Algorithmic Trading and Quantitative Strategies  book cover
1st Edition

Algorithmic Trading and Quantitative Strategies

ISBN 9781498737166
Published August 6, 2020 by Chapman and Hall/CRC
450 Pages 20 Color Illustrations

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Book Description

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.



Table of Contents

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


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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