Maple is building a research environment designed to move financial hypotheses from data to evidence — infrastructure for financial research, simulation, and validation.
The problem isn't a lack of ideas, and it isn't a lack of data — there's an abundance of both. Before an idea can be tested, someone has to collect, clean, and align the right data. To test it properly, someone has to build simulation tools, model logic, and validation workflows. To keep the result useful, someone has to preserve the assumptions well enough that the work can be repeated or inspected.
That tax is what slows research down, pulls technical people away from research itself, narrows which questions get asked, and leaves scattered, hard-to-trust outputs behind.
Most financial software starts with a category — charts, data, backtests, portfolios. Maple starts with what you're trying to understand, and is designed to carry that question all the way through.
The earlier version of Maple was built primarily to help algorithmic traders backtest and validate trading strategies. That's no longer a complete description of the company.
Backtesting remains part of the picture — a historical backtest is one piece of evidence, not a conclusion. Maple is being built as the broader system that evidence comes from: data, hypotheses, models, simulation, and validation working together.
The engine is the product — the shared foundation that turns fragmented data and models into reproducible research evidence.
"Testing a hypothesis is easy. Trying to break it is harder."
Maple is not designed to make strategies look good. It's designed to help researchers understand whether an idea deserves confidence — using concepts including:
Plain-language write-ups of the research approach, the validation concepts behind it, and how the company thinks about trust — kept current as Maple develops.
A small team split deliberately in two directions: company, product, and architecture on one side; distribution, messaging, and customer feedback on the other.
Maple is a small, early team. If the problem described in the Overview and Approach tabs is one you'd want to work on — research, engineering, or growth — send us a note. No formal opening required.
The original Maple prototype focused primarily on trading-strategy testing. It's no longer being presented as the current product — the lessons from building it are shaping the broader research infrastructure now in development.
This site is intentionally not a finished storefront. There's no login, no pricing, no dashboard to click into yet. What follows is what we're building, why it matters, and how to follow along.
A Canadian financial technology and research company. Canadian identity runs through the brand deliberately, not decoratively.
Good research doesn't remove uncertainty — it helps us understand uncertainty better. That's the standard the platform is designed toward.
Join the Maple waitlist. No account, no password — just a way to reach you as the platform comes together.