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Friday, March 14, 2025
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IEX Grows Displayed Market Share

By
Shanny Basar
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Equities exchange IEX has tripled its displayed market share since June 2024 after introducing new pricing tiers, but said it has maintained market quality.

 Bryan Harkins, IEX

Bryan Harkins, president of IEX Group, told Markets Media: “We are particularly proud of our displayed market share as market quality has not been sacrificed for growth. On the contrary, our displayed growth has improved the performance of our dark venue.”

IEX Group announced the appointment of Harkins as its president in May 2024. Before joining IEX his roles included head of markets at Cboe Global Markets overseeing equities, derivatives and foreign exchange trading; president of BIDS Trading; and head of markets at BATS Global Markets.

IEX embarked on a journey last year to find the sweet spot between performance and pricing and because of this, the exchange tweaked its pricing a few times throughout the year, according to Harkins. As a result, some liquidity providers gave IEX a shot and had a positive experience, which propelled growth in displayed trading. This growth comes as off-exchange trading of US equities grew to over 50% in the final quarter of 2024, according to data from industry association SIFMA.

 Source: SIFMA

Harkins admitted the displayed trading on exchanges has become increasingly challenging over the last five to 10 years.

“That makes our product especially useful in this environment where the Speed Bump and the Signal provide protection mechanisms for liquidity providers and allow them to post with greater consistency and confidence,” he added.

IEX was the first US equities exchange to power its order types with a machine learning-based mathematical formula, the Signal, also known as the crumbling quote indicator. The Signal predicts which way the market will move in order to protect customers from trading at a price that will imminently become stale.The Signal is enabled by the IEX Speed Bump, which gives IEX Exchange a time buffer in which to determine if a price is unstable before trades are executed.

In addition, the SEC approved IEX’s Discretionary Limit, or D-Limit, order type in 2020. D-Limit protects liquidity providers from potential adverse selection resulting from latency arbitrage trading strategies, and was introduced to encourage members to submit more displayed limit orders to the exchange. D-Limit uses the power of the IEX Signal to move an order out of the way if the price is about to become imminently stale i.e. the order avoids being “run over” when the price is unstable.

IEX said in a blog in August 2024 that institutional brokers using the lit D-Limit order receive over 40 mils per share in pre-trade price improvement, which comes out to nearly 0.65 basis points across all D-Limit volume, an increase of over 50% from April 2024.

Source: IEX

Harkins argued the displayed public market is essential in providing a healthy overall U.S. equity market.

“At IEX, we are pro-competition, and we understand the utility of off-exchange trading and alternative trading systems (ATSs), but it is very important to allow exchanges to be able to compete,“ he added.

SIFMA said the ATSs with the largest shares in the fourth quarter of last year were UBS ATS, Intelligent Cross and Goldman Sachs’s Sigma X2. IntelligentCross uses AI to optimize price discovery and matches orders near-continuously to achieve maximum price stability after trades.

“There is more innovation in how to design a market to improve outcomes and that is a great trend,” said Harkins. “We continue to be a leader in the space and welcome the competition.”

Options exchange

Harkins said: ”I think 2024 was the start of our next chapter at IEX, and we’re building from an amazing foundation.”

IEX has filed with the US Securities and Exchange Commission to expand outside equities and launch an options exchange. The new venue’s rule book has been published on the SEC website, according to Harkins.

“We continue to work with options market participants on the design of our exchange and we are knee-deep in the development phase,” Harkins added.

If IEX receives regulatory approval, it will become the nineteenth US options exchange. In August 2024 Miami International Holdings launched the newest options exchange, MIAX Sapphire electronic exchange, which will be followed by a physical trading floor this year.

Harkins said: “We can’t just clone our equities exchange, but the core focus of improving outcomes for liquidity providers by protecting against adverse selection remains.”

FLASH FRIDAY: Growth of ATSs: Implications for Liquidity and Market Structure

By
Anna Lyudvig
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FLASH FRIDAY is a weekly content series looking at the past, present and future of capital markets trading and technology. FLASH FRIDAY is sponsored by Instinet, a Nomura company.

One of the most notable trends shaping the equity market in recent years is the continued growth of Alternative Trading Systems (ATS), including dark pools. These privately operated platforms facilitate the buying and selling of securities outside traditional public exchanges, offering advantages like greater anonymity and the ability to execute large trades with minimal market impact.

The surge of ATS platforms can be traced back to the implementation of Regulation NMS (National Market System) in 2007, which sought to improve fairness in U.S. equity markets. Regulation NMS mandated that orders be executed at the best available price across all market venues, paving the way for ATS as alternatives to traditional exchanges. By offering additional venues for executing trades, these platforms helped improve market liquidity.

Drivers of ATS and Dark Pool Growth

Brian Hyndman

Several factors are fueling the expansion of ATS platforms and dark pools. Brian Hyndman, CEO of Blue Ocean Technologies, pointed to better liquidity and more favorable execution prices during core market hours. He also highlighted the growing popularity of the overnight session, as traders can manage their risk in real-time, rather than waiting for the market to open at 9:30 AM.
“There is also a big segment of retail customers in APAC that want to be able to trade US stocks during their daytime hours,” Hyndman told Traders Magazine.

Steve Miele, CEO of Kezar Markets, added that technological innovation has been a major driver of ATS development. “In recent years, we’ve seen many new products come to market that enhance execution quality,” he said.

Likewise, Khody Azmoon, CEOand co-founder, Head of Business Development & Product Strategy, BLOX Markets, noted the rise of advanced trading mechanisms like speed bumps and trajectory-based crossing to improve execution flexibility and efficiency.

