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Shroomy Web3 Oracle – NFT/Crypto Floor Price Forecaster
x402
Shroomy Web3 Oracle – NFT/Crypto Floor Price Forecaster is a paid API for AI agents from oracle.the-undesirables.com, paid per call via x402, $0.05/call, status unknown (last checked 2026-10-27).
Fetches real-time NFT or crypto floor prices via Alchemy and runs 20,000-iteration Heston stochastic Monte Carlo simulations to produce probabilistic price forecasts across user-specified time horizons.
Price / call$0.05
Success—
StatusUnknown
How this service works
TCG card grading, conformal risk forecasting, and market intelligence. Powered by x402 micropayments — USDC on Base, USDC on Solana, or USDG on Robinhood Chain; the 402 offers all three and the agent picks a leg. Free tools require no payment. Paid tools return HTTP 402 — sign a payment and retry with the payment proof header.
Output
Returns current floor price in ETH, forecast percentile distribution (5th, 25th, 50th, 75th, 95th) over the requested time horizon, Heston model parameters used (drift, vol-of-vol, mean reversion, long-term variance), number of simulations run (20,000), network, contract address, and a status field. Priced at $0.05 USDC per call.
Example use cases
Portfolio risk assessment for NFT holdings
“I'm holding a bunch of Pudgy Penguins and want to understand my downside risk. Can you run a 90-day forecast on the collection floor and show me what the worst 5% of outcomes looks like versus the median?”
Smart liquidation timing for collection floor
“We're thinking about liquidating our CloneX position over the next two weeks. Give me a probabilistic forecast for the floor price so we can figure out the best time window to minimize slippage and lock in fair value.”
Volatility-adjusted offer strategy for NFT market makers
“We're setting bid-ask spreads for Art Blocks contracts and need to know the expected price movement and volatility over the next 7 days. Run a Heston simulation so we can calibrate our risk margins properly.”
Technical details
Endpoint
GEThttps://oracle.the-undesirables.com/api/v1/crypto-oracle?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need institutional-grade probabilistic price forecasting for a specific NFT collection or crypto asset on Ethereum mainnet (or other supported networks), combining live on-chain floor price data with rigorous stochastic volatility modeling. Prefer this over simple price lookups when you need a full distribution of outcomes (percentiles), not just a point estimate. Best for risk management, portfolio analysis, or investment decision support requiring Heston-model-based confidence intervals.
Side effects
- Invalid or non-existent contract address returns error
- Unsupported network parameter causes failure
- Alchemy data unavailable for illiquid NFT collections with no floor price history
- Missing required query parameters (days, contract_address, network) returns bad request
- Rate limiting or upstream Alchemy API outage causes timeout
Input
{
"days": 30,
"coin_id": "ethereum",
"network": "eth-mainnet",
"contract_address": "0xBC4CA0EdA7647A8aB7C2061c2E118A18a936f13D"
}Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
{
"data": {
"days": 30,
"model": "merton_jump_diffusion",
"source": "alchemy_merton_oracle",
"network": "eth-mainnet",
"contract": "0xBC4CA0EdA7647A8aB7C2061c2E118A18a936f13D",
"currency": "ETH",
"simulations": 20000,
"model_params": {
"drift_mu": 0.1,
"jump_mean_mu_j": -0.08,
"diffusion_sigma": 0.7,
"jump_vol_sigma_j": 0.15,
"jump_intensity_lambda": 4
},
"risk_metrics": {
"VaR_95": 6.1007,
"CVaR_95": 5.4494,
"VaR_95_pct": -32.14,
"CVaR_95_pct": -39.38
},
"verifiability": {
"round": 6210873,
"beacon": "drand-league-of-entropy",
"method": "Monte Carlo seeded from the public drand randomness beacon — the seed is committed publicly each round and cannot be cherry-picked. Re-fetch the round and reproduce.",
"reproduce": "rng = numpy.random.default_rng(int(randomness, 16)); Z = concat(rng.standard_normal(n_sims//2), -that) for antithetic variates; draw N=rng.poisson(lambda_jump*days/365, n_sims) and J=rng.normal(N*mu_j, sqrt(max(N,1))*sigma_j); apply the terminal Merton/GBM formula.",
"randomness": "2ac25a2737b56cede44bb4ed5794b990a3d2cf3c06d721b24f406a8785b68728",
"exact_params": {
"mu": 0.1,
"days": 30,
"mu_j": -0.08,
"model": "merton_jump_diffusion",
"sigma": 0.7,
"n_sims": 20000,
"sigma_j": 0.15,
"lambda_jump": 4,
"current_price": 8.99
},
"provably_fair": true,
"verify_round_url": "https://api.drand.sh/public/6210873"
},
"current_floor_price": 8.99,
"forecast_percentiles": {
"5th": 6.1007,
"25th": 7.6494,
"50th": 8.8875,
"75th": 10.2773,
"95th": 12.6101
}
},
"tool": "crypto_oracle",
"price": "$0.05",
"status": "ok"
}Schema · Response
{
"type": "json",
"example": {
"status": "ok",
"forecast": {
"50th_percentile": 0.52,
"95th_percentile": 1.1
},
"floor_price": 0.45,
"model_params": {
"drift_mu": 0.1,
"diffusion_sigma": 0.7,
"jump_intensity_lambda": 4
}
}
}Tags
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