Settings & Parameters
🔧 Settings at a Glance​
Sentinel uses carefully selected default settings to provide reliable risk insights:
- Instrument type: Perpetual futures only (no dated/expiring futures)
- Margin mode: Isolated (segregated) only — no cross-margin
- Model: "Price paths based on recent market behavior" (GBM baseline)
- Lookback period: 180 days of past price moves
- Trade length: 10 days (240 hours)
- Contract types: Linear and Inverse
- Funding cost: Uses the most recent exchange-published rate from the last interval
- Number of simulated paths: 7,500
🧠What These Settings Mean​
Understanding these parameters helps you interpret Sentinel's results more effectively:
| Setting | What it means | Default in Sentinel |
|---|---|---|
| Model | A baseline price model using recent market behavior (GBM). Think "many plausible price paths." | GBM |
| Lookback | How much past data we use to estimate typical moves. Longer = smoother; shorter = more reactive. | 180 days |
| Trade length | How long we hypothetically hold the position before closing. | 10 days (240 hours) |
| Contract types | How P&L is denominated. • Linear (USDT‑margined): P/L in USD/USDT. • Inverse (coin‑margined): P/L in the coin. | Linear & Inverse |
| Funding cost | Periodic payments between longs and shorts. We use the latest rate posted by the exchange and apply it during the simulation. • Positive rate: longs pay. • Negative rate: longs receive. | Latest published rate (applied at each funding time, e.g., every ~8h) |
Customizing Settings​
While Sentinel's default settings are designed to work well for most users, we understand that different trading strategies may require different parameters.
If you need more control over the simulation parameters, please reach out to sentinel@bitpulse.io. Our team can provide customized solutions with:
- Different lookback periods
- Alternative price models
- Custom funding rate scenarios
- Specific shock scenarios
- Additional risk metrics
Technical Considerations​
The default settings represent a balance between:
- Computational efficiency - Running simulations quickly enough for real-time use
- Statistical robustness - Using enough simulations for reliable results
- Market relevance - Capturing recent market behavior while filtering out noise
These settings have been validated through extensive backtesting across various market conditions.