Celes Renata
4ffde8cc06
- Database migration 018 with 13 tables for trading engine state - Trading engine service (services/trading/) with 12 pure computation modules: position sizer, stop-loss manager, reserve pool, circuit breaker, risk tier controller, correlation matrix, tax lots, trading window, gradual entry, notifications, micro-trading, backtester - Core TradingEngine with pre-trade evaluation pipeline and integration wiring - FastAPI HTTP service with 14 endpoints (health, config, decisions, metrics, backtest) - Performance tracker with Sharpe ratio, drawdown, profit factor computation - 194 Python tests (165 property-based + 29 integration) - Frontend: 13 TanStack Query hooks, 7 dashboard panels, tabbed Trading Engine page - Helm chart entry, network policy, nginx proxy, ingress for trading-engine - Shared infrastructure: enums, Redis keys, TradingConfig in AppConfig
399 lines
12 KiB
Python
399 lines
12 KiB
Python
"""Typed JSON schemas for document intelligence, trend summaries, and recommendations."""
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from __future__ import annotations
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import uuid
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from datetime import datetime
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from enum import Enum
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from typing import List, Optional
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from pydantic import BaseModel, Field
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# --- Enums ---
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class DocumentType(str, Enum):
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ARTICLE = "article"
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FILING = "filing"
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TRANSCRIPT = "transcript"
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PRESS_RELEASE = "press_release"
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MACRO_EVENT = "macro_event"
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class SourceType(str, Enum):
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MARKET_API = "market_api"
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NEWS_API = "news_api"
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FILINGS_API = "filings_api"
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WEB_SCRAPE = "web_scrape"
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BROKER = "broker"
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class Sentiment(str, Enum):
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POSITIVE = "positive"
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NEGATIVE = "negative"
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NEUTRAL = "neutral"
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MIXED = "mixed"
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class CatalystType(str, Enum):
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EARNINGS = "earnings"
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PRODUCT = "product"
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LEGAL = "legal"
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MACRO = "macro"
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SUPPLY_CHAIN = "supply_chain"
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M_AND_A = "m_and_a"
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RATING_CHANGE = "rating_change"
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OTHER = "other"
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class TrendDirection(str, Enum):
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BULLISH = "bullish"
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BEARISH = "bearish"
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MIXED = "mixed"
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NEUTRAL = "neutral"
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class ActionType(str, Enum):
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BUY = "buy"
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SELL = "sell"
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HOLD = "hold"
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WATCH = "watch"
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class RecommendationMode(str, Enum):
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INFORMATIONAL = "informational"
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PAPER_ELIGIBLE = "paper_eligible"
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LIVE_ELIGIBLE = "live_eligible"
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class TrendWindow(str, Enum):
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INTRADAY = "intraday"
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ONE_DAY = "1d"
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SEVEN_DAY = "7d"
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THIRTY_DAY = "30d"
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NINETY_DAY = "90d"
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class ImpactType(str, Enum):
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SUPPLY_DISRUPTION = "supply_disruption"
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DEMAND_SHIFT = "demand_shift"
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COST_INCREASE = "cost_increase"
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REGULATORY_PRESSURE = "regulatory_pressure"
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CURRENCY_IMPACT = "currency_impact"
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COMMODITY_SHOCK = "commodity_shock"
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TRADE_BARRIER = "trade_barrier"
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GEOPOLITICAL_RISK = "geopolitical_risk"
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class SeverityLevel(str, Enum):
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LOW = "low"
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MODERATE = "moderate"
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HIGH = "high"
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CRITICAL = "critical"
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class MarketPositionTier(str, Enum):
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GLOBAL_LEADER = "global_leader"
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MULTINATIONAL = "multinational"
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REGIONAL = "regional"
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DOMESTIC = "domestic"
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class EstimatedDuration(str, Enum):
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SHORT_TERM = "short_term"
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MEDIUM_TERM = "medium_term"
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LONG_TERM = "long_term"
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# --- Autonomous Trading Engine Enums ---
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class TradingDecisionType(str, Enum):
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ACT = "act"
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SKIP = "skip"
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class CircuitBreakerTriggerType(str, Enum):
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DAILY_LOSS = "daily_loss"
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SINGLE_POSITION = "single_position"
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VOLATILITY = "volatility"
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MANUAL = "manual"
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class ReservePoolTriggerType(str, Enum):
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PROFIT_SIPHON = "profit_siphon"
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EMERGENCY_LIQUIDATION = "emergency_liquidation"
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MANUAL_ADJUSTMENT = "manual_adjustment"
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INITIAL = "initial"
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class NotificationChannel(str, Enum):
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SMS = "sms"
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EMAIL = "email"
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class RiskTierName(str, Enum):
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CONSERVATIVE = "conservative"
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MODERATE = "moderate"
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AGGRESSIVE = "aggressive"
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# --- Document Intelligence ---
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class CompanyImpact(BaseModel):
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ticker: str
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company_name: str
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relevance: float = Field(ge=0, le=1)
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sentiment: Sentiment
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impact_score: float = Field(ge=0, le=1)
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impact_horizon: str
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catalyst_type: CatalystType
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key_facts: List[str] = Field(default_factory=list)
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risks: List[str] = Field(default_factory=list)
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evidence_spans: List[str] = Field(default_factory=list)
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class ModelMetadata(BaseModel):
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provider: str = "ollama"
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model_name: str = ""
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prompt_version: str = ""
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schema_version: str = "2.0.0"
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class DocumentIntelligence(BaseModel):
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document_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
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document_type: DocumentType = DocumentType.ARTICLE
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summary: str = ""
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companies: List[CompanyImpact] = Field(default_factory=list)
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macro_themes: List[str] = Field(default_factory=list)
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novelty_score: float = Field(ge=0, le=1, default=0.5)
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source_credibility: float = Field(ge=0, le=1, default=0.5)
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extraction_warnings: List[str] = Field(default_factory=list)
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confidence: float = Field(ge=0, le=1, default=0.5)
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model: ModelMetadata = Field(default_factory=ModelMetadata)
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# --- Trend Summary ---
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class MarketContext(BaseModel):
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"""Recent market data features for a symbol, used to enrich aggregation."""
