When markets get tough, as crypto did in late January, investment firms need all the help they can get to make the right decisions quickly. It is therefore not surprising that many are turning to AI, the shiniest new weapon in the arsenal, to analyze and suggest ways to minimize losses and even make money.
Nearly all (96%) executives at a surveyed group of trading firms that collectively manage about $14 trillion in assets said AI is already playing a major role in core investment processes, according to research recently conducted by Nickel Digital Asset Management. But it is not enough, a human hand is still needed, said Anatoly Crachilov, founding partner and CEO of the firm.
AI is transforming quantitative trading, as it is almost every other industry and human endeavor. In addition to the large language models (LLMs) that seem to have permeated so much of daily life, there are also machine learning and predictive AI approaches that analyze historical data to predict what will come next. However, they are weak in identifying incorrect information that can lead to erroneous conclusions and poor decision making.
“It’s a very tough market. AI won’t save you; it’s not a savior,” Crachilov said in an interview.
Despite the fall in crypto prices that engulfed the market at the end of last month, London-based Nickel, which operates a multi-manager platform that allocates to more than 80 teams, remains positive for the year. “Maybe an achievement in itself,” Crachilov said.
The transition between crypto trading and AI is becoming most advanced in areas such as risk management. While AI may still struggle to outperform high-speed sniper bots targeting the latest low-liquidity crypto tokens, a sweet spot is where sentiment and data-driven models can learn to manage risk.
Each manager linked to Nickel operates within a well-defined risk framework that includes maximum withdrawal limits at times of increased volatility. Sometimes human intervention and an “old school” approach is needed, Crachilov explained, as opposed to relying on data-driven, machine-learned automation.
“If the market gets into trouble, as it has on a couple of occasions recently, sometimes you have to exercise discipline and stop the leaders who break [max drawdown] limits, whether it’s AI driving their strategy or not,” Crachilov said. “Ultimately, there’s a hard stop to how much pain we would allow in the portfolio.”
Questions about how much human involvement there should be in AI-driven trading strategies, or how a human override is triggered, were too technical and nuanced for Nickel’s relatively high-level survey of managers, Crachilov said.
He said Nickel runs “a military-like operation” where a rich data flow collects over 100 million data points from the underlying ledger every 24 hours. “Although this part is very well informed, it still requires human involvement. And we are still in conversation with managers, even in the middle of the night,” Crachilov said.
The natural evolution towards becoming fully automated still needs to account for the possibility of erroneous or incomplete data feeds from places like crypto exchanges, according to Crachilov.
For example, a human would realize that data indicating a particular position is down 100% was probably the result of something wrong with a data feed, he said. But an automated AI system can mechanically enforce a limit when it wasn’t required.
“You need a human overlay. The whole crypto ecosystem is still very fragile. And some of the exchanges can time out for 15 minutes, or see wrong data or produce patches of bad data, which can inadvertently force the system to shut down some of the leaders for no good reason,” Crachilov said.
It really comes down to the company’s risk management philosophy, which is to remove a single point of failure from any point in the process, said Nickel’s head of investor relations Charles Adams.
“If there was an independent agent monitoring the entire portfolio, let’s say something goes wrong with it, the risks could be potentially catastrophic,” he said. “The whole point is that we have this very well-diversified fund split between over 80 managers today across hundreds if not thousands of sub-accounts on exchanges, and it’s very important for us to remove the single point of failure.”
