This presents an opportunity as well as a challenge to the sell-side. If they can help their clients streamline and enhance workflows to reduce manual intervention, minimise errors, and accelerate trading and investment decisions, and if they can provide them with a way to lower operational costs while also enabling fast and profitable responses to market opportunities to better generate alpha, they can gain a serious competitive advantage.

Servicing the buy side ecosystem

With multi-asset strategies becoming more and more common, firms increasingly look to their sell-side providers to facilitate trading across a diverse range of instruments and asset classes through a single interface – whether UI or API – and to handle complex orders involving multiple instrument types, such as structured trades or multi-asset baskets for example, across different trading venues and diverse markets.

There is also an increasing emphasis on data-driven decision-making. While systematic and quantitative traders have always relied on data and models, fundamental and ‘quantamental’ firms are increasingly relying on data-driven insights to drive – or at least support – their investment and trading strategies. Firms now seek from their sell-side providers not only market data, analytics, and research, but also well-documented open APIs that allow them to seamlessly integrate such data into their proprietary models to inform and execute their trading strategies.

“The real challenge for the sell-side is adopting a technology strategy that balances their own internal needs with the ever-changing needs of their clients, one that effectively serves both,” observes Medan Gabbay, Chief Revenue Officer of multi-asset trading solutions vendor Quod Financial. “The buy side have their own technological ecosystem, made up of Portfolio Management Systems, Order Management Systems, applications for creating and managing trading strategies, various types of analytics tools, spreadsheet-based models, and a wide range of other systems they use in their day-to-day trading activities across the front, middle and back office. Forward-looking sell-side firms understand that a key part of their role is to facilitate this ecosystem, by using their technology to help clients trade their chosen markets in the way they want to trade them, as well as providing the necessary analytics and data in a format that helps them identify trading opportunities and manage their investment strategies.”

The key question for the sell side is, how to achieve the necessary agility in technology that will enable them to respond to the changing demands of their clients?

Moving beyond the buy-build debate

Several options exist. There are a number of well-established vendors who sell ‘off-the-shelf’ trading platforms, which can address many of the sell side’s needs. These platforms provide a range of essential features such as liquidity access, connectivity, order and execution management, analytics, and market data handling. However, while such off-the-shelf systems are generally adequate for day one requirements, they often lack the flexibility to rapidly adapt to changing customer needs and the dynamic nature of the markets. Firms relying solely on these platforms might therefore find themselves constantly behind the curve, limited as they are by their vendor’s upgrade and development cycles.

At the other end of the spectrum, firms may opt to build their own bespoke platforms tailored to their own specific requirements.  While this offers maximum control over the design and development process, it’s an expensive and complex undertaking, and is out of reach for most firms, other than tier one banks with substantial technology budgets.

A third option is becoming increasingly popular amongst forward-looking firms, that of buy and build. Vendor platforms that are built on modern, scalable, and adaptable technology, can be quickly deployed to meet a firm’s immediate needs and then adapted, customised and expanded as requirements evolve.

This type of approach offers various benefits, according to Gabbay. “Platforms built on this type of architecture are highly interoperable, easily integrating with other systems on both the front end – through desktop widgets for example – and the back end, through APIs. They are also much more scalable, capable of being deployed on hosted services including Cloud, on-premise, or a hybrid of the two, which leads to improved performance and better customisation to the clients specific infrastructure requirements. Additionally, being built around a component-based architecture, they offer flexibility and allow for rapid customisation, as individual modules can be created and adapted to suit specific customer requirements, new areas of functionality, evolving business processes, or changing regulatory and market structures.”

Gabbay points out that trading platforms architected in this way can also be more easily integrated with clients’ trading desks. This level of integration benefits both the client – for example through more efficient and transparent trade execution and real-time order/position monitoring – and the sell-side firm itself, by providing a better understanding and greater visibility of their clients’ activities and workflow.

For sell-side firms with limited resources, or those that believe their resources can be better invested in creating IP and not rebuilding existing technology, this approach can offer the best of both worlds – the rapid implementation and comprehensive functionality of a vendor platform, together with the flexibility, adaptability, scalability and capacity for integration of a custom-built solution. By adopting such an approach, a firm can distinguish itself from competitors who use generic or outdated vendor platforms, and compete more effectively with larger tier one banks that have developed their own solutions.

Artificial intelligence and machine learning

Another strategic choice for the sell-side is how to make best use of Artificial Intelligence (AI) and Machine Learning (ML). Although neither are new in Capital Markets, interest in AI has exploded since OpenAI introduced ChatGPT in November 2022. Since then, firms have identified a wide range of applications for Generative AI (GenAI) and the use of Large Language Models (LLMs).

One area where GenAI can add significant value in modern, component-based trading platforms, explains Gabbay, is its ability to accelerate the development and testing lifecycle, by automating coding processes and influencing all disciplines involved in defining, building, testing, operating, and supporting complex requirements. This allows firms to bring new functionality to market much more quickly than was previously possible.

“GenAI can generate test scenarios automatically by analysing the code base and understanding the purpose of different components,” he says. “It can then identify potential test cases, simulate different scenarios, and generate test data, thereby eliminating the need for manual test scenario creation. Additionally, by leveraging ML and AI algorithms, it can simulate user interactions, input test data, and validate the expected outputs. This automation reduces the reliance on manual testing, speeds up the testing process, saves time and effort, and improves overall efficiency.”

Outside of GenAI, modern trading platforms can also utilise ML within algorithmic trading, identifying and exploiting patterns in trade execution by analysing market conditions, liquidity, and order book dynamics. By scrutinising vast amounts of historical and real-time order book data to identify patterns and trends, ML-trained algorithms can determine the optimal timing, price, and quantity for executing trades, thus minimising transaction costs and market impact.

