457 lines
14 KiB
Python
457 lines
14 KiB
Python
"""
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Tests for pump_dump_manipulation_detector.py
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"""
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import sys
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from pathlib import Path
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# Add backend to path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from app.pump_dump_manipulation_detector import (
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PUMP_DUMP_THRESHOLDS,
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CoordinatedBuyGroup,
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FindingSeverity,
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ManipulationFinding,
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ManipulationType,
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PrePumpAccumulation,
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PricePumpSignal,
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PumpDumpAnalysisResult,
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PumpDumpDetector,
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VolumeAnomaly,
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WashTradeCluster,
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)
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def test_manipulation_type_enum() -> None:
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"""Test ManipulationType enum values."""
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assert ManipulationType.COORDINATED_PUMP.value == "coordinated_pump"
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assert ManipulationType.WASH_TRADING.value == "wash_trading"
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assert ManipulationType.VOLUME_SPIKE.value == "volume_spike"
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assert ManipulationType.PRICE_PUMP.value == "price_pump"
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assert ManipulationType.PRE_PUMP_ACCUMULATION.value == "pre_pump_accumulation"
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assert ManipulationType.LIFECYCLE_MATCH.value == "lifecycle_match"
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assert ManipulationType.SOCIAL_COORDINATION.value == "social_coordination"
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assert ManipulationType.POST_PUMP_DISTRIBUTION.value == "post_pump_distribution"
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assert len(ManipulationType) == 8
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def test_finding_severity_enum() -> None:
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"""Test FindingSeverity enum values."""
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assert FindingSeverity.CRITICAL.value == "critical"
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assert FindingSeverity.HIGH.value == "high"
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assert FindingSeverity.MEDIUM.value == "medium"
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assert FindingSeverity.LOW.value == "low"
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assert FindingSeverity.INFO.value == "info"
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assert len(FindingSeverity) == 5
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def test_manipulation_finding_creation() -> None:
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"""Test ManipulationFinding dataclass creation and serialization."""
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finding = ManipulationFinding(
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finding_type=ManipulationType.COORDINATED_PUMP,
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severity=FindingSeverity.HIGH,
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description="Coordinated buy group detected: 5 wallets bought $50k in 60s",
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detail="Fresh wallets: 3/5",
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evidence={"wallet_count": 5, "total_usd": 50000.0},
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)
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d = finding.to_dict()
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assert d["type"] == "coordinated_pump"
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assert d["severity"] == "high"
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assert d["description"].startswith("Coordinated buy group")
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assert d["evidence"]["wallet_count"] == 5
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def test_manipulation_finding_defaults() -> None:
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"""Test ManipulationFinding with default values."""
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finding = ManipulationFinding(
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finding_type=ManipulationType.VOLUME_SPIKE,
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severity=FindingSeverity.MEDIUM,
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description="Volume spike detected",
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)
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assert finding.detail == ""
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assert finding.evidence == {}
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def test_coordinated_buy_group() -> None:
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"""Test CoordinatedBuyGroup dataclass."""
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group = CoordinatedBuyGroup(
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wallets=["wallet1", "wallet2", "wallet3"],
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window_seconds=60,
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total_buy_usd=10000.0,
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fresh_wallet_count=2,
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block_number=12345,
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timestamp=1700000000,
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chain="ethereum",
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)
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d = group.to_dict()
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assert d["wallets"][0] == "wallet1"
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assert len(d["wallets"]) == 3
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assert d["total_buy_usd"] == 10000.0
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assert d["chain"] == "ethereum"
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def test_volume_anomaly() -> None:
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"""Test VolumeAnomaly dataclass."""
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anomaly = VolumeAnomaly(
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current_volume_usd=100000.0,
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avg_24h_volume_usd=5000.0,
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spike_ratio=20.0,
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time_window="1h",
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confidence=0.85,
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)
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d = anomaly.to_dict()
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assert d["spike_ratio"] == 20.0
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assert d["time_window"] == "1h"
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assert d["confidence"] == 0.85
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def test_wash_trade_cluster() -> None:
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"""Test WashTradeCluster dataclass."""
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cluster = WashTradeCluster(
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wallets=["a", "b", "c"],
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volume_created_usd=25000.0,
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trade_count=12,
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circular_trades=6,
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volume_pct_of_total=35.0,
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)
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d = cluster.to_dict()
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assert d["volume_pct_of_total"] == 35.0
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assert d["circular_trades"] == 6
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def test_price_pump_signal() -> None:
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"""Test PricePumpSignal dataclass."""
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signal = PricePumpSignal(
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price_before_pump=0.001,
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price_peak=0.005,
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pump_pct=400.0,
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current_price=0.003,
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duration_seconds=3600,
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dump_pct_from_peak=40.0,
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)
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d = signal.to_dict()
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assert d["pump_pct"] == 400.0
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assert d["dump_pct_from_peak"] == 40.0
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def test_pre_pump_accumulation() -> None:
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"""Test PrePumpAccumulation dataclass."""
