Executive summary
• This report looks at how practitioners in our audience use AI across the quant trading work-flow, viz., what works, what they want to learn next, and where they struggle. The findings draw on registrant data, survey responses and live poll data from the Algo Trading Conference 2025 conducted by QuantInsti in September.
• AI helps most in research and idea generation, code scaffolding, documentation, and back-testing support. It’s less effective in areas shaped by market intuition, data quality, and market microstructure.
• Most learners want to go deeper into machine learning, strategy design and validation, automation, data engineering for clean, reliable data, and risk management or portfolio construction.
• We see the same frictions across roles, viz., data readiness, leakage control, robust validation, translating research into trading in live markets, and risk management in live markets. The emphasis changes at different experience levels but the themes are consistent.
• The key point that we see: move fast but stay disciplined. Build and use AI to remove friction, and have humans accountable for validation, execution quality, and risk.
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@report{krishnamoorthy2025state,
title = {State of Algorithmic Trading Education 2025: What Practitioners Are Learning and Automating},
author = {Krishnamoorthy, V.},
collaborators = {Khandelwal, N. and Bhattacharya, M. and Gonz{\'a}les Tanaka, J. C.},
institution = {QuantInsti},
year = {2025},
month = nov,
type = {Research Report},
doi = {10.5281/zenodo.17732323},
url = {https://doi.org/10.5281/zenodo.17732323}
}
About the Author
Vivek Krishnamoorthy, Head - Content & Research, QuantInsti | Ex-ICICI Bank
Vivek Krishnamoorthy serves as the Head of Content and Research at QuantInsti and specializes in time-series modeling, strategy backtesting, and Python-based trading workflows. At QuantInsti, he develops curriculum, delivers quant finance lectures, mentors research projects, and leads initiatives that integrate machine learning and language-model-assisted workflows into trading strategy development and deployment.
His career combines hands-on industry experience with academic research and training: he worked as a credit analyst at ICICI Bank in Singapore and India, taught finance and quantitative methods as an Assistant Professor at Symbiosis Institute of Business Management in Pune, India, and conducted academic financial research at McMaster University in Canada before transitioning to full-time quant education.
He has co-authored Python Basics and A Rough-and-Ready Guide to Algorithmic Trading, and writes and speaks widely on algorithmic trading. Vivek holds an MBA in Finance from Nanyang Technological University and a B.E. in Electronics & Telecom from Mumbai University (VESIT).
In his personal time, he enjoys reading on Indic philosophy and Vedanta, learning Sanskrit and practicing meditation, and long-distance running.