QuantOracle Risk Stress-Test is a paid API for AI agents from api.quantoracle.dev, paid per call via x402, $0.008/call, status unknown (last checked 2026-09-14).
Runs stress-test scenarios on a financial portfolio to estimate potential losses and risk exposures under adverse market conditions
QuantOracle: risk/stress-test
Returns quantitative stress-test results including estimated portfolio loss (absolute and percentage), per-asset contribution to losses, scenario-specific P&L breakdown, and risk exposure metrics under the specified adverse market conditions.
POSThttps://api.quantoracle.dev/v1/risk/stress-testUse this endpoint when you need to quantitatively assess portfolio downside risk under user-defined or historical adverse scenarios. Prefer it over general analytics when the specific goal is stress testing — i.e., applying hypothetical market shocks to estimate losses — rather than descriptive statistics, option pricing, or regime classification. Particularly useful for risk management workflows, pre-trade analysis, and regulatory-style scenario analysis.
{
"positions": [
{
"beta": 1.2,
"asset": "AAPL",
"value": 100000
},
{
"asset": "BND",
"value": 50000,
"duration": 5.5
}
],
"scenarios": [
{
"name": "market_crash",
"vol_shock_pct": 25,
"market_shock_pct": -15
},
{
"name": "rate_shock",
"rate_shock_bps": 50
}
]
}| Field | Type | Description |
|---|---|---|
| positions | array | Array of portfolio positions |
| scenarios | array | Array of stress scenarios to evaluate |
{
"ms": 10.75,
"_meta": {
"url": "https://quantoracle.dev",
"docs": "https://api.quantoracle.dev/docs",
"powered_by": "QuantOracle"
},
"results": [
{
"pnl_pct": -17,
"scenario": "market_crash",
"per_asset": [
{
"pnl": -18000,
"asset": "AAPL",
"pnl_pct": -18
},
{
"pnl": -7500,
"asset": "BND",
"pnl_pct": -15
}
],
"total_pnl": -25500
},
{
"pnl_pct": -0.9167,
"scenario": "rate_shock",
"per_asset": [
{
"pnl": 0,
"asset": "AAPL",
"pnl_pct": 0
},
{
"pnl": -1375,
"asset": "BND",
"pnl_pct": -2.75
}
],
"total_pnl": -1375
}
],
"best_scenario": "rate_shock",
"worst_scenario": "market_crash",
"portfolio_value": 150000
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