feat: output schema conformance to spec Section 8
- VelocityMetrics: hype_velocity/pub_velocity range -100 to +100 (was 0-1) - EventFlag: full spec Section 8.4 compliance * Added: asset, industry, value, source_credibility, num_sources, detail_factor * Added: base_impact, t_zero, decay_remaining, half_life_minutes, impact_duration_minutes * Added: direction, is_scheduled, triggered_at, sources, details_extracted * Added: flag_type (FlagType enum per FLAG_TYPE_FOR_EVENT catalogue) * Added: flags (sub-tags list) * Legacy compat fields with validators for migration - FlagType enum: 80+ flag types per spec Section 8.5 (verbal, technical, governance, market, regulatory, social, macro, manipulation) - AssetSentiment: added contributing_events dict (fear_driver, greed_driver, velocity_driver) - IndustrySentiment: added hype_velocity, pub_velocity, contributing_events - MarketSentiment: added contributing_events - SentimentOutput: added schema_version (default 2) and engine_version (default 2.0.0) - All 46 core NLP unit tests pass
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@@ -7,11 +7,11 @@ from pydantic import BaseModel, Field, field_validator
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class VelocityMetrics(BaseModel):
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"""Hype and publication velocity metrics"""
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hype_velocity: float = Field(..., ge=0.0, le=1.0, description="Rate of sentiment acceleration")
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pub_velocity: float = Field(..., ge=0.0, le=1.0, description="Publication rate velocity")
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"""Hype and publication velocity metrics (per spec: -100 to +100)"""
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hype_velocity: float = Field(..., ge=-100.0, le=100.0, description="Rate of acceleration of hype-weighted mentions per hour")
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pub_velocity: float = Field(..., ge=-100.0, le=100.0, description="Rate of acceleration of raw publication count per hour")
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velocity_direction: str = Field(default="neutral", description="accelerating | decelerating | neutral")
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window_minutes: int = Field(default=15, description="Velocity computation window")
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window_minutes: int = Field(default=60, description="Velocity computation window (spec: 60 min)")
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source_count: int = Field(default=0, description="Number of sources in window")
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unique_assets: int = Field(default=0, description="Unique assets mentioned in window")
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@@ -27,20 +27,167 @@ class PumpDumpScore(BaseModel):
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last_update_ts: float = Field(..., description="Last score update timestamp")
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class FlagType(str, Enum):
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"""FLAG_TYPE_FOR_EVENT catalogue per spec Section 8.5"""
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# Verbal/Linguistic
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FLAG_VERBAL_BULLISH = "FLAG_VERBAL_BULLISH"
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FLAG_VERBAL_BEARISH = "FLAG_VERBAL_BEARISH"
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FLAG_VERBAL_UPGRADE = "FLAG_VERBAL_UPGRADE"
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FLAG_VERBAL_DOWNGRADE = "FLAG_VERBAL_DOWNGRADE"
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FLAG_VERBAL_GUIDANCE_RAISE = "FLAG_VERBAL_GUIDANCE_RAISE"
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FLAG_VERBAL_GUIDANCE_CUT = "FLAG_VERBAL_GUIDANCE_CUT"
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FLAG_VERBAL_BUYBACK_ANNOUNCEMENT = "FLAG_VERBAL_BUYBACK_ANNOUNCEMENT"
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FLAG_VERBAL_DIVIDEND_DECLARE = "FLAG_VERBAL_DIVIDEND_DECLARE"
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FLAG_VERBAL_EARNINGS_BEAT = "FLAG_VERBAL_EARNINGS_BEAT"
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FLAG_VERBAL_EARNINGS_MISS = "FLAG_VERBAL_EARNINGS_MISS"
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FLAG_VERBAL_REVENUE_GROWTH = "FLAG_VERBAL_REVENUE_GROWTH"
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FLAG_VERBAL_PARTNERSHIP = "FLAG_VERBAL_PARTNERSHIP"
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FLAG_VERBAL_EXPANSION = "FLAG_VERBAL_EXPANSION"
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FLAG_VERBAL_CONTRACT_WIN = "FLAG_VERBAL_CONTRACT_WIN"
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# Technical
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FLAG_TOKEN_UNLOCK = "FLAG_TOKEN_UNLOCK"
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FLAG_TOKEN_BURN = "FLAG_TOKEN_BURN"
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FLAG_TOKEN_MINT = "FLAG_TOKEN_MINT"
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FLAG_STAKING_REWARD = "FLAG_STAKING_REWARD"
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FLAG_STAKING_SLASHING = "FLAG_STAKING_SLASHING"
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FLAG_HARD_FORK = "FLAG_HARD_FORK"
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FLAG_SOFT_FORK = "FLAG_SOFT_FORK"
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FLAG_PROTOCOL_UPGRADE = "FLAG_PROTOCOL_UPGRADE"
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FLAG_AIRDROP = "FLAG_AIRDROP"
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FLAG_BRIDGE_INTEGRATION = "FLAG_BRIDGE_INTEGRATION"
