What is a conformal risk forecast for trading cards?

A conformal risk forecast is TCG Oracle's default price forecast model. Instead of assuming a mathematical model of how prices move, it calibrates prediction bands on thousands of real card price histories, so a 90% band is built to actually contain the true price about 90% of the time. It is distribution-free (no model assumptions), regime-aware (calm and volatile markets get different bands), and fully deterministic — the same card and date always produce the same numbers, so anyone can reproduce and verify a forecast. Each forecast returns a point estimate, probability the price rises, 50% and 90% bands, VaR95/VaR99 downside risk, and two letter grades: Safe-Hold (capital preservation) and Momentum (30-day direction). Available free at /api/v1/forecast.

How does Monte Carlo simulation work for trading cards?

Monte Carlo simulation is TCG Oracle's opt-in scenario engine (model=gbm or model=merton on /api/v1/simulate; the default model is the conformal risk forecast). It uses random sampling to model the range of possible future card prices across two stochastic finance models: GBM (Geometric Brownian Motion — the foundational continuous-time model for asset price dynamics) and Merton Jump-Diffusion (extends GBM with Poisson-driven jumps to capture sudden price spikes from hype events, reprints, or format changes). Each simulation runs 20,000 paths and returns full percentile bands (5th through 95th) with VaR/CVaR risk metrics and complete model parameter transparency.

Should I grade my trading card? Is it worth grading?

TCG Oracle's Grade-or-Not engine answers this by combining your card's predicted PSA grade with the PSA fee schedule (from ~$100 for the Regular tier — the cheaper Economy/Value tiers have been paused — up to $600 for express), estimated shipping costs ($15-30), and the market value difference between raw and graded copies. It returns a GO/NO-GO verdict with three profit scenarios: best-case, predicted, and worst-case. If the expected ROI exceeds your threshold, grade it. If not, sell raw.

What is the best API for trading card price prediction?

TCG Oracle is the only collectibles API with calibrated, reproducible conformal risk forecasts — honest VaR and Safe-Hold/Momentum letter grades, deterministic so anyone can verify the numbers — plus opt-in institutional-grade stochastic models (GBM and Merton Jump-Diffusion) seeded from the public drand randomness beacon. With 12 paid and 16 free API endpoints, it covers grading, simulation, arbitrage detection, portfolio optimization, and market data. It's available via MCP (Model Context Protocol), x402 micropayments on Coinbase Bazaar, and A2A Agent Cards for autonomous agent discovery. Pricing starts at $0.015 per risk forecast — and the conformal forecast board is free.

How accurate is AI card grading compared to PSA?

TCG Oracle exposes an open, public accuracy channel at /api/v1/accuracy. Anyone can query it, and anyone can submit their real PSA/BGS grade (POST /api/v1/accuracy/report) to hold the model accountable — as reports come in it publishes mean absolute error, hit rates (predictions within ±0.5 of actual grade), and grade distribution. On the forecasting side, every prediction is scored automatically at 30-day maturity, with the first cohort settling in mid-July 2026. Publishing a public, independently verifiable accuracy channel at all is the unusual part — most card-grading AIs never expose one.

TCG Oracle — Financial Intelligence for Collectibles

Q: What trading card games does TCG Oracle support?

TCG Oracle covers 446K+ products across 25 games including: Pokémon, Magic: The Gathering, Yu-Gi-Oh!, Digimon, One Piece, Flesh and Blood, Lorcana, Dragon Ball Super, Cardfight!! Vanguard, MetaZoo, Union Arena, Star Wars Unlimited, Gundam, and more. Data is sourced from daily TCGCSV market snapshots with eBay sold listing integration.

The only trading card API with conformal risk forecasting (Monte Carlo opt-in), AI grading, and portfolio optimization. 446K+ products across 25 games. 9 API endpoints. Pay-per-call with USDC.

What Is AI Card Grading?

