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TradingAgents/tests/unit/agents/test_correlation_analyst.py

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"""Tests for Correlation Analyst agent.
Issue #15: [AGENT-14] Correlation Analyst - cross-asset, sector rotation
These tests define the logic locally to avoid langchain import issues.
"""
import pytest
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from unittest.mock import Mock, MagicMock
from enum import Enum
pytestmark = pytest.mark.unit
# ============================================================================
# Local Definitions (matching correlation_analyst.py)
# ============================================================================
class CorrelationStrength(str, Enum):
"""Classification of correlation strength."""
VERY_STRONG_POSITIVE = "very_strong_positive"
STRONG_POSITIVE = "strong_positive"
MODERATE_POSITIVE = "moderate_positive"
WEAK_POSITIVE = "weak_positive"
NEGLIGIBLE = "negligible"
WEAK_NEGATIVE = "weak_negative"
MODERATE_NEGATIVE = "moderate_negative"
STRONG_NEGATIVE = "strong_negative"
VERY_STRONG_NEGATIVE = "very_strong_negative"
class SectorPhase(str, Enum):
"""Economic cycle phase for sector rotation."""
EARLY_CYCLE = "early_cycle"
MID_CYCLE = "mid_cycle"
LATE_CYCLE = "late_cycle"
RECESSION = "recession"
class SectorLeadership(str, Enum):
"""Sector leadership classification."""
LEADING = "leading"
LAGGING = "lagging"
IMPROVING = "improving"
WEAKENING = "weakening"
# ============================================================================
# Helper Functions (matching correlation_analyst.py)
# ============================================================================
def _calculate_correlation(series1: pd.Series, series2: pd.Series) -> float:
"""Calculate Pearson correlation between two series."""
if len(series1) < 2 or len(series2) < 2:
return 0.0
min_len = min(len(series1), len(series2))
s1 = series1.iloc[-min_len:].values
s2 = series2.iloc[-min_len:].values
if np.std(s1) == 0 or np.std(s2) == 0:
return 0.0
return float(np.corrcoef(s1, s2)[0, 1])
def _calculate_rolling_correlation(
series1: pd.Series,
series2: pd.Series,
window: int = 20
) -> pd.Series:
"""Calculate rolling correlation between two series."""
if len(series1) < window or len(series2) < window:
return pd.Series([])
min_len = min(len(series1), len(series2))
s1 = series1.iloc[-min_len:]
s2 = series2.iloc[-min_len:]
rolling_corr = s1.rolling(window=window).corr(s2)
return rolling_corr.dropna()
def _classify_correlation(corr: float) -> CorrelationStrength:
"""Classify correlation coefficient into strength categories."""
if corr >= 0.8:
return CorrelationStrength.VERY_STRONG_POSITIVE
elif corr >= 0.6:
return CorrelationStrength.STRONG_POSITIVE
elif corr >= 0.4:
return CorrelationStrength.MODERATE_POSITIVE
elif corr >= 0.2:
return CorrelationStrength.WEAK_POSITIVE
elif corr > -0.2:
return CorrelationStrength.NEGLIGIBLE
elif corr > -0.4:
return CorrelationStrength.WEAK_NEGATIVE
elif corr > -0.6:
return CorrelationStrength.MODERATE_NEGATIVE
elif corr > -0.8:
return CorrelationStrength.STRONG_NEGATIVE
else:
return CorrelationStrength.VERY_STRONG_NEGATIVE
def _detect_correlation_breakdown(
rolling_corr: pd.Series,
threshold_change: float = 0.3
) -> dict:
"""Detect significant correlation breakdown events."""
