advanced-algorithmic-trading

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1.5 How Does This Book Differ From "Successful Algorithmic
Trading"?
Successful Algorithmic Trading was written primarily to help readers think in rigourous quantitative
terms about their trading. It introduces the concepts of hypothesis testing and backtesting
trading strategies. It also outlined the available software that can be used to build backtesting
systems.
It discusses the means of storing financial data, measuring quantitative strategy performance,
how to assess risk in quantitative strategies and how to optimise strategy performance. Finally, it
provides a template event-driven backtesting engine on which to base further, more sophisticated,
trading systems.
It is not a book that provides many trading strategies. The emphasis is primarily on how to
think in a quantitative fashion and how to get started.
Advanced Algorithmic Trading has a different focus. In this book the main topics are Time
Series Analysis, Machine Learning and Bayesian Statistics as applied to rigourous quantitative
trading strategies.
Hence this book is largely theoretical for the first three sections and then highly practical for
the fourth, which discusses the implementation of actual trading strategies in a sophisticated,
but freely-available backtesting engine.
More strategies have been added to this book than in the previous version. However, the main
goal is to motivate continued research into strategy development and to provide a framework for
achieving improvement, rather than presenting specific "technical analysis"-style prescriptions.
This book is not a book that covers extensions of the event-driven backtester presented in
Successful Algorithmic Trading, nor does it dwell on software-specific testing methodology or
how to build an institutional-grade infrastructure system. It is primarily about mathematical
modelling and how this can be applied to quantitative trading strategy development.
1.6 Software Installation
Over the last few years it has become significantly easier to get both Python and R environments
installed on Windows, Mac OS X and Linux. This section will describe how to easily install
Python and R in a platform-independent manner.
1.6.1 Installing Python
In order to follow the code for the Bayesian Statistics and Machine Learning chapters (as well as
one chapter in the Time Series Analysis section) it is necessary to install a Python environment.
The most straightforward way to achieve this is to download and install the free Anaconda
distribution from Continuum Analytics at https://www.continuum.io/downloads.
The installation instructions are provided at the link above and come with nearly all of the
necessary libraries needed to get started with the code in this book. Other libraries can be easily
installed using the "pip" command-line tool.
Anaconda is bundled with the Spyder Integrated Development Environment (IDE), which
provides a Python syntax-highlighting text editor, an IPython console for interactive workflow/visualisation
and an object/variable explorer for debugging.