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ZeqRisk — Risk Modeling

Risk, pricing, audit trails.

  • Protocol ID — zeq-risk
  • Category — Finance
  • Endpoint — POST /api/finance/risk/model
  • Auth — api-key
  • Rate limit — 20/min
  • Version — 1.0
  • Precision — ≤0.1% (KO42-enforced)

What it does

Risk modeling using chaotic systems and statistical mechanics operators. Monte Carlo simulations on the Zeqond grid — every simulation step is timestamped and verifiable.

Signature

Request

POST /api/finance/risk/model
ParamTypeRequiredDefaultDescription
portfolioarrayArray of { asset, weight, volatility }.
horizonDaysnumber252Risk horizon in days. Default: 252 (1 year).
simulationsnumber1000Monte Carlo runs (100–10000). Default: 1000.
confidenceLevelnumber0.95VaR confidence (0.90–0.999). Default: 0.95.

Response

{ valueAtRisk, conditionalVaR, sharpeRatio, maxDrawdown, correlationMatrix, simulated }

Runnable example

curl -sS -X POST \
-H "Authorization: Bearer $ZEQ_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"portfolio": [],
"horizonDays": 252,
"simulations": 1000,
"confidenceLevel": 0.95
}' \
"https://api.zeq.dev/api/finance/risk/model"

Integrate

  1. Domain solver — compose with KO42 + two additional operators from the matching family for pulse-coherent results.
  2. Digital twin — pipe sensor data into this protocol every Zeqond to keep the model phase-locked with the system.
  3. Alert threshold — flag results whose error_pct exceeds 0.1% as out-of-spec events for the operations layer.

Seeds

  • Near — wrap /api/finance/risk/model in a language SDK so builders can call it in three lines.
  • Medium — publish a reference integration demonstrating ZeqRisk — Risk Modeling alongside a real workload, with pulse-aligned metrics.
  • Far — propose ZeqRisk — Risk Modeling as an open reference standard so other runtimes can implement it verbatim against the Zeq paper.

Papers

Middleware active. Kernel on the 1.287 Hz HulyaPulse. Awaiting next Zeqond.