The Regulatory Landscape: Scrutiny and Compliance

Despite their rapid growth, dark pools and ATS platforms are under increasing scrutiny from regulators. The U.S. Securities and Exchange Commission (SEC) has raised concerns about the lack of transparency on these platforms, particularly regarding price discovery and potential disadvantages to retail investors.

Hyndman emphasized that ATS platforms are regulated entities that must comply with SEC and FINRA (Financial Industry Regulatory Authority) rules. “As volume grows, ATS platforms will need to adhere to Fair Access standards and even Reg SCI (Systems Compliance and Integrity) standards,” he explained.

Azmoon also noted that U.S. equities ATSs and dark pools face growing regulatory pressure around risk controls and oversight. In particular, FINRA has indicated increased focus on extended and overnight trading in 2025, raising compliance concerns for platforms operating in these hours.

One area of concern is the rise of private rooms (also known as hosted pools), according to Azmoon. He explained that these rooms, initially created as efficient solutions for brokers managing internalized orders, are now being used in ways that raise regulatory questions. Brokers sponsoring private rooms through ATSs, creating “dark pools within dark pools,” could compromise transparency.

Furthermore, broker-owned ATSs have faced scrutiny due to potential conflicts of interest, particularly when managing retail order flow. Azmoon noted that this has led to a trend toward independent ATS launches, allowing brokers to reduce their regulatory exposure.

Technological Advancements

The rise of high-frequency trading (HFT) and algorithmic trading has played a key role in the growth of ATS platforms. These technologies, designed to execute trades at lightning-fast speeds, are particularly suited for dark pools and other ATS venues that prioritize minimal slippage and low-latency execution.

Steve Miele

Miele highlighted the contribution of these technologies in shaping a more efficient marketplace. “The advancements in trading technology have improved transparency and made the market safer and more reliable for investors,” he said.

As technology continues to evolve, the demand for faster, more efficient trading platforms has only increased, further boosting the popularity of ATS and dark pools.

Technological innovations have significantly reduced operational costs, improved execution speed, and enhanced the reliability of ATS platforms. According to Azmoon, launching a fully functional ATS is now possible for under a million dollars, although regulatory and operational expenses remain substantial.

“Modern technology stacks now achieve round-trip processing times of 50 microseconds or less, compared to legacy systems operating at 100 microseconds,” he noted. Additionally, next-generation platforms now offer superior redundancy solutions, ensuring system resilience by virtually eliminating data loss through real-time failover configurations.

Market Fragmentation and Liquidity Challenges

Since the implementation of Regulation NMS in 2007, the U.S. equity market structure has become more fragmented, with over 20 exchanges and nearly 30 ATSs operating today. This fragmentation continues to grow as more liquidity shifts toward dark pools and ATS platforms, raising concerns about price discovery and the accessibility of best execution.

Despite these concerns, Miele argued that the robust routing technology in place today enhances market accessibility. “Modern routing systems allow market participants to access deeper pools of liquidity across various platforms,” he said, countering the argument that fragmentation harms price discovery.

Khody Azmoon

Azmoon, however, emphasized that liquidity fragmentation remains an issue, with off-exchange trading volumes surpassing those on public exchanges. “We anticipate greater fragmentation and increased off-exchange liquidity over time,” he warns.

The SEC is aware of the fragmentation issue and has been taking steps to address it through amendments to equity market structure rules, particularly regarding Tick Sizes, Access Fees, and Transparency of Better-Priced Orders. These changes aim to shift liquidity back onto exchanges and promote a more balanced and accessible market structure.

As Miele argued, the U.S. market is designed to be fragmented. With nearly 20 exchanges and 30 ATSs, “there are rules in place to ensure customers receive the best execution,” he stated, emphasizing the technology that consolidates and distributes market data so that traders can access the best prices.

A Fragmented Yet Efficient Market

The growth of ATS platforms and dark pools has dramatically reshaped the U.S. equities market, introducing both opportunities and challenges. While regulatory scrutiny and concerns over liquidity fragmentation remain, technological advancements continue to enhance the efficiency and reliability of these platforms. With a strong regulatory framework in place, the market will likely continue to evolve, becoming even more fragmented, but also more accessible for participants seeking deeper liquidity pools and improved execution.

As the market structure continues to evolve, traders and regulators alike will need to navigate the balance between innovation, competition, and transparency to ensure the continued growth of the ATS landscape.

Traders Leverage Data Science to Improve Execution

By
Anna Lyudvig
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The role of data science in financial markets has seen considerable evolution in recent years. However, according to Stephen Ponzio, Managing Director, Head of Electronic Trading at BTIG, the fundamentals of data science and machine learning in trading have remained relatively unchanged despite the growing recognition of their value.

Speaking on the sidelines of the Equity Leaders Summit in Miami this week, he said that the use of data science, particularly in financial trading, often builds upon well-established statistical methods that have been around for a long time. Yet, the key challenge arises from the misconception that simply analyzing data can provide all the insights needed for optimal decision-making.

Stephen Ponzio

“First of all, a lot of the techniques that are used in machine learning, data science, or statistical analysis have been around for a long time,” Ponzio told Traders Magazine. “It’s nice that people recognize that you can learn a lot from looking at data, but the danger is thinking you can learn more than you really can.”

A prime example of this issue can be found in evaluating execution performance. Many firms strive to achieve the best prices for trades by monitoring metrics like venue analysis or spread capture. However, relying on these metrics as proxies for performance can be misleading. Venue analysis, for instance, might suggest that certain trading platforms or exchanges are superior, but according to Ponzio, “neither of those things… are correlated with performance.” In fact, the complexity of trading algorithms means that performance can’t simply be measured by looking at the source of fills.

