Maple Research is building infrastructure for financial research, simulation, and validation. The simplest way to describe it: Maple sits between financial data and financial models, and is designed to help someone move from a financial question to defensible evidence more efficiently than the current, fragmented way of doing that work.
Research designed around the question, not the tool. Most financial software starts with a category — a charting platform, a data provider, a backtesting engine, a portfolio dashboard. Maple starts with what someone is trying to understand, and is designed to carry that question through a consistent path: question → data → model → test → simulate → validate → evidence.
Why "research infrastructure" and not "a backtester"
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 — but Maple is being built as the broader system that evidence comes from: data, hypotheses, models, simulation, and validation working together. The engine behind that is the product; a backtesting screen is one thing it's designed to produce.
What Maple is not
Maple is not a signals service, an automated trading bot, a robo-advisor, or a brokerage — it does not place trades, does not move real money, and does not promise or imply any level of return. It is also not trying to be another charting platform, another basic strategy backtester, or a Bloomberg Terminal replacement. Every output Maple is designed to produce — a backtest, a validation read, a piece of AI-assisted commentary — is meant to inform a person's own research, never a recommendation to act on.
Who Maple is being built for
Maple isn't aimed at one audience. It's being designed for algorithmic traders who need more than a historical equity curve, quantitative researchers investigating relationships and signals beyond trading strategies, market researchers studying behaviour across regimes and asset classes, portfolio researchers working on construction and risk, and developers who want programmable research infrastructure rather than only a graphical interface. Longer term, that same research and simulation infrastructure could become something other financial platforms integrate.
Where things stand
Maple Quantitative Research Inc. is a Canadian financial technology and research company, currently in active development. The original prototype is not being presented as a finished, current product — the lessons from building it are shaping the broader architecture now underway. This knowledge base describes the direction, not a finished storefront. See Team for who's building it, or to apply if you'd like to work on it.