dennisthiessenandClaude Opus 5
7fdcac3b55
Follows 5ea0785, which renamed the user-visible labels. This finishes the pass
so code, docs and operator output use one vocabulary: README (pipeline list,
route table, FRED row), the methodology doc title, .env.example and config
comments, the snapshot model / event-study / service / test docstrings, the
scheduler section headers and morning-pipeline docstring, the TopBar status
text ("bullish regime" -> "bullish trend"), and the four "Regime monitor:" log
prefixes.
Deliberately NOT changed, because "market regime" is also a standard finance
term and most occurrences are not this job: the backtest caveat "~6 months is
roughly one market regime" in backtest_service, README, BacktestPanel and every
generated reports/*.json; "a regime shift" in TrackRecordPanel; and the
capacity-bracket findings doc. Renaming those would have made the text wrong.
Also unchanged, being persisted or externally linked rather than wording: the
regime_monitor / market_regime job ids, the regime_quadrant_enabled setting key,
the /regime route, METHODOLOGY and the snapshot fields, the service/test module
filenames, and docs/research/regime-monitor-v3.md's path (referenced from commit
messages). The doc now carries a one-line note recording the old name and why
those identifiers still use it.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
714 lines
26 KiB
Python
714 lines
26 KiB
Python
"""Pure-function tests for the v3 AI/Tech Risk Monitor contract."""
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from __future__ import annotations
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import copy
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import json
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from datetime import date, timedelta
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import pytest
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from pydantic import ValidationError as PydanticValidationError
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from sqlalchemy import select
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from app.models.regime_snapshot import RegimeSnapshot
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from app.routers import market as market_router
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from app.services import breadth_service, regime_monitor_service as rms
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from app.services.regime_monitor_service import (
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DEFAULT_CONFIG,
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HY_OAS_ELEVATED,
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HY_OAS_MILD,
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HY_OAS_STRESSED,
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STATE_BANDS,
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WARNING_BANDS,
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WARNING_WEIGHTS,
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_compute_index,
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_score_pillars,
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band_for,
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breadth_level_score,
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drawdown_pct,
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f2_credit_spreads,
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current_observation,
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fundamental_overlay,
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p1_trend_break,
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p2_death_cross,
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p3_drawdown,
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p4_relative_strength,
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p5_volatility,
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score_warning_sensors,
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w3_credit_impulse,
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warning_sensor_scores,
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)
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def _dated(values: list[float], end: date = date(2026, 6, 26)) -> list[tuple[date, float]]:
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return [
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(end - timedelta(days=len(values) - 1 - index), value)
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for index, value in enumerate(values)
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]
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def test_band_for_is_per_axis():
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assert band_for(10, STATE_BANDS) == "stable"
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assert band_for(20, STATE_BANDS) == "watch"
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assert band_for(50, STATE_BANDS) == "elevated"
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assert band_for(80, STATE_BANDS) == "breaking"
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# Warning's realized range is far narrower, so it gets its own thresholds.
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assert band_for(45, STATE_BANDS) == "watch"
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assert band_for(45, WARNING_BANDS) == "elevated"
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assert band_for(60, WARNING_BANDS) == "breaking"
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def test_price_sensors_are_stress_only():
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smh_under = [100.0] * 199 + [50.0]
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qqq_above = [100.0] * 200
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assert round(p1_trend_break(smh_under, qqq_above) or 0, 1) == 66.7
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bearish = [300.0 - index for index in range(260)]
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healthy = [100.0 + index * 0.5 for index in range(260)]
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assert (p2_death_cross(bearish, bearish) or 0) > 0
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assert p2_death_cross(healthy, healthy) == 0
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def test_drawdown_sensor_keeps_headroom_past_a_twenty_percent_fall():
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"""v2 pegged at 100 on a 20% drawdown, losing all resolution deeper in."""