Ponzio further illustrated this with the example of a broker’s algorithm sending orders to an ATS (Alternative Trading System). “By merely tracking the execution venue, one cannot conclude that the algorithm is performing well or poorly,” he said. In reality, the algorithm may be employing different strategies at various stages of a trade, using different venues, order types, and parameters to achieve specific goals. “Algorithms are complicated,” Ponzio explained. “They go through different phases, using different venues, different order types, different prices, and different tactics at different times.”

While data science promises to unlock new levels of insight, it is essential to understand its limitations, particularly in evaluating execution performance. Smaller trading firms, in particular, face significant challenges when trying to understand whether their executions are on track or going wrong.

Challenges for Smaller Firms

The most pressing challenge for smaller firms in trading is acquiring enough data to draw meaningful conclusions. For example, when comparing the performance of two brokers, small firms may lack sufficient order flow to distinguish between them effectively. “If you’re trying to distinguish the difference between broker A and broker B… you probably need about 5,000 orders apiece,” Ponzio noted. Smaller firms, however, often don’t have that much data at their disposal.

Beyond the volume of data, firms must also ensure their systems are configured correctly to track and interpret execution results. Without an experienced team to analyze this data, smaller firms may outsource the task to third-party companies. However, as Ponzio pointed out, simply shipping off data for evaluation might not uncover important issues. “If you’re just shipping it off to a TCA provider… are they going to go talk to the trader? Are they going to figure out why the performance is off?”

For smaller firms, one potential solution is outsourcing trading and execution to more systematic third-party providers. However, this creates another challenge—ensuring that the third-party service providers can appropriately evaluate brokers across different clients’ needs and execution strategies. Ponzio acknowledged that this is a tough problem but suggested a more systematic approach to broker evaluation in such cases.

Despite the challenges, there are promising developments in the world of crossing and algorithmic trading. One such advancement is the concept of “trajectory crossing.” This technique matches buy and sell orders at a higher level, allowing them to be executed at the average market price over a defined period, such as five minutes. By matching orders in this way, the algorithm reduces frictions and eliminates the spread cost, benefiting both the buyer and seller.

“Each side is going to get exactly the average price… it’s a gain for the algorithm on both sides,” Ponzio explained. Trajectory crossing not only improves price execution but also helps avoid market frictions, creating a win-win for participants. “A few brokers and ATSs have already implemented trajectory crossing in their pools, further enhancing its potential,” he said.

Another promising development is the rise of private rooms within ATSs, where only selected brokers participate. This setup enables brokers to internally match their own orders, which can help mitigate market frictions and optimize the trading process. “You can have an ATS where the participants are limited to certain brokers, again, to reduce some of the frictions,” said Ponzio. Such developments are beneficial for streamlining internal trading operations and improving price execution.

The Future of Algorithmic Trading

Looking forward, algorithmic trading will continue to dominate, with the expectation that more traders will rely on algorithms to navigate the complexities of the modern market. As Ponzio succinctly puts it, “It doesn’t make sense to trade manually in the market.”

However, while algorithms are essential for efficiently navigating multiple exchanges and ATSs, there remains a role for human intuition, particularly in decisions about when and how aggressively to trade. Ponzio noted that human traders should be responsible for determining the overall strategy—when to trade, how much to trade, and the level of aggressiveness to apply. Once these decisions are made, the algorithm handles the execution.

“Problems arise when low-level decisions are controlled manually, when it’s not clear how those decisions affect performance.,” he pointed out. As algorithmic trading continues to evolve, the challenge will be in refining systems to adapt to the nuanced requirements of individual trades.

In conclusion, while data science continues to drive improvements in trading strategies, especially through innovations like trajectory crossing and private ATS rooms, the future of algorithmic trading will require a balanced approach. Algorithms will be indispensable for optimizing execution, but human oversight will remain crucial in ensuring that the overall strategy aligns with the firm’s goals.

Gen AI to Elevate Financial Performance of Banks

By
Editorial Staff
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Artificial Intelligence (AI) is rapidly becoming the foundation of banking strategies, redesigning operational transformation and the reinvention of business models in the pursuit of healthier financial outcomes while addressing risk and compliance requirements, according to IBM.

According to the IBM Institute for Business Value 2025 Outlook for Banking and Financial Markets, Gen AI adoption is set to soar.

While only 8% of banks were developing generative AI systematically in 2024, 78% had a tactical approach, according to the findings.

As banks move from pilots to execution, more are redefining their strategic approach to service expansion, including agentic AI. 

Shanker Ramamurthy

“We are seeing a significant shift in how generative AI is being deployed across the banking industry as institutions shift from broad experimentation to a strategic enterprise approach that prioritizes targeted applications of this powerful technology,” said Shanker Ramamurthy, IBM Consulting’s Global Managing Director Banking & Financial Markets.

“As banks and other financial institutions around the world gear up for a pivotal year of investing in transformation, technology, and talent, we anticipate their efforts coalescing around initiatives using generative AI to level up customer experience, boost operational efficiency, reduce risks and modernize IT infrastructure.”

According to the findings, steady banking convergence is giving way to contrasting financial performance. Re-imagining the business model/processes and, importantly, execution will separate the winners from the rest, the report said.

In addition, 60% of banking CEOs surveyed acknowledge they must accept some level of risk to harness automation advantages and enhance competitiveness.

While over 16% of clients worldwide are comfortable with a branchless, fully digital bank as their primary banking relationship, competition is shifting from mass market digital offers to higher-value services, including embedded finance and advisory services to affluent investors and small and medium-size enterprises (SMEs), the report said.

