Now in its 15th year, The TRADE’s 2022 Algorithmic Trading Survey will be live for buy-side participation until 21 February, with ‘Long-only’ and ‘Hedge fund’ results due to be included in the Spring and Summer issues of The TRADE magazine, respectively. We encourage algorithmic trading providers to support client participation.

Last year the Survey received a record number of 1,468 ratings, across over 30 providers of algorithmic trading, yielding thousands of data points for analysis. The results highlighted that the percentage of funds trading via algorithms continues to rise, with traders opting for tried and tested strategies like volume-weighted average price (VWAP) during market uncertainty, as well as price improvement strategies, such as dark or alternative liquidity seeking algos. Whilst overall scores were high, both long-only and hedge fund managers called for improvements in key areas such as price improvement, execution consulting and customisation.

The Algorithmic Trading Survey is aimed at buy-side traders across all asset classes and regions. This year the Survey includes additional questions, to monitor the continued increase in the electronification of non-equity markets.

To participate in the survey, please click here.

The 2021 Algorithmic Trading Survey results for long-only and hedge fund clients can be viewed here.

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Speaking to The TRADE, Peter Ward, global head of futures and options electronic execution at JP Morgan, explains that while the volatility contributed to recent growth, adoption of futures algo trading has picked up pace with clients significantly in the last few years. 

Since 2016, futures volumes traded via algos at JP Morgan has increased 40% year-on-year. In fact, algos now comprise of almost 20% of the bank’s total futures trading flow, up significantly from roughly 4-5% in 2016 and 2017, figures seen by The TRADE have revealed.

The period of intense volatility in 2020 due to the global pandemic played a key role in the cumulative buy-side adoption of futures algos as traders became more accustomed to on-screen execution and liquidity.

“When liquidity is harder to source and there is more volatility, execution performance becomes challenged,” Ward explains. “Clients are driven to look at the problem areas in executions and that’s when we consult with them to figure out ways to bring in that performance. Maybe they should consider trading at higher volume at the open or close, or perhaps sitting out the first five minutes on the cash open because of the noise. All of that we can customise for them.

“I think the more challenges clients see in execution, the more opportunity there is for us to come in and help them, and the solution is increasingly the customised algorithm.”

Customised algorithms have become particularly popular with traders in 2020 and in recent years. Volumes on customised algos at JP Morgan have roughly tripled in each of the past three years, alongside a 21% increase in the number of custom algos in 2020 to almost 50 customisations, up from close to zero in 2017.

 The bank’s flagship liquidity-seeking algorithm, known as Aqua, is the most common foundation for modified client parameters and customisations. A classic example of customisation is where a client wants to follow a particular trading pattern but then switch urgency or strategy based upon predefined triggers.

JP Morgan rebuilt its algo platform around five years ago to provide the buy-side with more choice about the parameters they can set on their side for algorithms, and there are further customisations that the bank’s electronic traders can configure on behalf of clients. Ward adds this has allowed his team to have a “richer” dialogue with clients and demand is clearly there.

“There has always been demand for customised algos, even 10 years ago there was a lot of demand,” he says. “We just didn’t have a scalable way back then to adapt an algo to what a client really wanted. The reason for that is when a client wants something different, we needed developers to code that and then release it for implementation in the client’s platform, which takes a lot of time.” 

Reinforcement learning

The Aqua algorithm has been a particular area of focus for JP Morgan recently. It uses a technology referred to as reinforcement learning to create advanced signals on order routing and placement.

With reinforcement learning, which is a form of machine learning, the algorithm essentially learns from itself over time by looking back at previous signals that it has generated and evaluates performance. The signals will dictate whether the algo crosses the market or stays passive.

Reinforcement learning technology was first applied to a recently launched model of Aqua that is focused on navigating quarterly roll dates when futures contracts expire. It can be a high-volume period and volatile time for traders as everybody is typically rolling in the same week to the next expiration date. In recent years, this activity has evolved from manual, voice-based trading to more electronic, low-touch trading.

“Previously, a lot of this business was executed through voice desks and one reason for that was because trading systems out there couldn’t handle multi-leg products,” he says. “As those systems have been developed in the last few years, we found more of that activity moving to electronic channels.

“A lot of volume goes through on calendar rolls and the challenge is around optimising that experience for the clients rather than imposing a model of trading without looking at the particular client objective.”