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FLAG_API_DEPRECATION = "FLAG_API_DEPRECATION"
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FLAG_API_LIMIT_CHANGE = "FLAG_API_LIMIT_CHANGE"
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FLAG_FEATURE_RELEASE = "FLAG_FEATURE_RELEASE"
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FLAG_PERFORMANCE_DEGRADATION = "FLAG_PERFORMANCE_DEGRADATION"
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FLAG_SECURITY_AUDIT_PASS = "FLAG_SECURITY_AUDIT_PASS"
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FLAG_SECURITY_AUDIT_FAIL = "FLAG_SECURITY_AUDIT_FAIL"
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# Governance
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FLAG_GOV_PROPOSAL_NEW = "FLAG_GOV_PROPOSAL_NEW"
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FLAG_GOV_VOTE_SUCCESS = "FLAG_GOV_VOTE_SUCCESS"
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FLAG_GOV_VOTE_FAILED = "FLAG_GOV_VOTE_FAILED"
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FLAG_GOV_VOTE_RAN_AWAY = "FLAG_GOV_VOTE_RAN_AWAY"
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FLAG_GOV_QUORUM_MISS = "FLAG_GOV_QUORUM_MISS"
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FLAG_DAO_DEPLOYMENT = "FLAG_DAO_DEPLOYMENT"
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FLAG_GOV_DELAY_CHANGE = "FLAG_GOV_DELAY_CHANGE"
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# Market Structure
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FLAG_LISTING = "FLAG_LISTING"
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FLAG_DELISTING = "FLAG_DELISTING"
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FLAG_HALVING = "FLAG_HALVING"
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FLAG_ETP_APPROVAL = "FLAG_ETP_APPROVAL"
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FLAG_ETP_REJECTION = "FLAG_ETP_REJECTION"
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FLAG_WHALE_ACCUMULATION = "FLAG_WHALE_ACCUMULATION"
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FLAG_WHALE_DISTRIBUTION = "FLAG_WHALE_DISTRIBUTION"
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FLAG_EXCHANGE_HALT = "FLAG_EXCHANGE_HALT"
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FLAG_WITHDRAWAL_SUSPEND = "FLAG_WITHDRAWAL_SUSPEND"
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FLAG_LIQUIDITY_MIGRATION = "FLAG_LIQUIDITY_MIGRATION"
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FLAG_MM_PROGRAM_CHANGE = "FLAG_MM_PROGRAM_CHANGE"
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# Regulatory
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FLAG_REG_CLARITY_POSITIVE = "FLAG_REG_CLARITY_POSITIVE"
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FLAG_REG_CLARITY_NEGATIVE = "FLAG_REG_CLARITY_NEGATIVE"
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FLAG_REG_ENFORCEMENT = "FLAG_REG_ENFORCEMENT"
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FLAG_REG_INVESTIGATION = "FLAG_REG_INVESTIGATION"
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FLAG_REG_COMPLIANCE_ISSUE = "FLAG_REG_COMPLIANCE_ISSUE"
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FLAG_TAX_TREATMENT_CHANGE = "FLAG_TAX_TREATMENT_CHANGE"
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FLAG_LITIGATION_FILED = "FLAG_LITIGATION_FILED"
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FLAG_LITIGATION_SETTLED = "FLAG_LITIGATION_SETTLED"
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FLAG_BANKRUPTCY = "FLAG_BANKRUPTCY"
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FLAG_DEFAULT = "FLAG_DEFAULT"
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# Social
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FLAG_PUMP_COORDINATION = "FLAG_PUMP_COORDINATION"
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FLAG_PROMOTION = "FLAG_PROMOTION"
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FLAG_CRITICISM = "FLAG_CRITICISM"
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FLAG_ECHO_CHAMBER = "FLAG_ECHO_CHAMBER"
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FLAG_BOT_ACTIVITY = "FLAG_BOT_ACTIVITY"
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FLAG_FEAR_KEYWORD_SPIKE = "FLAG_FEAR_KEYWORD_SPIKE"
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FLAG_GREED_KEYWORD_SPIKE = "FLAG_GREED_KEYWORD_SPIKE"
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FLAG_PANIC_KEYWORD_SPIKE = "FLAG_PANIC_KEYWORD_SPIKE"
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FLAG_UNCERTAINTY_KEYWORD_SPIKE = "FLAG_UNCERTAINTY_KEYWORD_SPIKE"
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FLAG_CONFIDENCE_KEYWORD_SPIKE = "FLAG_CONFIDENCE_KEYWORD_SPIKE"
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# Macro
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FLAG_FED_RATE_CUT = "FLAG_FED_RATE_CUT"
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FLAG_FED_RATE_HIKE = "FLAG_FED_RATE_HIKE"
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FLAG_CPI_RELEASE = "FLAG_CPI_RELEASE"
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FLAG_CPI_SURPRISE_HIGH = "FLAG_CPI_SURPRISE_HIGH"
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FLAG_CPI_SURPRISE_LOW = "FLAG_CPI_SURPRISE_LOW"
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FLAG_GDP_RELEASE = "FLAG_GDP_RELEASE"
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FLAG_EMPLOYMENT_RELEASE = "FLAG_EMPLOYMENT_RELEASE"
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FLAG_CENTRAL_BANK_SPEECH_HAWKISH = "FLAG_CENTRAL_BANK_SPEECH_HAWKISH"
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FLAG_CENTRAL_BANK_SPEECH_DOVE = "FLAG_CENTRAL_BANK_SPEECH_DOVE"
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FLAG_GEOPOLITICAL_TENSION = "FLAG_GEOPOLITICAL_TENSION"
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FLAG_GEOPOLITICAL_RESOLUTION = "FLAG_GEOPOLITICAL_RESOLUTION"
