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Developing High-Frequency Trading Systems

You're reading from Developing High-Frequency Trading Systems Learn how to implement high-frequency trading from scratch with C++ or Java basics

Product type Paperback

Published in Jun 2022

Publisher Packt

ISBN-13 9781803242811

Length 320 pages

Edition 1st Edition

Languages

C++

Tools

Blockchain

Concepts

Financial Technology

Authors (3):

Sebastien Donadio

Sourav Ghosh

Romain Rossier

View More author details

Table of Contents (16) Chapters

Preface

1. Part 1: Trading Strategies, Trading Systems, and Exchanges

2. Chapter 1: Fundamentals of a High-Frequency Trading System FREE CHAPTER

3. Chapter 2: The Critical Components of a Trading System

4. Chapter 3: Understanding the Trading Exchange Dynamics

5. Part 2: How to Architect a High-Frequency Trading System

6. Chapter 4: HFT System Foundations – From Hardware to OS

7. Chapter 5: Networking in Motion

8. Chapter 6: HFT Optimization – Architecture and Operating System

9. Chapter 7: HFT Optimization – Logging, Performance, and Networking

10. Part 3: Implementation of a High-Frequency Trading System

11. Chapter 8: C++ – The Quest for Microsecond Latency

12. Chapter 9: Java and JVM for Low-Latency Systems

13. Chapter 10: Python – Interpreted but Open to High Performance

14. Chapter 11: High-Frequency FPGA and Crypto

15. Other Books You May Enjoy

Chapter 10: Python – Interpreted but Open to High Performance

In this chapter, we will introduce you to using Python in the high-frequency trading (HFT) system. Getting an HFT system using Python is problematic since Python was not built for speed and low latency. Because Python is the most used language and provides all the necessary libraries for data analysis, this language is the go-to in algorithmic trading. We will learn how to use HFT libraries in Python in this chapter.

We will cover the following topics:

Introducing Python
Python limitations in the context of HFT
How to use a C++ library in Python

We will provide you with the tools capable of transforming any C++ code into code that can be used by Python.

We will start by explaining why we should use Python, even in HFT.

The rest of the chapter is locked

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Authors (3)

Sebastien Donadio

Sebastien Donadio

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 Socit Gnrale 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.

See other products by Sebastien Donadio

Romain Rossier

Romain Rossier

Romain Rossier brings 19+ years of experience mostly as a Software Architect in the financial industry, specializing in low latency, high performance java software design and development. He is currently the Chief Architect for the HCTech FX Proprietary Trading engine. He also built and led the software development team at HCTech, where he oversaw and developed the HFT platform architecture for FX, Futures and Fixed Income. Prior to HCTech, Romain was Director of the Currenex lab where he led the team responsible for the development of the Currenex Intelligent Pricing System. Romain holds a Master of Science in Communication Systems from the Swiss Federal Institute of Technology in Lausanne.

See other products by Romain Rossier

Sourav Ghosh

Sourav Ghosh

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 with the most liquid global futures contracts. He is currently the vice president at an investment bank based in São Paulo, Brazil. He holds a master's 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.

See other products by Sourav Ghosh

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