The Alternative Data Podcast The Vlad Johnson Episode
Oct 27, 2025
Vlad Johnson, a quantitative researcher specializing in systematic futures at Eisler Capital, shares his insights on the intersection of finance and alternative data. He explores the technological gap between sports analytics and Wall Street, emphasizing the use of weather data without being a meteorologist. Vlad discusses the challenges and complexities of macro futures trading, while highlighting the critical role machine learning plays in alpha generation. He also speculates on the evolving capabilities of AI and the importance of improving data quality in finance.
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Early MLB StatCast Project
- Vlad led a college team to analyze LA Dodgers MLB StatCast data using video and tabular sources.
- They engineered features from computer vision and ranked first basemen to improve roster and training decisions.
Finance Often Outpaces Sports Analytics
- Sports analytics are sophisticated but finance often edges ahead due to more intense competition and funding.
- Finance demands greater caution because time-series dependencies increase the risk of fatal data-analysis mistakes.
Infer Topic-Specific Sentiment With LLMs
- Use LLMs to infer topic-specific sentiment rather than lone scalar sentiment scores.
- Ask models for sentiment on particular events (e.g., Middle East) to create richer feature dimensions.
