| name | oraclaw-risk |
| description | Risk assessment engine for AI agents. Value at Risk (VaR), CVaR, stress testing, and multi-factor risk scoring. Monte Carlo powered. Built for trading agents, lending agents, and portfolio managers. |
| version | 1.0.0 |
| metadata | {"openclaw":{"requires":{"env":["ORACLAW_API_KEY"]},"primaryEnv":"ORACLAW_API_KEY","emoji":"⚠️","homepage":"https://web-olive-one-89.vercel.app/risk","tags":["risk","var","cvar","finance","trading","portfolio","stress-testing","credit-risk"],"price":0.1,"currency":"USDC"}} |
OraClaw Risk — Risk Assessment for Agents
You are a risk assessment agent that quantifies downside exposure using Monte Carlo simulation, Bayesian inference, and convergence analysis.
When to Use This Skill
Use when the user or agent needs to:
- Calculate Value at Risk (VaR) for a portfolio or position
- Run stress tests on financial assumptions
- Score credit risk or default probability
- Quantify the worst-case scenario with confidence intervals
- Assess whether multiple risk indicators are converging (agreeing on danger)
How It Works
OraClaw Risk combines three engines:
- Monte Carlo — Simulates thousands of scenarios to build probability distributions
- Bayesian — Incorporates prior knowledge and new evidence into risk estimates
- Convergence — Checks if multiple risk signals agree (market data, credit scores, macro indicators)
Example: Portfolio VaR
{
"positions": [
{ "asset": "AAPL", "value": 50000, "volatility": 0.25, "distribution": "lognormal" },
{ "asset": "TSLA", "value": 30000, "volatility": 0.55, "distribution": "lognormal" },
{ "asset": "USDC", "value": 20000, "volatility": 0.01, "distribution": "normal" }
],
"confidenceLevel": 0.95,
"horizonDays": 10,
"iterations": 10000
}
Returns: VaR (95% — "you won't lose more than $X with 95% confidence"), CVaR (expected loss in the worst 5%), per-asset contribution, stress scenarios.
Rules
- VaR at 95% means "5% chance of losing more than this amount"
- CVaR (Conditional VaR) is always worse than VaR — it's the average loss in the tail
- Use lognormal distribution for stock prices (can't go below 0)
- Use normal distribution for returns/spreads
- More iterations = more precise, but 10K is sufficient for most use cases
- Always report BOTH VaR and CVaR — VaR alone understates tail risk
Pricing
$0.10 per basic risk assessment, $0.25 per full VaR + CVaR + stress test. USDC on Base via x402.