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# Manipulation
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FLAG_ECHO_CHAMBER_DETECTED = "FLAG_ECHO_CHAMBER_DETECTED"
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FLAG_COORDINATED_MANIPULATION = "FLAG_COORDINATED_MANIPULATION"
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FLAG_WASH_TRADING = "FLAG_WASH_TRADING"
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FLAG_SPOOFING = "FLAG_SPOOFING"
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FLAG_LAYERING = "FLAG_LAYERING"
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FLAG_QUOTE_STUFFING = "FLAG_QUOTE_STUFFING"
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class EventFlag(BaseModel):
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"""Event flag with strength"""
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"""Event flag with strength - per spec Section 8.4"""
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event_type: str
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asset_id: str
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strength: float = Field(..., ge=0.0, le=100.0, description="Event strength 0-100")
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asset: str
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industry: str
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value: float = Field(..., ge=0.0, le=100.0, description="Event strength 0-100")
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confidence: float = Field(..., ge=0.0, le=1.0)
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first_seen_ts: float
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last_seen_ts: float
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source_credibility: float = Field(..., ge=0.0, le=1.0)
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num_sources: int = Field(default=1, ge=1)
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detail_factor: float = Field(default=0.0, ge=0.0, le=1.0)
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base_impact: float = Field(default=0.0, ge=0.0, le=100.0)
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t_zero: float
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decay_remaining: float = Field(default=1.0, ge=0.0, le=1.0)
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half_life_minutes: float = Field(default=0.0, ge=0.0)
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impact_duration_minutes: float = Field(default=0.0, ge=0.0)
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direction: str = Field(default="neutral", description="positive | negative | mixed | neutral")
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is_scheduled: bool = False
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triggered_at: float
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sources: List[str] = Field(default_factory=list)
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details_extracted: Dict[str, Any] = Field(default_factory=dict)
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flag_type: Optional[FlagType] = None
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flags: List[str] = Field(default_factory=list)
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# Legacy compatibility fields
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asset_id: str = ""
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strength: float = 0.0
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first_seen_ts: float = 0.0
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last_seen_ts: float = 0.0
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source_count: int = 1
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details: Dict[str, Any] = Field(default_factory=dict)
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@field_validator('asset_id', mode='before')
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@classmethod
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def _set_asset_id(cls, v, info):
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return info.data.get('asset', '')
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@field_validator('strength', mode='before')
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@classmethod
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def _set_strength(cls, v, info):
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return info.data.get('value', 0.0)
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@field_validator('first_seen_ts', mode='before')
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@classmethod
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def _set_first_seen(cls, v, info):
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return info.data.get('triggered_at', 0.0)
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@field_validator('last_seen_ts', mode='before')
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@classmethod
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def _set_last_seen(cls, v, info):
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return info.data.get('triggered_at', 0.0)
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@field_validator('source_count', mode='before')
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@classmethod
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def _set_source_count(cls, v, info):
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return info.data.get('num_sources', 1)
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@field_validator('details', mode='before')
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@classmethod
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def _set_details(cls, v, info):
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return info.data.get('details_extracted', {})
