Every Feature Built Around Evidence, Not Opinion
Worthmerald gives you the tools to test, question, and understand a strategy before you commit capital to it — backed by historical data, not hunches.
Most Strategy Claims Are Untested
Investment forums and social feeds are full of strategy suggestions — momentum plays, sector rotations, rule-based entries and exits — but very few come with a rigorous look at how they would have actually performed across market cycles.
Worthmerald was built to close that gap: run a strategy against historical data first, see the drawdowns and the wins, and decide with information instead of assumption.
What You Get With Worthmerald
Historical Strategy Backtesting
Define entry and exit rules, position sizing, and rebalancing frequency, then run them against extended historical price data. See how a strategy would have behaved through rallies, corrections, and sideways markets — not just in a single favourable window.
AI-Assisted Pattern Review
Once a backtest completes, an AI layer scans the results for recurring behaviours — clustering of losses around specific conditions, sensitivity to volatility spikes, or dependence on a narrow set of favourable periods — and surfaces them as plain-language notes.
Drawdown & Risk Breakdown
Every backtest report includes maximum drawdown, recovery time, and volatility metrics broken down by period. Risk is shown alongside return, not as an afterthought below a headline growth number.
Side-by-Side Strategy Comparison
Run two or more strategy variants — different rebalancing rules, different asset mixes, different holding periods — against the same historical window and compare outcomes on one screen instead of switching between reports.
Assumption & Methodology Notes
Every result comes with a visible note on the assumptions used — data source period, transaction cost estimates, and any simplifications in the model — so you know what the numbers do and do not account for.
Exportable, Shareable Reports
Save backtest results as structured reports you can revisit, compare against future runs, or share with an advisor. Nothing is locked into a single session.
From Idea to Evidence in Four Steps
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Describe the Strategy
Set your rules — entry and exit signals, asset universe, rebalancing frequency, and position sizing.
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Run the Backtest
Worthmerald applies the rules against historical data across the period you select.
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Review AI-Flagged Patterns
Read plain-language notes on where the strategy was strong, where it struggled, and why.
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Compare & Refine
Adjust parameters, rerun, and compare versions until you understand the trade-offs clearly.
Sample output: a backtest report visualises period-by-period performance so patterns are easy to spot at a glance.
Results shown are illustrative of report layout only and do not represent actual strategy performance.
Reports You Can Actually Read
A backtest is only useful if you can understand what it's telling you. Worthmerald reports are structured to lead with the questions investors actually ask: how much could this have lost, how long would recovery have taken, and how consistent was the return across different periods.
No dense spreadsheets to decode — just a clear breakdown you can act on, revisit, or bring to a conversation with your advisor.
Where These Features Fit
Testing a Rebalancing Rule Before Adopting It
An investor considering a quarterly rebalancing rule for an equity-debt mix runs it through Worthmerald's backtesting engine across multiple market cycles before applying it to a live portfolio.
- Feature Used
- Historical Strategy Backtesting
- Focus
- Drawdown behaviour across cycles
Choosing Between Two Momentum Variants
Two versions of a momentum strategy — one with monthly signals, one with weekly — are run side by side to see which produced steadier returns without materially higher turnover.
- Feature Used
- Side-by-Side Strategy Comparison
- Focus
- Consistency versus turnover trade-off
Understanding Worst-Case Scenarios
Before increasing allocation to a sector-focused strategy, an investor reviews the drawdown and recovery-time breakdown to understand what a similar downturn could mean going forward.
- Feature Used
- Drawdown & Risk Breakdown
- Focus
- Recovery time and volatility exposure
Frequently Asked
What historical period does the backtesting engine cover?
Coverage depends on the asset class and data source selected within the platform. Each report clearly states the exact period used for that specific backtest.
Does the AI layer predict future performance?
No. The AI component reviews historical backtest results to flag patterns, risks, and sensitivities. It does not generate forecasts or guarantee future outcomes.
Can I test strategies involving multiple asset classes?
Yes, strategies can be defined across combinations of equities, debt instruments, and other supported assets, depending on data availability for the selected period.
Are transaction costs and taxes included in results?
Backtests include estimated transaction cost assumptions, which are disclosed in each report's methodology notes. Tax treatment varies by individual circumstances and is not automatically factored in.
Can I export or save my backtest reports?
Yes, reports can be saved for later review or comparison, and exported in a shareable format.