ML is also being increasingly used to develop intelligent Algo Wheels. These allow firms to analyse their incoming flow, so that the right execution strategies and order routing destinations can be automatically chosen, and optimised based on current market conditions and client-specific requirements.

Primary considerations for the sell side

Given the numerous challenges that sell-side firms face from a trading technology perspective, and the various choices they have available, what are the key considerations they need to take into account when evaluating trading platforms?

First of all, support and training are vital aspects of any technology implementation. Even the most intuitive platforms require a period of adaptation, and comprehensive training is crucial to maximise their potential. Vendors should provide robust support services to assist with both onboarding and continuous usage. This includes not only technical support but also strategic guidance to help teams leverage the platform’s full capabilities. Adequate training and support ensure that any investment in trading technology yields the maximum possible return.

Interoperability is another key factor. “A new trading platform should integrate seamlessly with existing systems to avoid operational disruptions,” advises Gabbay. “Ensuring smooth interoperability minimises the risk of data silos and ensures that all parts of your trading ecosystem can communicate effectively. This not only streamlines operations but also enhances data accuracy and decision-making processes.” Platforms that fail to integrate well can lead to significant headaches, requiring additional resources to bridge gaps between systems and potentially leading to costly errors.

Scalability is also essential for any trading platform. As trading volumes increase and new asset classes are added, the platform must scale efficiently to handle these changes. Scalability includes the ability to automate processes and manage higher trading volumes without performance degradation. “A scalable platform supports business growth by ensuring that system performance remains robust even as demands increase,” says Gabbay. “This scalability is not just about handling volume but also about expanding capabilities and accommodating new functionalities as trading strategies evolve.”

Flexibility around customisation is also important, according to Gabbay. “The platform should be capable of swiftly adapting to evolving workflows without causing bottlenecks,” he says. “Your technology shouldn’t become an obstacle, but a facilitator of change. Customisable platforms ensure that you can tailor the tools to meet specific trading needs.”

Key success factors

It’s clear that the dynamic nature of the buy-side presents both challenges and opportunities for sell-side firms. To stay competitive, banks and brokers need to consider a technology strategy that balances their internal needs with the ever-evolving demands of their clients. Whether choosing off-the-shelf platforms, bespoke solutions, or a hybrid approach, sell-side firms might want to prioritise agility, integration, and scalability in their technology stack.

Additionally, the strategic use of AI and ML can significantly enhance trading efficiency and decision-making processes. By embracing these advanced technologies and maintaining a flexible, client-centric approach, sell-side firms can not only meet the complex requirements of today’s market but also position themselves for sustained success in the future.

Robust support and training, seamless interoperability, and the ability to scale and customise are also critical factors that will determine the sell-side’s ability to capitalise on market opportunities and deliver superior value to their clients.

The post Evolving with the Market: Technology Strategies for Modern Sell Side Firms appeared first on A-Team.

By integrating Boltzbit’s AI technology, Liquidnet can now offer members and partner syndicate banks faster access to trading and data distribution, processing and displaying bond deals at a rate significantly faster than its previous parsing technology. This ensures that bonds are quickly available through the company’s deal announcement dashboard and new issue order book.

“This partnership improves the speed at which we can process messages, create, and then send structured data directly to our clients, which in turn allows them to quickly populate their OMS and prepare for trading,” says Mark Russell, Head of Fixed Income Strategy at Liquidnet, in conversation with TradingTech Insight. “The quicker we can do this, the better it is for those clients. Beyond this, the clients of our new issue Trading Platform (grey market) benefit as we are able to launch the new bonds on the screen earlier, giving those clients earlier access and more time to trade.  More trading time on our visible trading platform means more transparent data points, which is very useful for the syndicates and issuers as they get a view as to what is going on in the market.

“Structuring the bond data is not done in a single step, during the bond creation process we need to interpret the market chat, back and forth messaging, that drives the final structure of the bond,” explains Russell. “Our system needs to be able to capture and update any changes to the meta-data, such as coupons, issuers, benchmark, maturity etc. that describe the bond and feed those changes into the trading platform and other information platforms.”

He continues: “We’ve automated this process extensively with our partners at Boltzbit, creating a tool that handles the heavy lifting of structuring this data into a comprehensible bond format. Our partnership with Boltzbit is focused on speeding up and enhancing accuracy, bypassing traditional parsing tools and leveraging artificial intelligence instead.”

Boltzbit’s GenAI technology utilises the data captured from messages exchanged across various mechanisms and channels to create a large language model (LLM) that transforms the information into a structured and usable format.

“This process might seem simple, but it was actually extremely challenging,” explains Dr Yichuan Zhang, CEO and co-founder at Boltzbit. “Firstly, it involves very complex business processes. It’s not just about parsing one email; understanding the context of the conversations and the associated business processes is essential. Secondly, this is a highly specific solution, requiring the model to be extremely accurate and to follow the precise logic of the business flow around new issues. Finally, the solution needed to be highly secure and deployed in a way that allowed Liquidnet full control.”

Since the launch of its primary markets offering in 2022, Liquidnet has achieved record trading volumes in its new issue order book and increased participation from over 35 European syndicate banks, highlighting the company’s commitment to modernising primary markets and delivering substantial value to clients and the industry.

In addition to partnering with Boltzbit, Liquidnet has previously collaborated with NowCM and BondAuction, reinforcing its dedication to fostering efficiencies and connectivity for investors, banks, and issuers through strategic partnerships.

