Assessing Risks

Risk Assessment & Prognosis

Perform quantitative and qualitative risk analysis. This skill covers both financial risk (VaR, Sharpe) and Industrial risk (SLA breaches, throughput bottlenecks).

Process

1. Review Context: Determine if the risk is Financial, Operational, or Technical.
2. Select Methodology: Use statistical methods for large datasets; use heuristic analysis for process risks.
3. Generate Report: Produce a structured RiskReport with actionable mitigations.

Step 1: Financial Risk Metrics (Quantitative)

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)

Step 2: Industrial Risk (Operational)

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"
    }

Step 3: Predictive Anomaly Detection

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

Best Practices

• Contextualize: A 5% dip is "High Risk" for an equity fund but "Normal Variance" for picker UPH.
• Duality: Always report Risk Magnitude alongside Probability.
• Actionability: Every identified risk MUST have a corresponding mitigation strategy.

References

• .agent/knowledge/risk-management.json
• .agent/knowledge/risk-management-patterns.json
• .agent/knowledge/quantitative-theory.json

When to Use

Use this when requested to perform "Risk Analysis", "SLA Verification", or "Stress Testing".

Prerequisites

• Access to relevant project documentation
• Environmental awareness of the target stack