The short version
Every English-language One Piece TCG card with a TCGPlayer listing gets a daily price projection over two horizons, 7 days and 30 days. Each projection has a direction (up, down, or flat), an expected percentage move, and a confidence score. You can see them all on the price predictions page. These are statistical projections, not financial advice.
Where the price data comes from
Every prediction starts with TCGPlayer market price data, sourced through the free TCGCSV API. TCGCSV maintains daily snapshots of TCGPlayer's full catalog including market prices, low, mid, and high price tiers, and foil pricing.
WealthFamePower runs a sync job once per day at 4:00 PM Eastern. That job fetches the latest snapshot, updates each card's current price, appends a row to the price history table, generates a fresh round of baseline predictions, and checks user price alerts. Because the sync runs daily, price history accumulates by one day every day.
The full daily history starts in late January 2026 and has been collected continuously since then, giving the models a contiguous window of roughly 111 days to learn from. Every additional day of clean data makes the models incrementally more reliable.
The baseline statistical model
The baseline model is fully transparent and rule-based. For every card variant it computes three signals from the recent price series, combines them, and turns the result into a direction and an expected move.
1. Momentum
How much has the price moved over the lookback window? Cards trending up keep trending up more often than not over short horizons, so a strong recent gain pushes the prediction up. The signal is capped so a single outlier day cannot dominate.
2. Mean reversion
How far is today's price from its recent average? When a price spikes far above the average it tends to drift back down, and vice versa. We compute a z-score of the current price relative to the window mean, then flip the sign: a price well above the mean creates a "down" signal and a price well below creates an "up" signal.
3. Volatility
How choppy has the series been? Volatility does not push the direction one way or the other. Instead it shapes the expected magnitude (a volatile card can move further) and it discounts confidence (a volatile card is harder to forecast).
We average momentum and mean-reversion into a single ensemble score. If the score is above a small dead-band threshold the direction is "up"; below the negative threshold it is "down"; anything in between is "flat." The expected magnitude is then sized using the recent daily volatility and scaled by the square root of the horizon, which is the same approach used for sizing expected moves in equity options.
The machine learning model (beta)
The ML model is a LightGBM gradient-boosted ensemble. It is actually two models stitched together for each horizon: a classifier that picks direction (up, down, or flat) and a regressor that estimates the percentage magnitude of the move.
For each card it sees about 30 features per prediction:
- Price features: lagged prices at 1, 3, 7, and 14 days, rolling mean and standard deviation over 7 and 30 days, and returns over 1, 3, and 7 days.
- Card metadata: rarity, kind (Leader, Character, Event, Stage), set, number of colors, cost, power, life, counter, and the age of the set.
- Social signals: recent Reddit mentions, a derived social-attention score, and a spike percentage that catches sudden surges in chatter.
- Baseline signals: the momentum, mean-reversion, and volatility readings from the baseline model are themselves features, so the ML model can decide when to lean on them and when to ignore them.
Training uses our historical backtest table: for every past day we know what the price actually did seven and thirty days later, so the model learns from real realized outcomes rather than from human-labeled intuition.
Latest test-set accuracy
- 64% directional accuracy on the 7-day horizon
- 80% directional accuracy on the 30-day horizon
Accuracy reported on a held-out 20 percent test split from the most recent training run. A completely random three-class guess would score about 33 percent, so both models are learning real signal. The 30-day horizon scores higher because longer-term trends are noisier in the short term but cleaner over time.
The model is retrained roughly once per week against the latest data. You can toggle between the baseline and ML projections on the predictions page using the "Model" pill.
What "confidence" actually means
Confidence is the model's own honesty score, displayed as a percentage from 0 to 100. It combines three things:
- How much price history we have for that variant, relative to the lookback window the horizon needs.
- How volatile recent prices have been. A choppy series gets a confidence penalty because chop is harder to forecast.
- How many of the underlying signals we were able to compute. Missing signals lower the score.
Cards with a confidence below 30 percent show a "Data limited" badge so you can spot them at a glance. The confidence slider on the predictions page lets you filter to higher-conviction rows only.
What predictions do and do not tell you
Predictions answer
- Which direction is the price likely to move over the next 7 or 30 days?
- Roughly how big could that move be?
- How confident is the model in this read, given the data it has?
Predictions do not answer
- The exact day or hour to buy or sell.
- The impact of news, reprints, ban list changes, or surprise set announcements.
- Whether you personally should make a trade. Predictions are not financial advice.
How accurate are the predictions, really?
Accuracy is something you should be able to verify, not take on trust. Every prediction we make is replayed historically against the realized price change, and the results inform the confidence score and the calibration of the magnitude estimate. Hit rate is tracked per horizon, per confidence band, and per signal type, and we publish honest accuracy figures alongside the model description below.
Accuracy is not static. The models retrain against fresh data weekly, and the price history they learn from grows by one day every day, so the longer WealthFamePower runs the better the predictions get.
Disclaimer. Price predictions on WealthFamePower are statistical projections based on historical TCGPlayer market data and other public signals. They are provided for informational and educational purposes only and are not investment, financial, or trading advice. Past performance does not guarantee future results. You are responsible for your own buying and selling decisions.