How to Judge a Crypto Forecast: Start With the Naive Baseline
22 October 2026
Every forecast looks impressive until you ask a simple question: compared with what? A crypto price model that lands within one percent of the next hour sounds great, yet an hour is a short time, and prices often sit close to where they were a moment ago. The fix is a habit worth building early. Always set a forecast beside a baseline that takes no skill at all.
The baseline everyone should know
The simplest forecast says that the next price equals the last price. People call it persistence or the naive forecast. It needs no model, no training and no data beyond the latest close. Because markets absorb new information quickly, this humble rule is surprisingly hard to beat, especially over short horizons.
So when you read an accuracy number, ask for the persistence number next to it. If a model reports a mean absolute error of 0.3 percent at one hour, the useful question is what the last close would have scored over the same hours. A model earns attention when its error sits clearly below that line across many samples.
A worked example with round numbers
Imagine Bitcoin closes at $80,000, and over the next hour it drifts to $80,200. A persistence forecast of $80,000 misses by $200, which is 0.25 percent. A model that said $80,150 misses by $50, which is 0.06 percent. One example shows little, because luck matters. Repeat the comparison across a thousand hours and the average tells you whether the model adds anything. Averages over many samples are the heart of honest evaluation.
Pick the right error measure
- Mean absolute error averages the size of misses. It is easy to read in dollars or percent.
- Mean absolute percentage error scales misses by price, so Bitcoin and a small coin can sit on one chart.
- Direction hit rate counts how often the sign of the move was right. Pair it with size, because many small right calls can be erased by one large wrong call.
- Spread of the error shows how bad a bad day can be. Two models with equal averages can differ widely here.
Match the horizon to the question
Error grows with horizon. A one hour forecast, a one day forecast and a seven day forecast answer different questions and carry different widths of uncertainty. Compare each horizon against its own baseline, and expect wider bands further out. That widening is the model being truthful about what it can know.
Test on data the model has not seen
A forecast scored on the same history that shaped it will look better than it is. Good practice is to hold back a later slice of time, train on the earlier slice, and score only on the later slice. Then roll the window forward and repeat. Many small honest tests beat one big flattering test, and they show whether results stay steady across calm and wild periods.
Check the sample size
A result built on twenty observations can swing wildly. Look for the sample count beside any accuracy figure. The BitBank accuracy page reports error by horizon together with the number of samples behind it, so you can weigh how much trust a figure deserves. Pairs with few samples deserve a more cautious reading, and a missing figure is itself a useful signal to wait for more evidence.
A five step routine
- Open the forecast page and note the horizon you care about.
- Find the same pair and horizon on /accuracy and read the error and sample count.
- Compute or look up the persistence error for the same hours.
- Compare the width of the forecast band with the move you hope to capture. A band wider than the expected move says the model sees limited edge.
- Record your own predictions for a few weeks, so your judgement gets scored the same way.
Why this builds good habits
Beating a naive baseline is a modest goal, and it is the right one. It turns a vague feeling of accuracy into a measurable statement. It also protects you from comparisons that flatter a model, such as quoting an error figure without context. Once the habit is in place, you can read any forecast from any source with the same calm checklist.
Ready to practise? Browse live forecasts on BitBank, compare them with the published errors on /accuracy, and read how our own models are built in inside the forecasting models.
This article is educational and general information only. Forecasts describe uncertainty and offer no guarantee of future prices or results.