In response to the trend and client demand, JP Morgan developed a model of its Aqua strategy, known as the Roll Algo, which went live not long ago for the most recent US treasury roll in February. It has been especially popular with buy-side traders, according to Ward.

“The Roll Algo model focuses on maximising liquidity and pricing opportunities by using signals that help it understand when to cross the spread. It’s the most important area we are working on and has peaked the greatest interest from clients.

“It performed really well in February and there was a lot of client use in that period. With that, the algo learned a lot along the way so we can expect the performance in the next quarter’s roll to be improved.”

The Roll Algo is not the only new addition to JP Morgan’s new strategy line-up. Advanced strategies like Target to trade around the cash or futures close, Multi Leg Strategy for trading multiple instruments at the same time across futures and US treasuries, and options algos have also been developed by the bank.

Volumes in options on futures surged in 2020 as trading floors at major derivatives exchanges like CME that facilitate options trading were forced to shut down. As a result, liquidity shifted to low-touch and electronic channels and JP Morgan’s clients began to ask more questions about trading options through algorithms.

“Options on futures volumes have seen significant growth in the industry over the past few years and 2020 was a breakout year for liquidity on-screen,” Ward adds.

“With that said there are still challenges and nuances to trading them and that’s where we see opportunities to innovate and help our clients with their execution. This can be through simpler Peg and Cross type strategies and ultimately more targeted strategies using a delta or volatility reference.”

JP Morgan expects buy-side adoption of futures algo trading to continue increasing in the near future, having been driven by ongoing market developments and trends over the past few years.

Explicit regulatory requirements on best execution and growing appetite among the buy-side to address challenges in futures and options market structure have been instrumental in the growth of this trend. Best execution essentially forces traders to establish benchmarks to measure performance and trading through algorithms can provide an effective way to do this.

New products have also entered the market where liquidity is shared on multiple markets, which presents challenges in trading those products. Nifty derivatives, for example, are now tradeable in both Singapore and India after the Singapore Exchange (SGX) and India’s National Stock Exchange ended a two-year dispute which put SGX’s futures into question.

Other developments such as extended hours in futures markets also means there are now more hours to trade what is often the same amount of volume. Add periods of decreased liquidity and increased volatility to the mix, traders have progressively sought algorithmic strategies and automated solutions for consistent execution in volatile products, and when targeting cash settlement periods, for example. 

It’s not just JP Morgan that is doubling down on efforts in futures algo trading. In January, rival investment bank Citi rolled out a suite of execution algorithms, including its flagship Arrival strategy, for futures markets across all major exchanges in the US, Europe, and Asia Pacific.

In contrast to JP Morgan, the electronic traders at Citi handle all of the algo customisations on behalf of clients. Head of EMEA futures electronic execution at Citi, Gordon Ball, said at the time clients don’t want to enter numerous parameters to execute an order. He added: “the complexity of operating an intelligent algorithm and fine-tuning customisations sits with us, so our clients can focus on their overall investment and trading objectives”.

Elsewhere, a start-up founded by former global head of trading at AQR Capital Management, Hitesh Mittal, launched its own suite of execution algorithms in early 2020 that aims to reduce costs for the buy-side with customised and high-performance strategies. In December, BestEx Research secured $5 million in funding as it prepares to roll out its algos in futures markets.

Amid the arms race in this space, JP Morgan’s Ward predicts the pace of fixed income futures algo trading adoption, particularly customised algos, will continue apace in 2021. It remains a significant focus at JP Morgan as different buy-side clients are also now using algorithms to trade futures.

In the past few years, the type of buy-side client seeking algorithmic execution has shifted from being a relatively small number of large hedge fund clients to the more traditional managers, including pension funds, asset managers and insurance companies.

“Five years ago, there were pockets of interest in executing this way, depending on the specific trader or firm’s appetite. It’s now become far more mainstream, driven by broader electronification in fixed income markets as well more investment firms adopting more explicit execution benchmarks,” Ward concludes.

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The new platform includes benchmark algos such as time-weighted average price (TWAP), volume-weighted average price (VWAP) and Close, as well as other strategies and smart order types.

Its flagship algorithm is an implementation shortfall strategy, known as Arrival, which Citi said has been engineered from scratch on the platform. The bank added that Arrival was designed to minimise slippage by reducing the cost trade-off between market impact and price volatility across various market conditions in real-time.