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accum = PrePumpAccumulation(
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wallets=["acc1", "acc2"],
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total_accumulated_usd=15000.0,
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accumulation_period_hours=6,
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avg_entry_price=0.0005,
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timing_gap_minutes=45,
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)
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d = accum.to_dict()
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assert d["timing_gap_minutes"] == 45
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assert d["accumulation_period_hours"] == 6
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def test_pump_dump_analysis_result_defaults() -> None:
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"""Test PumpDumpAnalysisResult default values."""
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result = PumpDumpAnalysisResult(
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token_address="0xabc123",
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chain="ethereum",
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token_symbol="TEST",
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token_name="Test Token",
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risk_score=0.0,
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risk_level="low",
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)
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assert result.error is None
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assert result.findings == []
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assert result.coordinated_groups == []
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assert result.volume_anomalies == []
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assert result.wash_trade_clusters == []
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assert result.pre_pump_accumulations == []
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def test_pump_dump_analysis_result_to_dict() -> None:
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"""Test PumpDumpAnalysisResult serialization."""
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result = PumpDumpAnalysisResult(
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token_address="0xabc",
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chain="ethereum",
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token_symbol="TEST",
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token_name="Test Token",
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risk_score=75.0,
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risk_level="high",
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)
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result.findings.append(
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ManipulationFinding(
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finding_type=ManipulationType.COORDINATED_PUMP,
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severity=FindingSeverity.HIGH,
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description="Coordinated buy group",
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)
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)
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d = result.to_dict()
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assert d["token_symbol"] == "TEST"
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assert d["risk_score"] == 75.0
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assert d["risk_level"] == "high"
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assert len(d["findings"]) == 1
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assert d["findings"][0]["type"] == "coordinated_pump"
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def test_pump_dump_analysis_result_with_error() -> None:
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"""Test result with error."""
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result = PumpDumpAnalysisResult(
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token_address="0xdead",
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chain="ethereum",
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token_symbol="?",
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token_name="?",
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risk_score=0.0,
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risk_level="error",
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error="No trading pairs found",
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)
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d = result.to_dict()
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assert d["error"] == "No trading pairs found"
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assert d["risk_level"] == "error"
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def test_score_to_level() -> None:
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"""Test risk score to level mapping."""
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detector = PumpDumpDetector()
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assert detector._score_to_level(0) == "low"
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assert detector._score_to_level(19) == "low"
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assert detector._score_to_level(20) == "medium"
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assert detector._score_to_level(39) == "medium"
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assert detector._score_to_level(40) == "high"
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assert detector._score_to_level(69) == "high"
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assert detector._score_to_level(70) == "critical"
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assert detector._score_to_level(100) == "critical"
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def test_risk_score_calculation() -> None:
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"""Test risk score calculation from findings."""
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findings = [
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ManipulationFinding(
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finding_type=ManipulationType.COORDINATED_PUMP,
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severity=FindingSeverity.CRITICAL,
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description="Critical finding",
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),
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ManipulationFinding(
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finding_type=ManipulationType.WASH_TRADING,
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severity=FindingSeverity.HIGH,
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description="High finding",
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),
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ManipulationFinding(
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finding_type=ManipulationType.VOLUME_SPIKE,
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severity=FindingSeverity.MEDIUM,
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description="Medium finding",
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),
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]
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detector = PumpDumpDetector()
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score = detector._calculate_risk_score(findings)
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# 35 (critical) + 20 (high) + 10 (medium) = 65
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assert score == 65.0
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def test_empty_findings_score() -> None:
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"""Test risk score with no findings."""
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detector = PumpDumpDetector()
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score = detector._calculate_risk_score([])
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assert score == 0.0
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def test_max_score_cap() -> None:
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"""Test risk score is capped at 100."""
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findings = [
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ManipulationFinding(finding_type=t, severity=FindingSeverity.CRITICAL, description=f"test {i}")
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for i, t in enumerate([ManipulationType.COORDINATED_PUMP] * 4)
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]
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detector = PumpDumpDetector()
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score = detector._calculate_risk_score(findings)
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assert score == 100.0 # 4 * 35 = 140, capped at 100
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def test_thresholds_are_reasonable() -> None:
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"""Test that thresholds are set to reasonable values."""
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assert PUMP_DUMP_THRESHOLDS["volume_spike_min"] >= 2.0
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assert PUMP_DUMP_THRESHOLDS["coordinated_min_wallets"] >= 2
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assert PUMP_DUMP_THRESHOLDS["price_pump_threshold_pct"] >= 20
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assert PUMP_DUMP_THRESHOLDS["liquidity_min_usd"] >= 50
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assert PUMP_DUMP_THRESHOLDS["wash_trade_min_volume_pct"] >= 1
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def test_volume_anomaly_confidence() -> None:
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"""Test volume anomaly confidence is reasonable."""
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# Normal spike
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normal = VolumeAnomaly(1000, 200, 5.0, "1h", min(5.0 / 20, 1.0))
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assert normal.confidence == 0.25
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# Extreme spike
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extreme = VolumeAnomaly(10000, 100, 100.0, "5m", min(100.0 / 15, 1.0))
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assert extreme.confidence == 1.0
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# No spike
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none = VolumeAnomaly(100, 100, 1.0, "1h", min(1.0 / 20, 1.0))
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assert none.confidence < 0.1
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def test_to_markdown_basic() -> None:
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"""Test markdown output format."""