The outlook shares insights from analysis of industry C-suite leader sentiment, bank customer behavior and economic data from eight major markets—the United States, Canada, European Union, United Kingdom, Japan, China, India, and Japan – and what financial institutions and their ecosystem partners can glean from the trends.

EXECUTION MATTERS: How Algorithms Are Shaping the Future of Buy Side Trading

By
Anna Lyudvig
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(EXECUTION MATTERS is a Traders Magazine content series focused on the topics most important to traders and technologists in US equities and options markets. EXECUTION MATTERS is produced in collaboration with Lime Trading Corp.)

As buy-side firms increasingly turn to algorithmic trading, the need for customized strategies, real-time adaptability, and advanced technologies like AI and machine learning has never been greater. From achieving best execution to managing market risks, firms are focused on leveraging algorithms to optimize performance in today’s fast-moving markets.

This week, on the sidelines of the Equity Traders Summit in Miami, Traders Magazine caught up with Michael Warlan, Head of Global Trading, Third Avenue Management to discuss algos and how they are used by buy-side firms.

What are the key objectives for buy-side firms when implementing algorithmic trading strategies?

Michael Warlan

The key objectives for buy-side firms when implementing algorithmic trading strategies revolve around achieving best execution, which is foundational to any algorithmic approach. This involves ensuring that the strategies align with the firm’s investment mandates, the selected securities, and the criteria identified for optimal execution. Speed and scale are also critical, as reducing the time to execute trades and handling large volumes efficiently can minimize opportunity costs and limit market impact. Access to multiple points of liquidity is essential to maximize trading opportunities. Additionally, the reliability of the provider or counterparty is crucial; their strategies must be continuously monitored and tested to ensure they perform as intended. Finally, firms rely on algorithmic or electronic trading to mitigate the risk of information leakage.

How are algorithms tailored to the specific needs of buy-side firms?

Algorithms can be tailored to the specific needs of buy-side firms through various customization options. Most broker-provided strategies offer firms the flexibility to adjust or enhance a strategy based on their preferences. For instance, firms can modify the amount of stock displayed in a lit strategy, set a minimum fill quantity, add or remove specific destinations or venues, and place conditional orders. More advanced customizations might include setting parameters that link executions to other trades or adapting conditions in response to movements in the stock or overall markets. This level of customization allows the trader to influence the executions to better align with the firm’s objectives and market conditions.

How do market conditions impact the performance of algorithmic trading strategies?

Market conditions significantly impact the performance of algorithmic trading strategies. By design, these strategies use a framework based on expectations, so any unplanned events or news can disrupt performance. Sudden market movements triggered by economic reports, geopolitical events, or corporate announcements can lead to unexpected outcomes, making it essential for algorithms to incorporate mechanisms for handling such scenarios. By considering these market conditions, buy-side firms can better tailor their algorithmic strategies to achieve optimal performance.

How do buy-side firms ensure they achieve “best execution” when using algorithmic trading strategies?

Buy-side firms ensure they are achieving “best execution” using algorithmic trading strategies by constantly reviewing their activity through transaction cost analysis (TCA), strategy comparison, and rigorous testing. By analyzing these costs, firms can identify areas for improvement and adjust their strategies accordingly.

Comparing trades across different strategies allows firms to benchmark performance and determine which algorithms deliver the best results under various market conditions. This comparative analysis helps in refining strategies to better align with the firm’s execution objectives.

Backtesting and parallel testing are essential tools for firms with more specific demands.

By employing these methods, buy-side firms can continuously monitor and optimize their algorithmic trading strategies, ensuring they consistently achieve the best possible execution for their trades.

How are machine learning and artificial intelligence (AI) incorporated into algorithmic trading strategies?

Machine learning (ML) and artificial intelligence (AI) are increasingly integrated into algorithmic trading strategies to enhance their effectiveness and adaptability. These technologies can significantly improve strategy selection and execution by leveraging advanced predictive analytics and more accurate forecasts. ML and AI tools can analyze vast amounts of historical and real-time data to identify patterns and trends that may not be apparent through traditional methods. This allows for more informed decision-making and the continuous refinement of trading strategies.

Additionally, ML and AI can dynamically adjust trading instructions at the start and during the life of an order. By spotting trends and real-time market shifts, these tools can update strategies to better align with current market conditions, optimizing performance. This adaptability ensures the algorithms remain effective even in volatile or rapidly changing markets. Incorporating ML and AI into algorithmic trading strategies enables buy-side firms to achieve more precise and efficient execution, ultimately leading to better trading outcomes.

How much transparency and control do buy-side firms typically have when using third-party algorithmic trading providers?

Due to the competitive landscape, many providers are willing to collaborate closely with their clients to adjust or adapt strategies to meet specific needs. This willingness to customize and provide tailored solutions enhances performance and increases the likelihood of gaining a larger share of the client’s trading volume. Providers often align their services with the client’s requirements, offering transparency in their operations and strategy execution. This alignment fosters trust and ensures that the buy-side firms can monitor and control the trading process effectively, ultimately leading to better trading outcomes and stronger client-provider relationships.

What are the pros and cons of relying on external algorithmic trading providers?

One of the primary benefits is the ability to leverage the scale and innovation these providers offer. External providers often have access to larger data sets and real-time executions, which can enhance the analysis of performance and trading behaviors. This scale also means that providers can continuously innovate, benefiting clients from the collective insights and advancements generated by a broader user base. Additionally, outsourcing algorithmic trading can be cost-effective, especially for firms whose investment strategies are not heavily dependent on proprietary trading algorithms.