The post Liquidnet and Boltzbit Collaborate, Utilising GenAI to Accelerate Bond Primary Markets Workflow by 90% appeared first on A-Team.

QUODD’s QX Digital Platform gives customers access to market data functionality and content for front, middle and back-office workflows. S&P Global Market Intelligence now supplies the Platform with independent pricing and liquidity data for bonds, offering advanced security look-up and query capabilities using pre-defined or custom templates. Transaction data analysed and aggregated to generate pricing content encompasses nearly three million corporate and sovereign bonds, municipal bonds, and securitised products.

Integrating S&P Global Market Intelligence’s bond pricing and reference data with global equities and funds through QUODD is designed to enhance the QX Digital Platform’s display capabilities and connectivity for downstream wealth management users. The integration allows users to optimise their market data consumption, maximise their market data spend, reduce costs without compromising quality, and improve workflow efficiency. It supports daily pricing, reference data, and corporate actions while automating data usage entitlements for customised workflows.

“We have incorporated access to S&P pricing and reference data into our extensive content catalogue, which is a mix of proprietary and third-party data sets,” Bob Ward, CEO of QUODD, explains to TradingTech Insight. “This collectively amounts to 150 data sources and 250 billion data points in our data lake. We have made all this data available via several access points. Users can access this data as individual datasets via several communication methods (QX Marketplace), they can access digitally online and view and extract on demand (QX Digital), and now they can programmatically access multi-asset class data into third-party applications (QX Automate).”

QUODD has now signed numerous clients across multiple market segments with similar workflow concerns. The key drivers are timeliness, simplicity, and easy accessibility, as Ward outlines in the following use cases:

New issues research – New debt instruments are released into the market daily, and firms need pertinent terms and conditions to classify them correctly in their systems. The QX Digital Platform is tied into the real-time S&P bond reference data API to retrieve those details as soon as S&P does.

New asset setup – Banks price assets based on the issues that their clients hold. “A current customer told us this week that they set up 850 new assets in their system in June alone,” says Ward. “They are constantly accessing QX Automate to pull the data they need to properly set up those securities in their system based on asset type, sometimes multiple times a day. This gives them the timing and flexibility to retrieve data at any time to meet their client’s pricing needs.”

Price challenges – The Price Challenge process via the QX Digital Platform is supported by the integrated S&P Price Viewer tool, as Ward explains: “This tool gives our customers direct access to the S&P bond evaluators for price challenges. As bond pricing varies by provider, prices can differ, and customers need to confirm the most accurate evaluation. Price challenges are affirmed or updated usually within a few hours. The S&P pricing methodologies are transparent to all of our customers.”

Security master maintenance – “Most of our customers use QX not only for pricing but for global security master maintenance using our Corporate Actions solutions,” says Ward. “Many parameters can be set, such as Voluntary vs. Mandatory Date parameters, based on Effective Date or Announcement Date, or the ability to hone in on specific events that affect things like reorganizations, which affect shares and price. Maintenance tasks like identifier changes, name changes, M&A, etc., are all important in maintaining a security master.”

In addition, the platform has embedded proprietary calculators such as an Accrual & Amortization tool that provides the requisite buy & sell tickets on certain fixed income instruments, leveraging content from S&P to meet and exceed the functionality available in the legacy terminals.

Modern technologies and delivery models have been integrated into the QX Digital Platform to meet the new need for data on demand, and these innovations keep QUODD ahead of its competitors, says Ward. “Most providers today have layers and silos of technology, leading to increased inefficiencies and lower quality. QUODD is designed from the ground up for the future, and with our cloud-native platform, we can deliver our content into customisable client workflows that are turnkey, scalable, and cost-effective. Building from this platform allows us to meet customer needs today with very low switching costs while opening new options for even more advanced integrations as their digital strategy continues to evolve.”

Ward states that the two defining characteristics of the technology are a cloud-native platform that is purpose-built to power the full breadth of market data apps and APIs and the ability for companies to manage their preferred consumption model and frequency of data updates. “By virtue of technology reducing the friction of integrating and onboarding new sources of data on a self-service, on-demand, and connected basis, the QX Automate module delivers a superior experience, enabling customisation and integration at the same time; and because we are cloud-native with a modern tech stack, we can build faster and respond to customer requirements with more agility and transparency,” he says.

In terms of market data spend, Ward points out that under QUODD’s pricing model, clients only get charged for what they use and the frequency of that use. “This pricing approach, combined with a single integration point for a client’s entire security master, coupled with the improved workflow for the employees (no more swivel chairing), provides a very good value for our clients,” he says.

Looking ahead, QUODD has a number of customer-driven projects in the pipeline, including leveraging AI to help build third-party adapters at a quicker pace, and using AI to expand the company’s proprietary data sets.

The post QUODD Enhances QX Digital Platform with S&P Global Bond Data Integration appeared first on A-Team.

In addition to news content, LSEG will equip Dow Jones’s editorial teams with LSEG Workspace, incorporating the latest in workflow and productivity tools to support a data-driven newsroom environment. Journalists will benefit from comprehensive LSEG data sets, including Datastream, Fundamentals & Estimates, StarMine models, and SDC Platinum’s premier deal insights.

The Wall Street Journal’s coverage of mergers and acquisitions (M&A) and capital markets will be bolstered by over four decades of data, insights, and league tables provided by LSEG. Moreover, LSEG will serve as a key source of deals data, featuring prominently in the WSJ Investment Banking Scorecard.

The partnership will also see the co-development of an enhanced news experience within LSEG Workspace, curated by senior Dow Jones editors. This tailored news service, designed for the Workspace audience, is slated for launch in early 2025, with LSEG being Dow Jones’s first partner in this new enterprise-focused subscription model.

