advanced-algorithmic-trading

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The aim of this book is to provide the "next step" for those who have already begun their
quantitative trading career or are looking to try more advanced methods. In particular the book
will discuss techniques that are currently in deployment at some of the large quantitative hedge
funds and asset management firms.
Our main area of study will be that of rigourous statistical analysis. This may sound
like a dry topic, but rest assured that it is not only extremely interesting when applied to real
world data, but also provides a solid "mental framework" for how to think about future trading
methods and approaches.
Statistical analysis is a huge field of academic interest and only a fraction of the field can be
considered within this book. Trying to distill the topics important for quantitative trading is
difficult. However three main areas have been chosen for discussion:
• Bayesian Statistics
• Time Series Analysis
• Machine Learning
Each of these three areas is extremely useful for quantitative trading research.
1.2.1 Bayesian Statistics
Bayesian Statistics is an alternative way of thinking about probability. The more
traditional "frequentist" approach considers probabilities as the end result of many trials, for
instance, the fairness of a coin being flipped many times. Bayesian Statistics takes a different
approach and instead considers probability as a measure of belief. That is, opinions are used to
create probability distributions from which the fairness of the coin might be based on.
While this may sound highly subjective it is often an extremely effective method in practice.
As new data arrives beliefs can be updated in a rational manner using the famous Bayes’ Rule.
Bayesian Statistics has found uses in many fields, including engineering reliability, searching for
lost nuclear submarines and controlling spacecraft orientation. However, it is also extremely
applicable to quantitative trading problems.
Bayesian Inference is the application of Bayesian Statistics to making inference and predictions
about data. Within this book the main goal will be to study financial asset prices in order
to predict future values or understand why they change. The Bayesian framework provides a
modern, sophisticated mathematical toolkit with which to carry this out.
Time Series Analysis and Machine Learning make heavy use of Bayesian Inference for the
design of some of their algorithms. Hence it is essential that the basics of how Bayesian Statistics
is carried out are discussed first.
To carry out Bayesian Inference in this book a "probabilistic programming" tool written in
Python will be used, called PyMC3.
1.2.2 Time Series Analysis
Time Series Analysis provides a set of "workhorse" techniques for analysing financial time series.
Most professional quants will begin their analysis of financial data using basic time series methods.
By applying the tools in time series analysis it is possible to make elementary assessments
of financial asset behaviour.