transforming option trading into a continuous process of profit generation with tightly controllable parameters of risk and profitability.

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

In order to overcome certain obstacles and make more informed decisions in today’s markets, you need to use the appropriate models and apply careful analysis. Nobody understands this better than author Michael Harris. And now, with Profitability and Systematic Trading, he reveals how to achieve this goal, by discussing some of the most important trading concepts he’s worked on during twenty years of research and development in this field.

The financial markets industry is at the same crossroads as the automotive industry in the late 1970s. Margins are collapsing and customization is rapidly increasing. The automotive industry turned to quality and its no coincidence that in the money management industry many of the spectacular failures have been due largely to problems in quality control. The financial industry in on the verge of a quality revolution. New and old firms alike are creating new investment vehicles and new strategies that are radically changing the nature of the industry. To compete, mutual funds, hedge fund industries, banks and proprietary trading firms are being forced to quicklyy research, test and implement trade selection and execution systems. And, just as in the early stages of factory automation, quality suffers and leads to defects. Many financial firms fall short of quality, lacking processes and methodologies for proper development and evaluation of trading and investment systems. Authors Kumiega and Van Vliet present a new step-by-step methodology for such development. Their methodology (called K|V) has been presented in numerous journal articles and at academic and industry conferences and is rapidly being accepted as the preferred business process for the institutional trading and hedge fund industries for development, presentation, and evaluation of trading and investment systems. The K|V model for trading system development combines new product development, project management and software development methodologies into one robust system. After four stages, the methodology requires repeating the entire waterfall for continuous improvement. The discussion quality and its applications to the front office is presented using lessons learned by the authors after using the methodology in the real world. As a result, it is flexible and modifiable to fit various projects in finance in different types of firms. Their methodology works equally well for short-term trading systems, longer-term portfolio management or mutual fund style investment strategies as well as more sophisticated ones employing derivative instruments in hedge funds. Additionally, readers will be able to quickly modify the standard K|V methodology to meet their unique needs and to quickly build other quantitatively drive applications for finance. At the beginning and the end of Quality Money Management the authors pose a key question: Are you willing to change and embrace quality for the 21st century or are willing to accept extinction? The real gem in this book is that the concepts give the reader a road map to avoid extinction. Presents a robust process engineering framework for developing and evaluating trading and investment systems Best practices along the step-by-step process will mitigate project risk, model risk, and ensure data quality Includes a quality model for backtesting and managing market risk of working systems

'A Practical Guide to ETF Trading Systems' is about simple, rule-based trading systems of a trend following nature. This book reflects the author's belief that successful investing is not complex, that market timing works and that investors should spurn traditional actively-managed products in favour of managing their own investments using index-tracking funds. Providing a comprehensive introduction to rule-based trading, this book sets out in detail two specific systems which may be applied to exchange-traded funds (ETFs) and exchange-traded commodities (ETCs). Sceptics will maintain that mechanical systems do not work and that you cannot ignore the fundamentals. They are wrong. Sophisticated investors have profited handsomely over the years by following price trends on a purely mechanical basis and they will continue to do so. This guide will show you that systematic trading is likely to provide far better risk-adjusted returns than any conventional approach currently on offer from professional fund managers. There has never been a better time to benefit from the advantages of systematic investing. At a time when long-only traditionalists are fully invested in stocks and nursing huge losses, the systematic investor has exited the markets entirely and waits patiently for a signal to re-enter.