> For the complete documentation index, see [llms.txt](https://helios-finance.gitbook.io/helios-finance/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://helios-finance.gitbook.io/helios-finance/risk-framework/quickstart-1.md).

# Adaptive Risk Optimization (Mempool- & Volatility-Aware LTVs)

Helios’s risk engine is **fully algorithmic**: every Bitcoin block (\~10 min) it re-solves a convex optimization problem that chooses current **max-LTV** and **liquidation-bonus (LP)** values. No human flips a switch, no opaque AI—just a provably optimal LP model anchored to two on-chain data feeds:

| Input feed                                                                                                                             | Sample frequency | Why it matters                                               |
| -------------------------------------------------------------------------------------------------------------------------------------- | ---------------- | ------------------------------------------------------------ |
| **Mempool congestion (M)** – median sats/[vB](https://learnmeabitcoin.com/technical/transaction/size/#vbytes) for next-block inclusion | Every block      | Longer settlement time → greater liquidation delay risk      |
| **Realized BTC volatility (σ)** – 1 h and 24 h windows                                                                                 | Every block      | Larger price swings → larger potential collateral draw-downs |

**Formal objective**

Minimize **Expected Bad Debt (EBD)** subject to the hard constraint

<figure><img src="/files/z67XiUJCu7fAOWWJb2cT" alt=""><figcaption></figcaption></figure>

for every active loan, where![](/files/JRyfQj2evYGrDmRTAV7n) is collateral value after a worst-case liquidation delay. This caps the protocol-wide probability of under-collateralisation at **< 0.1 %**, equivalent to a 99.9 % one-block VaR.

If rising M or σ would push EBD above that ceiling, the solver **lowers max-LTV** and/or **raises LP** until the inequality is satisfied.

**Control loop & publication cadence**

1. **Oracle pull** – Fetch M, σ, order-book depth.
2. **Optimize (off-chain)** – Linear program returns LTV\*, LP\*.
3. **On-chain commit** – The algorithm writes the new limits into the contract’s state, and the updated rules apply automatically from the next block.
4. **Guard-band** – LTV cannot move >2 percentage points per block in calm markets, guaranteeing borrowers a minimum reaction window.

**Why this matters**

* **Predictable safety bar** – The 0.1 % insolvency target is auditable and stress-tested; every new block re-validates it.
* **Market-aligned incentives** – Higher LP during congestion guarantees liquidator profitability despite higher fees, ensuring prompt liquidations.
* **Capital efficiency** – In low-risk regimes lenders enjoy up to 80 % utilization while the solvency bound is still met.
* **Instant throttling** – During 2020-style mempool jams the engine would have cut LTV to 50 % within two blocks, preventing MakerDAO-type shortfalls.

Helios thus couples **Bitcoin settlement finality** with a risk framework that is as transparent as a margin rulebook and as fast as the chain itself.

All these adjustments happen algorithmically and frequently (potentially every block or every few blocks) as conditions evolve. This is a major innovation – rather than waiting for a governance vote after a market crash to adjust risk parameters (which is what happened in some DeFi incidents), Helios acts *preemptively and continuously*. It’s as if Helios has an embedded risk officer tweaking settings in real-time to keep the system safe yet efficient. This dynamic approach aims to maximize capital efficiency **without sacrificing solvency**.

From a due diligence perspective, this adaptive model is valuable because it reduces the reliance on manual intervention and can be stress-tested. It’s transparent (the rules and inputs are known) and can respond faster than human governance to emerging risks. For institutional partners, the fact that Helios is self-regulating to maintain a healthy risk profile provides confidence that extreme scenarios are being handled in a principled way.