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.
35 lines
1.3 KiB
Python
35 lines
1.3 KiB
Python
"""Halving calendar and position within the halving cycle."""
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import numpy as np
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import pandas as pd
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GENESIS = pd.Timestamp("2009-01-03")
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# Block heights 210k, 420k, 630k, 840k (UTC dates).
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HALVINGS = pd.DatetimeIndex(["2012-11-28", "2016-07-09", "2020-05-11", "2024-04-20"])
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# Later halvings are projected at the length of the last cycle. Block times drift
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# by weeks per cycle, which is noise at the resolution this is used.
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_LAST_CYCLE = HALVINGS[-1] - HALVINGS[-2]
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_PROJECTED = pd.DatetimeIndex([HALVINGS[-1] + k * _LAST_CYCLE for k in range(1, 6)])
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# Genesis starts cycle 0.
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CYCLE_STARTS = pd.DatetimeIndex([GENESIS]).append(HALVINGS).append(_PROJECTED)
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def cycle_position(dates) -> tuple[np.ndarray, np.ndarray]:
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"""
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For each date, return (cycle index, days since that cycle began).
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Cycle 0 runs from genesis to the first halving; a halving day is day 0 of
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the cycle it starts.
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"""
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dates = pd.DatetimeIndex(dates)
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if (dates < GENESIS).any():
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raise ValueError("date before genesis")
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if (dates >= CYCLE_STARTS[-1]).any():
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raise ValueError("date beyond projected halvings")
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index = CYCLE_STARTS.searchsorted(dates, side="right") - 1
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days = (dates - CYCLE_STARTS[index]).days
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return np.asarray(index), np.asarray(days)
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