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Building Low Latency Applications with C++

Building Low Latency Applications with C++

By : Sourav Ghosh
4.2 (15)
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Building Low Latency Applications with C++

Building Low Latency Applications with C++

4.2 (15)
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)
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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

Building the feature and computing complex features

In this section, we will build a minimal version of a feature engine. We will only compute two simple features – one (market price) that computes fair market prices based on the top of book prices and quantity and another (aggressive trade qty ratio) that computes how big a trade is compared to the top of book quantities. We will use these feature values to drive our market-making and liquidity-taking trading algorithms later in this chapter. The source code for the FeatureEngine class we will build here can be found in the Chapter9/trading/strategy/feature_engine.h file on GitHub. We discussed the details of this component in Chapter, Designing Our Trading Ecosystem, in the Designing a framework for low-latency C++ trading algorithms section.

Defining the data members in the feature engine

First, we need to declare the FeatureEngine class and define the data members inside this class. First, we will include the required...

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