Old School FX Traders Are Being Replaced By Algos With Names Like Viper

Inside BNP Paribas SA, a trader named Viper is now handling vast swaths of the French bank’s currency trading business.

Viper, along with Iguana and Chameleon, are code names for algorithms that the lender developed to make life easier for the hundreds of hedge fund managers and corporate treasurers that rely on its systems. The three live inside Rex — a reptile-themed trading tool that’s cemented BNP’s edge in the $7.5 trillion-a-day foreign exchange market.

BNP isn’t alone. Rivals like JPMorgan Chase & Co. and Goldman Sachs Group Inc. are also muscling in on algorithmic technology. For the old guard of Wall Street, the work has become crucial for keeping clients happy after a bevy of upstart market makers like Citadel Securities and XTX Markets flooded into FX in recent years.

“It’s a bit difficult for a trader to look at 10 different screens to get a price,” says Asif Razaq, BNP’s global head of foreign exchange algo execution services. “You can give us a basket of orders that you need to execute and upload it to Rex. It shows the client an execution plan: ‘This is what I’m going to do, I’m going to deploy Chameleon for this order, a Viper for this order.”

For decades, banks have been lamenting that a lack of data prevented them from automating FX markets like they had in equities. But swift adoption of algorithms in recent years has that changing — and fast. Before the pandemic, just 22% of hedge funds were using the technology to execute their currency trades. Now, close to half of them are.

It all means that gone are the days of the Barrow Boys, the boisterous traders who spent their days shouting bids in slang that deemed the Bank of England “The Old Lady” and the process of buying up £1 billion against the US dollar a “yard of cable.” Instead, with algorithms now handling more than 75% of the trading in spot markets for FX, the usual soundtrack for the trading floor is the hum of computers and the click-clack of keyboards.

“People coming into the dealing room often expect a lot of noise, with traders talking loudly and shouting on the phone,” said Alexis Laming, an algorithmic currency trader at Credit Agricole. “It’s not like what you see in the movies anymore.”

This new era isn’t without its downsides for the giants of Wall Street. With machines doing much of the work, it means trading desks are making a lot less for each currency trade than they did in the past. And without a human to intervene, there’s a heightened risk that something could go awry, especially in periods of extreme volatility.

Inside Rex

When Razaq first joined BNP Paribas in 2010, most of the bank’s currency trades would happen when a client phoned up the desk to place a bet. With the new job, Razaq had a tall order: Automate as much of the division’s processes as he could.

He got to work. A graduate of Queen Mary University of London, he came to BNP with an advanced degree in artificial intelligence after stints at UBS Group AG and Citigroup Inc. At first, he was focused on building algorithms that BNP’s own traders could use to better hedge their positions and manage risk.

Soon, though, the French lender decided to repackage those algorithms and give clients the ability to use them. That’s when Viper, Chameleon and Iguana were born.

It’s hard to overstate the importance of banks getting this right in FX, which counts itself as the largest financial market on the planet. Trading desks across Wall Street make billions of dollars every quarter off the business and it’s one of the few markets that gives them strong linkages to hedge fund giants and corporate treasurers alike.

With more of those clients looking to trade algorithmically, it’s meant the race is on for banks to cobble together teams that can develop the technology for them.

“It’s a no brainer for the banks,” said Paul Lambert, chief executive officer of data provider New Change FX. “Banks are pouring their money into technology because they know it’s an arms race.”

At BNP, the three algorithms all have different characteristics. Clients can use Viper when they want to be aggressive with their trades and get out of risky positions quickly. Chameleon, on the other hand, is better for executing larger transactions and allowing them to slowly dribble into the market without creating larger gyrations.

Iguana was the final algorithm that BNP built. It was created for when clients know they want to execute a certain trade over a specific time horizon, but they aren’t sure when within that window would be the best time to place the trade.

“Iguana says, ‘You give me the start time and end time and let me work out the best possible path within that window,’” says Razaq, who brings along a toy Rex from Walt Disney Co.’s Toy Story movies to every sales pitch. “The algos change based on time of day and liquidity and currency pair. Chameleon, Iguana and Viper were designed for when traders didn’t have time to fine tune them.”

Cheaper Product

At Citigroup Inc., home to one of the largest FX trading desks, the volume of algorithmic currency trades the firm does with regional banks soared by 200% in January compared with the same period a year earlier.

Initially, the bank was focused on providing these algorithms to asset managers and corporate clients; now, it’s increasingly been asked to make the offering available to hedge fund managers too.

“They like the cost efficiency of the product,” said Mark Meredith, Citigroup’s head of FX local markets e-trading.