if len(rolling_corr) < 10:
return {"detected": False, "details": "Insufficient data"}
corr_diff = rolling_corr.diff()
large_changes = corr_diff[abs(corr_diff) > threshold_change]
if len(large_changes) == 0:
return {"detected": False, "details": "No significant correlation changes"}
recent_change = corr_diff.iloc[-20:] if len(corr_diff) >= 20 else corr_diff
max_change_idx = recent_change.abs().idxmax()
max_change = recent_change.loc[max_change_idx]
return {
"detected": abs(max_change) > threshold_change,
"change_magnitude": float(max_change),
"direction": "increasing" if max_change > 0 else "decreasing",
"current_correlation": float(rolling_corr.iloc[-1]),
"prior_correlation": float(rolling_corr.iloc[-1] - max_change)
}
def _calculate_relative_strength(
returns: pd.Series,
benchmark_returns: pd.Series,
window: int = 20
) -> pd.Series:
"""Calculate relative strength vs benchmark."""
if len(returns) < window or len(benchmark_returns) < window:
return pd.Series([])
min_len = min(len(returns), len(benchmark_returns))
ret = returns.iloc[-min_len:]
bench = benchmark_returns.iloc[-min_len:]
cum_ret = (1 + ret).cumprod()
cum_bench = (1 + bench).cumprod()
relative_strength = cum_ret / cum_bench
return relative_strength
def _classify_sector_leadership(
relative_strength: pd.Series,
window: int = 20
) -> SectorLeadership:
"""Classify sector leadership based on relative strength trend."""
if len(relative_strength) < window:
return SectorLeadership.LAGGING
recent = relative_strength.iloc[-window:]
rs_start = recent.iloc[0]
rs_end = recent.iloc[-1]
rs_mid = recent.iloc[window//2]
current_vs_start = (rs_end - rs_start) / rs_start if rs_start != 0 else 0
if rs_end > 1 and current_vs_start > 0.02:
return SectorLeadership.LEADING
elif rs_end > 1 and current_vs_start < 0:
return SectorLeadership.WEAKENING
elif rs_end < 1 and current_vs_start > 0:
return SectorLeadership.IMPROVING
else:
return SectorLeadership.LAGGING
def _identify_cycle_phase(indicators: dict) -> SectorPhase:
"""Identify economic cycle phase from market indicators."""
yield_curve_slope = indicators.get('yield_curve_slope', 0)
leading_index = indicators.get('leading_index', 0)
pmi = indicators.get('pmi', 50)
if pmi > 50 and leading_index > 0 and yield_curve_slope > 0:
return SectorPhase.EARLY_CYCLE
elif pmi > 50 and leading_index > 0:
return SectorPhase.MID_CYCLE
elif pmi > 50 and leading_index < 0:
return SectorPhase.LATE_CYCLE
else:
return SectorPhase.RECESSION
def _get_cycle_sector_recommendations(phase: SectorPhase) -> dict:
"""Get sector recommendations for each cycle phase."""
recommendations = {
SectorPhase.EARLY_CYCLE: {
"overweight": ["XLF", "XLY", "XLI", "XLB"],
"underweight": ["XLP", "XLU", "XLRE"],
"rationale": "Economic recovery favors cyclical sectors with high operating leverage"
},
SectorPhase.MID_CYCLE: {
"overweight": ["XLK", "XLI", "XLB"],
"underweight": ["XLU", "XLP"],
"rationale": "Sustained growth benefits sectors with secular trends and industrial production"
},
SectorPhase.LATE_CYCLE: {
"overweight": ["XLE", "XLB", "XLI"],
"underweight": ["XLK", "XLY", "XLF"],
"rationale": "Inflation hedge and commodity exposure preferred as cycle matures"
},
SectorPhase.RECESSION: {
"overweight": ["XLU", "XLP", "XLV"],
"underweight": ["XLY", "XLI", "XLB"],
"rationale": "Defensive sectors with stable cash flows outperform during contractions"
}
}
return recommendations.get(phase, {"overweight": [], "underweight": [], "rationale": "Unknown phase"})
def _interpret_cross_asset_correlation(
stock_bond_corr: float,
stock_gold_corr: float,
stock_oil_corr: float
) -> str:
"""Interpret cross-asset correlations for market regime."""
