# Tokenomic Stress Testing

**Implementation Details**

**Class Names**

| Model Name             | Parameters Class           | Data Class          |
| ---------------------- | -------------------------- | ------------------- |
| TokenomicStressTesting | TokenomicStressQueryParams | TokenomicStressData |

**Import Statement**

```python
pythonCopyEditfrom satoshi_terminal.models.tokenomic_stress_testing import (
    TokenomicStressTesting,
    TokenomicStressQueryParams,
    TokenomicStressData,
)
```

***

**Parameters**

| Name                     | Type                    | Description                                                                      | Default | Optional |
| ------------------------ | ----------------------- | -------------------------------------------------------------------------------- | ------- | -------- |
| token\_symbol            | Union\[str, List\[str]] | Token symbol(s) to stress test (e.g., BTC, ETH, SOL).                            | None    | True     |
| stress\_event\_type      | str                     | Event type (e.g., "Supply Shock," "Market Illiquidity," "Governance Failure").   | None    | True     |
| projected\_time\_horizon | int                     | Time period (in days) to model the stress impacts.                               | 30      | True     |
| impact\_layer\_focus     | List\[str]              | Specific tokenomic layers to analyze (e.g., "Liquidity," "Holder Distribution"). | None    | True     |

***

**Data**

| Name                        | Type     | Description                                                           |
| --------------------------- | -------- | --------------------------------------------------------------------- |
| token\_symbol               | str      | Symbol of the token analyzed.                                         |
| simulated\_price            | float    | Predicted token price following the stress event.                     |
| liquidity\_drop\_percent    | float    | Estimated percentage of liquidity reduction during the stress period. |
| holder\_distribution\_delta | dict     | Shift in token distribution across small, medium, and whale holders.  |
| governance\_impact\_score   | float    | Impact on governance metrics, where applicable.                       |
| timestamp                   | datetime | Timestamp of the analysis execution.                                  |

***

**Key Features**

* **Multi-Layer Impact Simulation:** Evaluate token behavior across liquidity, governance, and market cap layers.
* **Advanced Holder Behavior Modeling:** Predict redistribution among holder classes after stress events.
* **Scenario Customization:** Design and test token-specific stress scenarios, such as early unlocking of staked tokens or major whale exits.
* **Risk Amplification Metrics:** Highlight cascading effects of correlated stress events across ecosystems.

***

#### Feature Documentation: Real-Time Regulatory Risk Map

***

**Implementation Details**

**Class Names**

| Model Name            | Parameters Class          | Data Class         |
| --------------------- | ------------------------- | ------------------ |
| RegulatoryRiskMapping | RegulatoryRiskQueryParams | RegulatoryRiskData |

**Import Statement**

```python
pythonCopyEditfrom satoshi_terminal.models.regulatory_risk_map import (
    RegulatoryRiskMapping,
    RegulatoryRiskQueryParams,
    RegulatoryRiskData,
)
```

***

**Parameters**

| Name              | Type                    | Description                                                                        | Default | Optional |
| ----------------- | ----------------------- | ---------------------------------------------------------------------------------- | ------- | -------- |
| region            | Union\[str, List\[str]] | Geographic region(s) to monitor for regulatory changes (e.g., US, EU, Asia).       | None    | True     |
| asset\_class      | Union\[str, List\[str]] | Type(s) of assets for regulatory analysis (e.g., "Utility Tokens," "Stablecoins"). | None    | True     |
| compliance\_focus | List\[str]              | Specific compliance areas to assess (e.g., "KYC Requirements," "AML Guidelines").  | None    | True     |
| time\_horizon     | int                     | Forward-looking analysis period (in months).                                       | 6       | True     |

***

**Data**

| Name                    | Type        | Description                                                                          |
| ----------------------- | ----------- | ------------------------------------------------------------------------------------ |
| region                  | str         | Geographic region assessed for risk.                                                 |
| regulatory\_score       | float       | Composite risk score (0-100) based on regulatory environment and compliance outlook. |
| recent\_policy\_changes | List\[dict] | Details of recent policy updates impacting assets in the region.                     |
| asset\_class            | str         | Type of asset analyzed for regulatory impact.                                        |
| timestamp               | datetime    | Timestamp of the analysis execution.                                                 |

***

**Key Features**

* **Dynamic Region Tracking:** Monitor changing regulations across multiple jurisdictions in real time.
* **Asset-Specific Compliance Scoring:** Provide granular compliance insights for different asset classes.
* **Forward-Looking Risk Modeling:** Simulate future regulatory scenarios based on policy trends and geopolitical factors.
* **Regulatory Dependency Mapping:** Highlight interdependencies between different regional compliance landscapes (e.g., EU-US alignment).
* **Alert Triggers:** Notify users of significant changes, such as a region banning or endorsing specific crypto activities.


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