Deutsche Bank AG, another giant in the world of FX, is also increasing its algorithmic offerings to hedge funds after seeing total volumes in such trading jump 40% in the final quarter of 2023 compared with the same period a year earlier.

“What we’re seeing more and more is clients looking at overall costs and saying, ‘It’s a lot easier for me to send an algo to a bank,’” said Vittorio Nuti, global head of listed derivatives and FX algo trading at Deutsche Bank.

Indeed, this new era of FX has come at a cost. A decade ago, as electronic trading was just beginning to take hold in FX, clients would pay banks around $50 for every $1 million they traded using the technology. With algorithms handling much of that work, that’s now halved to as low as $25 in some cases.

That might be good for clients’ returns. But it means that Wall Street giants have less money to invest and prepare for future technologies poised to shake up the market, says Ralf Donner, Goldman’s head of fixed income, currencies and commodities execution solutions.

“The reducing yield from fees is now at a point where it risks stifling further product innovation and investment in analytical tools for clients,” Donner said in an interview. “We fully understand that clients want to demonstrate best execution, but we want and expect them to focus on long-term all-in performance when evaluating us.”

Risky Business

The industry has quickly learned that the newfound prevalence of algorithmic trading in currency markets isn’t without its dangers. A growing number of asset managers have warned that there’s been a spate of macro events that the programs haven’t been able to cope with.

“We’ve seen dealers switch off algorithms when I assume they didn’t have enough data or key signals to really keep up with the pace of volatility,” Nafisa Yusuf, director of market structure at BlackRock Inc., said of the use of algorithms across asset classes at an industry conference earlier this year.

Indeed, the Bank for International Settlements found all the newfound automation in FX had made the market more vulnerable to bursts of volatility when faced with sudden moves in the British pound and Japanese yen.

“The problem with algorithms is that when there’s a catalytic event, liquidity dries up in a heartbeat,” said Joseph Pach, a former head of currency trading at a Bank of New York Mellon Corp. subsidiary. He now runs San Francisco-based Corcovado Investment Advisors. “When you have institutions that have very little appetite to take risk and their algorithms tend to be very sensitive to news, if something happens you can get these kinds of flash crash type of events.”

Regulations require that money managers obtain best execution on their trades, necessitating a paper trail to demonstrate that. While automation helps with record keeping, using algorithms can make it harder to prove that the best possible outcome was achieved, as the conditions of the trade and the available trading options are almost impossible to replicate.

Still, for hedge funds, asset managers and even central banks, the lure of cheaper transactions is hard to ignore. These investors also like the fact that algorithms give them greater anonymity when pulling off those bigger trades.

RBC Bluebay Asset Management, for instance, relishes the ability to hide sizable trades from other players by using algorithms that ‘slice and dice’ their execution. Basically the asset manager uses the technology to feed larger orders into the market in smaller chunks over a period of time instead of calling up a bank, which could leak future trading plans in trying to offload bigger positions.

“It’s better for risk — I want everything to be less touched, less keyboards stroked,” said Stuart Campbell, who leads fixed income trading at the asset manager. “In the FX world, you can pretty much trade any size through algos, there’s less and less need for me to pick up the phone to do a big block trade.”

Tougher Competition

With more clients looking to electronify their FX trading, banks are facing ever-greater competition from upstart market makers looking to capture more share from the incumbents. Currency markets are now home to around 90 different trading venues, up from just 20 in 2012, according to ION Markets. (Bloomberg LP, the parent company of Bloomberg News, also hosts a currency-trading platform.)

Take the alternative market maker XTX, which has zero human traders, only coders. It’s surged to become one of the top three spot liquidity providers in the currency market in recent years, leapfrogging some of the biggest banks. The firm’s success has turned its founder Alex Gerko into one of the UK’s wealthiest individuals, with a fortune worth $11.4 billion, according to calculations by the Bloomberg Billionaires Index.

Citadel Securities, which got its start in FX just a decade ago, has said it’s grown to become a top five liquidity provider on most major FX platforms.

As they try to keep up, there’s been a sea change in the type of talent that banks rely on to staff their trading desks. At NatWest Group Plc, for instance, the number of traders focused on Group-of-10 FX has dropped 70% in the last two decades. Instead, the bank has set up a broad team of developers and quantitative analysts in their stead.

“Automation hasn’t led to massive job cuts, just a change in roles,” said John Quayle, head of client algo execution at the British bank. “You still need individuals to manage the risk — you still need to be responsible and step in where possible, like an airline pilot.”