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.
84 lines
3.3 KiB
Python
84 lines
3.3 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 import data
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from btcmodel.evaluate import backtest
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from btcmodel.halving import HALVINGS, cycle_position
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from btcmodel.models import MODELS, CycleDrift
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def synthetic_prices(daily_return, start="2011-01-01", end="2024-11-26", noise=0.0, seed=0):
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"""Prices whose daily log return is `daily_return(cycle_day)` plus optional noise."""
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dates = pd.date_range(start, end, freq="D", name="date")
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_, day = cycle_position(dates)
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r = daily_return(day) + noise * np.random.default_rng(seed).standard_normal(len(dates))
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return pd.DataFrame({"close": 100 * np.exp(np.cumsum(r))}, index=dates)
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def test_cycle_position():
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index, day = cycle_position(pd.DatetimeIndex(["2009-01-03", "2012-11-27", *HALVINGS]))
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assert list(index) == [0, 0, 1, 2, 3, 4]
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assert list(day) == [0, 1424, 0, 0, 0, 0]
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# A projected halving about four years after the last one starts cycle 5.
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index, _ = cycle_position(pd.DatetimeIndex(["2028-06-01"]))
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assert index[0] == 5
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def test_cycle_model_recovers_a_cycle_shaped_drift():
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# Up for the first half of each cycle, down in the second half.
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def shape(day):
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return np.where(day < 700, 0.002, -0.001)
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prices = synthetic_prices(shape)
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drift = CycleDrift(prior_days=0).by_cycle_day(prices)
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assert drift[300] == pytest.approx(0.002, abs=2e-4)
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assert drift[1100] == pytest.approx(-0.001, abs=2e-4)
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def test_cycle_model_weights_recent_cycles_more():
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# The same day of the cycle returns less in each later cycle.
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prices = synthetic_prices(lambda day: np.zeros_like(day, dtype=float))
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cycle, _ = cycle_position(prices.index)
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r = 0.004 / 2.0**cycle
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prices["close"] = 100 * np.exp(np.cumsum(r))
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drift = CycleDrift(recency_half_life=0.25).by_cycle_day(prices)
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equal = CycleDrift(recency_half_life=1e9).by_cycle_day(prices)
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latest_complete = 0.004 / 2.0**3
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assert abs(drift[900] - latest_complete) < abs(equal[900] - latest_complete)
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@pytest.mark.parametrize("name", list(MODELS))
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def test_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 = MODELS[name].forecast(prices, horizons)
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assert f.log_quantiles.shape == (4, 100)
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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), "uncertainty should grow with horizon"
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def test_backtest_never_shows_models_the_future():
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prices = synthetic_prices(lambda day: 0.001 + 0 * day, noise=0.03)
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class Spy:
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name = "random_walk"
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def forecast(self, history, horizons):
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origin = history.index[-1]
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assert history.index.max() == origin
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assert (origin + pd.Timedelta(days=int(horizons.min()))) > history.index.max()
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return MODELS["random_walk"].forecast(history, horizons)
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scores = backtest([Spy()], prices)
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assert len(scores) > 0
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targets = scores.origin + pd.to_timedelta(scores.horizon, unit="D")
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assert targets.max() <= prices.index[-1]
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def test_development_data_stops_at_cutoff():
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prices = data.load_prices(until=data.DEV_CUTOFF)
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assert prices.index[0] == data.DATA_START
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assert prices.index[-1] == data.DEV_CUTOFF
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