Using Maple generally follows three stages: describing an idea, testing it against historical data, and examining how much to trust the result.
1. Describe the idea
A strategy in Maple is a set of explicit, testable rules — for example, an entry condition based on a moving-average crossover, an exit rule tied to a stop-loss or take-profit level, and an asset/timeframe to test it on. Maple's AI-assisted research tools can help translate a plain-English idea ("test whether a Bitcoin EMA pullback behaves differently on 15-minute versus 4-hour charts") into a structured strategy definition, or a strategy can be built directly in the Strategy Lab.
2. Backtest against historical data
Once a strategy is defined, Maple replays it against historical price data for the chosen asset and timeframe, recording every simulated trade the rules would have generated. This produces a full performance record: total return, win rate, drawdowns, trade-by-trade detail, and the underlying equity curve — all computed deterministically from the same rules every time, so results are reproducible.
3. Validate the result
A single backtest number is not evidence of anything on its own — it's a starting point. Maple runs a strategy's results through a set of independent validators (see Methodology) that check for overfitting, small-sample unreliability, regime-dependence, and drawdown risk most backtests don't surface on their own. The result is a confidence read on the strategy, not just a performance number.
Testing further
Beyond a single backtest, Maple also offers an autonomous research mode that tests many strategy variations on an asset and reports the strongest, more-robust candidates, and a paper-trading mode that lets a strategy run forward against live prices using simulated capital, so its behavior can be observed over time without risking real money.