Efficiently Managing an Order Book with Heaps in C++

Efficiently Managing an Order Book Using Heaps in C++ for Financial Trading Platforms

In financial trading platforms, maintaining an efficient order book is essential for high-performance trading. This article explores how to leverage heaps in C++ to organize and access trading orders based on price and timestamp. Using heaps ensures quick access to the most competitive orders while also addressing memory constraints.

Why Use a Heap?

A heap is an ideal data structure for managing a continuously sorted stream of data. In trading systems, where rapid access to the highest buy price and lowest sell price is critical, heaps provide an efficient solution. C++'s Standard Template Library (STL) offers strong support for heaps, which we can utilize to manage our order book effectively.

Structure of an Order Book

An order book typically consists of two primary heaps:

1. Max-Heap for Buy Orders: Stores buy orders with the highest prices given priority.

2. Min-Heap for Sell Orders: Stores sell orders with the lowest prices given priority.

Timestamp Management

Orders in each heap are prioritized by price. If prices are equal, the timestamp of each order dictates priority, ensuring the order book respects both price competitiveness and submission timing—critical during high-volume trading periods.

Memory Management Strategies

To prevent memory overflow in high-transaction environments, consider the following strategies:

1. Selective Storage: Store only orders within specific price thresholds or those likely to be executed. For example, a depth of 128 levels is often sufficient for exchanges like NSE. Maintain all orders up to the maximum level allowed.

2. Data Pruning: Regularly clear orders that have been executed, canceled, or are far from the current market price. Also, consider keeping separate heaps for each trading token.

By implementing these strategies, trading platforms can maintain an efficient, high-performance order book with minimal memory overhead, even under high transaction volumes.