Preface

In modern times, it is increasingly difficult to gain a significant competitive edge just by being faster than others, which means relying on sophisticated trading signals, predictive models, and strategies. In our book Learn Algorithmic Trading, we provide a broad audience with the knowledge and hands-on practical experience required to build a good understanding of how modern electronic trading markets and market participants operate, as well as how to go about designing, building, and operating all the components required to build a practical and profitable algorithmic trading business using Python.

You will be introduced to algorithmic trading and setting up the environment required to perform tasks throughout the book. You will learn the key components of an algorithmic trading business and the questions you need to ask before embarking on an automated trading project.

Later, you will learn how quantitative trading signals and trading strategies are developed. You will get to grips with the workings and implementation of some well-known trading strategies. You will also understand, implement, and analyze more sophisticated trading strategies, including volatility strategies, economic release strategies, and statistical arbitrage. You will learn how to build a trading bot from scratch using the algorithms built in the previous sections.

By now, you will be ready to connect to the market and start researching, implementing, evaluating, and safely operating algorithmic trading strategies in live markets.

Who this book is for

This book is for software engineers, financial traders, data analysts, entrepreneurs, and anyone who wants to begin their journey in algorithmic trading. If you want to understand how algorithmic trading works, what all the components of a trading system are, the protocols and algorithms required for black box and gray box trading, and how to build a completely automated and profitable trading business, then this book is what you need!

What this book covers

Chapter 1, Algorithmic Trading Fundamentals, explains what algorithmic trading is and how algorithmic trading is related to high frequency or low latency trading. We will discuss the evolution of algorithmic trading, from rule-based to AI. We will look at essential algorithmic trading concepts, asset classes, and instruments. You will learn how to set up your mind for algorithmic decisions.

Chapter 2, Deciphering the Markets with Technical Analysis, covers some popular technical analysis methods and shows how to apply them to the analysis of market data. We will perform basic algorithmic trading using market trends, support, and resistance.

Chapter 3, Predicting the Markets with Basic Machine Learning, reviews and implements a number of simple regression and classification methods and explains the advantages of applying supervised statistical learning methods to trading. 

Chapter 4, Classical Trading Strategies Driven by Human Intuition, looks at some basic algorithmic strategies (momentum, trend, mean-reversion), and explains their workings, as well as their advantages and disadvantages.

Chapter 5, Sophisticated Algorithmic Strategies, consolidates the basic algorithmic strategies by looking at more advanced approaches (statistical arbitrage, pair correlation), as well as their advantages and disadvantages.

Chapter 6, Managing Risk in Algorithmic Strategies, explains how to measure and manage risk (market risk, operational risk, and software implementation bugs) in algorithmic strategies. 

Chapter 7, Building a Trading System in Python, describes the functional components supporting the trading strategy based on the algorithm created in the preceding chapters. We will be using Python to build a small trading system, and will use the algorithm from the preceding chapters to build a trading system capable of trading. 

Chapter 8, Connecting to Trading Exchanges, describes the communication components of a trading system. We will be using the quickfix library in Python to connect the trading system to a real exchange.

Chapter 9, Creating a Backtester in Python, explains how to improve your trading algorithm by running tests with large amounts of data to validate the performance of your trading bot. Once a model is implemented, it is necessary to test whether the trading robot behaves as expected in the trading infrastructure (by checking for implementation-related mistakes).

Chapter 10, Adapting to Market Participants and Conditions, discusses why strategies do not perform as expected when deployed in live trading markets and provides examples of how to address those issues in the strategies themselves or the underlying assumptions. We will also discuss why strategies that are performing well slowly deteriorate in terms of performance and provide some simple examples to explain how to address this.

To get the most out of this book

Readers should have a basic knowledge of finance and Python.

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Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "This code will use the DataReader function from the pandas_datareader package."

A block of code is set as follows:

import pandas as pd
from pandas_datareader import data

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

 if order['action'] == 'to_be_sent':
             # Send order
             order['status'] = 'new'
             order['action'] = 'no_action'
             if self.ts_2_om is None:

Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "A mean reversion strategy that relies on the Absolute Price Oscillator (APO) trading signal indicator."

Warnings or important notes appear like this.

Tips and tricks appear like this.

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