sam
b0243adf61
Models are now a Composite of drift, volatility and (optional) shape components, so an experiment can swap one part against a fixed control. btcmodel/experiments.py holds seven experiments built from the ideas in the old branches (catalogued in docs/2024-ideas.md), each with its hypothesis and source, and a verdict rule fixed before anything ran. `just ab` runs them on development data. Results: - Shrinking the cycle drift, and a power-law trend (plain or reverting), beat their controls. The power law beats the random walk by 53-63% at 3-4 years with unbiased outcomes, so it is promoted to MODELS. - Every alternative volatility estimate (EWMA blends, other windows, reversion to a level or trend) is worse than the trailing 365-day window. Cycle-dependent volatility, heavy tails and stretched cycle phase show no reliable effect.
66 lines
3.2 KiB
Markdown
66 lines
3.2 KiB
Markdown
# Ideas in the 2024 model's history
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A catalogue of the modeling ideas in the old `model.py` and its branches
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(September 2026). Labels are referenced from `btcmodel/experiments.py`.
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"Fudge" means the constant or rule existed to hit backtest coverage/MAPE
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targets rather than to express a hypothesis.
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## Drift
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- **D1. Mean return by halving-cycle position.** The core idea throughout
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(initial commit, `Switch to simpler log-based projection`, `Implement trend
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smoothing`, `tuning-b`). Smoothing went Savitzky-Golay → 60-day centred mean
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→ Gaussian kernel with recency weights. The old code stretched every cycle
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to 1460 days (phase = fraction of the halving interval). Bugs: simple
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returns compounded as log returns (+~25%/yr bias) in the initial commit;
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genesis date off by a year; `tuning-3` regressed log price on row index
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across cycles. The "blend ratio of 0 is best?" commit was blending the
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simple- and log-return versions of the same quantity.
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- **D2. Drift damping.** Constants everywhere (×0.6–0.9, asymmetric, era- and
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cycle-position-keyed, a 3%/day cap, the fundamentals ×0.65–0.75). Fudge as
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implemented; the hypothesis underneath (the cycle drift is overfit) is real.
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- **D3. Diminishing returns.** Initial commit decayed drift by 0.9^(t/365);
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`old/backtests-trend-enhancement-1` regressed per-cycle returns on cycle
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number, but added that on top of the cycle drift (double counting) and
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clipped paths.
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- **D4. "Skew".** `loc += sign(μ)·0.087σ`, tuned so 68% coverage hit 68.1%.
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A fudge; its honest cousin is momentum.
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- **D5. Stock-to-flow.** A 30% blend of the daily % change in S2F into the
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drift. Broken: mismatched units, and S2F "halvings" on the wrong dates
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created a large fake post-halving drift.
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## Volatility
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- **V1. EWMA blend.** 30/90/180-day spans, weights .5/.3/.2 then .2/.5/.3,
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times a 1.2 fudge.
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- **V2. Adaptive windows / regime weights.** Short/long vol ratio rescales
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window lengths and blend weights; over-parameterised, and after the
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fundamentals rewrite the adaptive windows were computed but unused.
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- **V3. Cycle-position volatility.** `old/backtest-vol-cycle`.
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- **V4. Market maturity.** Volume growth, inverse vol, autocorrelation and a
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post-futures dummy, min-max normalised in-sample. Removed as "complexity
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without clear benefit".
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- **V5. "Fundamentals".** Supply growth, volume/supply and "depth" combined
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and clipped to [0.65, 0.75]: effectively a constant, grid-searched to match
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the era constants it replaced.
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- **V6. Market conditions.** Vol ratio, MA50−MA200 trend strength (≈30× too
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large from a units bug) and drawdown widening uncertainty.
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## Calibration (all fudges)
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- **C1. Era scaling keyed to the backtest's training start date**, with stacked
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factors (net ≈0.45× vol) and hand-picked era boundaries.
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- **C2. Horizon uncertainty multipliers**, caps and floors on daily vol.
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- **C3. Changing the quantile levels**: a "95%" band read off the ~81%
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quantiles past a year.
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## Shape
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- **S1. Skewed innovations**, with skew set per era (`tuning-1`).
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## Also
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Plumbing (backtest framework, multiprocessing, output handling, cycle and
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CDPR plots). The old NOTES.md described "machine learning" and "macro
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indicator" experiments; no such code ever existed.
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