# Risk Management

Pyron’s risk engine is built for precision, designed to support fast-moving capital while protecting the system from volatility, illiquidity, and liquidation spirals.Instead of relying on static configurations, Pyron continuously evaluates liquidator capacity, market depth, and price volatility, the three pillars of modern DeFi risk management.

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### **The Two Pillars** <a href="#the-two-pillars" id="the-two-pillars"></a>

**1. Liquidator Capacity:**\
\
**-** Pyron models how fast and effectively liquidators can act during market turbulence.This includes:

* Capital available to each liquidator
* Historical execution speed
* Adjustments for network congestion
* Conservative fallback estimates to ensure safe coverage

**Execution Capacity = Available Capital / Estimated Liquidation Time2. Market Depth & Recovery**\
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**-** Pyron estimates how much of an asset can be sold without causing major price impact — and how long it takes for liquidity to recover afterward.This includes:

* 90-day historical price and liquidity data
* Multi-layered smoothing (SMA, EMA, TMA) across short and long windows
* Variability discounting to penalize unstable markets

**Final output: A conservative, protocol-actionable risk parameter.**

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### Real-Time Risk Adjustments <a href="#real-time-risk-adjustments" id="real-time-risk-adjustments"></a>

Risk isn’t static, and neither is Pyron’s engine.All inputs feed into on-chain parameters that control collateral requirements, liquidation triggers, and borrow limits. This creates a feedback loop between real market behavior and protocol safety.Why this matters:

* In volatile markets, Pyron becomes stricter
* In stable conditions, it remains capital-efficient
* Liquidator assumptions are always conservative by default, not reactive after the fact

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### Infrastructure Fit <a href="#infrastructure-fit" id="infrastructure-fit"></a>

Fogo’s real-time, low-latency architecture is a key piece of this system.Speed matters, not just for user experience, but for safety.Without fast block confirmations and cheap fees, none of this dynamic modeling would be actionable.With Fogo, it is.[<br>](https://app.gitbook.com/o/ml7YGPQsgMcO7pvFx0vO/s/uSg6qshM531ZcXUVzd1I/health-factor)


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