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flat = [100.0] * 253
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down_20 = [100.0] * 252 + [80.0]
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down_30 = [100.0] * 252 + [70.0]
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down_45 = [100.0] * 252 + [55.0]
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assert drawdown_pct(down_20) == pytest.approx(20.0)
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leader_only_20 = p3_drawdown(down_20, flat)
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leader_only_30 = p3_drawdown(down_30, flat)
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assert leader_only_20 < leader_only_30 < 100.0
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# Full scale needs both legs at the deepest anchor, not one at 20%.
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assert p3_drawdown(down_45, down_45) == 100.0
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assert p3_drawdown(flat, flat) == 0.0
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def test_drawdown_blends_leader_and_confirm_instead_of_taking_the_max():
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"""max() let the more volatile leader own the whole price pillar."""
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flat = [100.0] * 253
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down = [100.0] * 252 + [72.0]
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both = p3_drawdown(down, down)
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leader_only = p3_drawdown(down, flat)
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assert leader_only == pytest.approx(both * 2.0 / 3.0)
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def test_credit_impulse_scores_widening_only():
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assert w3_credit_impulse([3.0] * 40) == 0.0
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# Tightening is not stress.
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assert w3_credit_impulse([4.0] * 21 + [3.0]) == 0.0
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# +35% over the lookback is full scale; half of it is half the score.
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assert w3_credit_impulse([3.0] * 21 + [3.0 * 1.35]) == pytest.approx(100.0)
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assert w3_credit_impulse([3.0] * 21 + [3.0 * 1.175]) == pytest.approx(50.0)
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# Fires while the OAS *level* is still far below the 3.5 mild anchor. This
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# is the pairing that lets the level stay purely anchored: dynamics live on
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# the Warning axis rather than being smuggled into State as a percentile.
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assert f2_credit_spreads([2.0] * 21 + [2.7]) == 0.0
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assert (w3_credit_impulse([2.0] * 21 + [2.7]) or 0) > 0
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assert w3_credit_impulse([3.0] * 5) is None
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def test_snapshot_records_upstream_history_spans():
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"""Guards the silent-truncation failure mode that caused this change."""
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end = date(2026, 6, 26)
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rising = [100.0 + index * 0.2 for index in range(700)]
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prices = {"SMH": _dated(rising, end), "QQQ": _dated(rising, end), "SPY": _dated(rising, end)}
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oas = [(end - timedelta(days=index), 4.0) for index in reversed(range(100))]
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result = _compute_index(
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prices, [(end, 20.0)], oas, {"f1_score": None, "f3_score": None},
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copy.deepcopy(DEFAULT_CONFIG), end, [(end, 55.0)], [(end, 20.0)], {end: 25},
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)
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assert result["data_quality"]["credit_history_days"] == 99
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assert result["data_quality"]["vix_history_days"] == 0
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def test_divergence_still_registers_when_price_confirms_the_breadth_loss():
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"""v2's hard price gate zeroed this sensor during every decline.
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On 2026-07-24 the basket shed 10 points of participation in 20 sessions
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while SMH fell 11.9%, and Warning printed exactly 0 as a result.
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"""
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days = [date(2026, 1, 1) + timedelta(days=index) for index in range(21)]
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breadth = {day: 70.0 for day in days[:1]} | {day: 70.0 - index for index, day in enumerate(days)}
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holding = [(day, 100.0) for day in days]
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falling = [(day, 100.0 - index * 0.9) for index, day in enumerate(days)]
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masked = breadth_service.compute_divergence_series(breadth, holding)[days[-1]]
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confirmed = breadth_service.compute_divergence_series(breadth, falling)[days[-1]]
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assert masked > confirmed > 0
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assert confirmed == pytest.approx(masked * breadth_service.DIVERGENCE_CONFIRMED_FLOOR)
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def test_warning_score_renormalises_over_available_sensors():
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full = {"breadth_divergence": 40.0, "relative_strength": 0.0, "credit_impulse": 20.0}
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assert score_warning_sensors(full) == pytest.approx(
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(40 * 45 + 0 * 30 + 20 * 25) / 100
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)
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partial = {"breadth_divergence": 40.0, "relative_strength": None, "credit_impulse": None}
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assert score_warning_sensors(partial) == 40.0
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assert score_warning_sensors(dict.fromkeys(full, None)) is None
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def test_warning_sensor_scores_covers_every_weighted_pillar():
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"""Guards the study/monitor shared definition against silent drift."""
