Quantmate is an agentic quant research environment where strategies don’t just get coded — they evolve. From natural language prompts to live testing and mutation, our system empowers professionals to explore, validate, and refine strategies at scale. With intelligent agents that learn from user input, market data, and performance feedback, Quantmate amplifies your decision-making — creating a faster, smarter way to discover alpha.
Learn more
Quantmate isn’t a rebalancing engine or static assistant — it’s a dynamic ecosystem of coding agents that evolve your ideas into intelligent, testable strategies. Instead of spending weeks coding, backtesting, and refining, users describe what they want to explore — and our system generates strategies, adapts them in real-time, and learns from results.
This is more than automation — it’s augmentation. Quantmate boosts the speed, scope, and intelligence of your entire investment research process, helping you iterate faster, surface new alpha, and continuously adapt to market dynamics.
Quantmate is an agentic quant research environment that transforms natural language into adaptive trading strategies. More than a tool, it’s a living system for evolving alpha through intelligent, explainable agents.
Instantly turn natural language ideas into fully executable strategies. Generate readable code, with optional explanations and full control for human refinement.
Evolve strategies through dynamic agent loops, mutation, and market feedback. Each strategy adapts over time — learning from past performance and user feedback and knowledge. Amplifying decision-making, not replacing it.
Evolve strategies through dynamic agent loops, mutation, and real-world feedback. Each strategy adapts over time — learning from past performance, user guidance, and embedded domain knowledge to amplify decision-making, not replace it.
A searchable, versioned archive of all generated strategies. Compare, fork, and revisit past ideas. Like GitHub, but for your custom repo of evolving alpha-generating strategies.
Instantly deploy dozens of strategies or evolved variants into paper trading environments. Gather real-time feedback and performance data, for agents to adapt, refine, and evolve even better strategies, fast.
Use your own EOD or intraday datasets. Upload CSVs or connect to any external data sources. Agents help you explore your data and generate context-aware strategies that reflect your data.
Co-Founder, CEO, Co-CAIO
Nicole is a globally recognized expert in LLMs and agentic architectures, advising institutions like the European Commission and IOSCO on GenAI. She’s delivered 50+ keynotes and workshops at Oxford, Cornell, JPMorgan, ACM, and more. She's a faculty member at the AI in Finance Institute an a researcher focused on agent-computer interfaces for self-evolving AI systems, she also is the author of two books in the domain (Manning and O'Reilly). She's the former deputy CEO and Chief Data Scientist of a regulated Crypto Fund.
Co-Founder, Co-CAIO & Head of R & D
David is a leading researcher in LLMs and reinforcement learning for finance. He developed a custom deep RL architecture for equities trading and has presented at QuantMinds and other global conferences. A faculty member at the AI in Finance Institute and former Columbia teaching fellow, he co-authored upcoming books on AI in Finance and LLMs, and holds a degree in Computational Mathematics and Data Science.
Join our waiting list to test the MVP or receive more information about our launch.
Be a part of our journey. Join our network to receive exclusive updates and explore investment opportunities with us.
