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This book brings together the latest research in the areas of market microstructure and high-frequency finance along with new econometric methods to address critical practical issues in these areas of research. Thirteen chapters, each of which makes a valuable and significant contribution to the existing literature have been brought together, spanning a wide range of topics including information asymmetry and the information content in limit order books, high-frequency return distribution models, multivariate volatility forecasting, analysis of individual trading behaviour, the analysis of liquidity, price discovery across markets, market microstructure models and the information content of order flow. These issues are central both to the rapidly expanding practice of high frequency trading in financial markets and to the further development of the academic literature in this area. The volume will therefore be of immediate interest to practitioners and academics. This book was originally published as a special issue of European Journal of Finance.
A limit order book is essentially a file on a computer that contains all orders sent to the market, along with their characteristics such as the sign of the order, price, quantity and a timestamp. The majority of organized electronic markets rely on limit order books to store the list of interests of market participants on their central computer. A limit order book contains all the information available on a specific market and it reflects the way the market moves under the influence of its participants. This book discusses several models of limit order books. It begins by discussing the data to assess their empirical properties, and then moves on to mathematical models in order to reproduce the observed properties. Finally, the book presents a framework for numerical simulations. It also covers important modelling techniques including agent-based modelling, and advanced modelling of limit order books based on Hawkes processes. The book also provides in-depth coverage of simulation techniques and introduces general, flexible, open source library concepts useful to readers studying trading strategies in order-driven markets.
The design of trading algorithms requires sophisticated mathematical models backed up by reliable data. In this textbook, the authors develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. These models are grounded on how the exchanges work, whether the algorithm is trading with better informed traders (adverse selection), and the type of information available to market participants at both ultra-high and low frequency. Algorithmic and High-Frequency Trading is the first book that combines sophisticated mathematical modelling, empirical facts and financial economics, taking the reader from basic ideas to cutting-edge research and practice. If you need to understand how modern electronic markets operate, what information provides a trading edge, and how other market participants may affect the profitability of the algorithms, then this is the book for you.
A fully revised second edition of the best guide to high-frequency trading High-frequency trading is a difficult, but profitable, endeavor that can generate stable profits in various market conditions. But solid footing in both the theory and practice of this discipline are essential to success. Whether you're an institutional investor seeking a better understanding of high-frequency operations or an individual investor looking for a new way to trade, this book has what you need to make the most of your time in today's dynamic markets. Building on the success of the original edition, the Second Edition of High-Frequency Trading incorporates the latest research and questions that have come to light since the publication of the first edition. It skillfully covers everything from new portfolio management techniques for high-frequency trading and the latest technological developments enabling HFT to updated risk management strategies and how to safeguard information and order flow in both dark and light markets. Includes numerous quantitative trading strategies and tools for building a high-frequency trading system Address the most essential aspects of high-frequency trading, from formulation of ideas to performance evaluation The book also includes a companion Website where selected sample trading strategies can be downloaded and tested Written by respected industry expert Irene Aldridge While interest in high-frequency trading continues to grow, little has been published to help investors understand and implement this approach—until now. This book has everything you need to gain a firm grip on how high-frequency trading works and what it takes to apply it to your everyday trading endeavors.
The primary goal of the book is to present the ideas and research findings of active researchers from various communities (physicists, economists, mathematicians, financial engineers) working in the field of "Econophysics", who have undertaken the task of modelling and analyzing order-driven markets. Of primary interest in these studies are the mechanisms leading to the statistical regularities ("stylized facts") of price statistics. Results pertaining to other important issues such as market impact, the profitability of trading strategies, or mathematical models for microstructure effects, are also presented. Several leading researchers in these fields report on their recent work and also review the contemporary literature. Some historical perspectives, comments and debates on recent issues in Econophysics research are also included.
This book brings together the latest research in the areas of market microstructure and high-frequency finance along with new econometric methods to address critical practical issues in these areas of research. Thirteen chapters, each of which makes a valuable and significant contribution to the existing literature have been brought together, spanning a wide range of topics including information asymmetry and the information content in limit order books, high-frequency return distribution models, multivariate volatility forecasting, analysis of individual trading behaviour, the analysis of liquidity, price discovery across markets, market microstructure models and the information content of order flow. These issues are central both to the rapidly expanding practice of high frequency trading in financial markets and to the further development of the academic literature in this area. The volume will therefore be of immediate interest to practitioners and academics. This book was originally published as a special issue of European Journal of Finance.
The models of portfolio selection and asset price dynamics in this volume seek to explain the market dynamics of asset prices. Presenting a range of analytical, empirical, and numerical techniques as well as several different modeling approaches, the authors depict the state of debate on the market selection hypothesis. By explicitly assuming the heterogeneity of investors, they present models that are descriptive and normative as well, making the volume useful for both finance theorists and financial practitioners. - Explains the market dynamics of asset prices, offering insights about asset management approaches - Assumes a heterogeneity of investors that yields descriptive and normative models of portfolio selections and asset pricing dynamics
Written by one of the leading authorities in market microstructure research, this book provides a comprehensive guide to the theoretical work in this important area of finance.
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.
A remarkable look at how the growth, technology, and politics of high-frequency trading have altered global financial markets In today’s financial markets, trading floors on which brokers buy and sell shares face-to-face have increasingly been replaced by lightning-fast electronic systems that use algorithms to execute astounding volumes of transactions. Trading at the Speed of Light tells the story of this epic transformation. Donald MacKenzie shows how in the 1990s, in what were then the disreputable margins of the US financial system, a new approach to trading—automated high-frequency trading or HFT—began and then spread throughout the world. HFT has brought new efficiency to global trading, but has also created an unrelenting race for speed, leading to a systematic, subterranean battle among HFT algorithms. In HFT, time is measured in nanoseconds (billionths of a second), and in a nanosecond the fastest possible signal—light in a vacuum—can travel only thirty centimeters, or roughly a foot. That makes HFT exquisitely sensitive to the length and transmission capacity of the cables connecting computer servers to the exchanges’ systems and to the location of the microwave towers that carry signals between computer datacenters. Drawing from more than 300 interviews with high-frequency traders, the people who supply them with technological and communication capabilities, exchange staff, regulators, and many others, MacKenzie reveals the extraordinary efforts expended to speed up every aspect of trading. He looks at how in some markets big banks have fought off the challenge from HFT firms, and how exchanges sometimes engineer technical systems to favor certain types of algorithms over others. Focusing on the material, political, and economic characteristics of high-frequency trading, Trading at the Speed of Light offers a unique glimpse into its influence on global finance and where it could lead us in the future.