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sensors = warning_sensor_scores(10.0, [100.0] * 70, [100.0] * 70, [3.0] * 40)
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assert set(sensors) == set(WARNING_WEIGHTS)
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def test_relative_strength_flat_or_better_is_zero():
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flat = [100.0] * 70
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rising = [100.0 + index for index in range(70)]
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falling = [100.0 - index * 0.5 for index in range(70)]
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assert p4_relative_strength(flat, flat) == 0.0
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assert p4_relative_strength(rising, flat) == 0.0
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assert (p4_relative_strength(falling, flat) or 0) > 0
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def test_volatility_and_breadth_zero_points():
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assert p5_volatility(15) == 0
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assert p5_volatility(30) == 100
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assert breadth_level_score(60) == 0
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assert breadth_level_score(20) == 100
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assert breadth_level_score(None) is None
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def test_credit_level_is_anchored_and_ignores_the_reference_window():
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"""The percentile leg is gone: the anchors already encode the long run.
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It ranked the level against whatever history the upstream series happened to
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serve, and that silently shrank from 10 years to 3 in April 2026 -- three
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uniformly tight years, against which an unremarkable spread scored as an
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extreme. Identical inputs must now score identically regardless of window.
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"""
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assert f2_credit_spreads([HY_OAS_MILD] * 100) == 0.0
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assert f2_credit_spreads([HY_OAS_ELEVATED] * 100) == 50.0
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assert f2_credit_spreads([HY_OAS_STRESSED] * 100) == 100.0
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assert f2_credit_spreads([]) is None
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# A level at the "mild" anchor is zero stress even when it tops its window.
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tight_window = [2.6] * 400 + [HY_OAS_MILD]
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assert f2_credit_spreads(tight_window) == 0.0
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# Only the latest observation matters; history cannot move the reading.
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assert f2_credit_spreads([9.0] * 400 + [3.0]) == f2_credit_spreads([2.6] * 400 + [3.0])
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def test_score_pillars_gates_band_below_75_percent_coverage():
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pillars = [
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{"id": "price", "label": "Price", "score": 80.0, "sensors": []},
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{"id": "breadth", "label": "Breadth", "score": 20.0, "sensors": []},
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{"id": "credit", "label": "Credit", "score": None, "sensors": []},
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{"id": "volatility", "label": "Vol", "score": None, "sensors": []},
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]
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result = _score_pillars(pillars, {"price": 40, "breadth": 25, "credit": 20, "volatility": 15})
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assert result["coverage"] == 65.0
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assert result["score"] is not None
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assert result["band"] is None
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def test_fundamental_overlay_never_replays_before_effective_date_and_expires():
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overrides = {
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"f1_score": 0.0,
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"f3_score": 100.0,
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"capex": {"GOOGL": "raising"},
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"good_news_stock_down": "yes",
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"fetched_at": "2026-06-01T10:00:00+00:00",
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"effective_date": "2026-06-02",
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}
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config = {**DEFAULT_CONFIG, "fundamental_staleness_days": 80}
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pending = fundamental_overlay(overrides, config, date(2026, 6, 1))
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assert pending["pending"] is True
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assert pending["available"] is False
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assert pending["capex"] is None
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# The effective date is still reported so a pending refresh is visible.
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assert pending["effective_date"] == "2026-06-02"
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live = fundamental_overlay(overrides, config, date(2026, 6, 2))
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assert live["available"] is True
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assert live["good_news_stock_down"] == "yes"
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assert live["earnings_stress"] == 100.0
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expired = fundamental_overlay(overrides, config, date(2026, 8, 22))
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assert expired["stale"] is True
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assert expired["available"] is False
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def test_live_observation_is_visible_before_its_effective_date():
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"""Refreshing must not look like it did nothing.
