Book Image

Building Low Latency Applications with C++

By : Sourav Ghosh
5 (1)
Book Image

Building Low Latency Applications with C++

5 (1)
By: Sourav Ghosh

Overview of this book

C++ is meticulously designed with efficiency, performance, and flexibility as its core objectives. However, real-time low latency applications demand a distinct set of requirements, particularly in terms of performance latencies. With this book, you’ll gain insights into the performance requirements for low latency applications and the C++ features critical to achieving the required performance latencies. You’ll also solidify your understanding of the C++ principles and techniques as you build a low latency system in C++ from scratch. You’ll understand the similarities between such applications, recognize the impact of performance latencies on business, and grasp the reasons behind the extensive efforts invested in minimizing latencies. Using a step-by-step approach, you’ll embark on a low latency app development journey by building an entire electronic trading system, encompassing a matching engine, market data handlers, order gateways, and trading algorithms, all in C++. Additionally, you’ll get to grips with measuring and optimizing the performance of your trading system. By the end of this book, you’ll have a comprehensive understanding of how to design and build low latency applications in C++ from the ground up, while effectively minimizing performance latencies.
Table of Contents (19 chapters)
1
Part 1:Introducing C++ Concepts and Exploring Important Low-Latency Applications
6
Part 2:Building a Live Trading Exchange in C++
10
Part 3:Building Real-Time C++ Algorithmic Trading Systems
14
Part 4:Analyzing and Improving Performance

Summary

In this chapter, our primary focus was on adding intelligence and sophistication to the market participants’ trading systems. First, we discussed our market-making and liquidity-taking trading strategies. We discussed the motivation behind these strategies, how they seek to profit in the markets, and the trading dynamics of these algorithms.

We implemented the important components that make up the intelligence around our trading strategies. The first one was the feature engine that’s used to compute trading features/signals from the market data so that they can be used by the trading strategies to make informed trading decisions. The next one was the position keeper, which is in charge of tracking a trading strategy’s positions and PnLs as the strategy’s orders are executed in the market. After, we looked at the order manager component, which sends and manages live orders in the market to simplify the trading strategy’s implementation....