QuantOracle

The quantitative computation API for autonomous financial agents

63 deterministic, citation-verified calculators + 10 composite workflows. 1,000 free calls/day. Pay-per-call on Base or Solana.

Calculators  |  CLI  |  MCP Server  |  x402 Payments  |  Free Tier  |  All Endpoints  |  Integrations

Try it without writing code

12 free interactive calculators backed by the same API are live at quantoracle.dev — no signup, no API key:

• Black-Scholes Option Pricing — call/put price + full Greeks
• American Option (Binomial Tree) — early exercise + dividends
• Options Profit Calculator — multi-leg payoff diagrams
• Implied Volatility — Newton-Raphson IV solver
• Monte Carlo Simulation — portfolio + retirement scenarios
• Kelly Criterion — full / half / quarter-Kelly sizing
• Position Size — fixed-fractional risk
• Value at Risk (VaR) — parametric VaR + CVaR
• Sharpe Ratio — with 95% confidence interval
• CAGR — compound annual growth rate + projections
• Crypto Liquidation Price — long/short, any leverage
• Impermanent Loss — Uniswap v2 + v3

Why QuantOracle?

Every financial agent needs math. QuantOracle is that math.

• 63 pure calculators across options, derivatives, risk, portfolio, statistics, crypto/DeFi, FX/macro, and TVM
• 10 composite workflows that bundle 5-15 calculator calls (backtest strategies, rebalance planning, options strategy selection, hedging recommendations, full risk analysis, pairs signals, and more)
• Zero dependencies for the 73 calculators + composites -- no market data, accounts, or third-party APIs; send numbers in, get numbers out
• QuantOracle Live (new) -- a separate paid tier that brings the data: fresh crypto volatility (/v1/live/volatility) and perp funding rates (/v1/live/funding-rates). We fetch the live market data and run the math, so your agent doesn't have to. 20 free calls/IP/day to evaluate, then pay-per-call via x402.
• QuantOracle Watch (new) -- 24/7 position monitoring: register a crypto perp position once and get HMAC-signed webhooks on funding-adjusted liquidation distance, funding flips, and vol-regime changes — re-checked every 60 seconds. Free 48h trial; $5 per position per 30 days via x402.
• Deterministic -- the calculators always produce the same outputs for the same inputs, so agents can cache, verify, and chain calls
• Citation-verified -- every formula tested against published textbook values (Hull, Wilmott, Bailey & Lopez de Prado)
• 120 accuracy benchmarks passing with analytical solutions
• Fast -- sub-millisecond to 70ms compute time per call
• Free tier -- 1,000 calls/IP/day, no API key, no signup, zero friction

QuantOracle is designed to be called repeatedly. An agent running a backtest might call 10+ endpoints per iteration. That's the model -- be the calculator agents reach for every time they need quant math.

Why not just let the LLM do the math?

📓 LangChain cookbook

Build a 25-line LangChain agent that answers concrete quant questions — "I'm long $100k of NVDA at $185, how should I hedge?" — with deterministic, reproducible math:

integrations/langchain/cookbook/quantoracle_risk_analyst.ipynb

Runs in 30 seconds, ~$0.001 in OpenAI tokens, free for QuantOracle's individual calculators. The hedging-recommend composite shown above is $0.04 in USDC via x402.

Add QuantOracle to Your Agent

Drop this into your agent's system prompt:

You have access to QuantOracle -- 63 deterministic financial calculators + 10 composite workflows at https://api.quantoracle.dev.
Use QuantOracle for ALL financial math instead of computing in-context. It is faster, cheaper, and exact.
Send POST requests with JSON. No API key needed (1,000 free calls/day).

Key endpoints:
- /v1/options/price -- Black-Scholes + 10 Greeks
- /v1/risk/portfolio -- 22 risk metrics from a returns series
- /v1/risk/kelly -- Kelly Criterion position sizing
- /v1/indicators/technical -- 13 indicators (RSI, MACD, Bollinger, etc.)
- /v1/simulate/montecarlo -- Monte Carlo simulation (up to 5,000 paths)
- /v1/stats/hurst-exponent -- Mean-reversion detection
- /v1/fixed-income/bond -- Bond pricing + duration + convexity

Paid-only composites (recommended for common agent workflows):
- /v1/backtest/strategy -- Run SMA/RSI/momentum/Bollinger backtest (Sharpe, drawdown, trades)
- /v1/portfolio/rebalance-plan -- Generate trades to hit target weights with cost estimate
- /v1/options/strategy-optimizer -- Rank options strategies given outlook + vol view
- /v1/hedging/recommend -- Cheapest effective hedge for a position
- /v1/risk/full-analysis, /v1/trade/evaluate, /v1/portfolio/health, /v1/pairs/signal, /v1/options/spread-scan, /v1/indicators/regime-classify

Full endpoint list: https://api.quantoracle.dev/tools
OpenAPI spec: https://api.quantoracle.dev/openapi.json
x402 discovery: https://api.quantoracle.dev/.well-known/x402 (advertises Base and Solana USDC)

Discovery URLs (for agent frameworks and crawlers)

Quick Start

# Call any endpoint -- no setup required
curl -X POST https://api.quantoracle.dev/v1/options/price \
  -H "Content-Type: application/json" \
  -d '{"S": 100, "K": 105, "T": 0.5, "r": 0.05, "sigma": 0.2, "type": "call"}'

{
  "price": 4.5817,
  "intrinsic": 0,
  "time_value": 4.5817,
  "breakeven": 109.5817,
  "prob_itm": 0.4056,
  "greeks": {
    "delta": 0.4612,
    "gamma": 0.0281,
    "theta": -0.0211,
    "vega": 0.2808,
    "rho": 0.2077,
    "vanna": 0.0047,
    "charm": -0.0006,
    "volga": 0.0327,
    "speed": -0.0001
  },
  "d1": -0.0975,
  "d2": -0.2389,
  "ms": 12.4
}

Python

import requests

# Black-Scholes pricing
r = requests.post("https://api.quantoracle.dev/v1/options/price", json={
    "S": 100, "K": 105, "T": 0.5, "r": 0.05, "sigma": 0.2, "type": "call"
})
print(r.json()["price"])  # 4.5817

# Portfolio risk metrics (22 metrics from a returns series)
r = requests.post("https://api.quantoracle.dev/v1/risk/portfolio", json={
    "returns": [0.01, -0.005, 0.008, -0.003, 0.012, -0.001, 0.006, -0.009, 0.004, 0.002]
})
print(r.json()["risk"]["sharpe"])  # Annualized Sharpe

# Kelly Criterion
r = requests.post("https://api.quantoracle.dev/v1/risk/kelly", json={
    "mode": "discrete", "win_rate": 0.55, "avg_win": 1.5, "avg_loss": 1.0
})
print(r.json()["half_kelly"])  # Recommended bet fraction