Combining Dow Jones’s real-time, industry-leading news with LSEG’s advanced classification, tagging, and search technologies, the collaboration aims to enrich news feed offerings for LSEG subscribers, who will gain access to Dow Jones’s text feeds, which LSEG will use to enhance its real-time news, news archive, and news analytics services.

David Schwimmer, CEO, LSEG, commented: “The inclusion of the latest news, commentary and analysis from Dow Jones and The Wall Street Journal is a powerful new addition for our LSEG Workspace users. Our partnership will also see Dow Jones benefit from our world class data and analytics capabilities to support a data-driven newsroom across all of its channels.”

Almar Latour, CEO of Dow Jones and Publisher of The Wall Street Journal, added: “This partnership with LSEG is key to delivering the world’s best news, information and analysis to business leaders across the globe. Combining the strength of both brands will serve the needs of LSEG Workspace users and enhance our newsrooms.”

The post LSEG and Dow Jones Forge Multi-Year Data and News Partnership appeared first on A-Team.

Options Technology supports trading at numerous venues worldwide with its managed infrastructure and connectivity services, along with private financial cloud services that combine hosting with direct market access, total cost of ownership reduction, and top-tier resiliency and security.

“One of the aims with this collaboration is to enable customers to utilise the Options Atlas feed more efficiently, says Grethe Brown, CEO of DiffusionData, in conversation with TradingTech Insight. “Our approach allows us to handle data transformation and distribution, significantly reducing egress costs by delivering only the specific data the trader needs. Unlike the standard practice of sending all data, we provide a sophisticated distribution that transmits only the delta—the difference between successive data points—resulting in greater efficiency and cost savings. Also, from a DiffusionData perspective, our team will benefit from collaborating with a larger entity like Options. This partnership will enhance our capabilities, providing a more comprehensive solution with Options’ consolidated data service than we currently offer.”

The Diffusion framework offers control over end-to-end data flow, creation of personalised data streams, and efficient data delivery through patented bandwidth optimisation, which will enable clients to fully leverage Options’ consolidated data service.

“There are numerous applications for personalised data streams. For instance, in FX liquidity provision, a bank might quote different FX rates to different tiers of customers,” says Brown. “Additionally, they may choose to apply a delay to some of their data, allowing them to charge lower fees to customers who receive the data with a 15-minute delay.”

Danny Moore, President and CEO of Options, commented: “Our partnership with DiffusionData represents a significant advancement in our ability to deliver robust and scalable data solutions to our clients. By integrating Diffusion’s cutting-edge data streaming technology with our consolidated data service, we are not only enhancing data delivery but also empowering our clients to gain real-time insights and make informed decisions faster and more efficiently. This collaboration underscores our commitment to providing innovative and reliable infrastructure that meets the evolving needs of the capital markets.”

Grethe Brown, CEO of DiffusionData, commented on the partnership: “By integrating our Diffusion framework with Options’ consolidated data service, we are providing clients with a powerful solution that combines real-time data streaming with unparalleled control and efficiency. This collaboration will enable users to harness the full potential of their data, delivering seamless and personalised data streams that drive better decision-making and operational performance. Together, we are setting a new standard for data delivery in the financial services industry.”

The post DiffusionData Partners with Options Technology to Integrate Real-Time Data Distribution appeared first on A-Team.

Considering the use of GenAI, an early audience poll question asked to what extent organisations are committed to GenAI applications. Some 46% said they are testing GenAI apps, 24% are using one or two apps, and 20% are using a number of apps. Nine percent are researching GenAI and 2% say there is nothing in the technology for them.

Value of GenAI applications

A second poll questioned which GenAI applications would be of most value to a delegate’s organisation. In this case, 53% of respondents cited predictive analytics, 39% risk assessment, 39% KYC automation, 28% fraud detection and 19% portfolio management.

The panel shared their own use cases, with one member experimenting with GenAI to produce programming code and creating an internal chat box for data migration, as well as scanning data to surface information that can be categorised, sorted, filtered and summarised to create ‘kind of conversational extracts that can be used.’

All agreed that GenAI produces some low hanging fruit, particularly in operational activities such as KYC automation, but that the technology is too young for many applications, leading firms to build capability internally before unleashing GenAI apps for customers as there is still work to do around issues such as risk integration and ensuring copyright and data protection are not compromised. One speaker said: “There is a lot of experimentation and some research to do before we’re confident that we can use this at scale.” Another added: “There are just not enough skilled people to allow us to push hard, even if we wanted to. There’s a real pinch point in terms of skills here.”

Risks of adopting GenAI

Turning to risk, a third audience poll asked the audience what it considered to be the biggest risk around adopting GenAI. Here data quality was a clear leader, followed by lack of explainability, hallucinations, data privacy and potential misuse. Considering these results, a speaker commented: “We’ve already got existing policies and governance frameworks to manage traditional AI. We should be using those to better effect, perhaps in response to people identifying data quality as one of the key risks.”

The benefits of AI and GenAI include personalisation that can deliver better products to consumers and improve the way in which they interact with technology. From a regulatory perspective, the technologies are focused on reducing financial crime and money laundering, and resulting enforcements against fraudulent activity.

On the downside, the challenges that come with AI technologies are many and include ethical risk and bias, which needs to be addressed and mitigated. One speaker explained: “We have a data science lifecycle. At the beginning of this we have a piece around the ethical risk of problem conception. Throughout the lifecycle stages our data scientists, machine learning engineers and future engineers have access to python libraries so that when they test models, things like bias and fairness are surfaced. We can then see and remediate any issues during the development phase so that by the time models come to validation and risk management we can demonstrate all the good stuff we’ve done.” Which leads us to the need, at least in the short term, for a human element for verification and quality assurance of GenAI models in their infancy.