Arrival also combines multiple strategies that are specifically tuned to micro market microstructure environments into one access point for reduced complexity.

“Our clients don’t want to enter numerous parameters to execute an order,” said Gordon Ball, EMEA head of futures electronic execution at Citi. “The complexity of operating an intelligent algorithm and fine-tuning customisations sits with us, so our clients can focus on their overall investment and trading objectives.”

The initiative was a multi-year project at Citi, and part of continued efforts to invest in its electronic execution services and capabilities, according to global co-head of futures, clearing and FX prime brokerage at Citi, Sabrina Wilson.

“We’ve made a number of key hires to drive this effort, bringing in expertise and a fresh perspective on our clients’ needs. The result is a global solution that focuses on individual market microstructure.”  Wilson added

In October, Wilson and Chris Perkins were promoted to global co-heads of futures, clearing and FX prime brokerage following the departure of Jerome Kemp who retired after nine years with Citi. Both were tasked with growing Citi’s client franchise and increasing electronic distribution.

“It was critically important for us to build algorithms specifically for listed derivatives, catering to the unique characteristics of these diverse instruments and their market microstructures,” added Wilson.

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Now in its 14th year, The TRADE’s 2021 Algorithmic Trading Survey will be live for buy-side participation until 26 February, with ‘Long-only’ and ‘Hedge fund’ results due to be included in the Spring and Summer issues of The TRADE magazine, respectively. We encourage algorithmic trading providers to support client participation.

The TRADE has teamed up with global research and advisory firm, Aite Group, to produce analysis of the survey results and an in-depth research report based on 10 years of historical back-data from the survey, which is now available for market participants to purchase.

The survey produced some intriguing themes in 2020, with long-only results suggesting that access to dark pools and liquidity remains a key focus for asset managers while hedge funds have upped their use of algorithms to reduce market impact.

The average score from long-only respondents for the survey in 2020 was 5.71 – a slight decrease on the average score of 5.74 in the year prior, but an increase on the 2018 average score of 5.60.

In contrast, the average score from hedge fund respondents in the 2020 survey was 5.77, up slightly from the 2019 survey average score of 5.72. While this indicated the industry has made progress in quality of execution, there is still room for improvement. 

To participate in the survey, please click here.
The full 2020 Algorithmic Trading Survey long-only results can be viewed here, and hedge fund results can be viewed here.

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He shifted out of risk assets, shorted Italian government bonds and bought the yen as a flight to safety. In this case, human judgement was the key, and the shift was made “long before the rocket science told us to,” he says.

These days, Kramarczik is a consultant in trading algorithms, working from home in Frankfurt. He had a sense of déjà vu as the COVID-19 pandemic prompted market collapse. While algorithms did not cause the mess, they were “not very helpful in March,” he explains. There was the same sense of panic that he witnessed in 2007. “People think that if they use algorithms they are not emotionally involved,” he says. “But that’s not true.”

Kramarczik now uses simple algorithms to identify the best days of the month on which to buy securities, or the best hour within a day. He sees gold as a “safety net” against the massive amounts of liquidity that have been injected into the financial system. Even having reached record levels, gold can still provide “insurance for the future,” he believes.

Still, he treats algorithms as a tool rather than a guide. “If there are moves that are not in your database, it becomes difficult,” he says. “Sometimes the correlation disappears.”

Flash Crash

The benefits of algorithms are obvious: much more efficient investment research and faster trading execution. Algorithms are also used to reduce the market impact of big trades as they make it easier to subdivide orders, so the size of the trades will not be apparent.

The behavioural biases of traders, at least in theory, are eliminated. This doesn’t come for free: market exchanges still experience software glitches, the effect of which can be magnified and spread by algorithms.

“There is no argument” that algorithmic trading adds more liquidity to the markets, says Ashu Swami, chief technology officer at Apifiny, a global trading and financial value transfer network in New York. But, given their high volumes and automatic nature, “if they go wrong and are not contained, they can cause sharp swings in the market”. Swami previously led the high frequency market-making business at Morgan Stanley.

Algorithms are often used to instantly exploit even minor price discrepancies in the same security trading in different markets. The International Organisation of Securities Commissions (IOSCO) Technical Committee in 2011 found that algorithms can quickly transmit shocks rapidly from one market to the next, so amplifying systemic risk.

