Daniel Krause
Quantitative Research Director at AIFI
Daniel writes about quantitative finance, trading systems, and the ML models behind them. He previously led a quant research team at a mid-size systematic hedge fund and holds a PhD in applied mathematics from ETH Zurich. His work focuses on where academic ML research meets production trading systems.
Articles by Daniel Krause(6)
High-Frequency Trading and AI: Inside Modern Market Microstructure
HFT firms execute roughly 50% of US equity volume. The public narrative focuses on speed. The actual edge is increasingly about ML-based signal extraction and inventory management.
NLP for Investment Research: How AI Reads Earnings Calls and SEC Filings
Every quarter, roughly 10,000 US public companies file earnings reports and host conference calls. No human can read them all. NLP systems now extract tradeable signals from this firehose of financial text in minutes.
Alternative Data for Investing: Satellites, Web Scraping, and the New Information Edge
Hedge funds spent an estimated $7 billion on alternative data in 2024. Most of it was wasted. The categories that produce real alpha are narrower than the vendor pitch decks suggest.
How Quant Hedge Funds Actually Use Machine Learning
The phrase 'AI-powered trading' appears in hundreds of fintech pitch decks. What Two Sigma, Jane Street, and Citadel Securities actually do with ML looks nothing like the marketing.
AI in Insurance Underwriting: Computer Vision, Telematics, and What Comes Next
Traditional insurance underwriting takes days and relies on paper forms. AI systems analyze satellite imagery, driving behavior, and cyber attack surfaces to price risk in seconds.
Reinforcement Learning in Quantitative Trading: What Actually Works
Most academic papers on reinforcement learning for trading fail in production. But in execution optimization, portfolio rebalancing, and market making, RL is delivering real results at top quant firms.