Getting skills right

Skills were also discussed, with one panel member saying: “We are living in a constantly more complex world, no organisation can claim that all its workforce has the skill set necessary for AI and GenAI, but ultimately I am hopeful that we are going to create more jobs than we are going to destroy, although the shift is not going to be easy.” Another said: “In compliance, we will be able to move people away from being data and document gatherers and assessors of data in a manual way to understand risk models, have a better capability and play a more interesting part.”

Taking a step back and a final look at the potential of GenAI, a speaker concluded: “Figuring out how to make safe products that we can offer to our customers is the only way we have a chance of reaching any sort of utopian conclusion. We must chart the right course for society and for people at work, because we’re all going to be affected by generative AI.”

The post Practicalities of Implementing GenAI in Capital Markets appeared first on A-Team.

TTI: Stephen, can you elaborate on the rationale behind introducing usage-based pricing and risk-free trials in G8? How do these changes help clients better understand the value scaling of your platform and benefit from its flexibility and cost efficiency in developing and deploying trading technologies?

SM: Our aim with the usage-based pricing was to disrupt the traditional pricing model. Our platform accelerates development, allowing applications to be built in days rather than months or years. However, with traditional pricing, it can be challenging for clients to understand how such value scales from an MVP to a full production system. We explored various pricing models and, by reverse engineering our previous pricing for other trading technology solutions, we found that usage-based pricing based on virtual CPUs was the most effective metric. This approach resonates with clients because the need for more CPUs correlates with the complexity, number of users, or volume of transactions, aligning with their perception of the platform’s value. And it follows a tiered consumption model: from 0 to 10 CPUs, it’s $x per CPU; from 10 to 25, it’s $x minus 20%, and so on, so as usage increases, the per-CPU price decreases.

With the risk-free trials, we now offer a trial period with a nominal developer charge, meaning clients only incur costs at runtime, whether in a test or production environment. Our clients appreciate this low-risk, low-cost way to prototype and test new ideas quickly.

It’s also important to note that our platform is cloud-agnostic. Clients can run it on their private virtual machines, on-prem infrastructure, in their private cloud, or on public clouds like Amazon, Google, or Microsoft. Clients value this flexibility, as it allows for a multi-cloud strategy without incurring substantial fixed costs from a single cloud vendor. This also enables them to back up against different cloud providers.

TTI: Can you explain the different categories within the new Genesis Marketplace in G8 and how each category helps expedite the creation of trading and risk management applications in financial markets?

SM: The Marketplace comprises three different categories. The first category includes off-the-shelf solutions, such as the Trade Allocation Manager (TAM), a post-trade allocation confirmation system, and the Automated Quoting System (AQS), a multi-asset class quoting application that supports RFQ business flows between investment firms and their treasury desks or broker-dealers. We are continually expanding these solutions. The second category consists of specific industry vendor integrations, such as Bloomberg TOMS. The third category is what we call components, which perform specific functions commonly found in financial applications.  These are a combination of integration and workflow and include things like alerts, reporting, user management and FIX gateways.

When you use our platform, you have access to all three categories within the Marketplace. We are heavily investing in expanding the Marketplace so developers can utilise these components, with only a nominal developer fee, and pay based on CPU usage. We see this as a significant disruption to the traditional buy versus build paradigm.

TTI: How do your strategic partnerships with major financial institutions like Bank of America, BNY Mellon, and Citi influence the development direction of the Genesis platform? Can you elaborate on their role in shaping your product offerings and training initiatives?

SM: They play a crucial role. We have a strategic investor forum with these partners every six weeks, where they help guide our platform roadmap based on feedback from all our clients. We gather extensive input from clients about the functionalities and solutions they are building, or would like us to build within the platform, giving us a comprehensive understanding of their needs.

Accessibility and ease of use are major priorities for our partners because they want to get large groups of developers on Genesis.  So we’ve focused heavily on our training program, the Genesis Academy. Initially, we were doing a lot of the development work for our clients ourselves, but this has now shifted significantly. Our investors provided invaluable feedback on training materials, documentation, certification programmes, and what we call developer enablement and evangelism. They also wanted the ability to scale through their systems integrators and consulting partners, so we’ve collaborated with these parties to enable them to use the platform effectively.

A lot of the feedback revolves around the tools they want to see in the platform product roadmap, as each partner has different requirements. For instance, BNY Mellon, the largest custodian in the world, is using the platform to drive new business innovation. Citi and Bank of America are exploring how the platform can support new consortium opportunities or innovate around existing consortia.

Regarding tooling, we’ve recently released Genesis Create, a web-based tool that allows users to create new applications in minutes, and Genesis View, which enables rapid UI development by converting screenshots into UI code, using generative AI. It’s immensely valuable to have strategic investors who are deeply invested in our technology roadmap.

TTI: G8 includes enhancements in desktop interoperability and improved FDC3 support. How do these enhancements facilitate better integration and communication between different trading systems and platforms, and what role do open APIs play in this process?

There’s a lot happening around desktop interoperability, and interoperability in general. All our UI components are FDC3-ready out of the box. We are completely vendor-agnostic, working seamlessly with platforms such as OpenFin and interop.io on the desktop interoperability side, where we continue to expand our FDC3 capabilities. And with Genesis Create, you can enable FDC3 for your UI components from the outset.

