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.
45 lines
1.7 KiB
Python
45 lines
1.7 KiB
Python
import numpy as np
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import pandas as pd
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from scipy.stats import norm
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from btcmodel.forecast import Forecast, crps, pit
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def normal_crps(mu, sigma, y):
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"""Closed-form CRPS of N(mu, sigma^2) at y."""
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z = (y - mu) / sigma
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return sigma * (z * (2 * norm.cdf(z) - 1) + 2 * norm.pdf(z) - 1 / np.sqrt(np.pi))
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def test_crps_matches_closed_form_for_normal():
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f = Forecast.normal("2020-01-01", np.array([1, 2, 3]), mean=[0.0, 1.0, 2.0], sd=[1.0, 0.5, 2.0])
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y = np.array([0.3, -0.2, 5.0])
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expected = normal_crps(np.array([0.0, 1.0, 2.0]), np.array([1.0, 0.5, 2.0]), y)
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np.testing.assert_allclose(crps(f.log_quantiles, y), expected, rtol=0.02)
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def test_crps_prefers_the_right_forecast():
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y = np.array([0.0])
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good = Forecast.normal("2020-01-01", np.array([1]), 0.0, 0.1)
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biased = Forecast.normal("2020-01-01", np.array([1]), 0.5, 0.1)
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vague = Forecast.normal("2020-01-01", np.array([1]), 0.0, 2.0)
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assert crps(good.log_quantiles, y) < crps(biased.log_quantiles, y)
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assert crps(good.log_quantiles, y) < crps(vague.log_quantiles, y)
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def test_pit_and_intervals():
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f = Forecast.normal("2020-01-01", np.array([1, 1, 1]), 0.0, 1.0)
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np.testing.assert_allclose(
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pit(f.log_quantiles, [0.0, 1.0, -10.0]), [0.5, 0.841, 0.0], atol=0.01
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)
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lo, hi = f.interval(0.95)
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np.testing.assert_allclose(hi, 1.96, atol=0.01)
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np.testing.assert_allclose(lo, -1.96, atol=0.01)
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def test_from_samples_recovers_quantiles():
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rng = np.random.default_rng(0)
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f = Forecast.from_samples("2020-01-01", np.array([10]), rng.normal(0, 1, size=(200_000, 1)))
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np.testing.assert_allclose(f.quantile(0.5), 0.0, atol=0.02)
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assert f.dates[0] == pd.Timestamp("2020-01-11")
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