Meeting Multi-Asset Trading Challenges: Workable Approaches for Success in a Dynamic Capital Markets Arena – A white paper from Smarttrade



Meeting Multi-Asset Trading Challenges:

Workable Approaches for Success in a Dynamic Capital Markets Arena

a White Paper from

Meeting Multi-Asset Trading Challenges:

Workable Approaches for Success in a Dynamic Capital Markets Arena



There has been widespread change in the global capital markets in the last few years, and it has had significant effects on banks’ trading platforms. Banks must provide trading platforms that can access the global financial markets, manage increasingly complex transactions involving multiple asset classes, and handle a range of protocols and market structures that are constantly evolving. Add to this the requirements to respond to evolving regulatory mandates, client demands for increased functionality and transparency, and increasing volumes and volatility; and it’s clear sell-side banks face a new, challenging trading terrain where they must quickly react to changing markets in order to remain competitive.

Institutional dealing is extending globally with more liquidity sources to manage, and involves more and more disparate counterparties. This creates the need to aggregate, create orders, and deal or trade faster with clear market views in a fast moving trading environment. The stakes are higher. Clients demand transparency and best execution even while trading larger volumes in more volatile markets across various market structures and across multiple asset classes.

Meeting Multi-Asset Trading Challenges: Workable Approaches for Success in a Dynamic Capital Markets Arena



Multi-Asset Trading Landscape

Siloed trading operations are becoming less and less optimal in this new environment. Clients want sell-side banks to support complex trades, often involving multiple legs in multiple asset classes. These trades may include instruments traded in more than one geographic region, in more than one time zone, and with multiple settlement arrangements. To compete in this kind of trading environment, sell-side banks need harmonized trading systems that enable deeper and richer cross-asset functionality.

In addition, they now need systems that can monitor risk more holistically and in real time across client accounts with diverse holdings. The pressure for sell-side banks to more transparently manage exposure across all business comes from both regulators and banks’ own risk management mandates, both of which continue to evolve.

Focus on Dealing Systems

Tier 1 and Tier 2 sell-side banks generally grow both organically and by acquisition. In the process of this growth, they build or acquire a plethora of trading systems that are disparate in terms of infrastructure, architecture and software. Some are legacy systems using old code bases and outdated hardware, some are purpose-built and best-of-breed, some are asset-class specific, and some handle multiple asset classes. A typical large global financial institution might have as many as 20, 50 or even 100’s of separate trading platforms. Many of these platforms may have open systems with flexible and scalable architectures, but others may be brittle, unable to scale, and difficult to modify to comply with a changing market environment. These platforms often operate independently of one another, requiring a spider’s web of interconnections and interfaces to enable connectivity with clients and interoperability with compliance, risk management, clearing, and other back office functions.

Evolving market structure, regulatory pressure, client demands, cost reduction, and consolidation are forcing many firms to consider a new paradigm for trading systems. They look for ways to harmonize their trading infrastructure across multiple asset classes; re-using functionality, data models, database, message transports, and protocols while still supporting the distinct workflows required by each asset class and market. There are a number of business benefits obtained from unification of platforms, technology and processes:

Meeting Multi-Asset Trading Challenges: Workable Approaches for Success in a Dynamic Capital Markets Arena



Rationalize operations across asset classes by removing silos, eliminating redundant infrastructure, 1. and dramatically reducing latency as well as the maintenance and support costs associated with siloed IT environments.

Standardize messaging to the back office, enabling a unified and normalized approach to allocations, 2. clearing, settlement, and reporting.

Meet customer needs for more complex trades involving multiple transactions across multiple 3. asset classes.

Provide buy-side customers with the ability to trade different asset classes in the same manner, 4. with similar algorithms, and from one point of connectivity.

Manage risk more holistically by providing complete, real time views of exposure across asset classes 5. and traditionally siloed systems.

Better prepare trading desks to meet evolving regulatory requirements without having to recode 6. multiple systems.

Provide customers and regulators with improved transparency into pricing, liquidity, transaction cost, 7. and risk.

Integrated, Multi-Asset Trading Systems

A single, integrated dealing system can deliver dramatic improvements in cost reduction, risk control, and profitability. So what components would need to be included in such a system?

It would have to include functionality for liquidity aggregation, pricing, order management, position-keeping, smart-order routing, and internalization. Such a system should be modular, allowing a firm to turn on features or asset classes one at a time to manage migration. It should also enable the firm to turn on or off specific functionality at discreet levels (for example, turning on a specific liquidity seeking algorithm for use with equities in the US markets, modifying it for use in the EU, and leaving it disabled for futures).

