1st Edition

Algorithmic Trading and Quantitative Strategies

By Raja Velu, Maxence Hardy, Daniel Nehren Copyright 2021
    450 Pages 20 Color Illustrations
    by Chapman & Hall

    Algorithmic Trading and Quantitative Strategies provides an in-depth overview of this growing field with a unique mix of quantitative rigor and practitioner’s hands-on experience. The focus on empirical modeling and practical know-how makes this book a valuable resource for students and professionals.

    The book starts with the often overlooked context of why and how we trade via a detailed introduction to market structure and quantitative microstructure models. The authors then present the necessary quantitative toolbox including more advanced machine learning models needed to successfully operate in the field. They next discuss the subject of quantitative trading, alpha generation, active portfolio management and more recent topics like news and sentiment analytics. The last main topic of execution algorithms is covered in detail with emphasis on the state of the field and critical topics including the elusive concept of market impact. The book concludes with a discussion of the technology infrastructure necessary to implement algorithmic strategies in large-scale production settings.

    A GitHub repository includes data sets and explanatory/exercise Jupyter notebooks. The exercises involve adding the correct code to solve the particular analysis/problem.



    I Introduction to Trading

    1. Trading Fundamentals

    A Brief History of Stock Trading
    Market Structure and Trading Venues: A Review

    Equity Markets Participants

    Watering Holes of Equity Markets

    The Mechanics of Trading

    How Double Auction Markets Work

    The Open Auction

    Continuous Trading

    The Closing Auction

    Taxonomy of Data Used in Algorithmic Trading

    Reference Data

    Market Data

    Market Data Derived Statistics

    Fundamental Data and Other Datasets

    Market Microstructure: Economic Fundamentals of Trading

    Liquidity and Market Making

    II Foundations: Basic Models and Empirics

    2. Univariate Time Series Models

    Trades and Quotes Data and their Aggregation: From Point Processes to Discrete Time Series