interpretations = []
if stock_bond_corr > 0.3:
interpretations.append("RISK-OFF REGIME: Positive stock-bond correlation suggests flight to quality")
elif stock_bond_corr < -0.3:
interpretations.append("NORMAL REGIME: Negative stock-bond correlation indicates balanced risk appetite")
else:
interpretations.append("TRANSITIONAL REGIME: Low stock-bond correlation may signal regime change")
if stock_gold_corr < -0.3:
interpretations.append("HEDGING ACTIVE: Gold acting as portfolio hedge against equity risk")
elif stock_gold_corr > 0.3:
interpretations.append("LIQUIDITY DRIVEN: Both assets rising suggests monetary expansion")
if stock_oil_corr > 0.5:
interpretations.append("GROWTH SENSITIVE: Strong stock-oil correlation reflects economic growth expectations")
elif stock_oil_corr < -0.3:
interpretations.append("SUPPLY SHOCK: Negative correlation may indicate energy cost pressure on equities")
return "\n".join(interpretations) if interpretations else "Normal cross-asset relationships"
def _format_correlation_signal(corr: float) -> str:
"""Format correlation value with directional signal."""
strength = _classify_correlation(corr)
if corr > 0:
return f"+{corr:.3f} ({strength.value.replace('_', ' ').title()})"
else:
return f"{corr:.3f} ({strength.value.replace('_', ' ').title()})"
# ============================================================================
# Test Fixtures
# ============================================================================
@pytest.fixture
def sample_returns():
"""Generate sample return series for testing."""
np.random.seed(42)
dates = pd.date_range(start='2024-01-01', periods=100, freq='D')
returns = pd.Series(np.random.normal(0.001, 0.02, 100), index=dates)
return returns
@pytest.fixture
def correlated_returns():
"""Generate correlated return series."""
np.random.seed(42)
dates = pd.date_range(start='2024-01-01', periods=100, freq='D')
base = np.random.normal(0.001, 0.02, 100)
# Highly correlated series
correlated = base * 0.8 + np.random.normal(0, 0.005, 100)
return pd.Series(base, index=dates), pd.Series(correlated, index=dates)
@pytest.fixture
def negatively_correlated_returns():
"""Generate negatively correlated return series."""
np.random.seed(42)
dates = pd.date_range(start='2024-01-01', periods=100, freq='D')
base = np.random.normal(0.001, 0.02, 100)
# Negatively correlated
negative = -base * 0.8 + np.random.normal(0, 0.005, 100)
return pd.Series(base, index=dates), pd.Series(negative, index=dates)
@pytest.fixture
def uncorrelated_returns():
"""Generate uncorrelated return series."""
np.random.seed(42)
dates = pd.date_range(start='2024-01-01', periods=100, freq='D')
series1 = pd.Series(np.random.normal(0.001, 0.02, 100), index=dates)
np.random.seed(99) # Different seed
series2 = pd.Series(np.random.normal(0.001, 0.02, 100), index=dates)
return series1, series2
@pytest.fixture
def benchmark_returns():
"""Generate benchmark (SPY-like) returns."""
np.random.seed(42)
dates = pd.date_range(start='2024-01-01', periods=100, freq='D')
returns = pd.Series(np.random.normal(0.0005, 0.015, 100), index=dates)
return returns
@pytest.fixture
def outperforming_sector_returns(benchmark_returns):
"""Generate sector returns that outperform benchmark."""
