risk-assessment

name	risk-assessment
description	Assess portfolio risk using volatility, drawdown, and other risk metrics. Use when evaluating the risk characteristics of a portfolio or comparing risk profiles.

Risk Assessment Skill

Key Risk Metrics

Metric	Description	Calculation	Good Value
Volatility	Annualized standard deviation	σ × √252	< 15% conservative, < 25% aggressive
Max Drawdown	Worst peak-to-trough decline	min(cumulative returns)	> -20% conservative
Sharpe Ratio	Risk-adjusted return	(R - Rf) / σ	> 1.0 good, > 2.0 excellent
Sortino Ratio	Downside risk-adjusted return	(R - Rf) / σ_down	> 1.5 good
Value at Risk (VaR)	Maximum expected loss at confidence	Percentile of returns	95% VaR < 2% daily

Risk Assessment Process

Step 1: Analyze Volatility

import numpy as np

returns = prices.pct_change().dropna()
volatility = returns.std() * np.sqrt(252)  # Annualized
print(f"Annualized Volatility: {volatility:.2%}")

Step 2: Calculate Maximum Drawdown

def calculate_max_drawdown(returns):
    cumulative = (1 + returns).cumprod()
    rolling_max = cumulative.expanding().max()
    drawdown = (cumulative - rolling_max) / rolling_max
    return drawdown.min()

max_dd = calculate_max_drawdown(portfolio_returns)
print(f"Maximum Drawdown: {max_dd:.2%}")

Step 3: Compute Sharpe Ratio

risk_free_rate = 0.02  # 2% annual
excess_return = portfolio_return - risk_free_rate
sharpe = excess_return / volatility
print(f"Sharpe Ratio: {sharpe:.3f}")

Risk Tolerance Mapping

Risk Tolerance	Target Volatility	Max Drawdown Tolerance	Suitable Universe
Low	< 10%	> -15%	conservative
Medium	10-18%	> -25%	global_diversified
High	18-30%	> -40%	us_tech, aggressive

Risk Flags

Warn the investor if:

• Volatility exceeds their stated tolerance by > 20%
• Max drawdown exceeds tolerance threshold
• Sharpe ratio < 0.5 (poor risk-adjusted returns)
• Single position exceeds max_position constraint