Maple is in active development. This page describes the direction in broad terms, rather than a fixed, dated feature list — the previous prototype focused narrowly on trading-strategy backtesting, and the lessons from building it are shaping a broader research architecture.
Current focus
The immediate priority is the research engine itself: turning a described hypothesis into a precise model state, replaying it against historical conditions with realistic, point-in-time assumptions, and producing evidence that exposes its own uncertainty and limits rather than hiding them (see How Maple Works). Validation — the strongest part of Maple's original DNA — is being carried forward and deepened, not set aside.
Directions being explored
- Broadening the categories of financial data a research question can draw on — market, fundamental, macroeconomic, and alternative data — coherently, not just cumulatively.
- A research workspace designed to start from a hypothesis rather than a software category.
- Deeper simulation: alternative scenarios, execution assumptions, and parameter sensitivity treated as a core research capability, not an add-on.
- Expanding the validation concepts available and making their explanations more detailed for people who want to go deeper.
- Longer term, exploring how the same research and simulation infrastructure could become something other financial platforms integrate with, rather than only a standalone application.
What won't change
Regardless of which capabilities ship first, the underlying commitment stays fixed: research and validation over promotion, transparency about how results are computed, and no framing of Maple's output as financial advice or guaranteed performance.