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

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