However, relying on external providers may lead to concerns about slippage, mainly if many firms use similar strategies, which could diminish their effectiveness. Furthermore, placing orders through third parties increases the risk of information leakage, potentially compromising the confidentiality of trading strategies. Balancing these pros and cons is crucial for buy-side firms to ensure they achieve the best possible outcomes while managing associated risks.

What are the most significant trends in algorithmic trading for the buy-side over the next 5 years?

Over the next five years, several significant trends are expected to shape algorithmic trading for buy-side firms. One of the most prominent trends is the continued integration of AI and machine learning into trading strategies, with a growing emphasis on real-time applications. These technologies will enhance adaptive behavior and strategy selection, enabling algorithms to respond dynamically to market conditions while adding substantial value by allowing traders to manage larger volumes efficiently.

What’s on the Horizon for Traders and Brokerages in 2025?

By
TradingBlock
-

By Michael Martin, Vice President of Market Strategy, TradingBlock

Michael Martin

In 2025, traders and the online brokerages they leverage can expect major shifts that will bring both challenges and opportunities. These changes could be driven by rapid advancements in technology, shifting investor expectations, evolving regulations, AI’s growing influence and a new administration in the White House.

Navigating the year ahead will require adaptability and an understanding of emerging trends. Whether you are a seasoned institutional investor or an individual trader looking to stay ahead of the curve, the following issues could loom on the horizon.

New White House, New Regulatory Landscape?

President Donald Trump has tapped crypto backer and former SEC commissioner Paul Atkins for the agency’s top job. With Atkins serving as chair, can we expect more clearly defined regulations that support the proliferation of digital currencies, as well as rules around how they are handled by online brokerages?

Just two days into Trump’s presidency, Acting SEC Chairman Mark T. Uyeda, announced the formation of a new crypto task force “dedicated to developing a comprehensive and clear regulatory framework for crypto assets.” Additionally, in a major pivot, the SEC recently rescinded Staff Accounting Bulletin (SAB) 121, which previously guided how financial institutions should account for crypto assets held on behalf of their customers. 

With Trump back in the White House, can we also expect sweeping deregulatory efforts that may lead us into a bullish trend?

A Recipe for Volatility?

Despite Trump’s intention to boost the economy through deregulation, his tariffs could snarl supply chains, ignite trade wars and bring about market instability. Traders will likely pay closer attention to the sectors directly impacted by the latest announcements.

Sharp increases in the price of key resources can lead to stagflation – high inflation, high unemployment and slow economic growth. Combine this economic environment with the world’s ongoing wars and conflicts and you could have a potent recipe for market volatility.

Online brokerages should prepare themselves to meet the demands of traders who must quickly adapt their portfolios amid dynamic, unsavory market conditions. Options traders may increasingly use shorter-term options as they attempt to manage news cycle swings.

AI and Augmenting Human Decision Making?

Will AI proliferate and have a profound impact on securities-related research and decision-making? Firms that leverage AI while placing humans at the center of the decision-making process may have a greater chance of gaining market share.

While AI can process endless amounts of data, expedite research and surface valuable insights, the technology lacks the unique skills and insights of a professional who can interpret market sentiment, account for qualitative data, make nuanced decisions, adapt to dynamic market conditions and more.

Might active managers and stock pickers do well in 2025 by using AI to inform decisions rather than relying on AI to make decisions? Regulators will probably continue to review AI adoption and rollout to investors to ensure it doesn’t cross over into investment advice and to ensure investors understand the underlying basis of the research AI may serve up.

Additionally, brokerages will continue to adopt AI to augment traditional customer service roles and back-office services as they look to manage costs in a zero-commission world.

Growing Demand for Custom Routing Algorithms?

Liquidity fragmentation, which is when asset liquidity is scattered across multiple trading venues or platforms, will likely continue to hinder traders, asset managers and hedge funds looking to access available liquidity all at once.This fragmentation can lead to failed trades, delayed execution or inflated costs.

With 18 options exchanges alone, the need to solve this challenge has never been greater.As a result, we should expect to see a greater demand for custom routing algorithms, which promote liquidity management by dynamically scanning for and aggregating liquidity across venues. Done well, they allow traders to carry out orders efficiently across exchanges for smarter, faster and more cost-effective execution.

Balancing Tech with Customer Support?

With the Great Wealth Transfer underway, millennials are expected to inherit more than any other generation – a whopping $46 trillion over the next 25 years, according to Cerulli Associates. This means that the number of tech-savvy high-net-worth individuals and families could skyrocket.

As online brokerages work to build relationships with and meet the demands of these digital natives – who want customized, tech-enabled solutions accessible anytime from anywhere – they will have to strike a balance between leveraging low-friction technology and providing the person-to-person customer support that can make clients sticky. This includes leveraging a streamlined tech stack that can cater to a broad audience with evolving needs.

As we peer into the near future, it’s clear that traders and brokerages will face their fair share of hurdles.To succeed, they will need to embrace technology and be ready to quickly adapt to changing market trends.Those that stay ahead of the trends can emerge from this year even stronger.

Traders Must Prioritize Data Quality for Better Execution

By
Anna Lyudvig
-

As algorithmic trading and machine learning take a bigger role in the financial world, the importance of high-quality data has never been more critical. That was one of the themes that emerged from the discussions at the Equity Leaders Summit (ELS) in Miami this week. 

With the increasing complexity of financial markets, the need for accurate, real-time, and reliable data has never been more crucial. The panelists also discussed the dangers of relying on poor-quality or delayed data.

Elliot Banks

“For traders, we’re seeing an ever-increasing requirement for high quality data,” Dr Elliot Banks, Chief Product Officer at BMLL, told Traders Magazine.