A classic example is the ‘Flash Crash’ of 6 May 2010 when more than a trillion dollars was temporarily wiped off US equity prices. Computer programs exacerbated the damage by selling large volumes of stocks in response to the volatility. Greater use of market-wide circuit breakers was a result.

COVID-19

Rather than a financial or market-led crisis, COVID-19 is a crisis of health and the economy. Some argue that algorithmic trading allowed a prompt, if partial, recovery from the COVID-19 lows seen early this year. The danger now, Swami says, is one of contagion.

When an algorithm goes wrong, “the other algos either see it as an anomaly and pull their quotes, or they make unreasonable trading decisions based on the outlier prices”. Swami is confident that the problem will tend to be reduced over time: “More people using algos will lead to their democratisation and lessen deleterious contagion.”

The volatility seen in March was a function of the pandemic rather than being caused by algorithms, says Ray Ross, co-head of electronic trading at BMO Capital Markets in New York. BMO, he claims, performed well for its clients while using algorithms.

BMO uses algorithms to manage execution and market impact, rather than to generate trading ideas. Given the fact that they can be used for any kind of strategy, Ross sees little danger that they will all give off the same signal. Neither does Ross see algorithms as being responsible for increased numbers of failed trades in March. He sees settlement and clearing as the more likely candidates: “my guess is that it’s at that end.”

BMO Capital Markets has been functioning pretty much as usual with people working from home, Ross says. They will keep doing so for the foreseeable future and he expects the change in working culture to outlast the pandemic. Working from home is “a real improvement” due to technological improvements in the years before the pandemic.

Still, the ability to meet face-to- face with clients is missing, he says. That is what will drive decision-making about getting people back into the office. “There’s no way to make up for personal contact.” The most likely result is a “hybrid comeback”.

High-frequency trading

For Dejan Ilijevski, president at Sabela Capital Markets in Chicago, the pandemic has expanded the opportunities to use algorithms for high-frequency trading (HFT).

“When markets are slow and flat, there are no triggers, no edges, no opportunities for profits,” he explains. “Uncertainty is the main source for volatility, and the con- text around COVID-19 is all about uncertainty.”

Since market efficiency depends on price discovery, an increase in trading volumes and market participation leads to fairer asset prices, Ilijevski argues. HFT has also led to lower transactional costs for investors, he adds.

BMO’s Ross takes a more nuanced view. He does not expect there to be a decline in high-frequency strategies. Under normal conditions, he counters, high-frequency traders reduce volatility, but at times of stress, they can serve to create a “phantom” illusion of liquidity. The order books look full, but no-one is really willing to step in and take the risk. “People think there’s more liquidity in the market than there really is.”

Liquidity auctions

Ross also points to US volatility auctions as showing a weak point in algorithms. The US had a level 1 cross-market trading halt on 9 March for the first time in 20 years, and this was followed by three more such halts.

Market-wide circuit breakers force a pause to let markets reset from extraordinary spikes in volatility. The halts led to volatility auctions designed to allow price discovery.

“That’s not really what we saw,” Ross says.

Since auctions are a key form of price discovery, lack of participation is likely to have an impact on the market once the auction is over, Ross contends. He points to post-volatility auction trading volumes and price moves which were significantly higher compared with the first minute of trading after a typical opening auction: stabilisation has still not been achieved. Some brokers were completely unable to support participation in the auctions.

The most likely explanation, he believes, is that algorithms were not coded to handle such events. The algorithm “only really knows what it has seen before,” and so chose to sit the auctions out, leaving the markets to fend for themselves.

Managing an automated options market making model for about 20 years taught Steve Sosnick that successful algorithms “need to be tweaked constantly”. A shock that is beyond the normal scope of a model “doesn’t invalidate the algorithms inside it, but it does force a larger re-evaluation of its underlying premises,” says Sosnick, now chief strategist at Interactive Brokers in Connecticut.

“Profitability should never take a back seat to risk management,” he says. “We learned the hard way who focused on reward without sufficient focus on risk.”

There’s no way that algorithms and HFT are going to go away, Sosnick adds. The key is managing control. Practitioners must “maintain risk controls that work even in extraordinary circumstances”.

Artificial intelligence

Algorithms can be divided between those that simply follow the rules laid down in the programme, and those that are linked to machine learning and artificial intelligence (AI). In the latter case, the aim is for these “self-learning” algorithms to be updated automatically in a changing situation, without human intervention.