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The stored snapshot keeps the effective-date gate so a rebuild cannot
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backdate an observation, but the live card reports that date instead of
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blanking the content -- otherwise a Friday refresh stays invisible until
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Monday.
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"""
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overrides = {
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"f1_score": 50.0,
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"f3_score": 100.0,
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"capex": {"GOOGL": "holding"},
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"good_news_stock_down": "yes",
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"reasoning": "fresh read",
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"fetched_at": "2026-06-01T10:00:00+00:00",
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"effective_date": "2026-06-02",
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}
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config = {**DEFAULT_CONFIG, "fundamental_staleness_days": 80}
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before = date(2026, 6, 1)
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record = fundamental_overlay(overrides, config, before)
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now = current_observation(overrides, config, before)
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# Same day, same observation: the record hides it, the live reading shows it.
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assert record["capex"] is None and record["reasoning"] is None
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assert now["capex"] == {"GOOGL": "holding"}
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assert now["reasoning"] == "fresh read"
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assert now["capex_stress"] == 50.0
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assert now["earnings_stress"] == 100.0
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# ...while still reporting when the stored record picks it up.
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assert now["pending"] is True
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assert now["effective_date"] == "2026-06-02"
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assert now["available"] is True
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# Staleness still expires the live reading.
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assert current_observation(overrides, config, date(2026, 8, 22))["stale"] is True
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assert current_observation(overrides, config, date(2026, 8, 22))["available"] is False
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def test_an_uncollected_observation_is_not_reported_as_collected():
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"""The default override is placeholders, not a reading.
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``capex`` defaults to "unknown" for every hyperscaler and the reaction to
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"mixed". Surfacing those as an observation made the card claim a read that
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never happened.
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"""
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names = DEFAULT_CONFIG["tickers"]["hyperscalers"]
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nothing_collected = {
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"f1_score": None,
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"f3_score": None,
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"capex": {name: "unknown" for name in names},
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"good_news_stock_down": "mixed",
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"reasoning": None,
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"fetched_at": None,
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"effective_date": None,
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"source": "default",
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}
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blank = current_observation(nothing_collected, DEFAULT_CONFIG, date(2026, 8, 7))
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assert blank["observed"] is False
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assert blank["available"] is False
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assert blank["capex"] is None
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assert blank["good_news_stock_down"] is None
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assert blank["reasoning"] is None
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# One real observation flips it, placeholders and all.
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collected = {**nothing_collected, "fetched_at": "2026-08-07T10:00:00+00:00", "source": "gemini"}
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assert current_observation(collected, DEFAULT_CONFIG, date(2026, 8, 7))["observed"] is True
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def test_fundamentals_do_not_move_the_warning_score():
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"""The v3 complaint: a maxed-out LLM read must not silently do nothing.
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It no longer feeds Warning at all, so Warning is identical either way and
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the observation is reported beside the score instead of buried in it.
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"""
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end = date(2026, 6, 26)
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rising = [100.0 + index * 0.2 for index in range(700)]
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prices = {"SMH": _dated(rising, end), "QQQ": _dated(rising, end), "SPY": _dated(rising, end)}
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args = (prices, [(end, 20.0)], [(end - timedelta(days=i), 4.0) for i in reversed(range(100))])
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tail = (copy.deepcopy(DEFAULT_CONFIG), end, [(end, 55.0)], [(end, 20.0)], {end: 25})
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quiet = _compute_index(*args, {"f1_score": None, "f3_score": None}, *tail)
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screaming = _compute_index(
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*args,
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{
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"f1_score": 100.0,
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"f3_score": 100.0,
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"capex": dict.fromkeys(DEFAULT_CONFIG["tickers"]["hyperscalers"], "cutting"),
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"good_news_stock_down": "yes",
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"effective_date": "2026-06-01",
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},
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*tail,
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)
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assert quiet["warning"]["score"] == screaming["warning"]["score"]
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assert {p["id"] for p in quiet["warning"]["pillars"]} == set(WARNING_WEIGHTS)
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assert screaming["fundamental_overlay"]["available"] is True
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assert screaming["fundamental_overlay"]["capex_stress"] == 100.0
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def test_capex_score_separates_holding_from_raising():
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"""v2 mapped raising and holding both to 0, so a boom read identical to a
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deceleration and the sensor carried no information."""
