Positioning upstream of Bloomberg

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 fund
• Using high-frequency trading to attract day-one allocators in hedge fund fundraising
• Why a verifiable track record matters more than terms when raising capital
• How to set fees, lockups, and redemptions as a first-time manager
• When investor relations and performance diverge and how to keep LPs during drawdowns
• Why Domeyard shut down and the scalability limits of HFT
• Building Databento as an API-first market data/market data API platform for algorithmic and quantitative trading
• Solving data licensing and usage rights with clean tick data, order book data, and better market microstructure coverage
• Competing 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-sales
• A bottom-up purchase at a major AI company as proof that PLG works for market data APIs
• Adoption by options market makers, quant funds, and AI in finance teams for research, alternative data, and NLP for markets use cases
• Cheaper backtesting and better trading infrastructure but tougher alpha generation in 2025
• A public roadmap and user upvotes to prioritize datasets that matter to quants and quantitative trading workflows
• Advance commitments that de-risk new exchange integrations and ensure day-one usage
• Incumbents copying features as validation that Databento leads in market data APIs
• The AI-in-finance arms race and why data quality decides machine learning trading, risk management, and Sharpe ratio outcomes
• How macro conditions change fundraising outcomes for startups and hedge funds
• Career advice for aspiring quants: learn market structure/market microstructure, data engineering, rigorous backtesting, portfolio construction, and build real trading strategies