    Trading Decisions as Short-Term Forecast Decisions

    Stochastic Processes: Some Properties

    Some Descriptive Tools and their Properties

    Time Series Models for Aggregated Data: Modeling the Mean

    Key Steps for Model Building

    Testing for Nonstationary (Unit Root) in ARIMA Models: To Difference or Not To

    Forecasting for ARIMA Processes

    Stylized Models for Asset Returns

    Time Series Models for Aggregated Data: Modeling the Variance

    Stylized Models for Variance of Asset Returns


    3. Multivariate Time Series Models

    Multivariate Regression

    Dimension-Reduction Methods

    Multiple Time Series Modeling

    Co-integration, Co-movement and Commonality in Multiple Time Series

    Applications in Finance

    Multivariate GARCH Models

    Illustrative Examples


    4. Advanced Topics

    State-Space Modeling

    Regime Switching and Change-Point Models

    A Model for Volume-Volatility Relationship

    Models for Point Processes

    Stylized Models for High Frequency Financial Data

    Models for Multiple Assets: High Frequency Context

    Analysis of Time Aggregated Data

    Realized Volatility and Econometric Models

    Volatility and Price Bar Data

    Analytics from Machine Learning Literature

    Neural Networks

    Reinforcement Learning

    Multiple Indicators and Boosting Methods


    III Trading Algorithms

    5. Statistical Trading Strategies and Back-Testing

    Introduction to Trading Strategies: Origin and History

    Evaluation of Strategies: Various Measures

    Trading Rules for Time Aggregated Data

    Filter Rules

    Moving Average Variants and Oscillators

    Patterns Discovery via Non-Parametric Smoothing Methods

    A Decomposition Algorithm

    Fair Value Models

    Back-Testing and Data Snooping: In-Sample and Out-of-Sample Performance


    Pairs Trading

    Distance-Based Algorithms


    Some General Comments

    Practical Considerations

    Cross-Sectional Momentum Strategies

    Extraneous Signals: Trading Volume, Volatility, etc

    Filter Rules Based on Return and Volume

    An Illustrative Example

    Trading in Multiple Markets

    Other Topics: Trade Size, etc

    Machine Learning Methods in Trading


    6. Dynamic Portfolio Management and Trading Strategies

    Introduction to Modern Portfolio Theory

    Mean-Variance Portfolio Theory

    Multifactor Models

    Tests Related to CAPM and APT

    An Illustrative Example

    Implications for Investing

    Statistical Underpinnings

    Portfolio Allocation Using Regularization

    Portfolio Strategies: Some General Findings

    Dynamic Portfolio Selection

    Portfolio Tracking and Rebalancing

    Transaction Costs, Shorting and Liquidity Constraints

    Portfolio Trading Strategies


    7. News Analytics: From Market Attention and Sentiment to Trading

    Introduction to News Analytics: Behavioral Finance and Investor

    Cognitive Biases

    Automated News Analysis and Market Sentiment

    News Analytics and Applications to Trading

    Discussion / Future of Social Media and News in Algorithmic Trading

    IV Execution Algorithms

    8. Modeling Trade Data

    Normalizing Analytics

    Order Size Normalization: ADV

    Time-Scale Normalization: Characteristic Time

    Intraday Return Normalization: Mid-Quote Volatility

    Other Microstructure Normalization

    Intraday Normalization: Profiles

    Remainder (of the Day) Volume

    Auctions Volume

    Microstructure Signals

    Limit Order Book (LOB): Studying Its Dynamics

    LOB Construction and Key Descriptives

    Modeling LOB Dynamics

    Models Based on Hawkes Process

    Models for Hidden Liquidity

    Modeling LOB: Some Concluding Thoughts

    9. Market Impact Models


    What is Market Impact

    Modeling Transaction Costs

    Historical Review of Market Impact Research

    Some Stylized Models

    Price Impact in the High Frequency Setting

    Models Based on LOB

    Empirical Estimation of Transaction Costs

    Review of Select Empirical Studies

    10. Execution Strategies

    Execution Benchmarks: Practitioner’s View

    Evolution of Execution Strategies

    Layers of an Execution Strategy

    Scheduling Layer

    Order Placement

    Order Routing

    Formal Description of Some Execution Models

    First Generation Algorithms

    Second Generation Algorithms

    Multiple Exchanges: Smart Order Routing Algorithm

    Execution Algorithms for Multiple Assets

    Extending the Algorithms to Other Asset Classes

    V Technology Considerations

    11. The Technology Stack

    From Client Instruction to Trade Reconciliation

    Algorithmic Trading Infrastructure

    HFT Infrastructure

    ATS Infrastructure

    Regulatory Considerations

    Matching Engine

    Client Tiering and other Rules

    12. The Research Stack

    Data Infrastructure

    Calibration Infrastructure

    Simulation Environment

    TCA Environment



    Raja Velu is a professor of Finance and Analytics in Whitman School of Management at Syracuse University. He served as a Technical Architect at Yahoo! in the Sponsored Search Division and was a visiting scientist at IBM-Almaden, Microsoft Research, Google and JPMC. He has also held visiting positions at Stanford's Statistics department, Indian School of Business, the National University of Singapore, and Singapore Management University.

    Maxence Hardy is a Managing Director and the Head of eTrading Quantitative Research for Equities and Futures at J.P.Morgan, based in New York. Mr. Hardy is responsible for the development of agency algorithmic trading strategies for the Equities and Futures divisions globally.

    Daniel Nehren is a Managing Director and the Head of Statistical Modelling and Development for Equities at Barclays. Based in New York, Mr. Nehren is responsible for the development of algorithmic trading and analytics products. Mr. Nehren has more than 19 years of experience in equity trading working for some of the most prestigious financial firms including Citadel, J.P Morgan, and Goldman Sachs.

    "This work does a marvelous job of emphasizing the dual significance of determining the fair value of an asset as well as designing the optimal way to interact with the markets. Optimizing valuation is equally important to optimizing order execution. Both skills must be mastered to avoid selection bias and capturing value. This book must be read!"
    -Peter J. Layton, Principal, Blackthorne Capital Management, LLC

    "An outstanding and timely synthesis of the state of art algorithmic trading ideas. I will recommend it to all who is serious on the foundations."
    -Guofu Zhou, Frederick Bierman and James E. Spears, Professor of Finance, Olin Business School, Washington University in St. Louis

    "This book is a rigorous yet practical introduction to the subject and takes the reader to some advanced concepts in quantitative algo trading. [...] What really makes the book stand out in terms of the learning experience for the interested reader is its no-nonsense attitude to hands-on-learning. The authors have the ethos that algo trading is not a spectator sport and that the best way to learn is to get their hands dirty and find out for themselves. In this spirit, each section contains a set of practical examples and exercises with data available in the corresponding GitHub repository. [...]."
    -Gordon Lee, in Quantitative Finance, January 2023

    "I enjoyed reading this excellent book. I strongly recommend this book to mathematical and applied statisticians generally and to portfolio analysts in particular."
    -Ramalingam Shanmugam, in the Journal of Statistical Computation and Simulation, June 2023