The core of this type of integrated system is a reliable, scalable, low latency, and high-throughput messaging capability. Scalability is crucial. Equities, options, and forex volumes have all been growing for years. As more of the products traditionally traded over the counter (OTC) are migrated to central clearing and electronic trading, volume in these assets is expected to swell dramatically. Systems used for trading these instruments will need to scale easily using industry-standard hardware.

Meeting Multi-Asset Trading Challenges: Workable Approaches for Success in a Dynamic Capital Markets Arena



An open architecture that facilitates easy integration with external systems is also crucial. Open application programming interfaces (APIs) can simplify and standardize integration, making the trading platform more flexible, and more customizable. These APIs should allow integration of applications to extend functionality, custom analytics and algorithms, specialized compliance and risk checks, and client-, market- or asset class-specific functionality.

Such a system would also need a standard set of adaptors to facilitate quick and flexible integration with counterparties for both liquidity and order inflows and pricing and execution outflows. It should have multiple deployment options: internally within the firm, hosted/colocated, distributed across multiple centers, with all corresponding monitoring tools depending on the option chosen.

A standardized data model needs to be flexible and robust enough to preserve all the nuances and unique identifiers present in each asset class while normalizing the data to enable common processes to handle liquidity aggregation, order book management, order routing, state management, etc.

Lastly standardized tools should be available for managing and monitoring connectivity, latency, and performance across counterparty connections, liquidity venues, back office connections, and internal processes.

Aggregating Liquidity

Banks need to easily identify, connect to, collect and aggregate prices or market data from a growing number of increasingly diverse liquidity sources. Market data comes in different formats and often in different protocols, depending on the source and instrument. These market data require normalization to effectively capture prices, apply analytics, and determine the best available prices for each instrument set up to trade. These capabilities must support both the most simple and the most complex versions of pricing, providing capabilities to capture and determine top of book for OTC and listed flow based on price, size, and spread.

Price Distribution

Approaches to price distribution can vary by asset class, by trading objectives, and by client. A multi-asset system needs the ability to customize pricing on a client-specific basis, applying risk analytics, credit checks, spread data, and trading limits based on customer-specific criteria.

Meeting Multi-Asset Trading Challenges: Workable Approaches for Success in a Dynamic Capital Markets Arena



In addition, the system must be flexible in the way it distributes prices. Some clients may wish to receive every price update streamed into high-frequency trading systems, while others may require only incremental snapshots of conflated data. Some clients may need conflation to preserve system resources or to support click-trading. Scalability and resilience are also critical, enabling the system to function smoothly during periods of extraordinarily high volume and volatility.

Order Management

Effective order capture, whether from internal or external sources, is another given in a high-speed, multi-asset trading environment. Standardized formats ensure orders are normalized in the trading book and properly managed, tracked and reported throughout their lifecycles. A critical feature of an order management component is the ability to track and monitor state throughout the order’s lifecycle. By standardizing the approach to order management across asset classes, firms can better track state and monitor complex, multi-leg orders involving multiple asset classes, each with multiple executions and fills.

In addition, a single, multi-asset platform gives traders and risk managers a holistic understanding of client positions, outstanding orders, and buying power across all their trading activity to better manage pre-trade risk in real-time. By aggregating all the order flow and positions for all asset classes together, firms can also get a real-time view of P&L dynamically and apply analytics as needed to ensure the highest level of risk management while pursuing best execution.

Internalization and Matching

To internalize or cross orders for best execution and maximum advantage to the bank also requires advanced tools that route to match orders within trading books. Normalized data model technology ensures parallel buys and sells in the trading book execute and move to trade reporting. A matching engine should be flexible, accepting and matching internalized orders based on pre-set rules implemented by asset type, security being traded, and by region to accommodate varying market structure and regulatory demands.

With increased volume and the technological means to pre-evaluate orders, sell-side banks can establish their own dark pools for trading in different instruments. The ability to show or not show the trade – based on pre-set rules – will need to be an integral part of order management with the continuing development of these internalized markets.

Meeting Multi-Asset Trading Challenges: Workable Approaches for Success in a Dynamic Capital Markets Arena



Smart Order Routing

Dynamic trading environments require sophisticated smart-order routing capabilities, and multi-asset trading systems demand even more sophistication. The system must be able to route orders for execution, separating multi-leg trades for routing to different execution points. The system should support execution algos to ensure best execution. A router should also manage the life cycles of orders, reconciling “child” orders to parent orders for reporting, allocations, settlement, and for proving best execution. The router should have access to the current top of book and depth information from the liquidity aggregator to manage best execution strategies.

