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Algorithmic Trading and Quantitative Strategies

Algorithmic Trading and Quantitative Strategies

Authors
Publisher Taylor & Francis Inc
Year 14/07/2020
Pages 434
Version hardback
Readership level College/higher education
ISBN 9781498737166
Categories Economic statistics, Investment & securities, Applied mathematics
Delivery to United States

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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.

Algorithmic Trading and Quantitative Strategies

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


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

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