We’ve always maintained an open approach, with open APIs from the beginning. On the server side, our technology integrates with various vendor systems such as Murex, Calypso, Fidessa, TT, ION, SS&C, Bloomberg, and Symphony. Whether it’s server-side data integration or technical APIs like REST and MQ, we ensure quick data transfer. Additionally, we support industry-specific protocols such as FIX and SWIFT, maintaining openness and flexibility across our platform.

TTI: What impact have the free trials and training had on client adoption, and how are consulting partners contributing to this new approach?

SM: We’ve received excellent feedback from our current clients, many of whom previously used our managed applications or products. They’ve expressed that they always wanted to use the platform, and now, with no real barriers to entry, they can. They can start using it for free, get comprehensive training, and access the Marketplace. They are only charged at runtime, once they see the value in a test or production environment. We’ve also engaged with new clients who had heard about us but hadn’t interacted with us before. They now understand how to engage with us, and clients who weren’t platform users but had specific solutions built by us can now see how scalable and user-friendly our technology is, thanks to the tooling and extensive training materials we provide. The pricing model also makes sense to them.

We also collaborate closely with consulting partners, who find this proposition very appealing. Previously, they focused solely on the professional services aspect, but now they can also participate in revenue shares by introducing new clients and implementing solutions for them. This approach encourages the entire industry to think differently about the buy versus build dilemma, or what we now call ‘buy to build’. This model allows clients to enjoy the benefits of both buying a solution and having the flexibility to build on it. This concept has really resonated with our client base.

TTI: To wrap up, where do you see the most significant opportunities for software innovation in the financial markets industry, and how does Genesis help firms address their innovation backlogs?

SM: There are several areas, but I’ll highlight two key ones. Firstly, there’s a significant focus on replacing spreadsheets and end-user computing systems. This process can be very complex because it’s not just about the spreadsheets themselves but also the workflows and how these spreadsheets are communicated within an organisation. These systems often have numerous integrations, with data being uploaded and downloaded between different systems. We see a lot of work in this area.

Another critical area is vendor consolidation. We frequently get asked if we can replace a specific vendor. However, rather than simply ripping and replacing existing systems, we excel at what we call ‘vendor scaffolding’. This approach involves integrating our technology on top of, next to, below, or around existing vendor technologies. It enables clients to start innovating quickly around their current technology stacks. Additionally, this method allows for the clean-up and optimisation of the existing vendor technology implementation. Vendor scaffolding is a significant use case because it enables clients to innovate rapidly without the substantial risk of replacing entire systems.

TTI: Thank you, Stephen

The post Unveiling G8: Genesis CEO Discusses New Platform Features and Strategic Trends appeared first on A-Team.

Opening the summit and introduced by A-Team president and chief content officer Andrew Delaney, Edward J. Achter from the office of applied AI at HSBC set the scene for the day, noting the need to build AI and GenAI products that are responsible and ethical and can be scaled, and describing the importance of educating and engaging the workforce to ensure solutions are used effectively and ethically.

In more detail, the keynote speaker explained the explosion of interest in AI and GenAI following the release of ChatGPT and a change in conversation at financial institutions. He also warned of risks inherent to the technology including fairness and bias, data privacy, and the deliberate spread of false information. To mitigate risk and create value, Achter emphasised the need to get your data house in order and, perhaps a long time in the asking, pay attention to data leaders as data is the lifeblood of AI and GenAI applications.

Also important to consider are regulatory requirements around AI and GenAI, addressing the carbon emission costs of using LLMs, and perhaps most importantly, writing a clear company policy that can be shared with all stakeholders. Demonstrating the benefits of AI and GenAI products can turn scepticism into an understanding of benefits, including productivity gains that can be measured, and change negative perspectives into positive approaches to doing more with the technology.

Ultimately, a skilled workforce, educated customers, technology used in the right context of conduct, and confidence across the organisation will result in value creation.

The post AI in Capital Markets Summit Tracks Evolution of GenAI and Value Creation appeared first on A-Team.

Advancements in technology and data analytics have led to more detailed, granular, and multi-dimensional TCA, which now considers factors such as order size, market conditions, and timing. Buy-side firms are increasingly using TCA to assess broker performance and optimise trading strategies, integrating real-time data for immediate feedback on trading performance. There is now a greater emphasis on pre-trade, intra-trade, and post-trade analysis, with the growth of custom benchmarks and sophisticated statistical models to better capture trading costs.

TCA is now being more broadly adopted across different asset classes, including fixed income, derivatives, and FX. Machine learning and AI are being incorporated into TCA models for predictive analytics, improved accuracy, and adaptive algorithms. The future of TCA will likely continue to innovate, leveraging new technologies and expanding its application across diverse financial instruments and markets.

TradingTech Insight has compiled a list of the top TCA solutions to consider in 2024. This list is based on a combination of A-Team Group research and entries, along with voting by our readership of TCA solution providers in our recent TradingTech Insight Awards USA and TradingTech Insight Awards Europe.

