1st Edition

Quantitative Trading Algorithms, Analytics, Data, Models, Optimization

    379 Pages
    by Chapman & Hall

    379 Pages 30 Color Illustrations
    by Chapman & Hall

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    The first part of this book discusses institutions and mechanisms of algorithmic trading, market microstructure, high-frequency data and stylized facts, time and event aggregation, order book dynamics, trading strategies and algorithms, transaction costs, market impact and execution strategies, risk analysis, and management. The second part covers market impact models, network models, multi-asset trading, machine learning techniques, and nonlinear filtering. The third part discusses electronic market making, liquidity, systemic risk, recent developments and debates on the subject.



    Evolution of trading infrastructure

    Quantitative strategies and time-scales

    Statistical arbitrage and debates about EMH

    Quantitative funds, mutual funds, hedge funds

    Data, analytics, models, optimization, algorithms

    Interdisciplinary nature of the subject and how the book can be used

    Supplements and problems

    Statistical Models and Methods for Quantitative Trading

    Stylized facts on stock price data

    Time series of low-frequency returns

    Discrete price changes in high-frequency data

    Brownian motion at the Paris Exchange and random walk down Wall Street

    MPT as a \walking shoe" down Wall Street

    Statistical underpinnings of MPT

    Multifactor pricing models

    Bayes, shrinkage, and Black-Litterman estimators

    Bootstrapping and the resampled frontier

    A new approach incorporating parameter uncertainty

    Solution of the optimization problem

    Computation of the optimal weight vector

    Bootstrap estimate of performance and NPEB

    From random walks to martingales that match stylized facts

    From Gaussian to Paretian random walks

    Random walks with optional sampling times

    From random walks to ARIMA, GARCH

    Neo-MPT involving martingale regression models

    Incorporating time series e_ects in NPEB

    Optimizing information ratios along e_cient frontier

    An empirical study of neo-MPT

    Statistical arbitrage and strategies beyond EMH

    Technical rules and the statistical background

    Time series, momentum, and pairs trading strategies

    Contrarian strategies, behavioral _nance, and investors' cognitive biases

    From value investing to global macro strategies

    In-sample and out-of-sample evaluation

    Supplements and problems

    Active Portfolio Management and Investment Strategies

    Active alpha and beta in portfolio management

    Sources of alpha

    Exotic beta beyond active alpha

    A new approach to active portfolio optimization

    Transaction costs, and long-short constraints

    Components of cost of transaction

    Long-short and other portfolio constraints

    Multiperiod portfolio management

    The Samuelson-Merton theory

    Incorporating transaction costs into Merton's problem

    Multiperiod capital growth and volatility pumping

    Multiperiod mean-variance portfolio rebalancing

    Dynamic mean-variance portfolio optimization

    Dynamic portfolio selection

    Supplementary notes and comments


    Econometrics of Transactions in Electronic Platforms

    Transactions and transactions data

    Models for high-frequency data

    Roll's model of bid-ask bounce

    Market microstructure model with additive noise

    Estimation of integrated variance of Xt

    Sparse sampling methods

    Averaging method over subsamples

    Method of two time-scales

    Method of kernel smoothing: Realized kernels

    Method of pre-averaging

    From MLE of volatility parameter to QMLE of [X]T

    Estimation of covariation of multiple assets

    Asynchronicity and the Epps effect

    Synchronization procedures

    QMLE for covariance and correlation estimation

    Multivariate realized kernels and two-scale estimators

    Fourier methods

    Fourier estimator of [X]T and spot volatility

    Statistical properties of Fourier estimators

    Fourier estimators of spot co-volatilities

    Other econometric models involving TAQ

    ACD models of inter-transaction durations

    Self-exciting point process models

    Decomposition of Di and generalized linear models

    Joint modeling of point process and its marks

    McCulloch and Tsay's decomposition

    Realized GARCH and other predictive models

    Jumps in e_cient price process and power variation

    Supplementary notes and comments


    Limit Order Book: Data Analytics and Dynamic Models

    From market data to limit order book (LOB)

    Stylized facts of LOB data

    Book price adjustment

    Volume imbalance and other indicators

    Fitting a multivariate point process to LOB data

    Marketable orders as a multivariate point process

    Empirical illustration

    LOB data analytics via machine learning

    Queueing models of LOB dynamics

    Diffusion limits of the level-1 reduced-form model

    Fluid limit of order positions

    LOB-based queue-reactive model

    Supplements and problems

    Optimal Execution and Placement

    Optimal execution with a single asset

    Dynamic programming solution of problem (6.2)

