Quantitative and Industrial risk assessment with VaR, drawdown analysis, SLA breach forecasting, and process reliability metrics.
Perform quantitative and qualitative risk analysis. This skill covers both financial risk (VaR, Sharpe) and Industrial risk (SLA breaches, throughput bottlenecks).
RiskReport with actionable mitigations.Compute standard risk metrics for time-series data.
import numpy as np
import pandas as pd
from scipy import stats
def compute_var(returns: pd.Series, confidence: float = 0.95) -> float:
"""Compute 95% Value-at-Risk."""
return -returns.quantile(1 - confidence)
def compute_sharpe(returns: pd.Series, rf=0.0) -> float:
"""Annualized Sharpe Ratio (252 periods)."""
return (returns.mean() - rf/252) / returns.std() * np.sqrt(252)
Analyze process data for potential SLA breaches or bottlenecks.
def analyze_sla_risk(
actual_uph: float,
target_uph: float,
remaining_volume: int,
remaining_hours: float
) -> dict:
"""Assess risk of failing to meet shipment deadline."""
needed_uph = remaining_volume / remaining_hours
gap = needed_uph - actual_uph
risk_level = "High" if gap > actual_uph * 0.2 else "Low"
return {
"risk_level": risk_level,
"needed_uph": needed_uph,
"current_gap": gap,
"mitigation": "Increase labor allocation" if risk_level == "High" else "Monitor"
}
Use Z-scores to identify extreme operational risks.
def detect_operational_outliers(df, column='processing_time'):
z_scores = stats.zscore(df[column])
critical_risks = df[np.abs(z_scores) > 3]
return critical_risks
.agent/knowledge/risk-management.json.agent/knowledge/risk-management-patterns.json.agent/knowledge/quantitative-theory.jsonUse this when requested to perform "Risk Analysis", "SLA Verification", or "Stress Testing".