Learn Algorithmic Trading

Understand the fundamentals of algorithmic trading to apply algorithms to real market data and analyze the results of real-world trading strategies

Key Features

• Understand the power of algorithmic trading in financial markets with real-world examples
• Get up and running with the algorithms used to carry out algorithmic trading
• Learn to build your own algorithmic trading robots which require no human intervention

Book Description

It's now harder than ever to get a significant edge over competitors in terms of speed and efficiency when it comes to algorithmic trading. Relying on sophisticated trading signals, predictive models and strategies can make all the difference. This book will guide you through these aspects, giving you insights into how modern electronic trading markets and participants operate.

You'll start with an introduction to algorithmic trading, along with setting up the environment required to perform the tasks in the book. You'll explore the key components of an algorithmic trading business and aspects you'll need to take into account before starting an automated trading project. Next, you'll focus on designing, building and operating the components required for developing a practical and profitable algorithmic trading business. Later, you'll learn how quantitative trading signals and strategies are developed, and also implement and analyze sophisticated trading strategies such as volatility strategies, economic release strategies, and statistical arbitrage. Finally, you'll create a trading bot from scratch using the algorithms built in the previous sections.

By the end of this book, you'll be well-versed with electronic trading markets and have learned to implement, evaluate and safely operate algorithmic trading strategies in live markets.

What you will learn

• Understand the components of modern algorithmic trading systems and strategies
• Apply machine learning in algorithmic trading signals and strategies using Python
• Build, visualize and analyze trading strategies based on mean reversion, trend, economic releases and more
• Quantify and build a risk management system for Python trading strategies
• Build a backtester to run simulated trading strategies for improving the performance of your trading bot
• Deploy and incorporate trading strategies in the live market to maintain and improve profitability

Who this book is for

This book is for software engineers, financial traders, data analysts, and entrepreneurs. Anyone who wants to get started with algorithmic trading and understand how it works; and learn the components of a trading system, protocols and algorithms required for black box and gray box trading, and techniques for building a completely automated and profitable trading business will also find this book useful.

Table of Contents

1. Algorithmic Trading Fundamentals
2. Deciphering the Markets with Technical Analysis
3. Predicting the Markets with basic Machine Learning
4. Classical Trading Strategies
5. Sophisticated Algorithmic Strategies
6. Managing Risk of Algorithmic Strategies
7. Building a Trading System in Python
8. Connecting to trading exchanges
9. Creating a Backtester in Python
10. Adapting to market participants and changing financial markets

Sebastien Donadio is the Chief Technology Officer at Tradair, responsible for leading the technology. He has a wide variety of professional experience, including being head of software engineering at HC Technologies, partner and technical director of a high-frequency FX firm, a quantitative trading strategy software developer at Sun Trading, working as project lead for the Department of Defense. He also has research experience with Bull SAS, and an IT Credit Risk Manager with Société Générale while in France. He has taught various computer science courses for the past ten years in the University of Chicago, NYU and Columbia University. His main passion is technology but he is also a scuba diving instructor and an experienced rock-climber.

Sourav Ghosh has worked in several proprietary high-frequency algorithmic trading firms over the last decade. He has built and deployed extremely low latency, high throughput automated trading systems for trading exchanges around the world, across multiple asset classes. He specializes in statistical arbitrage market-making, and pairs trading strategies for the most liquid global futures contracts. He works as a Senior Quantitative Developer at a trading firm in Chicago. He holds a Masters in Computer Science from the University of Southern California. His areas of interest include Computer Architecture, FinTech, Probability Theory and Stochastic Processes, Statistical Learning and Inference Methods, and Natural Language Processing.