Every tool built for long-horizon financial decisions
The Profits Circle combines historical market backtesting with AI-driven scenario modeling so households can stress-test plans before committing real capital.
Access PlatformCore Capability Snapshot
A complete toolkit for evidence-based planning
Each module is designed to translate raw historical data and probabilistic modeling into decisions households can actually act on.
Historical Scenario Replay
Run any portfolio allocation against decades of historical market data to see how it would have performed across recessions, rallies, and rate cycles.
AI-Assisted Projections
Machine learning models surface forward-looking probability ranges rather than single-point guesses, helping frame realistic best- and worst-case outcomes.
Milestone Tracking
Attach financial goals — retirement, education, property — to specific timeframes and see which allocations keep each milestone on track.
Drawdown & Volatility Views
Visualize maximum historical drawdowns and volatility bands for a given strategy so risk tolerance is matched before exposure, not after.
Side-by-Side Strategy Comparison
Place multiple allocation strategies next to each other with consistent metrics, making trade-offs between growth and stability easier to evaluate.
Source & Assumption Logs
Every backtest and projection ships with a visible log of data sources, time windows, and modeling assumptions used to generate the result.
Designed around how households actually decide
Most planning tools assume a single advisor relationship or a static spreadsheet. The Profits Circle was built for households that want to test their own assumptions, revisit them as life changes, and understand the historical context behind every number shown.
Instead of a single projected outcome, every analysis is presented as a range — anchored in verifiable historical data — so decisions are made with the uncertainty visible rather than hidden.
From raw data to a decision-ready report
The same four-step process runs behind every backtest, keeping results consistent and auditable.
Define the portfolio or plan
Input an allocation, savings schedule, or goal timeline manually or import an existing structure.
Select the historical window
Choose specific decades, market regimes, or let the engine run against the full available data range.
Apply the predictive layer
The AI model generates forward-looking probability bands based on historical patterns and current inputs.
Review the structured report
Results are returned as charts, drawdown tables, and plain-language summaries, with assumptions logged alongside.
| Scenario | Window | Max Drawdown | Outcome Range |
|---|---|---|---|
| Balanced 60/40 | 1990–2024 | -28% | Wide |
| Growth-Tilted | 1990–2024 | -41% | Wider |
| Conservative | 1990–2024 | -14% | Narrow |
Where households put these features to use
Retirement timeline stress-testing
Compare how a planned withdrawal rate would have held up across multiple historical retirement start dates, including periods of high inflation or extended downturns.
Education fund allocation review
Map a savings schedule against a fixed enrollment date and see which allocation mixes historically kept pace with tuition growth over a 10–15 year window.
Property purchase readiness
Model a shorter-term savings goal with lower risk tolerance, checking how conservative allocations performed over 3–5 year historical windows.