Your AI-native alpha researchand backtesting stack.
- Idea to verifiable backtest. In seconds.
Inspect the code. Account for every trade.
- A research copilot. Across your stack.
Automate the busywork. Keep the decisions.
- Let your research compound.
Reuse your signals and strategies. Build on every experiment.
Built for independent quants, research teams, and systematic funds.
Describe the research. Watch the platform do the work.
AutoQuant is not a code generator beside the product. It works through the product, with tools for data discovery, signal and strategy creation, backtest execution, and result analysis.
Inspect dataChecking access, axes, coverage, and cadence
DoneCreate signalsSaving authored, reusable research artifacts
DoneRun strategyExecuting and persisting the backtest
RunningWatch the research take shape.
Choose a step to explore
A question worth testing.
Your next signal, strategy, and backtest
can start with one prompt.
Personal enough for one researcher. Structured enough for a fund.
Your data, signals, strategies, and experiments stay connected. Start solo, share with your team, and build on what you've already learned. The foundation for your research today, and your trading infrastructure tomorrow.
Put your next idea to the test.
Help shape the future of AI-native research.
Questions, answered.
What is OpQuant?
OpQuant is an AI-native quantitative research platform that connects data discovery, signal research, strategy construction, backtesting, and eventually live deployment in one workspace.
Can a strategy run without signals?
Yes. Strategies can be deterministic or consume one or many signals. Signals depend on one or more data variables, while every backtest executes a strategy.
Does it work for individuals and teams?
Yes. The same ownership model supports a solo researcher as well as organizations with separate teams, entitlements, billing, and visibility boundaries.
How does AutoQuant work?
AutoQuant can search available data, create reusable research artifacts, start backtests, read results, and explain its work while preserving the same permissions as the user directing it.