What our forecasts are
Each price-forecast section on a coin page is a piece of editorial analysis written by a named author, reviewed by a named editor, and dated. It uses our quantitative model as one input alongside named-analyst price targets, on-chain data, and the editor’s own judgment.
What our forecasts are not
They are not algorithmic outputs. They do not recompute by the second. They do not predict which scenario among bull/base/bear will materialise — instead they describe what would have to happen for each path, and they name the indicators a reader can monitor to see which path is playing out.
How we layer editorial review on top of the model
- Research bundle. The author compiles named-analyst price targets (Standard Chartered, Bitwise, 21Shares, VanEck, Galaxy Digital, Bernstein, Fidelity Digital Assets, Messari, named exchange research teams), recent news from the past 90 days, and protocol fundamentals (supply schedule, staking yield, validator economics, ETF status, on-chain metrics).
- Editor’s take. The author writes a methodological thesis: which drivers matter most, where they disagree with the model’s weighting, and why.
- Scenarios. The author describes three paths (descriptively named, never labelled “Bull/Bear/Base”) and the chain of events that would have to occur for each.
- Catalysts. The author names five to seven coin-specific catalysts to watch, with dates or triggers and inline citations.
- Editor review. A senior editor reads the draft, checks every claim against its citation, and signs off with a dated stamp.
- Quality gate. An automated gate runs eleven checks before publish: banned-phrase scan, denylist-source scan, attribution-regex check, citation-count floor, scenario-not-target check, coin-specificity find-and-replace, byline-fields-populated, methodology-link resolves, disclaimer-placement, schema validity, editor’s-take depth.
- Refresh cadence. Tier-1 coins are reviewed monthly. Tier-2 quarterly. Tier-3 bi-annually. Major catalysts (halving, ETF approval, custodian collapse, major hack, regulation milestone) trigger an ad-hoc review.
Source standards
We cite Standard Chartered, Bitwise, 21Shares, VanEck, Galaxy Digital, Bernstein, Fidelity Digital Assets, Messari, Glassnode, The Block, Bloomberg, Reuters, DL News, Disruption Banking, and named exchange research teams (Binance Research, Coinbase Institutional, OKX Insights).
We do not cite InvestingHaven, WalletInvestor, DigitalCoinPrice, CaptainAltcoin, CoinDataFlow, CryptoPotato, Changelly, FXEmpire, CoinFomania, CCN, Cryptopolitan, BeInCrypto, CryptoSlate, Coinpedia, or CoinCodex — these are retail-aggregator forecast farms and would degrade our analysis. Full source-tier policy: editorial standards.
What we do not forecast
Coins outside our tier-1/tier-2 institutional-coverage allowlist receive on-chain analysis only, with no price targets. Stablecoins receive mechanism and peg analysis, not price prediction. Pretending otherwise would force fabricated coverage, which is what we built this methodology to prevent.
Corrections
If you find an error, email [email protected]. Material corrections are stamped on the page with the correction date and a summary of the change. Full correction policy: editorial standards → corrections.
Our quantitative model — for transparency
Our prediction model is a quantitative momentum-and-volatility composite designed for explainability over complexity. This page documents the full pipeline so anyone can audit, replicate, or critique it.
Inputs
The model takes four classes of input:
- Price returns — log returns over 1-day, 7-day, 30-day, and 1-year windows.
- Realized volatility — standard deviation of returns from the 7-day hourly price series (downsampled from CoinPaprika and Binance).
- Sentiment overlay — the alternative.me Fear & Greed Index, applied as a small daily drift adjustment (±0.08%/day at extreme readings).
- Mean-reversion anchor — the 7-day moving average, used as a damping factor on medium and long horizons.
Composition
Drift is blended differently per horizon:
- Short (24h, 7d): 50% × 1d return + 30% × 7d return + 20% × 30d return (all per-day in log space)
- Medium (30d, 3M, 6M): 20% × 7d + 50% × 30d + 30% × 1y
- Long (1Y, 2025–2030): 30% × 30d + 70% × 1y, with stronger mean-reversion damping
Bands
Base case = price × exp(drift × horizon_days). Bear/bull bands = ±1.5σ × √horizon_days. Under a log-normal distribution this captures roughly 85% of outcomes. Bear is clamped to 0.5× ATL as a floor; bull is clamped to 100× current as a ceiling.
Update cadence
Model output is cached for 60 seconds and recomputed on every page load. Underlying price data refreshes hourly from CoinPaprika. The Fear & Greed value refreshes every 12 hours.
What this model is not
- It is not a trading signal. It is a research tool.
- It does not incorporate fundamentals (supply schedules, protocol upgrades, regulatory news).
- It does not read derivatives positioning or order-book depth.
- Forecasts beyond 30 days are scenario bands, not point predictions.
Accuracy tracking
Every prediction is logged in a public table. At 24h, 7d, and 30d we revisit and score directional accuracy. As the log accumulates, this page will show a running tally.
Questions about the model? Email us.
Historical accuracy
We log every prediction the moment it is generated. As of today, we have 0 predictions in the log spanning 0 days. We need at least 30 days of history before publishing a meaningful accuracy score — this section will fill in automatically as the log accumulates.
Here is what we are measuring:
- Directional accuracy — was the sign of our 24h, 7d, and 30d forecasts correct?
- Mean absolute error (MAE) — average percentage distance between forecast base case and realized price.
- Calibration — did 85% of realized prices fall inside our bear-bull bands? (Our bands are calibrated to roughly that confidence level.)
This page updates automatically as we accumulate the required minimum sample. No retroactive editing of past predictions is permitted.