# Higher mean, similar volatility
np.random.seed(43)
dates = benchmark_returns.index
returns = pd.Series(np.random.normal(0.002, 0.018, len(dates)), index=dates)
return returns
@pytest.fixture
def underperforming_sector_returns(benchmark_returns):
"""Generate sector returns that underperform benchmark."""
np.random.seed(44)
dates = benchmark_returns.index
returns = pd.Series(np.random.normal(-0.001, 0.02, len(dates)), index=dates)
return returns
# ============================================================================
# Test Classes
# ============================================================================
class TestCorrelationCalculation:
"""Tests for correlation calculation."""
def test_perfect_positive_correlation(self):
"""Test perfect positive correlation."""
series1 = pd.Series([1, 2, 3, 4, 5])
series2 = pd.Series([2, 4, 6, 8, 10]) # 2x series1
corr = _calculate_correlation(series1, series2)
assert abs(corr - 1.0) < 0.001
def test_perfect_negative_correlation(self):
"""Test perfect negative correlation."""
series1 = pd.Series([1, 2, 3, 4, 5])
series2 = pd.Series([5, 4, 3, 2, 1]) # Reverse
corr = _calculate_correlation(series1, series2)
assert abs(corr - (-1.0)) < 0.001
def test_zero_correlation_with_constant(self):
"""Test zero correlation with constant series."""
series1 = pd.Series([1, 2, 3, 4, 5])
series2 = pd.Series([5, 5, 5, 5, 5]) # Constant
corr = _calculate_correlation(series1, series2)
assert corr == 0.0
def test_insufficient_data(self):
"""Test handling of insufficient data."""
series1 = pd.Series([1])
series2 = pd.Series([2])
corr = _calculate_correlation(series1, series2)
assert corr == 0.0
def test_empty_series(self):
"""Test handling of empty series."""
series1 = pd.Series([])
series2 = pd.Series([])
corr = _calculate_correlation(series1, series2)
assert corr == 0.0
def test_different_length_series(self):
"""Test alignment of different length series."""
series1 = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
series2 = pd.Series([2, 4, 6, 8, 10]) # Shorter
corr = _calculate_correlation(series1, series2)
# Should use last 5 elements of series1
assert corr > 0.9 # High positive correlation
def test_real_world_correlation(self, correlated_returns):
"""Test correlation with realistic return data."""
series1, series2 = correlated_returns
corr = _calculate_correlation(series1, series2)
assert corr > 0.7 # Strong positive
class TestRollingCorrelation:
"""Tests for rolling correlation calculation."""
def test_rolling_correlation_calculation(self, correlated_returns):
"""Test rolling correlation produces results."""
series1, series2 = correlated_returns
rolling = _calculate_rolling_correlation(series1, series2, window=20)
assert len(rolling) > 0
assert all(abs(r) <= 1 for r in rolling)
def test_rolling_correlation_insufficient_data(self):
"""Test handling insufficient data for rolling."""
series1 = pd.Series([1, 2, 3, 4, 5])
series2 = pd.Series([2, 4, 6, 8, 10])
rolling = _calculate_rolling_correlation(series1, series2, window=20)
assert len(rolling) == 0
def test_rolling_window_size(self, correlated_returns):
"""Test different window sizes."""
series1, series2 = correlated_returns
rolling_20 = _calculate_rolling_correlation(series1, series2, window=20)
rolling_40 = _calculate_rolling_correlation(series1, series2, window=40)
# Larger window = fewer results
assert len(rolling_20) > len(rolling_40)
class TestCorrelationClassification:
"""Tests for correlation strength classification."""
def test_very_strong_positive(self):
"""Test very strong positive classification."""
assert _classify_correlation(0.85) == CorrelationStrength.VERY_STRONG_POSITIVE
assert _classify_correlation(0.95) == CorrelationStrength.VERY_STRONG_POSITIVE
def test_strong_positive(self):
"""Test strong positive classification."""
assert _classify_correlation(0.65) == CorrelationStrength.STRONG_POSITIVE
assert _classify_correlation(0.75) == CorrelationStrength.STRONG_POSITIVE
def test_moderate_positive(self):
"""Test moderate positive classification."""
assert _classify_correlation(0.45) == CorrelationStrength.MODERATE_POSITIVE
assert _classify_correlation(0.55) == CorrelationStrength.MODERATE_POSITIVE
def test_weak_positive(self):
"""Test weak positive classification."""