Tracking the state of an order – monitoring the level of fills versus open orders, knowing where the child orders are and the state of those open orders, and knowing the status of internal and external markets enables cross-asset trading desks to dynamically manage trading activity. Along with the order’s state, current views of market price/volume activity, internalized or crossing trade books, and rules, such as credit tolerance and client preferences, further enable dynamic management of orders to ensure best execution.

Messaging Layer Real-time Multi-asset Trading Environment (Pre-Execution) Normalization to a Standard Data Model Price and Order Aggregation Smart Order Routing Business Rules/Algos Crosses Internalization Liquidity Sources Other OTC Fixed Income Futures Equities FX Other OTC Fixed Income Futures Equities FX

Order Entries Credit/Pricing Rules Price Distribution



eal-time Multi-asset Trading Environment (Pre-Execution)Meeting Multi-Asset Trading Challenges: Workable Approaches for Success in a Dynamic Capital Markets Arena 8

The Importance of Post-Trade

Rapidly increasing volumes across asset classes like equities, options, futures, and FX create challenges because most legacy post-trade processing systems have difficulty keeping up during spikes in activity. Automation of post-trade operations is growing in importance and in complexity as firms work to consolidate their trading activity in multiple assets that clear and settle in multiple markets, across different time horizons, and with different settlement arrangements.

Systems that enable post trade convergence, providing all the messages related to order handling and executions in a standardized data model, with infrastructure capable of handling sharp volume spikes, can simplify post-trade challenges and therefore streamline post-trade operations.

A standardized approach can also help optimize risk management by enabling real-time clearance and settlement reporting. By processing in real time, firms gain global, real-time views of actual positions and exposure. This provides them with a better grasp of client opportunity and risk in a demandingly dynamic trading environment.

This approach also adds flexibility to respond to market structure changes. When, for example, interest rate swap trading becomes centralized, volumes are likely to grow exponentially. Sell-side banks with established operations in a multi-asset trading environment will be better prepared to quickly scale to meet this new demand. Accommodating new volume will impact everything from the trading desk operations to clearing and settlement.

A standardized data model ensures that post-trade reporting is uniform and universally accessible firm-wide. This way, all the post-trade systems for confirmations, allocations, settlement, the accounting systems, post-trade risk management and regulatory reporting systems can be updated in real time as trades complete. Real-time Multi-asset Trading Environment (Post-Execution)

Exchange OTC Dark Pool Profit & Loss Clearing & Settlement Messaging Layer Data Model Execution Reporting Consolidated Book


eal-time Multi-asset Trading Environment (Post-Execution)Meeting Multi-Asset Trading Challenges: Workable Approaches for Success in a Dynamic Capital Markets Arena 9


We operate today in a global trading environment that is significantly different from just a few years ago. Volumes are growing rapidly in every asset class, complexity is increasing as clients demand abilities to effect multi-leg trades in a variety of instruments. Asset classes are more correlated than ever, creating needs to effectively manage complex trading workflows to achieve best execution and effectively hedge. Successful trading in this environment requires not only algorithms and speed, but integrated tools to provide order life cycle intelligence for risk, position and asset management, always with an eye toward firm-wide impact.

Solutions must work in open architectures to enable integration across trading functions with multiple applications while remaining flexible and responsive to client and internal trading needs based on business demand and anticipated regulatory mandates.

Business demands from buy-side clients will be driving these processes, even while sell-side banks face resource pressures from regulatory changes, consolidation and cost-cutting. Clients will continue to look to the sell-side to handle more complex trades and provide tools to facilitate their trading and trade reporting. At the same time, the need to effectively manage exposure and risk across all client business also requires effective real-time tools. Implementing a standardized, multiple-asset trading platform that unifies all this trading activity and workflow can create an opportunity for firms to service a client’s entire book of business, enhance execution quality, improve risk management, and reduce infrastructure and support costs.

About smartTrade Technologies

Founded in 1999 by former IT and trading professionals from Citigroup, Credit Agricole and Société Générale, smartTrade provides the industry’s most sophisticated Liquidity Management System (LMS) to banks, broker-dealers, ECNs, asset managers and large hedge funds. Cross asset by design, smartTrade’s LMS platform performs best execution as defined by both the market and your firm.

For more information, visit

© 2012 smartTrade Technologies |


About carlarweir
I am a Cross Asset FIX Connectivity SME working for international banks. i also sit on the FIX Protocol Ltd Global Steering Committee, and am Co-Chair of the Global Cross Asset Committee. I also have a very strong background in Mobile Telephony, Cloud computing, SaaS, IaaS, PaaS, Big Data, innovation, and am a pioneer in Pseudolite technology.

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