AgentSkillsCN

Validate Credit Exposure

验证信用风险敞口

SKILL.md

Skill: Validate Credit Exposure

Domain

financial_services

Description

Validates counterparty credit exposure calculations including potential future exposure, credit limits, and collateral adequacy for risk management.

Tags

credit-risk, exposure, counterparty, collateral, limits, trading

Use Cases

  • •Credit limit monitoring
  • •PFE calculation validation
  • •Collateral adequacy check
  • •Exposure aggregation

Proprietary Business Rules

Rule 1: Exposure Calculation

Mark-to-market plus potential future exposure.

Rule 2: Netting Validation

Validation of netting agreement application.

Rule 3: Collateral Coverage

Assessment of collateral against exposure.

Rule 4: Limit Breach Detection

Identification of credit limit breaches.

Input Parameters

  • •counterparty_id (string): Counterparty identifier
  • •positions (list): Current positions with counterparty
  • •market_data (dict): Current market prices
  • •netting_agreements (dict): Netting arrangement details
  • •collateral (dict): Posted collateral information
  • •credit_limits (dict): Approved credit limits

Output

  • •current_exposure (float): Current credit exposure
  • •potential_exposure (float): Potential future exposure
  • •collateral_coverage (dict): Collateral adequacy
  • •limit_utilization (dict): Limit usage analysis
  • •breach_alerts (list): Any limit breaches

Implementation

The validation logic is implemented in exposure_validator.py and references data from credit_parameters.json.

Usage Example

python
from exposure_validator import validate_exposure

result = validate_exposure(
    counterparty_id="CPTY-001",
    positions=[{"type": "swap", "notional": 10000000, "mtm": 250000}],
    market_data={"rates": {"USD": 0.05}, "fx": {"EURUSD": 1.08}},
    netting_agreements={"type": "ISDA", "enforceable": True},
    collateral={"cash": 100000, "securities": 50000},
    credit_limits={"total": 5000000, "unsecured": 2000000}
)

print(f"Current Exposure: ${result['current_exposure']:,.0f}")

Test Execution

python
from exposure_validator import validate_exposure

result = validate_exposure(
    counterparty_id=input_data.get('counterparty_id'),
    positions=input_data.get('positions', []),
    market_data=input_data.get('market_data', {}),
    netting_agreements=input_data.get('netting_agreements', {}),
    collateral=input_data.get('collateral', {}),
    credit_limits=input_data.get('credit_limits', {})
)