Currently, trading firms are spending enormous resources on cleaning and scrubbing data to make it usable for traders, he said.

“Firms should understand the usability, reliability and ease of use when evaluating data sources. Otherwise, you’ll spend four times as long cleaning the data as deriving useful value from it,” he commented.

Banks explained that when it comes to historical data, all facets of quality are important. 

That means data that is: accurate – interpreted correctly with edge cases in the data handled correctly, with no gaps or crossed books; consistent – there are 16 venues across the US equity market alone. Having a consistent, normalised schema that is global in nature is essential; and complete – no missing fields that have been dropped during capturing processes.   

Banks emphasized that one of the biggest challenges traders face is accessing clean, high-quality historical data for backtesting and execution analysis. With poor-quality data—whether outdated, incomplete, or misformatted—traders risk making decisions based on inaccurate information. 

He noted that quants spend up to 80% of their time cleaning and preparing data, and as data volumes grow, the need for reliable historical data has never been more critical.

“And with ever growing volumes of data, access to good historical data is becoming ever more important,” he commented.

“We’re seeing an increased demand for high quality data from traders, whether that’s for traditional backtesting or TCA, or to fuel innovative new solutions leveraging the latest techniques in machine learning and AI,” he said. 

“We’re most excited by the fact that high quality, consistent historical data means that firms can spend more time on these solutions, rather than data cleaning,” he added.

In the age of algorithmic trading, high-quality data is crucial, confirmed Rob Laible is BMLL’s Head of Americas.

Rob Laible

Speaking on the sidelines of the conference, he said that to build effective models, you need a large, well-maintained data set (or data lake) because if there are gaps or anomalies in the data, the machine learning model won’t recognize those issues. So, the foundation is a solid database with reliable data, and once that’s established, you can start applying machine learning to it.

Complex markets will continue to require ever more complex decision making around routing and trading decisions. There are 16 US equity exchanges now, but more on the horizon, Banks said, adding that’s the same in the US options markets. 

“And demands on firms to continue to prove execution quality in this increasingly complex environment will only increase the demand on firms to have high quality, consistent historical market data,” he said.

In today’s fragmented equity markets, with multiple exchanges and many different ways to execute, having high quality historical data is essential, Banks said. 

“Understanding which venue has the highest probability of being filled, or what exchange is setting best prices most often, is a critical question for anyone making routing or execution decisions,” he said.

“And these questions are impossible to answer without extremely high quality historical data. One firm told us that “if historical data quality is only at 99%, you might as well not bother”,” he said.

However, not all data sources are created equal. The accuracy of the data provided can vary, which is why selecting reliable sources is paramount. 

“There are some potentially nice regional players or single market players, or people go to particular exchange. You get a fee, but if you’re a global player there are not a lot of avenues you can go down if you’re not going to do it yourself,” Laible said.

As the demands for better execution and complex decision-making continue to grow, it’s clear that traders must prioritize high-quality data to stay competitive. Ensuring data integrity not only improves performance but allows firms to focus on innovating with AI and machine learning rather than getting bogged down by cleaning and correcting datasets.

Cboe Hires Meaghan Dugan as Head of U.S. Options

By
Press Release
-

CHICAGO, Feb. 5, 2025 — Cboe Global Markets, Inc. (Cboe: CBOE), the world’s leading derivatives and securities exchange network, today announced Meaghan Dugan has joined the company as Head of U.S. Options. This appointment marks the latest expansion of Cboe’s Global Derivatives team, which has recently welcomed several new hires and key promotions to further strengthen its business development, market intelligence, and sales capabilities across the U.S., Europe and APAC in response to increasing global client demand.


Meaghan Dugan

“This is an incredibly exciting time for Cboe’s Global Derivatives business as we continue to unlock new opportunities for growth by scaling up globally across every critical function,” said Catherine Clay, Global Head of Derivatives at Cboe.

“Customer demand for accessing Cboe’s derivatives markets and products has continued to grow rapidly, and in many ways, our hiring mirrors our customers’ evolving needs: expanding globally, while requiring tailored solutions for individual markets. Having local teams on the ground, supported by world-class research and content, is expected to further deepen our customer engagement and grow our global client base. We couldn’t be more excited to welcome Meaghan Dugan and our other recent hires, whose exceptional talent and expertise will help fuel Cboe’s continued success on a global scale.”

Ms. Dugan brings more than 20 years of experience in listed options trading and market making. As Head of U.S. Options at Cboe, Ms. Dugan will be responsible for overseeing the business strategy, competitive positioning, market structure and market development for Cboe’s U.S. options business. Previously, she was Head of Options at the New York Stock Exchange, overseeing NYSE Amex Options and NYSE Arca Options markets. Prior to joining NYSE, she spent 11 years at Bank of America, most recently as Head of Product for U.S. Electronic Options and Global Future Algorithms. Previously, she worked for Morgan Stanley, originally in San Francisco as a Lead Market Maker in its Automated Market Making business then later in MSET, an Electronic Trading Team delivering trading algorithms and solutions to institutional and buy-side trading.

“I have long admired Cboe for not only its remarkable success in U.S. options, but also its ongoing expansion into a truly global derivatives powerhouse,” said Meaghan Dugan, Head of U.S. Options at Cboe. “Since founding the U.S. listed options market more than 50 years ago, Cboe has continued to lead the global industry through relentless innovation, exporting its world-class products, services and market expertise to new regions around the world. I am incredibly excited to join a team that shares my spirit of innovation and commitment to excellence, and I look forward to helping propel Cboe’s U.S. options business into its next phase of growth.”

