sam
cfc27a38de
Replace the 2024 model (model.py, ~2000 lines) with the btcmodel package, the baseline for future work: - Forecasts are quantiles of log price at each horizon, scored with CRPS in a walk-forward backtest (origins every 30 days from 2014, horizons 1 month to 4 years). Skill is relative to a zero-drift random walk, with circular block-bootstrap intervals and a count of independent windows. - Development data stops at 2024-11-26, the last day the 2024 model saw. Later outcomes are a holdout, scored only by `backtest --holdout`. - Models: random_walk, drift_rw, and cycle (the 2024 model's cycle-position drift, now kernel-smoothed and recency-weighted). On development data nothing beats the random walk with confidence; cycle loses at every horizon. - Prices: the Investing.com archive moves to data/ (cut at 2024-11-26; its last row was intraday) and is extended with Coinbase daily closes by `update`. Also: Nix flake dev shell (Python 3.13, pandas 3), ruff in place of black, pytest suite, and a rewritten README. NOTES.md is removed as inaccurate, and poetry is dropped.
Bitcoin Price Model
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):
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_rwleads at 2-4 years (+13-18% skill, but the intervals span zero).cycleloses 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:
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 through 2024-11-26, then Coinbase Exchange daily closes (UTC).