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.
114 lines
4.7 KiB
Markdown
114 lines
4.7 KiB
Markdown
# Bitcoin Price Model
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<p align="center">
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<img src="https://img.izismile.com/img/img5/20120417/640/i_have_no_idea_what_im_doing_meme_640_07.jpg" alt="I have no idea what I'm doing" />
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</p>
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**Don't take this seriously. It's all in good fun.**
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## The 2024 edition
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In November 2024 I asked Claude (3.6 Sonnet, via copy and paste in Claude Web)
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to help build a Bitcoin price model. After a lot of branching it produced ~2000
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lines of cycle analysis, "market fundamentals", era adjustments and Monte Carlo,
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and forecasts like this one (as of 2024-11-14):
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In September 2026 a newer Claude scored the forecasts it made on 2024-11-27
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against what actually happened:
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| | 2024 forecast (median) | Actual |
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|---|---|---|
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| Cycle top | 2025-10-21, $199K | 2025-10-06, $124.7K |
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| 2026-06-30 | $133K (95% floor $65K) | $58.5K |
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| 2026-09-23 | $124K | $84.4K |
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It called the timing of the top within 15 days, 19 months ahead, but put the
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level ~60% too high. "The price stays at $92.7K" was a better forecast (18%
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mean error against 68%). Its "95%" intervals were really ~81% intervals past a
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year out, thanks to a fudge factor that narrowed them at long horizons.
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That code lives in the jj/git history. This is the rewrite.
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## How it works now
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A forecast is a probability distribution of the price at each horizon, not a
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line. Every model produces quantiles of log price, and every model is scored
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the same way:
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- **Walk-forward.** Every 30 days from 2014 on, each model sees only the data up
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to that day and forecasts 1 month to 4 years ahead.
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- **CRPS.** Each forecast is scored against what happened with the continuous
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ranked probability score, in log-price units (0.1 ≈ "typically 10% off"). It
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rewards being sharp and being calibrated at once, and can't be gamed by
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narrowing or widening intervals.
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- **Baselines.** Skill is reported relative to a zero-drift random walk, with a
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90% block-bootstrap interval. Nearby forecasts overlap heavily, so the report
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also shows `windows`: the number of genuinely independent outcomes.
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- **Holdout.** Development only sees data up to 2024-11-26, the last day the
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2024 model saw. Everything after it is held out, and is scored only by
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`just holdout`, once per round of model changes. (Caveat: we already know
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roughly what happened in 2025-26, so it isn't perfectly blind.)
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### Models
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- `random_walk`: zero drift; "it stays about here, give or take".
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- `drift_rw`: drift equal to the last four years' average; "it keeps doing what
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it did last cycle".
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- `cycle`: the 2024 model's one real idea. Expected return depends on the day of
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the halving cycle, estimated from past cycles, with recent cycles weighted
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more.
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### Findings so far (development data)
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- Nothing beats the random walk with any confidence at any horizon. At 2-4
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years there are only 3-5 independent outcomes in the whole history.
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- `drift_rw` leads at 2-4 years (+13-18% skill, but the intervals span zero).
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- `cycle` loses to both at every horizon, and so does every setting tried
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(recency half-life 0.25-2 cycles, smoothing bandwidth 15-60 days). The cycle
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*shape* costs accuracy. The level is the problem: each cycle has grown less
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than the last (log return 4.0, 2.6, 2.0, i.e. roughly ×55, ×13, ×7), so any
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average of past cycles overshoots.
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That points at diminishing returns as the structure worth modelling, e.g. a
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power-law trend, which is next.
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## Usage
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With Nix:
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```sh
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nix develop
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just update # fetch new daily prices from Coinbase
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just backtest # score models on development data -> output/backtest/
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just forecast # forecast from the latest price -> output/forecast/
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just test
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just holdout # score on held-out outcomes; sparingly
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```
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Without Nix, any Python 3.13 with numpy, pandas 3, scipy and matplotlib works:
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`python -m btcmodel --help`.
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## Layout
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```
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btcmodel/
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data.py price loading (Investing.com archive + Coinbase), dev cutoff
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halving.py halving calendar, position in cycle
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forecast.py Forecast (quantiles of log price), CRPS, PIT
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evaluate.py walk-forward backtest and summary
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models/ one file per model family; register new ones in __init__.py
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plots.py fan chart, skill and calibration charts
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```
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A model is any object with a `name` and
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`forecast(history, horizons) -> Forecast`. `history` is a date-indexed frame
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holding everything up to the forecast origin and nothing after it. Other data
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sources (hash rate, on-chain metrics, macro series) can join it as extra
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columns. Anything published with a lag must be shifted to the date it was
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actually available, or the backtest will quietly cheat.
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Price data: [Investing.com](https://www.investing.com/crypto/bitcoin/historical-data)
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through 2024-11-26, then Coinbase Exchange daily closes (UTC).
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