Multi-Dimensional Risk Profiling
Implementation Details
Class Names
MultiDimensionalRiskProfiling
MultiDimensionalRiskQueryParams
MultiDimensionalRiskData
Import Statement
pythonCopyEditfrom satoshi_terminal.models.multi_dimensional_risk_profiling import (
MultiDimensionalRiskProfiling,
MultiDimensionalRiskQueryParams,
MultiDimensionalRiskData,
)Parameters
protocol_name
str
Name of the protocol to analyze (e.g., "Uniswap," "Aave").
None
False
risk_categories
List[str]
Specific risk categories to focus on (e.g., "Technical," "Market," "Governance").
None
True
time_horizon
int
Forward-looking time horizon for risk projection (in days).
30
True
dependency_depth
int
Depth of dependency mapping for protocol relationships (e.g., nested contracts).
3
True
Data
protocol_name
str
Name of the protocol analyzed.
risk_layer
str
Specific layer of risk (e.g., "Market," "Technical," "Governance").
risk_score
float
Composite risk score (0-100) for the specific risk layer.
dependency_map
dict
Nested mapping of protocol dependencies and their associated risks.
cascading_risk_amplification
float
Projected amplification of risks through interdependencies.
timestamp
datetime
Timestamp of the analysis.
Key Features
Layered Risk Assessment: Break down protocol risks into multiple layers, including technical (smart contract vulnerabilities), market (liquidity and volatility), and governance (centralization risks).
Dependency Risk Mapping: Identify critical interdependencies between protocols, such as lending platforms relying on external price oracles.
Cascading Risk Simulation: Model how failures in one component (e.g., oracle failure) can propagate to dependent protocols.
Dynamic Weight Adjustments: Allow users to adjust the weight of each risk category based on their strategy or exposure.
Custom Risk Models: Support for user-defined risk parameters and thresholds tailored to specific protocols or assets.
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