High-Performance Computing in Financial Systems

The financial world moves at a breakneck speed, generating and consuming massive volumes of data each second. The sheer magnitude and speed at which this data flow necessitates systems capable of high-performance computing—systems that can retrieve, process, store, and analyze data in real-time, while ensuring efficiency, reliability, and scalability. In this dynamic landscape, it is our strategic choices and astute implementations that spell the difference between success and mediocrity.

In this chapter, we delve into the specifics of implementing robust, scalable, and efficient financial systems. These systems must not only adapt to the demands of vast, complex data streams but also facilitate the execution of complex trading algorithms and strategies. This task is akin to assembling an intricate timepiece; each component must be meticulously chosen, carefully calibrated, and precisely integrated with the others to maintain...

Technical requirements

lmportant note

The code provided in this chapter serves as an illustrative example of how one might implement a high-performance trading system. However, it is important to note that this code may lack certain important functions and should not be used in a production environment as it is. It is crucial to conduct thorough testing and add necessary functionalities to ensure the system’s robustness and reliability before deploying it in a live trading environment. High-quality screenshots of code snippets can be found here: https://github.com/PacktPublishing/C-High-Performance-for-Financial-Systems-/tree/main/Code%20screenshots.

Implementing the LOB and choosing the right data structure

In the financial industry, the efficiency and effectiveness of decision-making hinges on the ability to process real-time data swiftly and accurately. We will examine the essential task of retrieving and storing this data, laying the groundwork for how financial systems handle the vast amounts of data that course through their veins every second.

We are not just dealing with the sheer volume of data but also the velocity at which it arrives and changes. In the financial markets, prices and other market indicators can shift in microseconds, causing a cascade of effects across different instruments and markets. A system that is not well-equipped to handle such data at this speed can miss out on lucrative opportunities or even incur significant losses.

To keep pace with the ever-changing financial landscape, it’s crucial to design systems that can efficiently manage real-time data. This involves taking into account...

Implementing data feeds

The data feed, which provides a continuous stream of market data, is the lifeblood of any trading system. It delivers the raw information that will feed the LOB and have trading strategies to make decisions.

As we saw, processing this data in real time is a significant challenge. Market data can arrive at extremely high rates, especially during periods of high market volatility. Moreover, the data must be processed with minimal latency to ensure that trading decisions are based on the most up-to-date information.

In this section, we will explore how we can implement data feeds in our high-performance trading system using C++. We will discuss various aspects of real-time data processing, including network communication, low-latency data techniques, and the use of a FIX engine. As a practical example, we will also go deep into the QuickFIX engine (a very well-known library) and how we can implement network communication in C++ based on our LOB implementation...

Implementing the Model and Strategy modules

This module is the brain of the system, responsible for making trading decisions based on real-time market data. The Strategy module continuously reads the market data from the LOB, applies various trading strategies, and triggers orders when certain criteria are met. The implementation of this module requires careful design and optimization to ensure low latency and high throughput, which are critical for the success of high-frequency trading.

In our proposed architecture, the Strategy module is designed as a separate module that operates concurrently with the LOB and other modules of the system. It communicates with the LOB through a ring buffer, which is a lock-free data structure that allows efficient and concurrent access to market data. The Strategy module continuously polls the ring buffer in a busy waiting loop, ensuring that it can immediately process new market data as soon as it arrives.

To further optimize the performance...

Implementing the Messaging Hub module

Messaging Hub is the module that will serve as a conduit for real-time market data between the LOB and the various non-latency-sensitive modules within the system. Its primary function is to decouple the hot path, which is the real-time data flow from the LOB, from the rest of the system, ensuring that the hot path is not burdened with the task of serving data to multiple modules.

This module is designed to operate concurrently with the LOB and the Strategy module, receiving real-time data updates from the LOB and distributing them to the subscribed modules. This design allows the LOB to focus on its core task of maintaining the state of the market, while Messaging Hub handles the distribution of this data to the rest of the system.

The architecture of Messaging Hub is based on the publish-subscribe pattern, a popular choice for real-time data distribution in modern trading systems. In this pattern, Messaging Hub acts as a publisher, broadcasting...

Implementing OMS and EMS

The OMS and EMS are critical components of orders being generated by the system. The OMS is responsible for managing and tracking orders throughout their lifecycle, while the EMS is responsible for routing orders to the appropriate trading venues. Both systems need to operate with high performance and reliability to ensure efficient and effective trading operations.

The OMS is designed to manage active and filled orders. It validates orders received from the strategy module, keeps them in an active order vector, and then sends them to the EMS. The OMS also receives execution reports from the FIX engine, updates order statuses, and moves filled orders to a filled order vector. If an order is canceled, it is removed from the active orders. The OMS also has a function to forward filled orders to a database, although the implementation, depending on requirements, could change.

The OMS could also connect to a messaging hub to receive market data updates. These...

Implementing RMS

The RMS is responsible for assessing and managing the risks associated with trading activities. An effective RMS ensures that the trading activities align with the firm’s risk tolerance and comply with regulatory requirements. It provides the real-time monitoring of risk metrics, performs pre-trade risk checks, and conducts post-trade analysis.

The RMS is designed as a modular system, allowing for scalability and ease of maintenance. It is integrated with the OMS and the EMS to receive and process order and position data. The RMS also connects to the messaging hub to ingest and preprocess market data for the cases where it needs to calculate exposures and current prices.

It also comprises several components, each responsible for a specific function:

• Data ingestion and preprocessing: This component collects market data from the messaging hub and position data from the OMS. The data is then preprocessed for further analysis.
• Risk metrics calculation...

Measuring system performance and scalability

Maintaining optimal system performance is not just a one-time task; it is something that continuously evolves while data grows exponentially. It is an ongoing process that requires constant vigilance. This is where the importance of measuring and monitoring system performance and scalability comes into play. As such, it is crucial to continuously measure and monitor system performance to ensure that it meets the required standards and can scale to accommodate growing data volumes and user demands.

We go by “what gets measured gets managed,” and that has never been true in this industry. Without a clear understanding of a system’s performance, it is impossible to manage it effectively or make informed decisions about its future.

Constantly measuring and monitoring system performance allows us to do the following:

• Understand the system’s behavior under different workloads
• Identify potential bottlenecks...

Summary

In this chapter, we have dived deep into the heart of high-performance systems, exploring the intricate details of data structures, system architecture, and the implementation of key modules. We have examined the critical role of the LOB and the importance of choosing the right data structure to ensure optimal performance. We have also discussed the implementation of other essential modules, such as the order management system, execution management system, and risk management system.

We have further explored the importance of identifying performance bottlenecks. We discussed various profiling and benchmarking techniques to identify potential areas of improvement and ensure the system is operating at its peak. We also touched on the importance of key performance metrics and how they can be used to measure system performance.

Finally, we discussed the challenges and strategies associated with scaling systems to handle increasing volumes of data. We explored different approaches...