Profitability & risk models
Specify the data, assumptions, and uncertainty needed before a mining model can support decisions.
Model specification only. A calibrated profitability optimizer and mining decision engine are not implemented.
Model scope
Profitability analysis needs both an accounting basis and a clearly stated operating scenario. Expected revenue, realized receipts, and cash balance should remain separate outputs. An expected value does not guarantee a positive realized result.
Scenario operating result = scenario revenue − operating costs
Full-cost result = scenario operating result − allocated capital costsRequired inputs
| Input family | Required detail |
|---|---|
| Work & network | Accepted work, unit convention, difficulty history, delivery uptime, and pool mode. |
| Revenue | Applicable miner reward, transaction fees, payout terms, conversion prices, and observation times. |
| Owned equipment | Acquisition cost, useful life, residual value, power, hosting, and maintenance assumptions. |
| Rented hashpower | Contract price and duration, delivery, refund conditions, pool charges, and switching constraints. |
Capital & operating costs
Owned hardware and rented hashpower have different economic commitments. A model must identify which costs continue during idle time and which change with actual operation. A prepaid rental is not equivalent to freely switching owned equipment on and off.
For protocol accounting, keep first-stage rental expenses separate from the second-stage allocation basis. Do not subtract an already funded rental again from the 30 / 60 / 10 split. A full operating-profit report may include the expense once in its own accounting view.
Risk & sensitivity
- Use multiple difficulty, delivery, payout, and settlement-price scenarios rather than one favorable point estimate.
- Expose zero-block outcomes, stale data, missing histories, pool fees, conversion costs, and unavailable liquidity.
- Separate forecast uncertainty from data-quality uncertainty.
- Validate units and reward assumptions against the target network before comparing models.
Decision-support roadmap
A future engine would compare defined operating choices, document uncertainty, and provide an auditable explanation. Automatic execution is a separate planned capability.
- Specify the model and preserve its versioned inputs.
- Calibrate on historical data and evaluate on held-out periods.
- Compare against simple baselines and publish errors and limitations.
- Run observed-only monitoring before any execution proposal.