# Bitcoin Price Model

I have no idea what I'm doing

**Don't take this seriously. It's all in good fun.** ## The 2024 edition In November 2024 I asked Claude (3.6 Sonnet, via copy and paste in Claude Web) to help build a Bitcoin price model. After a lot of branching it produced ~2000 lines of cycle analysis, "market fundamentals", era adjustments and Monte Carlo, and forecasts like this one (as of 2024-11-14): ![moooooon](docs/2024-forecast.png) In September 2026 a newer Claude scored the forecasts it made on 2024-11-27 against what actually happened: | | 2024 forecast (median) | Actual | |---|---|---| | Cycle top | 2025-10-21, $199K | 2025-10-06, $124.7K | | 2026-06-30 | $133K (95% floor $65K) | $58.5K | | 2026-09-23 | $124K | $84.4K | It called the timing of the top within 15 days, 19 months ahead, but put the level ~60% too high. "The price stays at $92.7K" was a better forecast (18% mean error against 68%). Its "95%" intervals were really ~81% intervals past a year out, thanks to a fudge factor that narrowed them at long horizons. That code lives in the jj/git history. This is the rewrite. ## How it works now A forecast is a probability distribution of the price at each horizon, not a line. Every model produces quantiles of log price, and every model is scored the same way: - **Walk-forward.** Every 30 days from 2014 on, each model sees only the data up to that day and forecasts 1 month to 4 years ahead. - **CRPS.** Each forecast is scored against what happened with the continuous ranked probability score, in log-price units (0.1 ≈ "typically 10% off"). It rewards being sharp and being calibrated at once, and can't be gamed by narrowing or widening intervals. - **Baselines.** Skill is reported relative to a zero-drift random walk, with a 90% block-bootstrap interval. Nearby forecasts overlap heavily, so the report also shows `windows`: the number of genuinely independent outcomes. - **Holdout.** Development only sees data up to 2024-11-26, the last day the 2024 model saw. Everything after it is held out, and is scored only by `just holdout`, once per round of model changes. (Caveat: we already know roughly what happened in 2025-26, so it isn't perfectly blind.) ### Models - `random_walk`: zero drift; "it stays about here, give or take". - `drift_rw`: drift equal to the last four years' average; "it keeps doing what it did last cycle". - `cycle`: the 2024 model's one real idea. Expected return depends on the day of the halving cycle, estimated from past cycles, with recent cycles weighted more. ### Findings so far (development data) - Nothing beats the random walk with any confidence at any horizon. At 2-4 years there are only 3-5 independent outcomes in the whole history. - `drift_rw` leads at 2-4 years (+13-18% skill, but the intervals span zero). - `cycle` loses to both at every horizon, and so does every setting tried (recency half-life 0.25-2 cycles, smoothing bandwidth 15-60 days). The cycle *shape* costs accuracy. The level is the problem: each cycle has grown less than the last (log return 4.0, 2.6, 2.0, i.e. roughly ×55, ×13, ×7), so any average of past cycles overshoots. That points at diminishing returns as the structure worth modelling, e.g. a power-law trend, which is next. ## Usage With Nix: ```sh nix develop just update # fetch new daily prices from Coinbase just backtest # score models on development data -> output/backtest/ just forecast # forecast from the latest price -> output/forecast/ just test just holdout # score on held-out outcomes; sparingly ``` Without Nix, any Python 3.13 with numpy, pandas 3, scipy and matplotlib works: `python -m btcmodel --help`. ## Layout ``` btcmodel/ data.py price loading (Investing.com archive + Coinbase), dev cutoff halving.py halving calendar, position in cycle forecast.py Forecast (quantiles of log price), CRPS, PIT evaluate.py walk-forward backtest and summary models/ one file per model family; register new ones in __init__.py plots.py fan chart, skill and calibration charts ``` A model is any object with a `name` and `forecast(history, horizons) -> Forecast`. `history` is a date-indexed frame holding everything up to the forecast origin and nothing after it. Other data sources (hash rate, on-chain metrics, macro series) can join it as extra columns. Anything published with a lag must be shifted to the date it was actually available, or the backtest will quietly cheat. Price data: [Investing.com](https://www.investing.com/crypto/bitcoin/historical-data) through 2024-11-26, then Coinbase Exchange daily closes (UTC).