Tokenomic Stress Testing
Implementation Details
Class Names
TokenomicStressTesting
TokenomicStressQueryParams
TokenomicStressData
Import Statement
pythonCopyEditfrom satoshi_terminal.models.tokenomic_stress_testing import (
TokenomicStressTesting,
TokenomicStressQueryParams,
TokenomicStressData,
)Parameters
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
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
RegulatoryRiskMapping
RegulatoryRiskQueryParams
RegulatoryRiskData
Import Statement
Parameters
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
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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