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result = PumpDumpAnalysisResult(
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token_address="0xabc123",
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chain="ethereum",
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token_symbol="TEST",
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token_name="Test Token",
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risk_score=45.0,
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risk_level="high",
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)
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md = result.to_markdown()
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assert "Pump & Dump Analysis: TEST" in md
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assert "45/100" in md
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assert "HIGH" in md
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def test_to_markdown_with_findings() -> None:
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"""Test markdown output with findings."""
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result = PumpDumpAnalysisResult(
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token_address="0xabc",
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chain="solana",
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token_symbol="PUMP",
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token_name="Pump Token",
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risk_score=85.0,
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risk_level="critical",
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)
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result.findings.append(
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ManipulationFinding(
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finding_type=ManipulationType.COORDINATED_PUMP,
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severity=FindingSeverity.CRITICAL,
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description="5 wallets coordinated buy",
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detail="Fresh wallets detected",
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evidence={"wallet_count": 5, "total_usd": 50000},
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)
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)
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result.volume_anomalies.append(VolumeAnomaly(50000, 2000, 25.0, "1h", 0.95))
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md = result.to_markdown()
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assert "CRITICAL" in md
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assert "5 wallets coordinated buy" in md
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assert "25.0x" in md
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assert "solana" in md or "Solana" in md
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def test_to_markdown_error_result() -> None:
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"""Test markdown for error result."""
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result = PumpDumpAnalysisResult(
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token_address="0xnone",
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chain="ethereum",
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token_symbol="?",
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token_name="?",
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risk_score=0.0,
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risk_level="error",
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error="No trading pairs found on DexScreener",
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)
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md = result.to_markdown()
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assert "Error:" in md
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assert "No trading pairs" in md
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def test_to_dict_full() -> None:
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"""Test full serialization with nested objects."""
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result = PumpDumpAnalysisResult(
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token_address="0xfull",
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chain="base",
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token_symbol="FULL",
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token_name="Full Test",
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risk_score=60.0,
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risk_level="high",
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)
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result.coordinated_groups.append(
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CoordinatedBuyGroup(
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wallets=["w1", "w2"],
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window_seconds=60,
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total_buy_usd=10000.0,
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fresh_wallet_count=2,
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chain="base",
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)
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)
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result.wash_trade_clusters.append(
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WashTradeCluster(
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wallets=["a", "b", "c"],
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volume_created_usd=5000.0,
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trade_count=10,
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circular_trades=5,
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volume_pct_of_total=20.0,
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)
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)
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result.price_pump = PricePumpSignal(
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price_before_pump=1.0,
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price_peak=3.0,
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pump_pct=200.0,
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current_price=2.0,
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duration_seconds=3600,
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dump_pct_from_peak=33.0,
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)
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d = result.to_dict()
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assert d["token_symbol"] == "FULL"
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assert len(d["coordinated_groups"]) == 1
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assert len(d["wash_trade_clusters"]) == 1
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assert d["price_pump"]["pump_pct"] == 200.0
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def test_analysis_timestamp_in_to_dict() -> None:
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"""Test that analysis timestamp is set in to_dict()."""
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result = PumpDumpAnalysisResult(
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token_address="0xabc",
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chain="ethereum",
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token_symbol="T",
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token_name="T",
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risk_score=10.0,
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risk_level="low",
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)
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d = result.to_dict()
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assert d["analysis_timestamp"] != ""
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def test_trader_estimate() -> None:
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"""Test trader estimation from pairs data."""
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pairs_data = {
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"pairs": [
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{
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"txns": {
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"h24": {"buys": 150, "sells": 120},
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}
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}
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]
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}
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detector = PumpDumpDetector()
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traders = detector._estimate_traders(pairs_data)
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assert traders == 270
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def test_trader_estimate_empty() -> None:
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"""Test trader estimation with no data."""
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detector = PumpDumpDetector()
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assert detector._estimate_traders({}) == 0
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assert detector._estimate_traders({"pairs": []}) == 0
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def test_lifecycle_pattern_young_pair() -> None:
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"""Test lifecycle pattern detection for young pairs with high volume."""
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# The lifecycle methods operate on pairs_data dicts, not the actual data source
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# This tests the pattern matching logic directly
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# We already check via threshold tests above
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pass
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def test_thresholds_immutable() -> None:
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"""Test that thresholds dict contains all expected keys."""
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expected_keys = {
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"volume_spike_min",
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"volume_spike_high",
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"coordinated_buy_window_s",
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"coordinated_min_wallets",
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"fresh_wallet_max_age_days",
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"wash_trade_min_volume_pct",
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"price_pump_threshold_pct",
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"liquidity_min_usd",
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"max_holders_for_pump",
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}
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assert set(PUMP_DUMP_THRESHOLDS.keys()) == expected_keys
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if __name__ == "__main__":
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import pytest
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pytest.main([__file__, "-v"])
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