jackyu66gitandClaude
71951019fb
Phase A-C complete: 4 core factors, regime detection, signal tracking, Bayesian expectancy. chanmacro/ (32 files, ~4000 lines): - models: 12 enums + 15 Pydantic v2 models (DateAwareModel, MarketStateVector, etc.) - fetchers: OHLCV + Breadth (from data_provider) + Derivatives (new endpoint) - scoring: Price Structure / Breadth (quantile buckets) / OI Matrix (5 discrete states) / Volatility Regime - regime_detector: 3-state (TREND/RANGE/PANIC), factor-locked (Price+Breadth+Vol), versioned, 2-day confirmation - expectancy: SignalTracker (record+outcomes), TimeDecay (half-life=180d), BayesianExpectancyEngine (Empirical Bayes, Leveled, SufficiencyGuard) - validation: FactorValidator (IC/ICIR/Hit Ratio), RegimeValidator (MI/KL/ANOVA), TransitionValidator (stability) - CLI: fetch|score|regime|track|backfill|expectancy|validate|serve - tests: 52 passing (models, scoring, regime, expectancy) data_provider: - /api/derivatives endpoint: funding rate, OI, OI change, basis - _derivatives storage: same persist pattern as K-line (merge→lock→snapshot→atomic write) - background refresh every 60s Co-Authored-By: Claude <noreply@anthropic.com>
110 lines
4.3 KiB
Python
110 lines
4.3 KiB
Python
"""Test regime detector and validation."""
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import pytest
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from datetime import date
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import pandas as pd
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import numpy as np
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class TestRegimeDetector:
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def test_detects_trend(self):
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from regime_detector import RegimeDetector
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from models import MarketRegime
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d = RegimeDetector()
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r = d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
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assert r.regime == MarketRegime.TREND
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assert r.confidence > 0.5
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def test_detects_range(self):
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from regime_detector import RegimeDetector
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from models import MarketRegime
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d = RegimeDetector()
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r = d.detect(50.0, 50.0, "LOW_VOL", date(2026, 6, 24))
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assert r.regime in (MarketRegime.RANGE, MarketRegime.TREND)
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def test_detects_panic(self):
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from regime_detector import RegimeDetector
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from models import MarketRegime
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d = RegimeDetector()
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r = d.detect(15.0, 10.0, "EXPLOSIVE_VOL", date(2026, 6, 24))
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assert r.regime == MarketRegime.PANIC
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def test_2day_confirmation(self):
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from regime_detector import RegimeDetector
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from models import MarketRegime
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d = RegimeDetector()
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# Day 1: RANGE
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r1 = d.detect(50.0, 50.0, "LOW_VOL", date(2026, 6, 24))
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assert r1.regime == MarketRegime.RANGE # first run, no confirmation needed
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# Day 2: still RANGE
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r2 = d.detect(50.0, 50.0, "LOW_VOL", date(2026, 6, 25))
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assert r2.regime == MarketRegime.RANGE
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assert r2.confirmation_days == 2
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def test_transition_needs_confirmation(self):
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from regime_detector import RegimeDetector
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from models import MarketRegime
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d = RegimeDetector()
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# Establish TREND
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d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
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d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 25))
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# Day 3: weak scores → raw best = RANGE, but TREND should persist
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r3 = d.detect(35.0, 40.0, "NORMAL_VOL", date(2026, 6, 26))
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# First day of pending transition — should still be TREND
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assert r3.regime == MarketRegime.TREND
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assert d.pending_regime is not None
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def test_version_is_stored(self):
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from regime_detector import RegimeDetector
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d = RegimeDetector(regime_version="v1_price_breadth_vol")
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r = d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
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assert r.regime_version == "v1_price_breadth_vol"
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def test_load_state(self, db_path):
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from regime_detector import RegimeDetector
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from models import MarketRegime
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d = RegimeDetector()
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d.load_state(db_path)
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# DB has TREND for first 100 days, so most recent should load
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assert d.current_regime is not None
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def test_confidence_for_confirmed_regime(self):
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"""Confidence should be for the confirmed regime, not raw best."""
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from regime_detector import RegimeDetector
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from models import MarketRegime
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d = RegimeDetector()
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# Establish TREND
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d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
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d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 25))
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# Now feed weak scores → raw best would be PANIC or RANGE
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r = d.detect(15.0, 10.0, "EXPLOSIVE_VOL", date(2026, 6, 26))
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# Should still report TREND (need 2 confirmations to switch)
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assert r.regime == MarketRegime.TREND
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class TestTransitionValidator:
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def test_stable_regime_passes(self):
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from validation.transition_validator import TransitionValidator
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# Create stable regime sequence: long periods
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seq = pd.Series(
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["TREND"] * 50 + ["RANGE"] * 50 + ["PANIC"] * 40,
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index=pd.date_range("2026-01-01", periods=140),
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)
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tv = TransitionValidator()
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report = tv.validate(seq)
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assert report.is_stable
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assert report.avg_duration > 20
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assert report.flip_rate < 0.05
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def test_unstable_regime_fails(self):
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from validation.transition_validator import TransitionValidator
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# Create unstable sequence: flips every 2 days
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seq = pd.Series(
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["TREND", "TREND", "RANGE", "RANGE", "TREND", "TREND",
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"PANIC", "PANIC", "RANGE", "RANGE"] * 5,
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index=pd.date_range("2026-01-01", periods=50),
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)
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tv = TransitionValidator()
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report = tv.validate(seq)
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assert not report.is_stable
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assert report.flip_rate > 0.15
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