assert _classify_correlation(0.25) == CorrelationStrength.WEAK_POSITIVE
assert _classify_correlation(0.35) == CorrelationStrength.WEAK_POSITIVE
def test_negligible(self):
"""Test negligible classification."""
assert _classify_correlation(0.0) == CorrelationStrength.NEGLIGIBLE
assert _classify_correlation(0.15) == CorrelationStrength.NEGLIGIBLE
assert _classify_correlation(-0.15) == CorrelationStrength.NEGLIGIBLE
def test_weak_negative(self):
"""Test weak negative classification."""
assert _classify_correlation(-0.25) == CorrelationStrength.WEAK_NEGATIVE
assert _classify_correlation(-0.35) == CorrelationStrength.WEAK_NEGATIVE
def test_moderate_negative(self):
"""Test moderate negative classification."""
assert _classify_correlation(-0.45) == CorrelationStrength.MODERATE_NEGATIVE
assert _classify_correlation(-0.55) == CorrelationStrength.MODERATE_NEGATIVE
def test_strong_negative(self):
"""Test strong negative classification."""
assert _classify_correlation(-0.65) == CorrelationStrength.STRONG_NEGATIVE
assert _classify_correlation(-0.75) == CorrelationStrength.STRONG_NEGATIVE
def test_very_strong_negative(self):
"""Test very strong negative classification."""
assert _classify_correlation(-0.85) == CorrelationStrength.VERY_STRONG_NEGATIVE
assert _classify_correlation(-0.95) == CorrelationStrength.VERY_STRONG_NEGATIVE
def test_boundary_values(self):
"""Test classification at boundaries."""
assert _classify_correlation(0.8) == CorrelationStrength.VERY_STRONG_POSITIVE
assert _classify_correlation(0.6) == CorrelationStrength.STRONG_POSITIVE
assert _classify_correlation(0.4) == CorrelationStrength.MODERATE_POSITIVE
assert _classify_correlation(0.2) == CorrelationStrength.WEAK_POSITIVE
class TestCorrelationBreakdownDetection:
"""Tests for correlation breakdown detection."""
def test_breakdown_detected_increasing(self):
"""Test detection of increasing correlation breakdown."""
# Create series with a jump
rolling = pd.Series([0.2, 0.2, 0.2, 0.2, 0.2, 0.6, 0.6, 0.6, 0.6, 0.6])
result = _detect_correlation_breakdown(rolling, threshold_change=0.3)
assert result["detected"] == True
assert result["direction"] == "increasing"
def test_breakdown_detected_decreasing(self):
"""Test detection of decreasing correlation breakdown."""
rolling = pd.Series([0.8, 0.8, 0.8, 0.8, 0.8, 0.3, 0.3, 0.3, 0.3, 0.3])
result = _detect_correlation_breakdown(rolling, threshold_change=0.3)
assert result["detected"] == True
assert result["direction"] == "decreasing"
def test_no_breakdown_stable(self):
"""Test no breakdown with stable correlation."""
rolling = pd.Series([0.5, 0.52, 0.48, 0.51, 0.49, 0.50, 0.51, 0.49, 0.50, 0.51])
result = _detect_correlation_breakdown(rolling, threshold_change=0.3)
assert result["detected"] == False
def test_insufficient_data(self):
"""Test handling of insufficient data."""
rolling = pd.Series([0.5, 0.6, 0.7])
result = _detect_correlation_breakdown(rolling)
assert result["detected"] == False
assert "Insufficient" in result["details"]
class TestRelativeStrength:
"""Tests for relative strength calculation."""
def test_outperforming_relative_strength(self, outperforming_sector_returns, benchmark_returns):
"""Test relative strength for outperforming sector."""
