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Daniel Chait, Managing Director, Lab49, asserts that CEP has a wide range of applications beyond algo/auto trading.
New Horizons for Complex Event Processing
Over the past few years, complex event processing (CEP) technology has grown from a niche category rooted in academia to one of the central pillars in the modern financial services technology stack. In this article, I will discuss the reasons why this new technology has become so widely adopted, demonstrate one of its main new uses, and preview where I see this industry headed.
History and trends
To understand CEP, and the central role it has come to play in financial services, it is useful to consider the broader market and technology environments within which this development has taken place. Over the past decade, data volumes have skyrocketed, new sources of liquidity have mushroomed, sell-side firms have created internal crossing networks, trading has become increasingly sophisticated and trading volumes in complex derivatives have spiked, collectively far outstripping the stock markets.
For these reasons, the old, batch-based technology model no longer suffices. In that model, firms would record their activity throughout the day, typically using a relational database, and then run a batch of programmes at the end of the day to process this data. For example, a fund would send orders to its broker throughout the trading day, storing these in a position-keeping database along the way. Then, at the close of the day, they (or their prime broker on their behalf) would re-compute their value at risk (VaR) as well as their net exposures to various factors, and use these closing values the next day to inform their trading decisions. The problem is that those nightly numbers are quickly out-of-date. As markets sped up and volumes rose, traders began demanding these numbers again at the middle of the day, then several times per day. These intraday batches came to be requested so frequently that systems designed to handle a single nightly run simply could not scale up to support the business needs.
This model has yielded to a new paradigm of continuous, event-driven systems. In this design, rather than store data for processing at a later stage, we set up the processes and then, as data arrives, act upon it immediately. For example, we might create a system to continuously re-price off-the-run US treasury bonds using a model dictating that each bond should be computed according to some
calculated spreads from a corresponding on-the-run bond. Then, with each new tick in the price of the on-the-run bond, we will trigger a cascade of analytic code to re-calculate the new prices for all the related off-the-run bonds. CEP engines are designed to enable this continuous, event-driven model.
Buy side takes control
Another interesting effect of the acceleration in the markets is that buy-side firms have been pressured to adopt cutting-edge technology, which for a long time was the purview only of the large sell-side institutions. And as buy-side firms look to take more control over their trading functions (for example direct market access, high-speed algorithmic trading, real-time risk), they are increasingly developing tighter technology integration with their broker/dealers. These two effects have demanded increasing sophistication of their IT operations. CEP, as one of the key new technologies enabling real-time financial services, is seeing rapid adoption within buy-side firms as part of this overall trend.
CEP first gained widespread adoption in automated and algorithmic trading. Auto/algo trading systems are ideally suited to CEP for several reasons: they often require low latency and high throughput; they employ complex logic in response to outside market events; and they frequently require connections to several outside trading venues. Before the advent of CEP engines, firms had to write much of this code themselves, adding to the cost, complexity and risk of building algorithmic trading solutions. Now, using a CEP platform, developers can focus on creating the business logic to implement a particular algorithm and let the platform handle details such as connecting to exchanges, triggering trading decision logic and maintaining consistency even in the face of network outages, hardware failures etc.
Additionally, certain features of CEP products are specifically geared towards automated and algorithmic traders. For example, firms need a way to test out their trading strategies and execution algorithms prior to deployment. Most CEP products provide sophisticated features for feeding in simulated data, recording and playing back live data, and debugging applications, providing a rich environment for teams to develop, test and refine their strategies.
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