The danger is that the speed at which these algorithms learn will outpace human capacity to manage and regulate them. According to a report in June from the International Organisation of Securities Commissions (IOSC), AI and machine learning (ML) can “create or amplify” risks for financial markets.

Regulators should consider requiring firms to have designated senior management responsible for monitoring and controlling AI and ML, the report says. Regulators should also require firms to continuously test algorithms to validate the results of the techniques used, the IOSC said. Further, compliance and risk

management functions need to be able to understand and challenge the algorithms that are produced, and conduct due diligence on third-party providers.

Ross says the danger of market manipulation is “a regulatory concern”. “The machine does not know the regulations. It could certainly come up with something that does not meet market norms.”

Still, he believes the same kind of dangers exist with human traders as with algorithms and “it’s easier to shut down the machine than the human”. Of course, rogue human traders are easier to prosecute once you find them. But who gets prosecuted when the algorithms go wrong?

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A new report on the buy-side’s perspective of algo trading, which analysed historic data spanning the past 10 years from 2011 to 2020, suggested that traders have found a middle ground in their algo usage.

The research showed the number of buy-side firms that execute less than 20% of trades algorithmically, declined from 26% in 2017 to just 17% in 2020. Similarly, the number of firms executing 80% or more of their trades algorithmically declined from 21% to just 13% over the three-year period. 

Results in 2020 revealed that 55% of buy-side firms currently execute between 40% and 80% of their total trading notional value through algorithms, while a majority of 34% of investment firms execute between 40% and 60% of trades using algos.

“Just as the buy-side gains familiarity with algo strategies, traders continue to develop a better understanding of when to ‘work’ orders,” the report, produced in partnership with consultancy Aite Group, said. “For instance, certain environments, especially during highly volatile market conditions such as those of the COVID-19 pandemic, necessitate a higher-touch approach or more traditional means to achieve optimal trading results.”

Elsewhere, the report revealed that 32% of surveyed funds indicated they have appetite to make use of additional algorithmic trading providers in the next 12 months, with around one in five managers reporting interest in adding algos from their most used providers.

“The confidence of existing providers to deliver on expectations depends highly on relationships and the trust that buy-side firms have with their sell-side counterparts to provide cutting-edge solutions and to perform to expectations,” the research outlined. 

The findings are based on data collected as part of The TRADE’s Algorithmic Trading Survey, which has remained a staple of the capital markets industry since 2008.

 “The Buy-Side Perspective of Algorithmic Trading, 2020: Amid the Chaos” research report, co-produced with Aite Group, is available to purchase here. This year’s hedge fund and long-only results of The TRADE’s Algorithmic Trading Survey are also available to view online.

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The statement of good practice for algo trading in the FICC markets from the FMSB outlined good conduct and governance for participants engaged in algorithmic trading across all FICC asset classes and markets, in particular, those who are not regulated as stringently.

“The use of algorithmic trading systems across FICC markets has increased significantly in recent years and having robust governance structures in place to help manage the risks associated with this rise in algorithmic trading is critical,” said global head of principal electronic trading, FX, rates, and credit at UBS, Ciara Quinlan.

The statement summarised 10 points that all market participants and venue operators should adhere to when interacting with algorithmic trading or algorithmic trading systems. The points include areas such as ensuring there is an appropriate governance framework and pre and post-trade controls to limit risk, record keeping, and ensuring all staff have sufficient training, among others.

“This statement of good practice seeks to promote good conduct and governance practices applicable to algorithmic trading activities and demonstrates the shared commitment of market participants to enhancing the integrity and functioning of FICC markets,” said Chris Dickens, chief operating officer EMEA for global markets at HSBC.

The statement of good practice follows a recent report from FMSB, which emphasised the current limitations and challenges in adopting algo trading in less liquid markets. It found adoption of execution algorithms offered by banks or dealers to clients on an agency basis for order execution remains lower in FICC, with fundamental issues around data and governance persisting.

FMSB concluded that a consultation will take place on 21 August to comment on the proposed new statement for algo trading in FICC markets, with the aim of it being formally published after scrutiny.

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According to the FICC Markets Standard Board (FSMB), the use of execution algorithms offered by banks or dealers to clients on an agency basis for order execution remains lower in FICC, with fundamental issues around data and governance persisting.

Execution algorithms are typically implemented by the buy-side to reduce execution costs and market impact, as well as assisting in best execution efforts. While they have been used in cash equities far longer for those reasons, it’s improbable that execution algorithms will deliver the same benefits in FICC.  