AI card grading uses computer vision to predict the grade a trading card would receive from professional grading services like PSA or Beckett. TCG Oracle uses a 3-stage pipeline:

Qwen Vision LLM analyzes corners, edges, and surface defects from the card image
OpenCV measures exact centering ratios programmatically (no LLM estimation)
BGS capping algorithm adjusts the final grade using professional grading rules (0.5 cap above lowest structural score)

This costs $0.10 per card via x402 USDC micropayments on Base network. Every grade response includes a free ROI verdict telling you whether grading is profitable.

The Default Model: Conformal Risk Forecasts

Most price "predictions" assume the market follows a tidy mathematical model. Cards don't. TCG Oracle's default forecast is conformal: instead of assuming how prices move, it calibrates its bands on thousands of real card price histories — so a 90% band is built to actually contain the real price about 90% of the time. Honest uncertainty, not false precision.

Distribution-free — no model assumptions to be wrong about. The bands come from measured forecast errors on real cards.
Regime-aware — calm markets get tight bands, volatile markets get wide ones. A drift-spike filter flags cards mid-mania where direction is untrustworthy.
Deterministic & reproducible — the same card on the same date always produces the same numbers. Anyone can re-run a forecast and verify it — which is exactly what lets soul prediction track records be judged fairly.
Graded output — every forecast returns a point estimate, prob_up, 50%/90% bands, VaR95/VaR99, and two letter grades: Safe-Hold (capital preservation, absolute scale) and Momentum (30-day direction).

Try it free — no key, no payment: the forecast board at /api/v1/forecast covers the top ~200 liquid cards, and /api/v1/forecast/{product_id} works for any of the 446K+ cards.

Opt-In: Monte Carlo Simulation for Trading Cards

When you want scenario simulation instead of calibrated bands — "what does a hype-jump world look like?" — switch to Monte Carlo with model=gbm or model=merton. Two stochastic finance models borrowed from Wall Street options pricing, seeded from the public drand randomness beacon so the draws are provably fair:

GBM (Geometric Brownian Motion) — The foundational continuous-time model for asset price dynamics with constant drift and volatility. Ideal for stable, liquid markets.
Merton Jump-Diffusion — Extends GBM with Poisson-driven jumps to capture sudden price spikes from hype events (a YouTuber opens a rare card, a reprint tanks supply, a format rotation bans a staple). Returns VaR/CVaR risk metrics alongside percentile bands.

Each simulation runs 20,000 paths and returns full percentile bands (5th through 95th) with complete model parameter transparency. You see exactly what drift, volatility, and jump parameters were used.

Should I Grade My Trading Card?

TCG Oracle's Grade-or-Not engine answers this by combining your card's predicted PSA grade with:

PSA fee schedule (from ~$100 Regular tier — cheaper tiers paused — up to $600 express)
Estimated shipping and insurance ($15–30)
Market value difference between raw and graded copies at each grade level

It returns a clear GO/NO-GO verdict with three profit scenarios: best-case, predicted, and worst-case. If the expected ROI exceeds your threshold, grade it. If not, sell raw and save the fees.

Supported Games

TCG Oracle covers 446K+ products across 25 games including Pokémon, Magic: The Gathering, Yu-Gi-Oh!, Digimon, One Piece, Flesh and Blood, Lorcana, Dragon Ball Super, Cardfight!! Vanguard, MetaZoo, Union Arena, Star Wars Unlimited, Gundam, and more.

API Access

TCG Oracle is available through 9+ discovery surfaces, each speaking a different protocol:

MCP — Model Context Protocol for Cursor, Claude Desktop, VS Code, Perplexity
x402 — Micropayments on Coinbase Bazaar for autonomous agents
A2A — Google Agent-to-Agent protocol for peer discovery
npm / PyPI — Package managers for ElizaOS and Python agents
REST API — OpenAPI spec at /docs

Last updated: May 21, 2026 • Built by The Undesirables LLC