Other recent appointments include Steven Jorgensen as Head of Derivatives Sales for Europe and the Middle East. With two decades of experience in global markets and expertise in derivatives, he is responsible for expanding the usage of Cboe’s US Derivatives products among European and Middle East clients and helping to grow Cboe Europe Derivatives (CEDX), its pan-European equity derivatives exchange. 

Jason Beck joined as Senior Director of Derivatives Sales, Head of Florida and U.S. Sell Side. With deep expertise in equity derivatives sales and trading, he is focused on strengthening Cboe’s U.S. sell-side client coverage, in addition to expanding Cboe’s regional presence in Florida and deepening its local client relationships.

Cboe also grew its APAC Derivatives Sales team by appointing Hiroshi Okitsu (Tokyo), James Paik (Hong Kong) and Lydia Stringer (Hong Kong) as Sales Directors, and Vincent Wang (Singapore) as Sales Manager. The team is focused on addressing client demand in APAC, a new and fast-growing market for Cboe’s derivatives products.

In addition, Cboe recently expanded its Derivatives Market Intelligence team under Mandy Xu, appointing Henry Schwartz as Vice President, Market Intelligence and Ed Tom as Senior Director, Market Intelligence, with plans to add one more hire in the APAC region. Mr. Schwartz brings extensive options trading, market making and data analytics experience, including as the former co-founder of Trade Alert. He is focused on expanding Cboe’s educational content for retail customers, with an emphasis on providing accessible data and insights on options markets, volume trends and products. Mr. Tom, with 35 years of experience in systematic (quantitative) and equity derivatives research, is responsible for strengthening Cboe’s institutional client engagement, providing data and analysis to help increase adoption of Cboe’s next-gen volatility products.

Source: Cboe

Why Your Trading System Is Costing You More Than You Think?

By
Quod Financial
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By Medan Gabbay, CRO at Quod Financial

Medan Gabbay

Order and Execution Management Systems (OEMS) are the most critical components for every sell side trading operation. But despite this, the industry has seen a noticeable lack of real innovation in this space. For sure we have seen better and faster algos, but the core function of maintaining state, routing orders for execution and sending the results back to buy-side clients really hasn’t changed.  So much so that many market participants view their OEMS as little more than glorified Excel spreadsheets—outdated, inflexible, and impossible to change. Yet, with the abundance of cutting-edge technology available today, why are firms still hesitant to modernize? The answer is simple: it’s not the technology that’s holding them back—it’s mindset, entrenched process, and resistance to change. 

Capital markets are at their most competitive in 2025. Look at how Citadel is looking to shake up Fixed Income trading in Europe just as they and their kind did in equity markets globally [Citadel Securities aims to become ‘material player’ in Eurozone bond trading]. But, worse of all, some traders see that navigating the inefficiencies of multiple screens, accepting dreadful UXs, and copy-pasting between them is actually their job. For operations teams, it’s even worse – papering over cracks, fixing problems that shouldn’t happen, or having to ruefully tell traders, “Well, that’s just how it is”. 

So, for brokerage and sell-side firms looking to stay relevant, this legacy mindset has to change. Familiarity and perceived reliability can become a bigger barrier to progress and innovation than the platforms themselves.  

Comfort, while convenient, comes at a cost. 

The buy-sides are under their own pressures to get the best possible execution outcomes and are more and more discerning about what their brokers truly offer. Just like in our daily lives, everything sits on top of technology and, for sell-side firms, that technology layer is always their OMS. 

“I’m happy with how I do things now.” – You 

This sentiment, heard all too often, reveals an underlying issue, a fear of change. In both business and life, standing still is never an option. Technology, when embraced, serves as a catalyst for positive change—enabling automation, reducing errors, and enhancing overall service quality. The reluctance to move away from legacy systems is understandable; change can be challenging. However, those growing pains are essential to staying relevant in an increasingly competitive landscape. Clinging to outdated systems might feel comfortable, but it’s holding firms back from achieving operational excellence and, ultimately, ceding competitive edge to their competitors.  

How to rethink your processes? 

Sometimes, meaningful change starts with a simple question: Why are we doing it this way? In many sell-side firms, long-standing processes remain untouched—unchallenged for over a decade. The reality is that technology offers an opportunity to break these shackles and reform processes that no longer align with trading today. By reassessing workflows and embracing automation, firms can unlock significant efficiencies that were previously unimaginable. 

Take data automation, for example. With the right OMS in place, firms can achieve real-time instrument creation as soon as an order arrives, eliminating the need for manual static data entry. This shift not only reduces the risk of rejected orders but also ensures seamless trading across any instrument, in any market, without human intervention.  

Similarly, process automation introduces intelligent solutions like Algo Wheels, Broker and Destination Choosers, and compliance automation. These capabilities empower firms to handle incoming orders with precision and flexibility, turning what was once a rigid process into a dynamic, rules-based workflow. 

For traders, by traders 

Speed and efficiency have also seen a dramatic transformation with modern OEMS solutions. Built by traders for traders, today’s systems are designed to remove the frustration of duplicate actions, unnecessary clicks, and missing functionality—elements that have long plagued legacy systems. The focus is now on intuitive workflows that allow traders to execute with confidence and speed, without being bogged down by system inefficiency. 

Moreover, integrations have become a critical component in delivering competitive advantages. A modern OMS enables seamless connectivity across the trading ecosystem, incorporating research, Transaction Cost Analysis (TCA), and real-time position monitoring and client interaction. In today’s fast-paced environment, any delay or lack of access to crucial data is not just an inconvenience—it’s an operational cost that hinders effective decision-making and, ultimately, the success and growth of your firm. 

