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Developing High-Frequency Trading Systems

Developing High-Frequency Trading Systems

By : Sebastien Donadio, Sourav Ghosh, Romain Rossier
4.1 (18)
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Developing High-Frequency Trading Systems

Developing High-Frequency Trading Systems

4.1 (18)
By: Sebastien Donadio, Sourav Ghosh, Romain Rossier

Overview of this book

The world of trading markets is complex, but it can be made easier with technology. Sure, you know how to code, but where do you start? What programming language do you use? How do you solve the problem of latency? This book answers all these questions. It will help you navigate the world of algorithmic trading and show you how to build a high-frequency trading (HFT) system from complex technological components, supported by accurate data. Starting off with an introduction to HFT, exchanges, and the critical components of a trading system, this book quickly moves on to the nitty-gritty of optimizing hardware and your operating system for low-latency trading, such as bypassing the kernel, memory allocation, and the danger of context switching. Monitoring your system’s performance is vital, so you’ll also focus on logging and statistics. As you move beyond the traditional HFT programming languages, such as C++ and Java, you’ll learn how to use Python to achieve high levels of performance. And what book on trading is complete without diving into cryptocurrency? This guide delivers on that front as well, teaching how to perform high-frequency crypto trading with confidence. By the end of this trading book, you’ll be ready to take on the markets with HFT systems.
Table of Contents (16 chapters)
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1
Part 1: Trading Strategies, Trading Systems, and Exchanges
5
Part 2: How to Architect a High-Frequency Trading System
10
Part 3: Implementation of a High-Frequency Trading System

Improving the speed of Python code in HFT

The critical components we defined during the previous chapters must run at high speed. Using any of the tools we described previously will help you create C/C++-like code and create performant Python code using libraries. It is essential to begin constructing a new algorithm in Python utilizing NumPy and SciPy while avoiding looping code by leveraging the vectorized idioms of both libraries. In reality, this means attempting to replace any nested for loops with similar calls to NumPy array functions. The purpose is to prevent the CPU from wasting time on the Python interpreter instead of crunching numbers for trading strategies.

However, there are situations when an algorithm cannot be efficiently expressed in simple vectorized NumPy code. The following is the recommended method in this case:

  1. Find the primary bottleneck in the Python implementation and isolate it in a dedicated module-level function.
  2. If there is a small but...
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Tech Concepts
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Programming languages
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