OneTick

Winner of Best TCA Solution 2024 in both our Trading Tech Insight Awards (Europe) and Trading Tech Insight Awards (US)

OneTick’s Best Execution (BestEx) and Transaction Cost Analysis (TCA) solution offers comprehensive tools for evaluating and optimizing trading performance. It measures execution algorithm performance, breaking down results by market and order characteristics, and checks for spread capture and front-running. The solution compares execution venues, includes venue-specific fees, and provides detailed slippage and spread capture metrics. It supports regulatory reporting (RTS 27/28, 605/606) and offers customizable TCA reports. Users can visualize trends, identify outliers, and analyze client flow toxicity. The hosted research environment and TCA API facilitate market data analytics with pre-configured functions and a powerful Python API. Additionally, pre-trade TCA insights and machine learning capabilities enhance decision-making and model management.

https://info.onetick.com/

Adroit Trading Technologies

Cross-asset TCA is native to Adroit’s buy-side EMS, capturing the entire market context at each step of voice and e-traded orders, from order creation and trade execution to post-trade.  With a single click, users can generate reports to demonstrate best execution and fairness across accounts. Adroit’s pre- and post-trade analysis incorporates disparate data types, including streamed quotes, firm responses to RFQs, evaluated prices, publicly reported trades, and ETF prices. Adroit’s focus is on OTC FICC, across cash and derivatives.

https://www.adroit-tt.com/

big xyt

big xyt’s Open TCA is an “analytics as a service” solution, built on a very powerful and efficient technology stack, and is accessible via web-based front-ends and APIs where derived data can be extracted and additional analysis can be performed. Pre- and post-trade TCA has to manage increasingly complex datasets at previously unmeasurable levels of granularity. This is a challenge for every buy- and sell-side firm.

big xyt harvests, stores and normalises granular data sets across 120 global markets (across equities, ETFs, listed derivatives and FX). With Open TCA, users can interrogate those datasets to estimate clients’ pre-trade costs, benchmark their trading when they executed, and calculate the cost when they did not.

As a privately owned company with no trading activities, big xyt is an unbiased and objective provider of data analysis. Clients and regulators see independence as an essential ingredient to avoid conflicts of interest or unnecessary leakage of information.

https://big-xyt.com/

Bloomberg

Bloomberg BTCA delivers robust multi-asset transaction cost analysis harnessing Bloomberg’s global market data across a wide range of trading benchmarks. BTCA offers impactful trading insights that help clients create and monitor optimal trading and execution strategies. Using BTCA’s powerful exception-based workflows, traders can efficiently meet a firm’s compliance and execution policies through granular analysis of the entire trade flow life cycle.

https://www.bloomberg.com/

eflow

eflow’s TZBE platform is a highly configurable platform that automates best execution tests, enriches trade data with market data curated from more than 250 sources, and generates highly granular TCA reporting. TZBE also generates valuable commercial insights by highlighting how a firm’s trading strategy can be executed more effectively.

The system automatically ingests data, tests against all core industry benchmarks, and reports on all instrument types and asset classes. Customisable parameters can be refined to mirror specific trading strategies, automatically accounting for variables and reducing false positives. TZBE also includes data archiving and indexing that complies with MiFID II and all other global regulations as standard, ensuring that you are one step ahead of your reporting obligations.

https://eflowglobal.com/

KX

KX accelerates the speed of data and AI-driven business innovation. Time series and vector data management are at the heart of the company’s products, which are independently benchmarked as the fastest on the market. KX’s customers process data at unmatched speed and scale and empower quantitative researchers and data scientists to launch, configure, run, and scale the most important capital markets analytics and AI workloads, such as options pricing, transaction cost analysis, and back-testing

KX technology enables the discovery of richer, actionable insights for faster decision making which drives competitive advantage and transformative growth for our customers.

https://kx.com/solutions/trading-analytics/

LIST

LIST’s Transaction Cost Analysis (TCA) is part of the ION LookOut product suite. ION LookOut TCA is designed to calculate over 80 key performance / cost metrics (KPM) to provide post-trade execution performance analytics to buy-side and sell-side investment firms. The calculated KPMs include execution statistics like participation rates, execution style, spread capture and venues distribution; execution benchmarks like VWAP and implementation shortfall; market status like prices, momenta, ADV, volatility, slippage, and reversions; and implicit costs like delay, spread, market impact, timing, and opportunity. ION LookOut TCA supports pre-configured and customizable reports that can be exported in various formats.

https://www.list-group.com/list-lookout/

Quod Financial

Quod Financial’s TCA stands out as one of the best in the capital markets due to its comprehensive real-time analytics and machine learning capabilities. The platform provides actionable insights, enabling traders to monitor and optimize execution performance instantly. Its unique integration of TCA outputs into OMS/EMS systems allows for immediate adjustments and improved trading strategies. Quod’s TCA offers pre-trade and post-trade analysis, robust benchmarking, and compliance with MiFID II regulations. The platform’s ability to manage big data and provide predictive analytics ensures informed decision-making, enhancing overall trading efficiency and performance. Quod Financial’s TCA features a powerful dashboard for detailed visualization and reporting, making it easier to track market impact, slippage, and hit ratios. Additionally, its AI/ML-driven recommendations for EMS configurations and strategy adjustments further enhance trading outcomes, providing a significant competitive edge in the capital markets.

https://www.quodfinancial.com/tca-best-execution-reporting/

S&P Global

S&P provides an independent TCA tool that provides global empirical performance data to help measure and manage best execution across multiple asset classes. Equity, FX, Fixed Income, CDS. Loans. MMI, Listed Futures and Options as well as OTC derivatives. Our tool combines execution, algorithmic, venue and smart order evaluation analytics to enhance trading-related execution quality management and reporting capabilities. Evolving market practices and global regulations such as MiFID II have increased scrutiny over best execution practice. As the concept of best execution has developed from the best available price at a point in time to a more holistic view of the investment process, the need to measure and manage increasingly complex trades and execution processes has grown significantly.

