Can It Pass is built for traders who already have strategy reports and want to know whether the combined basket actually deserves a prop challenge. Import the files, shape the portfolio, apply the rules, save the research trail and pressure-test the same setup with robustness tools before risking more capital.
In MetaTrader Strategy Tester, right-click the backtest report and export it in the Open XML / Excel format. Can It Pass reads MT5 XLSX exports directly, so you can start from the real report instead of rebuilding trades by hand.
Register once, then work from the same saved account every time. Your simulations, imported strategies, project history and optimizer runs stay attached to the workspace instead of living in throwaway spreadsheets.
Click Add MT5 XLSX and import one file or several at once. The platform combines the selected reports into one portfolio research workspace so you can test how the basket behaves together, not just how each strategy looks in isolation.
Use Full history when you want the widest possible view and are comfortable with each strategy joining the basket when its own backtest starts. Use Shared overlap when you want to evaluate only the dates where every enabled strategy has data at the same time.
Choose account size, number of phases, target percentages, max loss and daily loss. You can also narrow the simulation range if you want to test a specific market regime or a more recent deployment window.
The simulator gives you the challenge verdict, current profit, balance and equity curves, rule lines, statistics and strategy contribution in one cleaner decision layer. If the basket passes the challenge and later breaks in the funded monitor, the post-pass view makes that visible too.
Save the current setup before changing ranges, lots or rules. History lets you reopen older versions, duplicate them into a new branch and compare ideas without losing the earlier research trail.
Open Optimizer to run Monte Carlo, test sizing mixes in Auto Optimizer and inspect deeper diagnostics in Advanced Analytics. This is where one historical equity curve turns into a probability view, a drawdown distribution and a more honest robustness read.
Right-click the backtest report and export it as an Excel/Open XML file. That is the raw material the workflow expects.
Take the saved basket and turn one historical curve into pass probability, balance distribution and failure map.
Explore alternative multiplier mixes without overwriting the original setup. Save the optimized copy only if it earns it.
Reopen, fork and compare saved portfolio versions so the work becomes a process instead of a one-off test.
A lot of confusion disappears once users understand why both modes exist. You are not just changing a chart view. You are changing which dates the portfolio is allowed to use.
Build the basket, apply the rules and read the first decision. This is the main place to import reports, choose portfolio mode, set phases and see whether the setup passes or fails.
Keep the research process instead of starting over every time. Save versions, reopen older baskets, duplicate promising ideas and compare different configurations with less chaos.
Challenge the baseline instead of trusting one line. Monte Carlo estimates pass probability, Auto Optimizer explores alternative sizing mixes and Advanced Analytics gives you deeper portfolio diagnostics.
Use Full history when you want the broadest realistic span and do not mind each strategy joining the basket on its own start date. It is useful for seeing the whole research record first, especially when your systems were not all launched at the same time.
Use Shared overlap when you want the strictest apples-to-apples view. The portfolio window becomes only the dates where every enabled strategy has data, which is often the cleaner option when you want to judge correlation, diversification and challenge behavior under the same shared timeline.
Because Optimizer works best when the baseline is intentional. Saving first gives you a fixed reference point, makes versioning cleaner and lets you keep the original basket untouched while you branch into different sizing ideas later.
The product is most useful once you stop reading about it and run your own reports through the workflow. Build the basket in Simulator, keep the research trail in History and use Optimizer when the baseline starts to look promising.