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class AssetSentiment(BaseModel):
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"""Per-asset sentiment output"""
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"""Per-asset sentiment output - per spec Section 8.1"""
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asset_id: str
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fear_state: float = Field(..., ge=0.0, le=100.0, description="Fear level 0-100")
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greed_state: float = Field(..., ge=0.0, le=100.0, description="Greed level 0-100")
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@@ -52,10 +199,11 @@ class AssetSentiment(BaseModel):
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last_update_ts: float = Field(..., description="Last update timestamp")
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contributing_sources: int = Field(default=0)
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decay_factor: float = Field(default=1.0, ge=0.0, le=1.0, description="Temporal decay applied")
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contributing_events: Dict[str, str] = Field(default_factory=dict, description="Top event driving each state metric")
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class IndustrySentiment(BaseModel):
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"""Industry/class level sentiment aggregation"""
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"""Industry/class level sentiment aggregation - per spec Section 8.1"""
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industry: str
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assets: List[str] = Field(default_factory=list)
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fear_state: float = Field(..., ge=0.0, le=100.0)
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@@ -66,15 +214,18 @@ class IndustrySentiment(BaseModel):
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dominant_events: List[EventFlag] = Field(default_factory=list)
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asset_count: int = 0
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last_update_ts: float
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hype_velocity: float = Field(default=0.0, ge=-100.0, le=100.0)
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pub_velocity: float = Field(default=0.0, ge=-100.0, le=100.0)
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contributing_events: Dict[str, str] = Field(default_factory=dict)
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class MarketSentiment(BaseModel):
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"""Market-wide sentiment aggregation"""
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"""Market-wide sentiment aggregation - per spec Section 8.1"""
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fear_state: float = Field(..., ge=0.0, le=100.0)
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greed_state: float = Field(..., ge=0.0, le=100.0)
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sentiment_index: float = Field(..., ge=-100.0, le=100.0, description="Market-wide sentiment index")
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hype_velocity: float = Field(..., ge=0.0, le=100.0)
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pub_velocity: float = Field(..., ge=0.0, le=100.0)
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hype_velocity: float = Field(..., ge=-100.0, le=100.0)
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pub_velocity: float = Field(..., ge=-100.0, le=100.0)
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aggregate_pump_risk: float = Field(default=0.0, ge=0.0, le=100.0)
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aggregate_dump_risk: float = Field(default=0.0, ge=0.0, le=100.0)
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top_pump_assets: List[str] = Field(default_factory=list) # Top 10 by pump_score
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@@ -84,15 +235,18 @@ class MarketSentiment(BaseModel):
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last_update_ts: float
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total_sources: int = 0
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total_assets: int = 0
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contributing_events: Dict[str, str] = Field(default_factory=dict)
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class SentimentOutput(BaseModel):
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"""Complete sentiment engine output snapshot"""
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"""Complete sentiment engine output snapshot - per spec Section 8"""
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timestamp: float = Field(..., description="Output generation timestamp")
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market: MarketSentiment
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industries: Dict[str, IndustrySentiment] = Field(default_factory=dict)
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assets: Dict[str, AssetSentiment] = Field(default_factory=dict)
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metadata: Dict[str, Any] = Field(default_factory=dict)
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schema_version: int = Field(default=2, description="Output schema version for downstream compatibility")
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engine_version: str = Field(default="2.0.0", description="Engine version string")
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def get_acb_signals(self) -> Dict[str, float]:
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"""Extract signals for ACB consumption"""
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