“Execution algorithms should become very useful tools for driving efficiency in FICC market structure and to alleviate some of the cost pressures that the FICC industry faces, but they are unlikely to be a ‘magic bullet’ in the delivery and measurement of best execution,” the report from FMSB said.

Two fundamental issues that market participants should consider when rolling out execution algorithms in FICC were highlighted by FMSB, including potential conflicts of interest from providers. Banks largely trade in an agency role for cash equities, but they are more likely to act as principals with significant inventory in FICC, meaning clients could face competing interests of the bank or dealer provider.

Another issue is the lack of availability of data inputs in less liquid markets, compared to equities which dominates primary venues with continuous market data from various sources. For some fixed income products there is no primary venue, and some currencies may represent a small portion of the overall market, presenting considerable challenges in sourcing accurate market data.

According to the long-only results of The TRADE’s Algorithmic Trading Survey 2020, larger asset managers are likely to use several algo providers to manage multi-asset class portfolios. Beyond algorithms in equities, new regulations such as the uncleared margin rules, are fostering greater electronic trading in foreign exchange derivatives, and therefore the development of new algo trading solutions for that market.

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Known as Striker, the agency algorithm will initially support options in CME treasury futures, and QB expects to expand coverage across other CME futures products throughout this year, including options on Eurodollars and equity index.

Christian Hauff, co-founder and CEO of QB, explained that while electronic trading of options on CME futures instruments has seen huge growth recently, execution is still undertaken manually on-screen.

“Striker will assist traders to greatly improve their productivity by using this advanced algorithm to seek liquidity at the best price,” Hauff added. “In an unprecedented time, with the CME floor closed due to the current global crisis with COVID-19, we are thrilled to bring this much needed solution to the market which has been seeking an intelligent and purpose-built agency algorithm for the options on futures market.”

Transactions from the algorithm will be available to view in QB’s complementary transaction cost analysis (TCA), which the futures and US cash treasury algorithm and analytics provider said was another first for the industry.  

The new algorithm also incorporates real-time cointegration and implied pricing calculations, alongside its passive and aggressive child order logic.

“Striker is the culmination of an extensive research effort by our team to understand how to successfully trade options on futures, where liquidity can be a challenge, and where the fair price on any individual contract is hard to determine without looking at the entire complex.”

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The end-to-end, multi asset global trading platform includes execution algorithms and broker neutral transaction cost analysis (TCA), with agency broker Abel Noser set to rollout the algorithms developed by BestEx Research to more than 500 fund managers globally.

“BestEx Research is a new business model that is revolutionary in its impact and approach to solving the problem of performance drag in active fund managers’ returns due to high transaction costs,” Mittal commented. “Execution algorithms offered by banks and brokers have not evolved in over a decade and are stale, opaque and conflicted with their own internal liquidity pools, leaving managers to pay as much or more than management fees in implicit costs.”

Mittal established BestEx Research almost three years ago after leading global trading for hedge fund AQR Capital Management for just under four years. Prior to AQR Capital, Mittal spent over a decade with former agency broker ITG, now part of Virtu Financial, where he oversaw algorithmic trading and POSIT trading venue globally.

The BestEx Research platform aims to significantly cut down the implicit costs of algo trading with a more systematic and quantitative approach to execution. Its TCA system is also designed to measure and reduce transaction costs by attributing them to every aspect of an order that algorithms place, including venue, timing, size, price and order type.

“We aim to decouple execution algorithms from brokers and banks so institutions have choices and complete transparency into how each execution takes place,” Mittal added. “To date, a few sophisticated buy-side firms have built a subset of these capabilities in-house, but most firms have relied on standard broker algorithms. With our pure software model, hedge funds and asset managers can significantly reduce trading costs through customised high-performance execution algorithms and have enormous flexibility in bank or broker selection.”

Mittal concluded via a blog post on social media that the platform has been live for several months prior to this official launch, describing its first client as “one of the most performance sensitive firms on the street”, which is using BestEx Research’s equities algorithms globally to replace its current stack.

Institutional investors and asset managers are invited to rate the features and capabilities of their algo providers in The TRADE’s 2020 Algorithmic Trading Survey. Now in its 13th year, The TRADE’s 2020 Algorithmic Trading Survey will be live for buy-side participation until 28 February.

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