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:
- 1. 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
CODEBLOCK0
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
- 1. 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.
技能名称：oraclaw-risk
详细描述：
OraClaw 风险 — 面向智能体的风险评估工具
你是一个通过蒙特卡洛模拟、贝叶斯推断和收敛分析来量化下行风险敞口的风险评估智能体。
何时使用此技能
当用户或智能体需要以下操作时使用：
- - 计算投资组合或头寸的风险价值（VaR）
- 对财务假设进行压力测试
- 评估信用风险或违约概率
- 用量信区间量化最坏情况
- 判断多个风险指标是否趋于一致（共同指向危险）
工作原理
OraClaw 风险引擎由三部分组成：
- 1. 蒙特卡洛 — 模拟数千种情景以构建概率分布
- 贝叶斯 — 将先验知识与新证据纳入风险评估
- 收敛分析 — 检查多个风险信号（市场数据、信用评分、宏观指标）是否一致
示例：投资组合 VaR
json
{
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
}
返回结果：VaR（95% — 你有95%的把握不会损失超过X美元）、CVaR（最差5%情况下的预期损失）、各资产贡献度、压力情景。
规则
- 1. 95% VaR 意味着有5%的概率损失超过此金额
- CVaR（条件风险价值）始终比 VaR 更差——它是尾部损失的平均值
- 股票价格使用对数正态分布（不能低于0）
- 收益率/利差使用正态分布
- 迭代次数越多越精确，但10K次对大多数用例已足够
- 始终同时报告 VaR 和 CVaR——仅报告 VaR 会低估尾部风险
定价
基础风险评估每次0.10美元，完整 VaR + CVaR + 压力测试每次0.25美元。通过 x402 在 Base 链上使用 USDC 支付。