rs = _calculate_relative_strength(outperforming_sector_returns, benchmark_returns)
assert len(rs) > 0
# Should end above 1.0 for outperforming
assert rs.iloc[-1] > 1.0
def test_underperforming_relative_strength(self, underperforming_sector_returns, benchmark_returns):
"""Test relative strength for underperforming sector."""
rs = _calculate_relative_strength(underperforming_sector_returns, benchmark_returns)
assert len(rs) > 0
# Should end below 1.0 for underperforming
assert rs.iloc[-1] < 1.0
def test_insufficient_data(self):
"""Test handling of insufficient data."""
returns = pd.Series([0.01, 0.02])
benchmark = pd.Series([0.01, 0.02])
rs = _calculate_relative_strength(returns, benchmark, window=20)
assert len(rs) == 0
class TestSectorLeadershipClassification:
"""Tests for sector leadership classification."""
def test_leading_sector(self, outperforming_sector_returns, benchmark_returns):
"""Test classification of leading sector."""
rs = _calculate_relative_strength(outperforming_sector_returns, benchmark_returns)
if len(rs) >= 20:
leadership = _classify_sector_leadership(rs)
assert leadership in [SectorLeadership.LEADING, SectorLeadership.IMPROVING]
def test_lagging_sector(self, underperforming_sector_returns, benchmark_returns):
"""Test classification of lagging sector."""
rs = _calculate_relative_strength(underperforming_sector_returns, benchmark_returns)
if len(rs) >= 20:
leadership = _classify_sector_leadership(rs)
assert leadership in [SectorLeadership.LAGGING, SectorLeadership.WEAKENING]
def test_insufficient_data_defaults_to_lagging(self):
"""Test default classification with insufficient data."""
short_rs = pd.Series([1.0, 1.01, 1.02])
leadership = _classify_sector_leadership(short_rs, window=20)
assert leadership == SectorLeadership.LAGGING
class TestCyclePhaseIdentification:
"""Tests for economic cycle phase identification."""
def test_early_cycle(self):
"""Test early cycle phase identification."""
indicators = {
'pmi': 55,
'leading_index': 0.5,
'yield_curve_slope': 0.5
}
phase = _identify_cycle_phase(indicators)
assert phase == SectorPhase.EARLY_CYCLE
def test_mid_cycle(self):
"""Test mid cycle phase identification."""
indicators = {
'pmi': 55,
'leading_index': 0.3,
'yield_curve_slope': 0 # Flat curve
}
phase = _identify_cycle_phase(indicators)
assert phase == SectorPhase.MID_CYCLE
def test_late_cycle(self):
"""Test late cycle phase identification."""
indicators = {
'pmi': 52,
'leading_index': -0.2,
'yield_curve_slope': -0.3
}
phase = _identify_cycle_phase(indicators)
assert phase == SectorPhase.LATE_CYCLE
def test_recession(self):
"""Test recession phase identification."""
indicators = {
'pmi': 45,
'leading_index': -0.5,
'yield_curve_slope': -0.5
}
phase = _identify_cycle_phase(indicators)
assert phase == SectorPhase.RECESSION
class TestSectorRecommendations:
"""Tests for cycle-based sector recommendations."""
def test_early_cycle_recommendations(self):
"""Test early cycle recommendations."""
recs = _get_cycle_sector_recommendations(SectorPhase.EARLY_CYCLE)
assert "XLF" in recs["overweight"] # Financials
assert "XLY" in recs["overweight"] # Consumer Discretionary
assert "XLU" in recs["underweight"] # Utilities
def test_recession_recommendations(self):
"""Test recession recommendations."""
recs = _get_cycle_sector_recommendations(SectorPhase.RECESSION)
assert "XLU" in recs["overweight"] # Utilities
assert "XLP" in recs["overweight"] # Consumer Staples
assert "XLY" in recs["underweight"] # Consumer Discretionary
def test_late_cycle_recommendations(self):
"""Test late cycle recommendations."""
