Inputs stay attached
Dataset identity, rule version and tested period remain part of the saved report.
Rule-based backtesting
Explainable trading strategy backtesting.
Build a visual rule set, validate its historical data, state capital and execution costs, then inspect simulated performance trade by trade.
- Inputs
- Versioned and explicit
- Evidence
- Trade-level detail
- History
- Saved run context
Interactive backtest report
BTC/USDT · 1m · EMA cross · v3
2025-01-01 — 2025-12-31
Execution assumptions
Net return
16.8%
Max drawdown
-4.8%
Trades
248
Coverage
99.4 %
Equity16.8%
Drawdown-4.8%
| Trade | Side | Entry | Exit | Result |
|---|---|---|---|---|
| T-0248 | Long | $67,420 | $68,905 | 2.2% |
| T-0247 | Short | $66,980 | $66,340 | 1.0% |
| T-0246 | Long | $65,870 | $65,410 | -0.7% |
Hypothetical historical simulation based on the selected data and assumptions.
Evidence before outcome
A backtest is useful when its inputs remain inspectable.
Keep the dataset range, quality snapshot, strategy version and execution assumptions beside the report, so later comparisons use the same research context.
Costs remain visible
Capital, fees, slippage and risk assumptions are stated before interpreting a result.
Events stay traceable
Entries, exits and rule triggers connect headline metrics to the simulated trades behind them.
Reproducible workflow
From rule idea to an evidence trail.
Each step adds context that remains available when the report is reopened or compared.
- 01
Select and validate data
Choose the market, source and range; inspect coverage, freshness and known gaps.
- 02
Freeze the rule set
Save entries, exits and risk logic as an inspectable strategy version.
- 03
State the assumptions
Record capital, fees, slippage and execution settings before the run.
- 04
Run and monitor
Follow progress and events without separating them from the run context.
- 05
Inspect the evidence
Review equity, drawdown, metrics, trades and rule triggers together.
- 06
Save, compare and export
Reopen the same inputs later or compare them with another run.
Complete run context
Keep the whole run inspectable.
The report keeps the context needed to explain, reproduce and compare a historical simulation.
Data-quality preflight
Check requested coverage, freshness and known gaps before execution.
Versioned rule snapshots
Identify the exact entry, exit and risk logic behind a run.
Explicit cost assumptions
Keep capital, fees and slippage beside every result.
Progress and event history
Follow execution state and inspect the events recorded during the run.
Trade-level explainability
Trace every simulated entry and exit back to the active rules.
Saved comparisons and exports
Reopen run context, compare reports and carry evidence into external analysis.
Connected research
Continue through the research workflow.
Sweetspot analysisCompare neighboring parameter results and read the full performance surface.Explore product Research-ready datasetsInspect source, range, coverage, gaps and schema before the data enters a study.Explore product Backtesting dataImmutable Polymarket data packages for BTC, ETH and SOL across 5- and 15-minute markets, with native events, order-book depth, trades, settlements and an inspectable quality manifest.Explore product
Important risk information
Backtests and sweetspot analyses are hypothetical simulations based on historical data and selected assumptions. They can omit market effects and do not guarantee future results. Umbre Trading does not provide investment advice or execute trades.
MethodologyStart with explicit inputs
Run the next strategy with its assumptions attached.
Connect a versioned rule set to quality-checked historical data and an inspectable report.