Algorithmic trading and quantitative strategies

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

Trading in Multiple Markets

Other Topics: Trade Size, etc

Machine Learning Methods in Trading

Exercises

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

Exercises

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

Introduction

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

Conclusion

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.