class RecommendationDisclosure:
    # 1. Methodology Disclosure (REQUIRED)
    methodology_disclosure: str  # How the recommendation was generated

    # 2. Data Sources (REQUIRED)
    data_sources: list[str]      # All data sources with timestamps
    model_version: str           # ML model version used
    training_date: str           # Last model training date

    # 3. Risk Warnings (REQUIRED)
    risk_factors: list[str]      # Specific risks for this recommendation
    volatility_warning: bool     # If stock is high volatility
    liquidity_warning: bool      # If low trading volume

    # 4. Performance Disclaimer (REQUIRED)
    disclaimer: str = "Past performance does not guarantee future results."

    # 5. Confidence Level (REQUIRED)
    confidence_score: float      # 0.0 - 1.0
    uncertainty_disclosure: str  # What the model doesn't know

□ Methodology Disclosure
  - [ ] Algorithm description is included
  - [ ] Data sources are listed with timestamps
  - [ ] Model version is documented

□ Risk Warnings
  - [ ] "Past performance" disclaimer present
  - [ ] Stock-specific risk factors listed
  - [ ] Volatility/liquidity warnings if applicable
  - [ ] Sector concentration alert if needed

□ Fair Presentation
  - [ ] Balanced view of risks AND opportunities
  - [ ] No misleading performance claims
  - [ ] Clear distinction between historical and projected

□ Audit Trail
  - [ ] Recommendation ID generated
  - [ ] All inputs logged
  - [ ] Timestamp recorded
  - [ ] User interaction tracked

□ Limitations Statement
  - [ ] Scope of analysis disclosed
  - [ ] Data freshness limitations noted
  - [ ] Model confidence levels shown

# Every recommendation must be logged for SEC compliance
# Retention: 5+ years minimum

audit_log = {
    "event_id": str(uuid.uuid4()),
    "timestamp": datetime.utcnow().isoformat(),
    "event_type": "recommendation.generated",

    # Inputs
    "ticker": ticker,
    "input_data": {
        "fundamental_metrics": {...},
        "technical_indicators": {...},
        "sentiment_scores": {...},
        "data_timestamps": {...},
    },

    # Model Info
    "model_version": "v2.3.1",
    "model_training_date": "2025-01-15",

    # Output
    "recommendation": {
        "action": "BUY",
        "confidence": 0.78,
        "target_price": 155.00,
        "thesis": "...",
        "risk_factors": [...],
    },

    # Compliance
    "disclosures_included": True,
    "compliance_check_passed": True,
}

# Validate a recommendation object has all required disclosures
python -c "
from backend.services.compliance import SECComplianceValidator

validator = SECComplianceValidator()
result = validator.validate_recommendation(recommendation)

if not result.is_compliant:
    print('COMPLIANCE FAILURE:')
    for issue in result.issues:
        print(f'  - {issue}')
else:
    print('Recommendation is SEC compliant')
"

# Check audit trail completeness
python -c "
from backend.services.audit import AuditService

audit = AuditService()
gaps = audit.find_compliance_gaps(
    start_date='2025-01-01',
    end_date='2025-01-25'
)
print(f'Found {len(gaps)} audit trail gaps')
"

# Always wrap recommendation generation with compliance check
from backend.services.compliance import ensure_sec_compliance

@ensure_sec_compliance
async def generate_recommendation(ticker: str) -> Recommendation:
    # ... analysis logic ...
    return recommendation  # Decorator validates before returning