| name | stress-test |
| description | Portfolio stress test. Receives a list of holdings and identifies portfolio weaknesses through shock sensitivity, scenario analysis, and causal chain analysis. |
| argument-hint | [symbol list] [--scenario SCENARIO] e.g.: 7203.T,AAPL,D05.SI --scenario triple-meltdown |
| allowed-tools | Bash(python3 *) |
Portfolio Stress Test Skill
Parse $ARGUMENTS to determine the portfolio symbol list and scenario, then run the following command.
Execution Command
python3 /Users/kikuchihiroyuki/stock-skills/.claude/skills/stress-test/scripts/run_stress_test.py --portfolio <symbols> [--scenario <scenario>] [--weights <weights>]
Natural Language Routing
For natural language → skill selection, see .claude/rules/intent-routing.md.
Argument Parsing Rules
portfolio (symbol list — required)
Extract a comma-separated symbol list from user input. Convert space-separated input to comma-separated.
| User Input Example | --portfolio Value |
|---|
7203.T,AAPL,D05.SI | 7203.T,AAPL,D05.SI |
7203.T AAPL D05.SI | 7203.T,AAPL,D05.SI |
Toyota Apple | Convert to corresponding tickers first |
weights (holding ratios — optional)
Comma-separated ratio list matching the number of symbols. Defaults to equal weights (1/N per symbol).
| User Input Example | --weights Value |
|---|
0.5,0.3,0.2 | 0.5,0.3,0.2 |
50%,30%,20% | 0.5,0.3,0.2 (convert percentages to decimals) |
| omitted | Equal weight 1/N per symbol |
Scenario List
| Scenario | Description |
|---|
| triple-meltdown | Equities, FX, and bonds fall simultaneously. Stress across all asset classes |
| usd-jpy-surge | Sharp JPY depreciation. Rising import costs; overseas assets appreciate in JPY terms |
| us-recession | US economic downturn. Global demand contraction, risk-off |
| boj-rate-hike | Rising Japanese interest rates. Banks up, growth stocks and REITs down |
| us-china-conflict | Escalating trade friction. Supply chain disruption; hit to semiconductors and manufacturing |
| inflation-resurgence | Rising prices again. Real purchasing power declines; rate hike expectations |
| tech-crash | NASDAQ -30%. AI expectations collapse; tech stocks hit hard, flight to quality |
| jpy-appreciation | USD/JPY -20 yen. Foreign asset JPY-denominated values fall; hit to exporters |
| custom | User-specified scenario interpreted from natural language |
Output Format (10-Step Pipeline)
Present results in the following structured steps.
Step 1: Portfolio Overview
- Symbol list (symbol, name, sector, weight)
- Total market cap (estimated)
Step 2: Concentration Analysis
- Sector HHI / Region HHI / Currency HHI
- Identify highest concentration axis
- Risk level judgment (diversified / somewhat concentrated / dangerously concentrated)
Step 3: Shock Sensitivity Scores
- Assessment of each stock's beta, financial health, and valuation resilience
- Per-symbol shock sensitivity score (0–100)
Step 4: Scenario Definition
- Applied scenario name and description
- Macro variable changes (assumed movements in interest rates, FX, equities)
Step 5: Per-Symbol Impact Estimates
- Estimated loss rate per symbol
- Application of concentration multiplier
- Portfolio-weighted impact
Step 6: Portfolio-Wide Impact
- Estimated portfolio-wide loss rate
- Largest loss contributor
Step 6b: Correlation Analysis (KIK-352)
- Cross-symbol correlation matrix (Pearson correlation, 1-year daily returns)
- High-correlation pairs detection (|r| >= 0.7)
- Factor decomposition: regress each symbol against macro variables (USD/JPY, Nikkei 225, S&P 500, crude oil, US 10-year yield)
- LLM interpretation: For residual correlations not explained by factor regression, use domain knowledge to infer causes (e.g., supply chain dependencies, shared customer base). Clearly separate "confirmed factors (statistical)" from "inferred factors (estimated)"
Step 6c: VaR (Historical Data-Based Risk Metrics) (KIK-352)
- Calculate portfolio weighted returns from 1-year daily return history
- 95% VaR / 99% VaR (daily and monthly)
- Explain the difference from the scenario analysis (tail risk) in the stress test
Step 7: Causal Chain Analysis
- Explanation of cascading effects when the scenario occurs
- Cross-sector propagation paths
Step 8: Overall Assessment + Recommended Actions (KIK-352)
- Specific risk mitigation proposals (rule-based auto-generated + Claude supplement)
- Recommendations integrating concentration, correlation, VaR, and stress test results
- Hedge candidates (symbols and sectors)
- LLM supplement: In addition to rule-based recommendations, Claude should suggest sectors not in the portfolio and propose diversification targets informed by qualitative correlation causes
Execution Examples
python3 .../run_stress_test.py --portfolio 7203.T,AAPL,D05.SI
python3 .../run_stress_test.py --portfolio 7203.T,9984.T,6758.T --scenario triple-meltdown
python3 .../run_stress_test.py --portfolio 7203.T,AAPL,D05.SI --weights 0.5,0.3,0.2
python3 .../run_stress_test.py --portfolio 7203.T,AAPL --scenario "semiconductor supply chain collapse"
Knowledge Integration Rules (KIK-466)
When get_context.py output contains the following, integrate with stress test results:
- Previous stress test (StressTest): Compare with previous scenario and result. "Previous tech crash scenario: -18% → Current: -15% (improved: lower tech weighting)"
- Concern notes: If a flagged symbol takes heavy damage in the stress test, "concern note matches → consider countermeasures"
- Investment notes: Reference hedge strategy notes if available. "Previous lesson note: insufficient JPY hedge → still weak in USD surge scenario"