Tolerance for pain 

Despite the technology available, many firms continue to tolerate inefficiency simply because they have become accustomed to the daily pain of working within the constraints of legacy systems. However, the hidden costs are substantial—whether in terms of wasted time, higher risk, increased operational effort, or missed opportunities in client acquisition and retention. The reality is that if a process is difficult, it will either happen less frequently or, worse, not at all. Addressing these challenges head-on with an upgraded OMS is not just about keeping up; it’s about staying ahead. 

The Cost of Delaying Change 

You already know these challenges all too well — grappling with outdated systems day in and day out. So, what truly drives change? Why does the decision to modernize always get pushed to the next year, the next budget cycle? 

At Quod Financial, we believe the hesitation stems from the lack of a clear, viable alternative—one that doesn’t just replace legacy systems but redefines the way sell-side trading is managed. Our platform is built to bridge the gap between established workflows and the future of trading, offering the flexibility, automation, and innovation needed to transform your desk. This commitment to progress is at the core of our product—and our firm’s mindset. 

Devexperts Announces Futures Trading Platform

By
Anna Lyudvig
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Devexperts, a global software developer for the capital markets industry, has launched a turnkey futures trading platform that will enable brokers looking to expand into both futures and options on futures trading.

Peter Snasdell

“We have seen a steady increase in futures trading volumes over the last few years and there continue to be a number of international market factors that suggest this trend is likely to continue,” said Peter Snasdell, Senior Vice President at Devexperts.

“We are living in exciting times where we are seeing progress happening very quickly. Factors such as regulatory and policy shifts in response to change, for example, have led to market conditions that lend themselves favourably to options and futures trading,” he told Traders Magazine.

The idea of this platform is to be provided both as an “out-of-the-box platform white-labelled with a broker’s logo”, and as a flexible solution for those with legacy infrastructure and many integration points, Snasdell said.

The number of customization options is almost unlimited, examples include: implementing different/unique workflows, automation, specific risk rules and settings, layouts, integrations options (executing venues, market data, third party tools for marketing, support, analytics, etc.), he said.

The new futures trading platform provides brokers with access to futures and options on futures on CME and ICE; is partnered with a futures commission merchant; and features DOM ladder trading, with simplified, one-click order placing functionality removing complexity, and aligning with UX expectations on specialized futures platforms.

According to Snasdell, the depth-of-market (DOM) ladder provides a clear visualization of order groups, such as OCO (One-Cancels-the-Other) or OSO (One-Sends-the-Other) orders, and incorporates trade parameters like bracket order price levels.

“With one-click order placement, traders can execute orders instantly, reducing friction and ensuring fast execution in high-speed futures markets,” he said.

The platform’s customizable and adaptive UI allows traders to personalize their layouts to fit their trading style, while brokers can offer preset layouts designed for novice users, he said, adding that this helps new traders get started quickly without overwhelming them with unnecessary complexity.

“A clean, high-performance interface ensures optimal speed in data processing and execution, minimizing distractions while keeping real-time market insights front and center,” Snasdell said.

Other functions include a real-time order management system (OMS), with automated real-time calculation of SPAN margin requirements and built-in pre-trade validation of placed orders to ensure margin requirements are satisfied; as well as a post-trade monitoring function that ensures portfolios are automatically liquidated in case of market moves, whilst also allowing brokers to offer intraday margin discounts and liquidation levels according to their risk management practices.

“Our platform’s automated SPAN margin calculation is a key component of pre-trade risk controls, ensuring that orders comply with margin requirements before execution,” Snasdell said.

As part of CFTC Rule 1.73 compliance, pre-trade validation checks margin sufficiency, position limits, and overall exposure to prevent excessive risk-taking and market abuse, he said.

Beyond pre-trade validation, margin is continuously monitored post-trade to help brokers manage client risk dynamically, he added.

“By integrating these automated controls into the trading workflow, our platform enhances brokers’ ability to comply with regulatory requirements while protecting market integrity,” he said.

The platform provides full integration with executing destinations, with existing destinations including CQG, StoneX, and others.

“We see brokers and firms within the US market as being most likely to benefit from this platform most immediately,” Snasdell said.

This is for various reasons, he explained, the regulatory environment in the US means derivatives such as futures and options are particularly attractive instruments for US traders.

The existing infrastructure surrounding these is therefore strong which is of course a big advantage, he said.

“The market conditions we are currently seeing mean traders are looking for opportunities to trade on these instruments, so we are happy to be in a good position to be able to help both our existing and prospective customers meet this demand,” he said.

According to Snasdell , the platform is designed to meet the high-performance demands of experienced futures traders by delivering fast execution, advanced order types, and a customizable trading workflow.

“With an optimized, low-latency infrastructure, traders can execute orders with minimal slippage, a critical advantage for active traders and scalpers,” he said.

To support precise risk management and automation, the platform offers advanced order types, including bracket orders, OCO orders, and trailing stops, he said.

“By focusing on speed, flexibility, and execution control, our platform provides professional traders with the tools they need to compete effectively in the fast-moving futures markets,” he added.

Snasdell also said Devexperts has an AI bot called Devexa, which will soon be integrated across all their products, offering a wide range of benefits including knowledge base capabilities, natural language responsiveness, and automated tailored alerts.

“Devexa is also integrated with multiple external platforms, providing services such as market data analysis, real-time trading alerts, and CRM connectivity,” he said.

“The Devexa team is, in addition, now actively working on machine learning-based predictive tools which could help brokers with client profiling, segmenting them according to their areas of interests and thus providing personalized offers and news, helping to retain active traders and enhancing engagement,” he concluded.

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