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names = DEFAULT_CONFIG["tickers"]["hyperscalers"]
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assert rms._score_capex_states(dict.fromkeys(names, "raising"), names) == 0.0
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assert rms._score_capex_states(dict.fromkeys(names, "holding"), names) == 50.0
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assert rms._score_capex_states(dict.fromkeys(names, "cutting"), names) == 100.0
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assert rms._score_capex_states(
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{names[0]: "raising", **dict.fromkeys(names[1:], "holding")}, names
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) == 37.5
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assert rms._score_capex_states(
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{names[0]: "cutting", names[1]: "holding", names[2]: "unknown", names[3]: "unknown"},
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names,
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) is None
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def test_fundamental_api_rejects_numeric_ordinal_overrides():
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with pytest.raises(PydanticValidationError):
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market_router.RegimeFundamentalsUpdate(f3_score=75)
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@pytest.mark.asyncio
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async def test_legacy_numeric_fundamentals_do_not_leak_into_v3(monkeypatch):
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async def fake_value(_db, _key):
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return json.dumps({"f1_score": 75.0, "f3_score": 75.0, "source": "manual"})
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monkeypatch.setattr(rms.settings_store, "get_value", fake_value)
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result = await rms.get_fundamental_overrides(object())
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assert result["methodology"] == "v3"
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assert result["f1_score"] is None
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assert result["f3_score"] is None
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assert result["good_news_stock_down"] == "mixed"
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@pytest.mark.asyncio
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async def test_v2_observation_survives_the_methodology_bump(monkeypatch):
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"""A snapshot reseed must not throw away a hand/LLM-collected observation.
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The categorical format is unchanged, so the stored capex map is still valid;
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only the capex scale moved, and f1 is recomputed from the categories.
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"""
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names = DEFAULT_CONFIG["tickers"]["hyperscalers"]
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async def fake_value(_db, _key):
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return json.dumps({
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"methodology": "v2",
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"f1_score": 0.0, # stale v2 scale, must be recomputed
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"f3_score": 100.0,
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"capex": {names[0]: "raising", **dict.fromkeys(names[1:], "holding")},