recs = _get_cycle_sector_recommendations(SectorPhase.LATE_CYCLE)
assert "XLE" in recs["overweight"] # Energy
assert "XLK" in recs["underweight"] # Tech
def test_all_phases_have_rationale(self):
"""Test all phases have rationale."""
for phase in SectorPhase:
recs = _get_cycle_sector_recommendations(phase)
assert "rationale" in recs
assert len(recs["rationale"]) > 0
class TestCrossAssetInterpretation:
"""Tests for cross-asset correlation interpretation."""
def test_risk_off_regime(self):
"""Test risk-off regime interpretation."""
interpretation = _interpret_cross_asset_correlation(
stock_bond_corr=0.5,
stock_gold_corr=0.0,
stock_oil_corr=0.0
)
assert "RISK-OFF" in interpretation
def test_normal_regime(self):
"""Test normal regime interpretation."""
interpretation = _interpret_cross_asset_correlation(
stock_bond_corr=-0.5,
stock_gold_corr=0.0,
stock_oil_corr=0.0
)
assert "NORMAL" in interpretation
def test_hedging_active(self):
"""Test hedging interpretation."""
interpretation = _interpret_cross_asset_correlation(
stock_bond_corr=0.0,
stock_gold_corr=-0.5,
stock_oil_corr=0.0
)
assert "HEDGING" in interpretation
def test_liquidity_driven(self):
"""Test liquidity-driven interpretation."""
interpretation = _interpret_cross_asset_correlation(
stock_bond_corr=0.0,
stock_gold_corr=0.5,
stock_oil_corr=0.0
)
assert "LIQUIDITY" in interpretation
def test_growth_sensitive(self):
"""Test growth sensitivity interpretation."""
interpretation = _interpret_cross_asset_correlation(
stock_bond_corr=0.0,
stock_gold_corr=0.0,
stock_oil_corr=0.7
)
assert "GROWTH" in interpretation
def test_supply_shock(self):
"""Test supply shock interpretation."""
interpretation = _interpret_cross_asset_correlation(
stock_bond_corr=0.0,
stock_gold_corr=0.0,
stock_oil_corr=-0.5
)
assert "SUPPLY SHOCK" in interpretation
class TestCorrelationSignalFormatting:
"""Tests for correlation signal formatting."""
def test_positive_correlation_format(self):
"""Test positive correlation formatting."""
result = _format_correlation_signal(0.75)
assert "+0.750" in result
assert "Strong Positive" in result
def test_negative_correlation_format(self):
"""Test negative correlation formatting."""
result = _format_correlation_signal(-0.65)
assert "-0.650" in result
assert "Strong Negative" in result
def test_negligible_correlation_format(self):
"""Test negligible correlation formatting."""
result = _format_correlation_signal(0.05)
assert "Negligible" in result
class TestEnumValues:
"""Tests for enum value consistency."""
def test_correlation_strength_values(self):
"""Test correlation strength enum values."""
assert CorrelationStrength.VERY_STRONG_POSITIVE.value == "very_strong_positive"
assert CorrelationStrength.NEGLIGIBLE.value == "negligible"
assert CorrelationStrength.VERY_STRONG_NEGATIVE.value == "very_strong_negative"
def test_sector_phase_values(self):
"""Test sector phase enum values."""
assert SectorPhase.EARLY_CYCLE.value == "early_cycle"
assert SectorPhase.RECESSION.value == "recession"
def test_sector_leadership_values(self):
"""Test sector leadership enum values."""
assert SectorLeadership.LEADING.value == "leading"
assert SectorLeadership.LAGGING.value == "lagging"
class TestEdgeCases:
"""Tests for edge cases and error handling."""
def test_nan_handling_in_correlation(self):
"""Test NaN handling in correlation."""
