Implementing Low Latency Financial Data Feeds with C and Microsoft Technologies

For financial institutions and traders, the ability to access and process live financial data in real-time is critical. With changes in the market dictated by nano-seconds, implementing an efficient and low-latency live financial data feed system is essential. This article will explore the optimal approach for implementing such a system using C and Microsoft-related technologies.

Introduction

Financial market data is dynamic, and handling it requires robust and efficient mechanisms to maintain latency at the milisecond or sub-millisecond level. In this context, C (a low-level, high-performance programming language) and Microsoft technologies such as MSVC (Microsoft Visual C ) can offer significant advantages.

Key Considerations for Low Latency Feeds

The most crucial element in creating a low-latency financial data feed is to avoid blocking operations, especially within the data handling process. The two most important aspects to focus on are:

Threading Queues

One of the primary techniques involves using threaded queues. This method ensures that data handling operations do not block the main feed processing thread, thereby minimizing latency. A separate thread runs the queue, and as soon as a data point (e.g., a quote) is received, it is enqueued and processed immediately. This approach allows the main thread to continue processing data without interruption.

Conflation Mechanisms

If the primary interest is in the most recent value of a quote, conflation mechanisms can be employed. This involves merging or discarding adjacent quotes that have the same ticker, reducing noise while maintaining crucial information. Conflation can help minimize the number of updates processed, further reducing the overall latency.

Real-Time Data Display

If the objective is to display live quotes in a real-time grid, several considerations come into play. The first step is to ensure that the grid can handle fast updates.

Updating the Grid

Determining how often to update the grid is crucial. Real-time updates on every tick might be too frequent for the user and could lead to performance issues. Regular updates, say every second or two, might be more appropriate. Additionally, the system must efficiently handle these updates to ensure no significant delay in displaying the latest quotes.

Using Disruptor Framework in C

For those working with C, it is worth considering the C port of the well-regarded Disruptor framework. Originally developed in Java, the Disruptor framework is now available in C and is used by several trading firms with C stacks. This high-performance framework is designed to handle massive throughput, making it ideal for high-frequency trading applications.

The Disruptor Framework

The Disruptor framework is based on the concept of a ring buffer, which is a non-blocking approach for managing events. It is particularly effective in reducing contention and improving throughput. By eliminating traditional locking mechanisms, the Disruptor can achieve extremely low latencies and high throughputs, making it an excellent choice for implementing low-latency financial data feeds in a C environment.

Conclusion

In conclusion, implementing a low-latency live financial data feed using C and Microsoft technologies requires a focused approach on minimal blocking operations and high-performance data handling mechanisms. Utilizing threaded queues and conflation techniques can significantly reduce latency. For real-time grid applications, setting appropriate update frequencies can enhance user experience. Lastly, leveraging the C port of the Disruptor framework can provide the necessary performance and throughput for high-frequency trading environments.

Keywords: Low Latency, C Programming, Microsoft Technologies, Financial Data Feed, Threading Queues