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"good_news_stock_down": "yes",
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"source": "gemini",
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"fetched_at": "2026-07-24T14:25:47+00:00",
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"effective_date": "2026-07-27",
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})
|
|
|
|
monkeypatch.setattr(rms.settings_store, "get_value", fake_value)
|
|
|
|
result = await rms.get_fundamental_overrides(object())
|
|
|
|
assert result["source"] == "gemini"
|
|
assert result["good_news_stock_down"] == "yes"
|
|
assert result["effective_date"] == "2026-07-27"
|
|
assert result["f1_score"] == 37.5 # recomputed on the v3 scale, not the stored 0.0
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_unlock_does_not_redate_a_fundamental_observation(monkeypatch):
|
|
stored = {
|
|
"methodology": "v3",
|
|
"f1_score": 100.0,
|
|
"f3_score": 0.0,
|
|
"capex": dict.fromkeys(DEFAULT_CONFIG["tickers"]["hyperscalers"], "cutting"),
|
|
"good_news_stock_down": "no",
|
|
"locked": True,
|
|
"source": "manual",
|
|
"fetched_at": "2026-06-01T10:00:00+00:00",
|
|
"effective_date": "2026-06-02",
|
|
}
|
|
saved: dict = {}
|
|
|
|
async def fake_get(_db):
|
|
return dict(stored)
|
|
|
|
async def fake_update(_db, _key, value):
|
|
saved.update(json.loads(value))
|
|
|
|
monkeypatch.setattr(rms, "get_fundamental_overrides", fake_get)
|
|
monkeypatch.setattr(rms, "update_setting", fake_update)
|
|
|
|
result = await rms.set_fundamental_overrides(object(), locked=False)
|
|
|
|
assert result["locked"] is False
|
|
assert result["fetched_at"] == stored["fetched_at"]
|
|
assert result["effective_date"] == stored["effective_date"]
|
|
assert saved == result
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_manual_fundamentals_are_categorical_and_derived(monkeypatch):
|
|
names = DEFAULT_CONFIG["tickers"]["hyperscalers"]
|
|
current = {
|
|
"methodology": "v3",
|
|
"f1_score": None,
|
|
"f3_score": None,
|
|
"capex": dict.fromkeys(names, "unknown"),
|
|
"good_news_stock_down": "mixed",
|
|
"locked": False,
|
|
"reasoning": "old reasoning",
|
|
"fetched_at": None,
|
|
"effective_date": None,
|
|
"source": "default",
|
|
}
|
|
saved: dict = {}
|
|
|
|
async def fake_get(_db):
|
|
return dict(current)
|
|
|
|
async def fake_update(_db, _key, value):
|
|
saved.update(json.loads(value))
|
|
|
|
monkeypatch.setattr(rms, "get_fundamental_overrides", fake_get)
|
|
monkeypatch.setattr(rms, "update_setting", fake_update)
|
|
capex = {names[0]: "cutting", **dict.fromkeys(names[1:], "holding")}
|
|
|
|
result = await rms.set_fundamental_overrides(
|
|
object(), capex=capex, good_news_stock_down="mixed"
|
|
)
|
|
|
|
assert result["f1_score"] == 62.5 # one cutting (100) + three holding (50)
|
|
assert result["f3_score"] is None
|
|
assert result["good_news_stock_down"] == "mixed"
|
|
assert result["source"] == "manual"
|
|
assert result["locked"] is True
|
|
assert result["reasoning"] is None
|
|
assert saved == result
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_prior_snapshot_is_immutable_without_explicit_rebuild(db_session):
|
|
snapshot_date = date(2026, 6, 26)
|
|
first = {
|
|
"methodology": "v3",
|
|
"date": snapshot_date.isoformat(),
|
|
"state": {"score": 10.0, "band": "stable"},
|
|
"warning": {"score": 20.0, "band": "stable"},
|
|
}
|
|
changed = copy.deepcopy(first)
|
|
changed["state"] = {"score": 90.0, "band": "breaking"}
|
|
|
|
written, _ = await rms._upsert_snapshot(
|
|
db_session, first, rewrite_existing=True
|
|
)
|
|
await db_session.flush()
|
|