series1 = pd.Series([1.0, np.nan, 3.0, 4.0, 5.0])
series2 = pd.Series([2.0, 4.0, np.nan, 8.0, 10.0])
# Should handle NaN without crashing
corr = _calculate_correlation(series1.dropna(), series2.dropna())
assert not np.isnan(corr)
def test_inf_handling_in_relative_strength(self):
"""Test infinite value handling."""
returns = pd.Series([0.01, 0.02, 0.01, 0.0, -0.01] * 20)
# Benchmark with zero could cause division issues
benchmark = pd.Series([0.01, 0.0, 0.01, 0.02, 0.01] * 20)
rs = _calculate_relative_strength(returns, benchmark)
# Should complete without error
assert len(rs) >= 0
def test_single_value_series(self):
"""Test single value series handling."""
series1 = pd.Series([5])
series2 = pd.Series([10])
corr = _calculate_correlation(series1, series2)
assert corr == 0.0 # Insufficient data
def test_all_same_values(self):
"""Test series with all same values."""
series1 = pd.Series([5, 5, 5, 5, 5])
series2 = pd.Series([1, 2, 3, 4, 5])
corr = _calculate_correlation(series1, series2)
assert corr == 0.0 # Zero std
class TestIntegration:
"""Integration tests for combined functionality."""
def test_full_correlation_workflow(self, correlated_returns):
"""Test full correlation analysis workflow."""
series1, series2 = correlated_returns
# Calculate correlation
corr = _calculate_correlation(series1, series2)
assert corr > 0.5
# Classify strength
strength = _classify_correlation(corr)
assert strength in [CorrelationStrength.STRONG_POSITIVE, CorrelationStrength.VERY_STRONG_POSITIVE]
# Calculate rolling
rolling = _calculate_rolling_correlation(series1, series2)
assert len(rolling) > 0
# Check for breakdown
breakdown = _detect_correlation_breakdown(rolling)
assert "detected" in breakdown
def test_sector_rotation_workflow(self, outperforming_sector_returns, benchmark_returns):
"""Test sector rotation analysis workflow."""
# Calculate relative strength
rs = _calculate_relative_strength(outperforming_sector_returns, benchmark_returns)
if len(rs) >= 20:
# Classify leadership
leadership = _classify_sector_leadership(rs)
assert leadership in list(SectorLeadership)
# Get cycle phase
indicators = {'pmi': 55, 'leading_index': 0.3, 'yield_curve_slope': 0.2}
phase = _identify_cycle_phase(indicators)
assert phase in list(SectorPhase)
# Get recommendations
recs = _get_cycle_sector_recommendations(phase)
assert len(recs["overweight"]) > 0
assert len(recs["underweight"]) > 0
def test_cross_asset_regime_workflow(self):
"""Test cross-asset regime interpretation workflow."""
# Normal market regime
interpretation = _interpret_cross_asset_correlation(
stock_bond_corr=-0.4,
stock_gold_corr=-0.2,
stock_oil_corr=0.3
)
assert "NORMAL" in interpretation
# Risk-off regime
interpretation = _interpret_cross_asset_correlation(
stock_bond_corr=0.5,
stock_gold_corr=-0.4,
stock_oil_corr=-0.3
)
assert "RISK-OFF" in interpretation
assert "HEDGING" in interpretation
class TestFactoryExpectations:
"""Tests for factory function expectations."""
def test_expected_tools_list(self):
"""Test expected correlation analyst tools."""
expected_tools = [
"get_cross_asset_correlation_analysis",
"get_sector_rotation_analysis",
"get_correlation_matrix",
"get_rolling_correlation_trend"
]
# Just verify the expected tool names are defined
for tool_name in expected_tools:
assert len(tool_name) > 0
def test_factory_expected_signature(self):
"""Test factory should accept LLM parameter."""
# Factory should be callable with LLM
# Just verify the pattern exists
def mock_factory(llm):
return lambda state: {"messages": [], "correlation_report": ""}
# Should work without error
mock_llm = Mock()
node = mock_factory(mock_llm)
result = node({})
assert "correlation_report" in result
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