Maple Research is a quantitative research platform built for traders, investors, and researchers who want to test trading ideas with evidence instead of guesswork. Maple helps people take a hunch — "does this pattern actually work?" — and turn it into a properly tested, statistically examined piece of research.
At its core, Maple is a backtesting and strategy-validation engine: it runs trading rules against historical market data, measures how they would have performed, and then subjects those results to a series of checks designed to catch the most common ways backtests mislead people — overfitting, small sample sizes, lucky drawdown periods, and results that don't hold up outside the exact window they were tuned on.
Maple is built by a small independent team in Canada, currently in a private alpha with a limited group of testers. The product is under active development, and this knowledge base reflects the platform as it exists today — not a finished, final product.
What Maple is not
Maple is not a financial advisor, a signal service, or an automated trading bot that places real orders on your behalf. It does not connect to a brokerage, does not move real money, and does not promise or guarantee any level of return. Every result Maple produces — a backtest, a confidence score, a piece of AI commentary — is a research output meant to inform your own thinking, not a recommendation to act on.
Who Maple is for
Maple is built for people who already think in terms of testable rules — a moving-average crossover, a mean-reversion setup, a volatility filter — and want a disciplined, repeatable way to check whether those rules hold up under scrutiny before trusting them with real capital (in whatever venue they choose to use, entirely outside of Maple).