rewritten, persisted = await rms._upsert_snapshot(
|
|
db_session, changed, rewrite_existing=False
|
|
)
|
|
row = (
|
|
await db_session.execute(
|
|
select(RegimeSnapshot).where(RegimeSnapshot.date == snapshot_date)
|
|
)
|
|
).scalar_one()
|
|
|
|
assert written is True
|
|
assert rewritten is False
|
|
assert persisted["state"]["score"] == 10.0
|
|
assert row.total_score == 10.0
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_routine_can_refresh_latest_trading_session_after_civil_day_rolls(
|
|
monkeypatch,
|
|
):
|
|
latest_date = date(2020, 1, 3)
|
|
config = copy.deepcopy(DEFAULT_CONFIG)
|
|
prices = {
|
|
"SMH": [(latest_date, 100.0)],
|
|
"QQQ": [(latest_date, 100.0)],
|
|
"SPY": [(latest_date, 100.0)],
|
|
}
|
|
rewrites: list[bool] = []
|
|
|
|
async def fake_config(_db):
|
|
return config
|
|
|
|
async def fake_overrides(_db):
|
|
return {"locked": True, "fetched_at": None, "effective_date": None}
|
|
|
|
async def fake_prices(_config, _start, _end):
|
|
return prices
|
|
|
|
async def fake_fred(_series_id, _start, _end):
|
|
return None
|
|
|
|
async def fake_breadth(_db, _symbols, window, min_tickers):
|
|
return {}, {}
|
|
|
|
async def fake_latest(_db):
|
|
return object(), {"methodology": "v3", "sensor_revision": rms.SENSOR_REVISION}
|
|
|
|
async def fake_upsert(_db, result, *, rewrite_existing):
|
|
rewrites.append(rewrite_existing)
|
|
return True, result
|
|
|
|
class FakeDB:
|
|
async def commit(self):
|
|
return None
|
|
|
|
monkeypatch.setattr(rms, "get_regime_config", fake_config)
|
|
monkeypatch.setattr(rms, "get_fundamental_overrides", fake_overrides)
|
|
monkeypatch.setattr(rms, "_fetch_prices", fake_prices)
|
|
monkeypatch.setattr(rms, "_fetch_fred_series", fake_fred)
|
|
monkeypatch.setattr(rms.breadth_service, "compute_breadth_details", fake_breadth)
|
|
monkeypatch.setattr(rms, "_latest_snapshot_row", fake_latest)
|
|
monkeypatch.setattr(rms, "_upsert_snapshot", fake_upsert)
|
|
|
|
result = await rms.update_regime_monitor(FakeDB())
|
|
|
|
assert result["date"] == latest_date.isoformat()
|
|
assert rewrites == [True]
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
@pytest.mark.parametrize(
|
|
("stored", "expect_reseed"),
|
|
[
|
|
({"methodology": "v3"}, True), # written before the marker existed
|
|
({"methodology": "v3", "sensor_revision": 1}, True),
|
|
({"methodology": "v3", "sensor_revision": rms.SENSOR_REVISION}, False),
|
|
],
|
|
)
|
|
async def test_a_stale_sensor_revision_reseeds_stored_history(
|
|
monkeypatch, stored, expect_reseed
|
|
):
|
|
"""Widening the OAS window has to reach rows that are already stored.
|
|
|
|
Routine runs recompute only the latest date, so without this trigger every
|
|
older row would keep the credit gap the wider window exists to close.
|
|
"""
|
|
sessions = [date.today() - timedelta(days=offset) for offset in reversed(range(10))]
|
|
prices = {symbol: [(day, 100.0) for day in sessions] for symbol in ("SMH", "QQQ", "SPY")}
|
|
written: list[date] = []
|
|
revisions: list[int] = []
|
|
|
|
async def fake_config(_db):
|
|
return copy.deepcopy(DEFAULT_CONFIG)
|
|
|
|
async def fake_overrides(_db):
|
|
return {"locked": True, "fetched_at": None, "effective_date": None}
|
|
|
|
async def fake_prices(_config, _start, _end):
|
|
return prices
|
|
|
|
async def fake_fred(_series_id, _start, _end):
|
|
return None
|
|
|
|
async def fake_breadth(_db, _symbols, window, min_tickers):
|
|
return {}, {}
|
|
|
|
async def fake_latest(_db):
|
|
return object(), stored
|
|
|
|
async def fake_upsert(_db, result, *, rewrite_existing):
|
|
written.append(date.fromisoformat(result["date"]))
|
|
revisions.append(result["sensor_revision"])
