How PokeFuture builds a sealed forecast
Each forecast combines historical pricing, comparable sealed products, demand signals, and machine-learning projections to produce a directional view of risk and upside. The framework is designed to support investment judgment, not to imply precision the underlying data cannot support.
At a glance
Approach
Machine-learning forecasts
Independent projections across 1-year, 3-year, and 5-year horizons.
Data sources
PriceCharting, TCGplayer, community signals
Multi-provider pricing combined with demand and engagement signals.
Process
How a forecast is built
Every product moves through the same five stages before a projection is published.
1. Historical pricing
The forecast is anchored to observed sealed price movement over time.
2. Comparable products
The product is benchmarked against peers from a similar era and supply profile.
3. Demand signals
Search activity, community engagement, and broader market momentum are factored in.
4. Model projection
Trained models project plausible price and return paths across each horizon.
5. Downside review
Bear scenarios are widened where reprint risk or limited data warrant additional caution.
Inputs
What the model evaluates
Forecasts draw on four families of inputs: price action, supply context, demand proxies, and peer-group behavior.
Historical pricing
Trajectories, momentum, volatility, drawdowns, and the density of available price history.
Comparable products
Peer products from similar eras, product types, and print-run environments.
Demand signals
Search interest, community engagement, and other indicators of collector activity.
Market context
Provider agreement, liquidity proxies, and explicit flags for any missing data.
Limitations
Where forecasts are less reliable
The framework is most useful for ranking opportunities and framing scenarios. It is least reliable when the underlying market signal is weak.
Recently released products with limited price history.
Sets with sparse comparables or atypical supply behavior.
Products exposed to elevated reprint risk.
Thin-liquidity products where realized exit prices may diverge from quoted prices.
Intended use
Forecasts are intended as one input within a broader research process — used to compare products, evaluate upside, and stress-test downside before sizing a position.
Data sources
What powers the forecasts
Forecasts are trained on a multi-source dataset that combines market pricing with collector demand signals.
Pricing
PriceCharting
Sealed product price history across multiple conditions
Marketplace
TCGplayer
Live market pricing and listing activity
Demand
Community signals
Search interest, Reddit, and forum engagement
Catalog
Set metadata
Era, product type, print-run profile, and release date
Get started
Explore sealed product forecasts
Open the forecast view to compare products, review downside scenarios, and evaluate projected outcomes across the catalog.