advanced-algorithmic-trading

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1.3 How Is The Book Laid Out?
The book is broadly laid out in four sections. The first three are theoretical in nature and
teach the basics of Bayesian Statistics, Time Series Analysis and Machine Learning, with many
references presented for further research. The fourth section applies all of the previous theory
to the backtesting of quantitative trading strategies using the QSTrader open-source backtesting
engine.
The book begins with a discussion on the Bayesian philosophy of statistics. The binomial
model is presented as a simple example with which to apply Bayesian concepts such as conjugate
priors and posterior sampling via Markov Chain Monte Carlo.
It then explores Bayesian statistics as related to quantitative finance, discussing a Bayesian
approach to stochastic volatility. Such a model is eligible for use within a regime detection
mechanism in a risk management setting.
In Time Series Analysis the discussion begins with the concept of serial correlation, applying
it to simple models such as White Noise and the Random Walk. From these two models more
sophisticated linear approaches can be built up to explain serial correlation, culminating in the
Autoregressive Integrated Moving Average (ARIMA) family of models.
The book then considers volatility clustering, or conditional heteroskedasticity, motivating the
famous Generalised Autoregressive Conditional Heteroskedastic (GARCH) family of models.
Subsequent to ARIMA and GARCH the book introduces the concept of cointegration (used
heavily in pairs trading) and introduces state space models including Hidden Markov Models
and Kalman Filters.
These time series methods are all applied to current financial data as they are introduced.
Their inferential and predictive performance is also assessed.
In the Machine Learning section a rigourous definition of supervised and unsupervised learning
is presented utilising the notation and methodology of statistical machine learning. The
humble linear regression will be presented in a probabilistic fashion, which allows introduction
of machine learning ideas in a familiar setting.
The book then introduces the more advanced non-linear methods such as Decision Trees,
Support Vector Machines and Random Forests. It then discusses unsupervised techniques such
as K-Means Clustering.
Many of the above mentioned techniques are applied to asset price prediction, natural language
processing and sentiment analysis. Subsequently full code is provided for systematic
strategy backtesting implementations within QSTrader.
The book provides plenty of references on where to head next. There are many potential
academic topics of interest to pursue subsequent to this book, including Non-Linear Time Series
Methods, Bayesian Nonparametrics and Deep Learning using Neural Networks. Unfortunately,
these exciting methods will need to wait for an additional book to be given the proper treatment
they deserve!
1.4 Required Technical Background
Advanced Algorithmic Trading is a definite step up in complexity from the previous QuantStart
book Successful Algorithmic Trading. Unfortunately it is difficult to carry out any statistical
inference without utilising some mathematics and programming.