Test strategies before they touch capital.
DAROS Backtest turns strategy ideas into evidence — historical testing with realistic cost assumptions, drawdown and regime analysis, and deployment-readiness reporting that tells you whether a strategy has earned the right to trade.
A backtest with zero brokerage, zero slippage and one lucky parameter set is not evidence — it is a story. DAROS Backtest prices in costs, splits results by regime and time-of-day, and stress-tests parameters, so strategies that reach deployment have already survived contact with reality on paper.
What breaks without it
These are the failure patterns DAROS Backtest is built to remove:
- Strategies deployed on gut feel instead of evidence
- Unrealistically clean backtests that ignore execution
- No brokerage, slippage or cost assumptions
- No clarity on where and when a strategy fails
- Overfitting risk hidden until live capital finds it
Built for
- Individual traders systematizing a discretionary edge
- Quant teams running research pipelines
- Prop desks vetting strategies before allocation
- Anyone who wants evidence before deployment
Spec sheet & configuration
Where this module sits in the pipeline, what it handles, and what its configuration looks like. Figures marked as design targets are benchmarked per deployment.
| DATA DEPTH | Multi-year intraday & EOD history — data-source dependent, normalized via Connect |
| COST MODEL | Brokerage · slippage · taxes / fees — configurable per venue |
| ANALYSIS | Drawdowns · distribution · time-of-day · regime splits · sensitivity grids |
| OUTPUTS | Trade-level logs · equity curve · deployment-readiness report |
| PARITY | Same rule engine in backtest, paper and live — no reimplementation drift |
Values are design targets and depend on deployment environment, broker APIs and data sources. Nothing here is a performance or return claim.
{
"strategy": "orb-fut-02",
"window": { "from": "2020-01-01", "to": "2026-06-30" },
"costs": {
"brokerage": "per_lot:22",
"slippage_bps": 4 // stress at 8, 12
},
"splits": ["regime", "time_of_day"],
"sensitivity": { "param": "lookback", "grid": [10,14,20,28] },
"report": "deployment_readiness"
}What DAROS Backtest does
Historical strategy testing
Run rules against historical data with event-level fidelity.
Brokerage & slippage assumptions
Cost models so results reflect tradable reality.
Drawdown reports
Depth, duration and recovery of every drawdown period.
Win/loss distribution
Trade-level distribution, streaks and expectancy analysis.
Time-of-day performance
See when in the session a strategy earns and bleeds.
Regime-wise performance
Trend, range and high-volatility behavior broken out separately.
Parameter sensitivity
Check whether results survive small parameter changes.
Trade-level logs
Every simulated entry, exit and cost, exportable for review.
Equity curve
Curve, underwater plot and rolling statistics.
Deployment-readiness reports
A structured summary of whether a strategy is fit to paper-deploy.
How it runs in practice
Idea to evidence
Define entry/exit rules → run against multi-year history with brokerage and slippage applied → review drawdowns, distribution and regime splits → export a readiness report.
Overfitting check
Sweep key parameters across a sensitivity grid → flag configurations whose edge disappears with small changes → keep only robust regions.
Cost-reality test
Re-run a promising strategy with progressively harsher slippage assumptions → find the cost level at which the edge breaks → decide with eyes open.
How it connects with other DAROS modules
No module runs alone. DAROS Backtest plugs into the rest of the platform:
All modules:
Discuss your backtesting and pipeline requirements.
Tell us what data you have, what you trade and what a ‘pass’ should mean. We will scope the testing and validation pipeline around it.