sam
082bcfbbbc
Add TrendReversionVol: deviations from the power-law trend follow a daily AR(1), so uncertainty levels off, optionally plus trend-parameter uncertainty with an autocorrelation-adjusted effective sample size. Two experiments, run under the unchanged verdict rule: - powerlaw-ou: +21% to +45% vs powerlaw at 2-4 years, but slightly negative point estimates at 1 month make it inconclusive. - cycle-on-powerlaw: inconclusive (+18% at 2 years, negative elsewhere). The holdout (outcomes after 2024-11-26) was scored once, for the four candidates fixed beforehand. powerlaw is the best long-horizon forecast (+45% and +58% vs the random walk at 2 and 3 years); nothing beats the random walk inside a year; cycle fails badly. Results are in the README.
86 lines
3.5 KiB
Python
86 lines
3.5 KiB
Python
import numpy as np
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import pandas as pd
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import pytest
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from btcmodel.experiments import EXPERIMENTS
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from btcmodel.halving import GENESIS
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from btcmodel.models.drift import CycleDrift, PowerLawDrift, ShrunkDrift
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from btcmodel.models.shape import StudentT
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from btcmodel.models.volatility import ReversionVol, TrailingVol, TrendReversionVol
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from .test_models import synthetic_prices
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ALL_VARIANTS = {m.name: m for e in EXPERIMENTS.values() for m in e.models}
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@pytest.mark.parametrize("name", list(ALL_VARIANTS))
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def test_experiment_models_produce_valid_forecasts(name):
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prices = synthetic_prices(lambda day: 0.001 + 0 * day, noise=0.03)
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horizons = np.array([1, 30, 365, 1460])
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f = ALL_VARIANTS[name].forecast(prices, horizons)
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assert np.isfinite(f.log_quantiles).all()
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assert np.all(np.diff(f.log_quantiles, axis=1) >= 0)
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lo, hi = f.interval(0.8)
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assert np.all(np.diff(hi - lo) > 0)
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def test_shrunk_drift_scales_linearly():
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prices = synthetic_prices(lambda day: np.where(day < 700, 0.002, -0.001))
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horizons = np.array([100, 1000])
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full = CycleDrift().expected_log_return(prices, horizons)
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np.testing.assert_allclose(
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ShrunkDrift(CycleDrift(), 0.5).expected_log_return(prices, horizons), full / 2
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)
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np.testing.assert_allclose(
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ShrunkDrift(CycleDrift(), 0.0).expected_log_return(prices, horizons), 0
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)
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def test_power_law_recovers_its_exponent():
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dates = pd.date_range("2011-01-01", "2024-11-26", freq="D", name="date")
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t = (dates - GENESIS).days.to_numpy()
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prices = pd.DataFrame({"close": 1e-17 * t**5.8}, index=dates)
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_, slope, _ = PowerLawDrift().fit(prices)
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assert slope == pytest.approx(5.8, rel=1e-6)
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expected = 5.8 * np.log((t[-1] + 365) / t[-1])
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assert PowerLawDrift().expected_log_return(prices, np.array([365]))[0] == pytest.approx(
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expected
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)
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def test_reversion_vol_matches_trailing_when_already_at_long_run():
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rng = np.random.default_rng(0)
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dates = pd.date_range("2011-01-01", "2024-11-26", freq="D", name="date")
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prices = pd.DataFrame(
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{"close": 100 * np.exp(np.cumsum(0.03 * rng.standard_normal(len(dates))))}, index=dates
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)
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horizons = np.array([30, 365, 1460])
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reverting = ReversionVol(now_span=1460, long_window=len(dates)).sd(prices, horizons)
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flat = TrailingVol(window=len(dates)).sd(prices, horizons)
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np.testing.assert_allclose(reverting, flat, rtol=0.05)
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def test_student_t_shape_has_unit_variance_and_fatter_tails():
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q = StudentT(4).standard_quantiles(None, np.array([1]))[0]
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assert q[-1] > 2.576 # beyond the normal 99.5% quantile
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assert np.interp(0.8413, np.linspace(0.005, 0.995, 100), q) < 1.0 # thinner shoulders
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def test_trend_reversion_vol_levels_off():
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# A power-law trend plus AR(1) deviations with a ~70-day half-life.
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rng = np.random.default_rng(0)
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dates = pd.date_range("2011-01-01", "2024-11-26", freq="D", name="date")
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t = (dates - GENESIS).days.to_numpy()
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gap = np.zeros(len(t))
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for i in range(1, len(t)):
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gap[i] = 0.99 * gap[i - 1] + 0.03 * rng.standard_normal()
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prices = pd.DataFrame({"close": np.exp(-40 + 5.8 * np.log(t) + gap)}, index=dates)
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horizons = np.array([30, 365, 1460, 5000])
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plain = TrendReversionVol().sd(prices, horizons)
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with_params = TrendReversionVol(parameter_uncertainty=True).sd(prices, horizons)
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assert np.all(np.diff(plain) >= 0)
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stationary = 0.03 / np.sqrt(1 - 0.99**2)
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assert plain[-1] == pytest.approx(stationary, rel=0.15)
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assert np.all(with_params >= plain)
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