How Trading Systems Function

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This post discusses a real world reference architecture using Big Data techniques and is more technical in nature. The final part of this series will focus on business recommendations for disruptive innovation in this area. With globalization driving the capital markets and an increasing number of issuers, one finds an increasing amount of complexity across a range of financial instruments and assets stocks, bonds, derivatives, commodities etc.

The business drivers as noted in the first post in this three part series from a Capital Markets perspective-. Re-tool existing trading infrastructures so that they are more integrated yet loosely coupled and efficient. Automating complex trading strategies that are quantitative in nature across a range of asset classes like equities, forex,ETFs and commodities etc.

Pure speed can only get a firm so far. Retrofitting existing trade systems to be able to accommodate a range of mobile clients who have a vested interest in deriving analytics. The need of the hour is to provide enterprise architecture capabilities around designing flexible trading platforms that are built around efficient use of data, speed, agility and a service oriented architecture.

The choice of open source is key as it allows for a modular and flexible architecture that can be modified and adopted in a phased manner — as you will shortly see. From the Sell side one needs to provide support for handling customer orders and managing trading positions. Figure 3 — Overall Trading Process flow. The intention in adopting a SOA or even a microservices architecture is to be able to incrementally plug in lightweight business services like performance measurement, trade surveillance, risk analytics, option pricing etc.

The data architecture is based on the lambda system developed by Nathan Marz. The lambda architecture solves the problem of computing arbitrary functions on arbitrary data in real time by decomposing the problem into three layers: The Lambda Architecture is aimed at applications built around complex asynchronous transformations that need to run with low latency say, a few seconds to a few hours which is perfectly suited to our business case.

Big Data Lambda Architecture. Your email address will not be published. Notify me of follow-up comments by email. Notify me of new posts by email. The business drivers as noted in the first post in this three part series from a Capital Markets perspective- 1.

Re-tool existing trading infrastructures so that they are more integrated yet loosely coupled and efficient 2. Automating complex trading strategies that are quantitative in nature across a range of asset classes like equities, forex,ETFs and commodities etc 3. Pure speed can only get a firm so far 4. In short support an iterative and DevOps based methodology.

A core requirement is to use Open Source Software and commodity Support a rule based trading model declarative that will evolve to supporting predictive analytics with ingrained support for both complex event processing CEP as well as business workflow ideally support for the BPMN standard notation Support integration with a wide variety of external participants across the globe. It is now deployed in a range of industries ranging from healthcare to manufacturing to IoT Across verticals.

Using AMQP avoids lock-in and costly bridging technology. This tier contains the definition and the runtime for rules for order management, routing, crossing, matching. In memory analytics provided by an in memory data grid or even using a Spark in memory layer The data layer is based on an Apache Hadoop platform and is architected based on a lambda architecture developed by Nathan Marz.

More on this in the below sections Figure 1 — Reference Architecture for Trading Platform The key components of the Trading Platform Architecture as depicted above are — Order Management System — which displays a rich interactive portal with a user interface; clients call in brokers via the telephone or place orders electronically. These orders are routed to the OMS. Bloomberg, Thomson Reuters etc.

The business rules approach adds another dimension to BPM by enabling one to leverage declarative logic with business rules to build compact, fast and easy to understand trading logic. An example of this is in a sector e. Complex Event Processing CEP — The term Event by itself is frequently overloaded and can be used to refer to several different things, depending on the context it is used. In our trading platform, when a sell operation is executed, it causes a change of state in the domain that can be observed on several actors, like the price of the securities that changed to match the value of the operation, the owner of the individual traded assets that change from the seller to the buyer, the balance of the accounts from both seller and buyer that are credited and debited, etc.

Intercepting a cloud of these events and having a business process adapt and react to them is key to have an agile trading platform. This layer also needs to deal with Data Governance.

References — Big Data Lambda Architecture http: Leave a Reply Cancel reply Your email address will not be published.