    Continuous-time models and calculus of variations

    Myth{the optimal deterministic strategies

    Multiplicative price impact model

    The model and stochastic control problem

    HJB equation for _nite-horizon case

    In_nite-horizon case T = 1

    Price manipulation and transient price impact

    Optimal execution with LOB

    Cost minimization

    Optimal strategy for Model 1

    Optimal strategy for Model 2

    Closed-form solution for block-shaped LOBs

    Optimal execution with portfolios

    Optimal placement

    Markov random walk model with mean reversion

    Continuous-time Markov chain model

    Supplements and problems

    Market Making and Smart Order Routing

    Ho and Stoll's model and the Avellanedo-Stoikov policy

    Solution to the HJB equation and subsequent extensions

    Impulse control involving limit and market orders

    Impulse control for the market

    Control formulation

    Smart order routing and dark pools

    Optimal order splitting among exchanges in SOR

    The cost function and optimization problem

    Optimal order placement across K exchanges

    A stochastic approximation method

    Censored exploration-exploitation for dark pools

    The SOR problem and a greedy algorithm

    Modi_ed Kaplan-Meier estimate ^ Ti

    Exploration, exploitation, and optimal allocation

    Stochastic Lagrangian optimization in dark pools

    Lagrangian approach via stochastic approximation

    Convergence of Lagrangian recursion to optimizer

    Supplementary notes and comments


    Informatics, Regulation and Risk Management

    Some quantitative strategies

    Exchange infrastructure

    Order gateway

    Matching engine

    Market data dissemination

    Order fee structure

    Colocation service

    Clearing and settlement

    Strategy informatics and infrastructure

    Market data handling

    Alpha engine

    Order management

    Order type and order qualifier

    Exchange rules and regulations

    SIP and Reg NMS

    Regulation SHO

    Other exchange-specific rules

    Circuit breaker

    Market manipulation

    Risk management

    Operational risk

    Strategy risk

    Supplementary notes and comments


    A Martingale Theory

    Discrete-time martingales

    Continuous-time martingales

    Markov Chain and Related Topics

    Generator Q of CTMC

    Potential theory for Markov chains

    Markov decision theory

    Doubly Stochastic Self-Exciting Point Processes

    Martingale theory, intensity process, self-excitation

    Hawkes process: Compensator and stationarity

    Estimation in point process models

    Asymptotic theory and likelihood inference

    Simulation of doubly stochastic SEPP

    Weak Convergence and Limit Theorems

    Donsker's theorem and its extensions

    Queuing system and limit theorems



    Xin Guo is the Coleman Fung Chair Professor of Financial Modeling in the department of Industrial Engineering and Operations Research, UC Berkeley. She founded the Berkeley Risk Analysis and Data Analytics Research (RADAR) Lab and holds a courtesy appointment with the Lawrence Berkeley National Lab. Prior to UC Berkeley, she was a Research Staff Member at the IBM T. J. Watson Research Center and an Associate Professor at Cornell University. Her main research interests are stochastic control, stochastic processes and applications. In addition to high frequency trading modeling and analysis, her recent research includes singular controls, impulse controls, non-linear expectations, mean-field games, and filtration enlargement with application to credit risk.

    Tze Leung Lai is a Professor of Statistics and, by courtesy, of Health Research and Policy in the School of Medicine and of the Institute for Computational & Mathematical Engineering (ICME) in the School of Engineering at Stanford University. He is Director of the Financial and Risk Modeling Institute, Co-Director of the Biostatistics Core of the Stanford Cancer Institute, and Co-Director of the Center for Innovative Study Design at the Stanford School of Medicine. He has held regular and visiting faculty appointments at Columbia University, UC Berkeley, and Nankai University, and holds advisory positions with the University of Hong Kong, Peking University, and Tsinghua University.

    Howard Shek is a senior researcher at Tower Research Capital, where he has built and led the Core Research team with a mandate that covers the wide spectrum of research topics in automated trading. He has over 15 years of quantitative research and trading experience in fixed-income arbitrage, market microstructure, volatility estimation, option pricing, and portfolio theory, and has held senior trading and research positions at Merrill Lynch and J. P. Morgan, focusing on proprietary trading in fixed-income derivatives.

    Samuel Po-Shing Wong is CEO and Chief Quant of 5Lattice Securities, a proprietary trading company in Hong Kong that develops quantitative trading algorithms and corresponding risk management methodologies from statistical data analysis and machine learning. He also teaches the course of Algorithmic Trading for Stanford Quantitative Finance Program in Hong Kong and serves as an Honorary Professor of the Department of Statistics and Actuarial Science at The University of Hong Kong.

    "All in all, it is certainly a welcome addition to the nascent literature on this intriguing subject and recommended reading for those interested in quantitative trading strategies—academics, practitioners, and students alike."
    ~The American Statistician, Mikko S. Pakkanen