|
|
# Every replayed row must be rewritable, or a reseed writes one row.
|
|
assert rewrite_existing is True
|
|
return True, result
|
|
|
|
class FakeDB:
|
|
async def commit(self):
|
|
return None
|
|
|
|
for name, value in (
|
|
("get_regime_config", fake_config),
|
|
("get_fundamental_overrides", fake_overrides),
|
|
("_fetch_prices", fake_prices),
|
|
("_fetch_fred_series", fake_fred),
|
|
("_latest_snapshot_row", fake_latest),
|
|
("_upsert_snapshot", fake_upsert),
|
|
):
|
|
monkeypatch.setattr(rms, name, value)
|
|
monkeypatch.setattr(rms.breadth_service, "compute_breadth_details", fake_breadth)
|
|
|
|
await rms.update_regime_monitor(FakeDB())
|
|
|
|
if expect_reseed:
|
|
assert written == sessions, "a reseed must replay the whole stored span"
|
|
else:
|
|
assert written == [sessions[-1]], "a current revision must not reseed"
|
|
assert set(revisions) == {rms.SENSOR_REVISION}
|
|
|
|
|
|
def test_the_rebuild_span_stays_inside_the_oas_window():
|
|
"""The reseed must not replay rows it cannot compute credit for.
|
|
|
|
Each replayed row needs W3's lookback inside the fetched OAS window; if the
|
|
replay reached further back than the fetch, the reseed would recreate the
|
|
very gap it exists to close.
|
|
"""
|
|
replay_calendar_days = rms.REBUILD_LOOKBACK_DAYS
|
|
w3_lookback_calendar = rms.W3_OAS_LOOKBACK * 7 / 5 # business days -> calendar
|
|
assert replay_calendar_days + w3_lookback_calendar <= rms.HY_OAS_WINDOW_DAYS
|
|
# ...and still covers the 400-session series the v3 cutover wrote.
|
|
assert replay_calendar_days >= 400 * 365 / 252
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_manual_llm_refresh_recomputes_latest_regime_snapshot(monkeypatch):
|
|
calls: list[str] = []
|
|
refreshed = {"f1_score": 0.0, "f3_score": 100.0}
|
|
|
|
async def fake_refresh(_db, force):
|
|
assert force is True
|
|
calls.append("refresh")
|
|
return refreshed
|
|
|
|
async def fake_recompute(_db):
|
|
calls.append("recompute")
|
|
return {"available": True}
|
|
|
|
monkeypatch.setattr(
|
|
market_router.regime_monitor_service,
|
|
"refresh_fundamental_overrides",
|
|
fake_refresh,
|
|
)
|
|
monkeypatch.setattr(
|
|
market_router.regime_monitor_service,
|
|
"update_regime_monitor",
|
|
fake_recompute,
|
|
)
|
|
|
|
response = await market_router.refresh_regime_fundamentals(
|
|
_admin=object(), db=object()
|
|
)
|
|
|
|
assert calls == ["refresh", "recompute"]
|
|
assert response.data == refreshed
|
|
|
|
|
|
def test_compute_index_uses_one_max_price_vote_and_has_no_combined_score():
|
|
end = date(2026, 6, 26)
|
|
rising = [100.0 + index * 0.2 for index in range(700)]
|
|
qqq = rising.copy()
|
|
smh = rising[:-1] + [rising[-1] * 0.75]
|
|
prices = {
|
|
"SMH": _dated(smh, end),
|
|
"QQQ": _dated(qqq, end),
|
|
"SPY": _dated(rising, end),
|
|
}
|
|
breadth = [(end, 55.0)]
|
|
divergence = [(end, 20.0)]
|
|
result = _compute_index(
|
|
prices,
|
|
[(end, 20.0)],
|
|
[(end - timedelta(days=index), 4.0) for index in reversed(range(100))],
|
|
{"f1_score": None, "f3_score": None},
|
|
copy.deepcopy(DEFAULT_CONFIG),
|
|
end,
|
|
breadth,
|
|
divergence,
|
|
{end: 25},
|
|
)
|
|
price = next(p for p in result["state"]["pillars"] if p["id"] == "price")
|
|
sensor_scores = [sensor["score"] for sensor in price["sensors"] if sensor["score"] is not None]
|
|
assert price["score"] == max(sensor_scores)
|
|
assert result["methodology"] == "v3"
|
|
assert "combined" not in result
|
|
assert result["basket"]["members_available"] == 25
|