Our TCA platform meets the rigorous performance verification needs of execution providers’ trading customers. Our solution allows for actionable insight to enhance and synchronize trading related execution quality and delivers trading intelligence valued by trading desks. Our service provides the metrics necessary to streamline performance-based order routing and help with optimal trade strategy selection. In addition, toxic liquidity and front-running surveillance tools accurately differentiate the performance of alternative execution channels as well as being able to fulfil an extensive compliance and regulatory use case. Using best in class benchmark data sets in asset class where price visibility is challenging, S&P also provides proprietary pre trade models as well as deep and wide peer data sets to provide context on trading activity.

https://www.spglobal.com/marketintelligence/en/mi/products/transaction-cost-analysis.html

Spacetime

Spacetime’s TradeFabric platform is a trade intelligence and insights platform designed to augment trader decision making throughout the trade lifecycle. It goes beyond traditional TCA services with a highly visual interface for flexible in-depth analysis, and real-time adaptive analytics for pre and in-trade insights that provide context and situational awareness of where and when traders need it most.

Sell side desks can maintain coverage levels more efficiently and respond quickly to potential issues and opportunities on client orders, while Buy Side desks can restore information flows, liquidity sourcing, and alerting they often lose when working an order themselves.

https://www.spacetime.io/

Trading Technologies

Trading Technologies places a strong emphasis on TCA across its platform from the newly acquired Abel Noser suite of multi-asset TCA products to the dynamic implementation of TCA into the TT Premium Order Type algo process managed by TT’s Quantitative Trading Solutions (QTS) unit. TCA is not only available for TT clients post-trade but employed to improve/optimize the algo suite as part of its comprehensive data science ingredient in the product’s design and application.

TT’s Abel Noser Solutions is the leading global provider of TCA solutions. Its secure universe of trade data and analysis helps hundreds of client firms achieve best execution/measure trading performance/evaluate strategies/compare costs/identify performance improvements in equities, fixed income, options, FX and futures. The Trade-Zoom post-trade multi-asset solution allows clients to benefit from sophisticated analytics as well as customizable reports and benchmarking against the largest universe of annualized market and trade data.

https://www.tradingtechnologies.com/
https://www.abelnoser.com/

TS Imagine

TS Imagine’s TCA empowers customers to optimise liquidity and make timely, accurate trading decisions with complete visibility into execution performance across all counterparties and venues. Users can tailor analytics using over 25 benchmarks, 50 market data venues, and various visualisation tools, enabling teams to make informed decisions regarding broker and algorithm selection, timing, and strategy. This leads to reduced costs and maximised opportunities through in-depth, data-driven performance insights. Customers can also rely on TS Imagine’s team of data scientists and analysts to manage their data, allowing them to focus on generating alpha and maximising returns.

https://tsimagine.com/data/transaction-cost-analytics/

The post The Top 12 Transaction Cost Analysis (TCA) Solutions in 2024 appeared first on A-Team.

“What continues to amaze me in this industry is that firms compete heavily to hire quantitative talent, only to have them spend 80% of their time cleaning unsuitable data. This has to change,” states Paul Humphrey, CEO of BMLL, in conversation with TradingTech Insight. “Considering the expense and investment in this talent, we should be drawing the very best from them. It’s like commissioning a painting from an artist and then asking them to redecorate the studio first; it just doesn’t make sense. There’s a lot of useless data occupying financial services firms. Instead of using a kdb+ estate to store raw data and having engineers try to make something of it, we can now provide best-in-class historical data in a format that suits the engine our customers are working on, so they can perform all the analysis they want right out of the gate.”

BMLL’s datasets capture full order book data across more than 100 trading venues, providing consistent granularity at Levels 3, 2, and 1. This data is utilised by banks, brokers, asset managers, hedge funds, global exchange groups, and academic institutions to gain insights into market behaviour.

INQDATA, a cloud-based data solutions provider, simplifies the ingestion, processing, storage, and management of analytic-ready market data. Its high-performance environment, powered by KX’s kdb+ high-performance time-series database, query language and analytics engine used extensively in financial markets, ensures rapid access to cleansed, real-time, and historical datasets.

The collaboration between BMLL and INQDATA enables data scientists and application developers to efficiently access and explore granular historical market data and analytics derived from Level 3 data. By ingesting BMLL data directly into their existing kdb+ estate, the integration allows users to better leverage their kdb+ environment to enhance their trading strategies, test new markets quickly, understand execution costs, and improve the development of quantitative models.

“For years, many in the marketplace have relied on a kdb+ estate to manage their real-time data and provide market analytics. kdb+ has been a popular installation due to its excellent handling of real-time data,” says Humphrey. “However, it’s not the best for just storing historical data. INQDATA addresses this by converting our data into a format that integrates seamlessly with kdb+ estates, allowing users to utilise the data immediately. And that aligns with our commitment to making our data available in the format clients prefer, a principle that also led to our recently announced partnership with Snowflake.”

The integration aims to democratise access to data and analytics, allowing market participants to utilise BMLL’s API library and quantitative analysis tools within their environment. By reducing the burden of data engineering and infrastructure, users can focus on conducting comprehensive analyses to improve trading outcomes.

“Strategy testing and optimisation are clear use cases,” says Humphrey. “Backtesting strategies to explore various what-if scenarios can be done easily and quickly within an existing kdb+ environment, essentially transforming historical data into pre-trade data to guide your algorithms.”

By combining BMLL’s data curation and analytics with INQDATA’s data management capabilities, the partnership offers market participants reliable historical market data without the need for extensive reformatting. The scalable cloud architecture provided by both companies supports large-scale quantitative and market microstructure analyses.

“Customers come to us for the uniqueness and the heavy lifting we’ve done on our content. It’s crucial for us to remain agnostic regarding how clients want their data. This approach is yet another delivery mechanism, providing data to clients in the format they prefer.”

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