
This report analyzes how practitioners use AI across the quantitative trading workflow, based on registration data, survey responses and live poll data from the Algo Trading Conference 2025. It examines where AI helps the most, the topics learners want to go deeper into, and the friction points that limit adoption. The study spans 13,000+ registrants, covering backgrounds, experience levels, and geographical regions. The report also includes learning trends, bottlenecks, and co-occurrence patterns between problem areas and learning goals.
quantitative finance, trading education, Machine learning, algorithmic trading, financial markets, AI in trading, LLMs
quantitative finance, trading education, Machine learning, algorithmic trading, financial markets, AI in trading, LLMs
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
