Quants worth following: Alex Reyfman

"Quants worth following" is our latest interview series highlighting thought leaders in the quantitative trading industry who actively share their knowledge and resources with the community. This week, we sat down with Alex Reyfman, a seasoned Wall Street veteran whose career spans almost 30 years in the financial industry.

Electronic trading has transformed equities, futures, and FX markets—but corporate bonds have been slower to adapt. Alex began his career at Lehman Brothers, moving from client research and strategy to portfolio management and trading, eventually focusing on electronic corporate credit trading at Barclays. He later joined Squarepoint Capital, a systematic hedge fund, where he helped develop some of the first buy-side automated trading systems for corporate bonds. In 2020, Alex and his co-founder, Michael Wong, left Squarepoint to launch Tradewell Technologies, a fintech startup dedicated to automating request for quote (RFQ) trading for corporate bonds.

In this interview, Alex shares his insights on the transformation of credit markets post-2008, the nuances of building trading algorithms for RFQ-driven markets, the evolving skill sets required in today’s quant landscape, and his journey from top-tier financial institutions to entrepreneurship.

Alex’s path to electronic trading began in quantitative research at AQR before the 2008 financial crisis, where he gained portfolio management expertise that led him to credit trading, particularly in single-name credit default swaps (CDS):

"As funny as it sounds today, in the early 2000s, single-name CDS were more liquid than corporate bonds. If you were running a systematic strategy on corporate credit, CDS was the more efficient instrument. Post-crisis and the regulations that came after, that's no longer the case, but back then, CDS was the way to go."

The post-crisis regulatory environment accelerated electronic trading in credit markets, opening doors for innovation in algorithmic trading:

"After the crisis, secondary trading became increasingly electronic. In markets like equities and futures, automation followed quickly, but in credit, electronic trading emerged years before true automation due to structural differences in the market."

"My initial role at Barclays was to work on the very first generation of market-making algos. At the time, while electronic trading—specifically RFQ trading—was growing rapidly in corporate bonds, the process remained entirely manual. The buy-side would send RFQs, and traders would manually type their responses.

That approach wasn’t scalable, so Barclays asked us to develop a system to automate RFQ responses and free up traders' time. What started as a simple efficiency tool quickly became something much more significant—a core risk and balance sheet management tool for banks. Today, roughly half of a bank's balance sheet is still dedicated to voice trading, while the other half flows through algorithmic and portfolio trading operations."

This shift raised an inevitable question: if dealers were using algorithms, shouldn't the buy-side follow? Recognizing this asymmetry, Alex pursued the opportunity after joining Squarepoint.

"At Squarepoint, we built a systematic long-short credit strategy with an automated execution system," Alex explains. "We developed one of the first, if not the first, fully automated trading systems for US corporate bonds on the buy-side."

"Portfolio and risk management in equities are ahead of credit, largely due to the availability of high-quality data. In equities, firms can access clean, reliable market data from providers like Databento. In credit, that’s not the case."

This data limitation forces credit market participants to take a different approach:

"Instead of subscribing to a market data feed, firms have two options. They can build their own pricing model—a real-time machine learning framework that ingests all available data, most of which is post-trade, and predicts where a corporate bond might trade. Or they can license a model from one of the vendors selling these solutions."

After pioneering buy-side algorithmic trading at Squarepoint, Alex and Michael launched Tradewell Technologies in 2020 to meet the growing demand for specialized automation in electronic credit markets.

"The opportunity is huge—electronic trading in U.S. investment-grade credit now accounts for 50% of trades, while high-yield credit is at 30%. Scaling further without automation isn't feasible."

"Automating trading and portfolio management is complex, and few have firsthand experience building robust, efficient systems for this space. While equities, futures, and FX execution algorithms solve different problems, RFQ-driven credit markets demand a specialized approach. In this niche, we've built the best team with the deepest expertise."

The U.S. corporate bond market is increasingly electronic, primarily trading through the RFQ protocol—a multi-step process initially designed for manual interaction between two counterparties over an electronic network.

"As trading volumes grow in both notional value and number of trades, manually handling RFQs becomes impractical. Automation is essential," Alex explains.

Traditionally, investors would send RFQs when they wanted to trade, and dealers would respond with an ask. This meant investors had to take liquidity and cross the bid-ask spread—buying at the ask or selling at the bid.

Tradewell Technologies automates this process, offering two key advantages:

1. Time savings – Traders no longer need to manually monitor multiple trading UIs across venues.
2. Passive execution – Investors can now execute trades passively, reducing transaction costs by buying near the bid and selling near the ask.

"In the past, investors always crossed the bid-ask spread to complete a trade. Now, they have the option to execute more patiently, improving liquidity access while lowering costs."

Shifting from employee to entrepreneur came with unexpected challenges for Alex:

"Building a business is tough. This is my first time, and the hardest part is not knowing what you don’t know. Over time, you realize, ‘Everyone around me expects me to be doing X, Y, and Z, and I had no idea that was even an issue.’"

Alex also gained insights into the venture capital ecosystem:

"VCs categorize companies into specific buckets—some seen as promising, others less so. If you're looking for funding, you need to be clear about where you fit and present your company in a way that resonates with investors."

For aspiring quants, Alex emphasizes the importance of staying at the forefront of AI and machine learning:

"If I were starting out today, I'd focus on developing a strong toolkit for identifying which frontiers in AI can be applied to financial data. That’s an open-ended challenge and not always easy for newcomers, but it’s where I see the biggest opportunities in our space."

For entrepreneurs, he recommends connecting with those who have already walked the path:

"Before diving in, spend time with founders at different stages—one year in, two years in, five years in. Understand their journeys. Being a founder is incredibly rewarding, but it’s not for everyone." He adds that self-awareness is key:

"The more you can answer for yourself—not just about risk and reward, but what your day-to-day life will actually look like—the better prepared you’ll be for success."

Watch the full interview on YouTube, where Alex dives deeper into the future of algorithmic trading in credit markets and the entrepreneurial lessons he’s learned along the way.