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 on the technology infrastructure necessary to implement algorithmic strategies in large-scale production settings.

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

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 than19 years of experience in equity trading working for some of the most prestigious financial firms including Citadel, J.P Morgan, and Goldman Sachs.

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 on the technology infrastructure necessary to implement algorithmic strategies in large-scale production settings.

A git-hub 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

Exercises

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

Exercises

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

Exercises

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

Evaluation

Pairs Trading

Distance-Based Algorithms

Co-Integration

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