As discussed in the previous article, a robust method for short-term predictions of financial time series is of great value, and many companies in the electronic trading industry are using machine learning methods to achieve this goal. As trading strategies that support machine learning become more prevalent, the speed at which machine learning algorithms can make decisions is once again becoming one of the critical factors in differentiating oneself from the competition.
Quantifying latency is difficult in a general framework, as it varies by trading strategy, exchange, etc. Such information can be derived by examining the timestamps of trading messages as they arrive at the exchange. The advantage of analyzing message data instead of limit order book data is that races become visible: When multiple market participants compete for the same trade, one can see the timestamp of the fastest participant on the finish line as well as the timestamps of the slower participants whose trade attempt failed.
A study by Aquilina et al. using the method of analyzing trade message data found that:
The analysis shows that 5 microseconds of delay can draw the line between a successful and unsuccessful trading strategy. Machine learning inference often takes up most of the latency of the trading loop. Thus, the above example shows the latency regime in which machine learning algorithms operate for high-speed trading.
In the era of data-driven decisions, every microsecond counts. In this article "Ultra-low Latency XGBoost with Xelera Silva", we discuss the optimization of xgboost, lightgbm and catboost for lightning-fast machine learning inference.
As ML-enabled trading strategies proliferate, companies must find ways to differentiate themselves. One trend we've seen over the past two years is trading companies moving machine learning into the hot path of the ultra-low latency trading cycle - a task that remains challenging as most machine learning does not operate at microsecond latency. Let's explore the transformative power of AI in trading together!
Since phones became smartphones, we've seen more and more smart devices. In the data center, a similar evolution is taking place as network interface cards become SmartNICs. Cloud architects, builders and operators see them as an opportunity to build better data centers. Analysts predict the SmartNIC market will become a $2B market by 2027. So, what is a SmartNIC? In this article, we dive into this technology, explore its features and shed light on its potential.