Odds on Open

John Knorring, a former Goldman Sachs natural gas trader, shares insights from his extensive trading career and his role in founding Green Tiger Markets. He discusses how the transition from pit trading to electronic markets transformed risk management and liquidity. Highlighting the importance of human judgment amidst algorithmic advancements, John explains the evolution of trading strategies and the impact of regulatory changes like Dodd-Frank. He also dives into opportunities in the Philippines’ electricity market, showcasing how transparent contracts can drive investment.

In this insightful discussion, Daniel Xystus, a hedge fund manager and former quant director at AIA, shares his extensive experience in launching hedge funds globally. He emphasizes that most hedge funds fail due to operational issues rather than bad trading decisions. Daniel explains the nuances of choosing fund domiciles like Cayman and Singapore, and highlights the significance of compliance and infrastructure. He also delves into the unique challenges of trading in Asia and the evolving role of Middle East family offices in the hedge fund landscape.

DeWayne Louis and Nishant Gurnani, partners at Versor, share insights from their $1.4 billion quantitative investment firm. DeWayne outlines innovative merger-arbitrage strategies, predicting competing bids with impressive accuracy. Nishant dives into the GETT managed futures strategy, emphasizing the role of alternative data and hypothesis-driven research. The duo discusses the importance of clean data, avoiding data-mining traps, and the integration of AI in finance, highlighting how human intuition underpins their machine learning innovations.

Doug Garber, a former analyst at Citadel and portfolio manager at Millennium Management, now leads Westport Alpha Group. In this chat, he reveals the contrasting cultures of Citadel's structured strategies versus Millennium's decentralized, entrepreneurial approach. Doug dives into the importance of deep research in portfolio construction, the challenges of transitioning from sell-side to buy-side, and the traits that make a successful analyst. He also shares personal insights on balancing high-pressure roles with family life and raising kind children.

In this engaging discussion, hedge fund manager Alix Pasquet shares his insights on why high IQ can lead to investment failures. He delves into the pitfalls of competing with equally sharp investors and the risks associated with generational wealth transfer. Drawing parallels from poker and backgammon, Alix emphasizes the importance of emotional intelligence and the dangers of overcomplicating strategies. He also reveals how AI is reshaping the finance landscape, creating 'fantasy stocks' that prioritize hype over substance, urging investors to focus on simplicity and adaptability.

Cory Paddock, co-founder and CEO of GBE, dives into energy trading with a focus on anticipating market shifts in a world increasingly driven by renewables. He shares insights on how true trading edge comes from understanding grid topology rather than just price charts. Paddock is optimistic about Gen Z's potential in quant finance, highlighting their fluency in coding and data analysis. At GBE, he fosters a culture where young traders can experiment and learn through real trading experiences, paving the way for a new generation of algorithmic trading talent.

Can you start a hedge fund as a college student? Christina Qi, co-founder of Domeyard, did—and later built Databento, a modern market data API used by top algorithmic trading and quantitative trading teams. We get into how high-frequency trading (HFT) actually works, why clean order book/tick market data matters for robust trading strategies, and how a product-led model beats “talk-to-sales.” Christina shares what it takes to compete with Bloomberg/Refinitiv, where AI in finance is headed, and how better data unlocks faster research, reliable execution, and scalable quantitative trading workflows.Christina also breaks down hedge fund fundraising as a first-time manager—what allocators look for, how to structure fees/lockups/redemptions, and why your track record is everything. We talk about 2025 algorithmic trading: easier tools, tougher alpha, and how to find edge with high-quality market data, disciplined backtesting, and strong risk management. She closes with career advice for aspiring quants: master market structure, build real trading strategies in Python, and apply machine learning trading where it truly adds value—not as hype, but as part of a rigorous AI in finance toolkit.We also discuss...Founding Domeyard in college and turning a summer strategy into an HFT hedge fundUsing high-frequency trading to attract day-one allocators in hedge fund fundraisingWhy a verifiable track record matters more than terms when raising capitalHow to set fees, lockups, and redemptions as a first-time managerWhen investor relations and performance diverge and how to keep LPs during drawdownsWhy Domeyard shut down and the scalability limits of HFTBuilding Databento as an API-first market data/market data API platform for algorithmic and quantitative tradingSolving data licensing and usage rights with clean tick data, order book data, and better market microstructure coverageCompeting with Bloomberg and Refinitiv by focusing upstream on raw market data (not dashboards)Winning with product-led growth and self-serve checkout instead of talk-to-salesA bottom-up purchase at a major AI company as proof that PLG works for market data APIsAdoption by options market makers, quant funds, and AI in finance teams for research, alternative data, and NLP for markets use casesCheaper backtesting and better trading infrastructure but tougher alpha generation in 2025A public roadmap and user upvotes to prioritize datasets that matter to quants and quantitative trading workflowsAdvance commitments that de-risk new exchange integrations and ensure day-one usageIncumbents copying features as validation that Databento leads in market data APIsThe AI-in-finance arms race and why data quality decides machine learning trading, risk management, and Sharpe ratio outcomesHow macro conditions change fundraising outcomes for startups and hedge fundsCareer advice for aspiring quants: learn market structure/market microstructure, data engineering, rigorous backtesting, portfolio construction, and build real trading strategies

Jason Hsu, co-founder of Research Affiliates and CIO of Rayliant, shares insights on smart beta and quantitative investing. He argues that simply investing in the S&P 500 is outdated, highlighting the inefficiencies in Asian markets due to retail speculation and governance risks. Jason discusses the evolution of factor investing, emphasizing its advantages over traditional indexing. He also explains how Rayliant employs machine learning to create robust portfolios and shifts retail investors from short-term trading to long-term value creation.

In this engaging discussion, Renee Yao, founder and CIO of Neo Ivy Capital, shares her journey from Citadel to creating a billion-dollar hedge fund leveraging AI. She reveals how self-evolving AI enables real-time trading and uncorrelated returns, challenging traditional investing methods. Renee discusses the significance of diverse strategies, her experiences during COVID, and the vital role of investor education in understanding AI complexity. She also offers career advice emphasizing focus, adaptability, and discipline—key strategies in both trading and life.

In a fascinating dialogue, Joe Mezrich, Founder of Metafoura LLC and former Managing Director at Nomura, shares insights from his extensive career in quantitative finance. He discusses how the sell side pioneered modern factor investing and the pitfalls of models like the Barra risk model during market crises. Joe emphasizes the importance of interpretability in machine learning, reflecting on how complexity can overshadow understanding. He also highlights the evolution of market-neutral strategies and the critical role of alternative data in today's finance.