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ticker: str = ""
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price_change_pct: Optional[float] = None # % change over the window
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avg_volume: Optional[float] = None # average daily volume
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volume_change_pct: Optional[float] = None # volume vs prior period
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volatility: Optional[float] = None # intra-window price std dev
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latest_close: Optional[float] = None
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latest_bar_at: Optional[datetime] = None
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bars_available: int = 0
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@property
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def has_data(self) -> bool:
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return self.bars_available > 0
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class DisagreementDetail(BaseModel):
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"""Represents an explicit disagreement between document signals.
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Rather than collapsing contradictory signals into a single score,
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this captures the nature of the disagreement so downstream consumers
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can inspect *why* signals conflict.
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Requirements: 6.4
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"""
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dimension: str = "" # e.g. "sentiment", "catalyst", "impact_horizon"
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positive_doc_ids: List[str] = Field(default_factory=list)
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negative_doc_ids: List[str] = Field(default_factory=list)
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positive_weight: float = 0.0
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negative_weight: float = 0.0
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description: str = ""
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class TrendSummary(BaseModel):
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entity_type: str = "company"
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entity_id: str = ""
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window: TrendWindow = TrendWindow.SEVEN_DAY
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trend_direction: TrendDirection = TrendDirection.NEUTRAL
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trend_strength: float = Field(ge=0, le=1, default=0.5)
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confidence: float = Field(ge=0, le=1, default=0.5)
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top_supporting_evidence: List[str] = Field(default_factory=list)
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top_opposing_evidence: List[str] = Field(default_factory=list)
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dominant_catalysts: List[str] = Field(default_factory=list)
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material_risks: List[str] = Field(default_factory=list)
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contradiction_score: float = Field(ge=0, le=1, default=0.0)
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disagreement_details: List[DisagreementDetail] = Field(default_factory=list)
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market_context: Optional[MarketContext] = None
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generated_at: datetime = Field(default_factory=datetime.utcnow)
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# --- Recommendation ---
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class PositionSizing(BaseModel):
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portfolio_pct: float = Field(ge=0, le=1, default=0.02)
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max_loss_pct: float = Field(ge=0, le=1, default=0.005)
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class Recommendation(BaseModel):
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recommendation_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
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ticker: str = ""
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action: ActionType = ActionType.WATCH
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mode: RecommendationMode = RecommendationMode.INFORMATIONAL
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confidence: float = Field(ge=0, le=1, default=0.5)
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time_horizon: str = ""
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thesis: str = ""
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invalidation_conditions: List[str] = Field(default_factory=list)
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position_sizing: PositionSizing = Field(default_factory=PositionSizing)
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evidence_refs: List[str] = Field(default_factory=list)
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model_metadata: ModelMetadata = Field(default_factory=ModelMetadata)
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generated_at: datetime = Field(default_factory=datetime.utcnow)
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# --- Global News Interpolation ---
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class GlobalEventSchema(BaseModel):
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event_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
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event_types: List[ImpactType] = Field(default_factory=list)
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severity: SeverityLevel = SeverityLevel.LOW
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affected_regions: List[str] = Field(default_factory=list)
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affected_sectors: List[str] = Field(default_factory=list)
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affected_commodities: List[str] = Field(default_factory=list)
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summary: str = ""
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key_facts: List[str] = Field(default_factory=list)
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estimated_duration: EstimatedDuration = EstimatedDuration.SHORT_TERM
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confidence: float = Field(ge=0, le=1, default=0.5)
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source_document_id: str = ""
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model_metadata: ModelMetadata = Field(default_factory=ModelMetadata)
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created_at: datetime = Field(default_factory=datetime.utcnow)
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class MacroImpactRecordSchema(BaseModel):
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event_id: str = ""
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company_id: str = ""
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ticker: str = ""
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macro_impact_score: float = Field(ge=0, le=1, default=0.0)
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impact_direction: str = "neutral"
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contributing_factors: List[str] = Field(default_factory=list)
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confidence: float = Field(ge=0, le=1, default=0.5)
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computed_at: datetime = Field(default_factory=datetime.utcnow)
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class ExposureProfileSchema(BaseModel):
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company_id: str = ""
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geographic_revenue_mix: dict[str, float] = Field(default_factory=dict)
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supply_chain_regions: List[str] = Field(default_factory=list)
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key_input_commodities: List[str] = Field(default_factory=list)
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regulatory_jurisdictions: List[str] = Field(default_factory=list)
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market_position_tier: MarketPositionTier = MarketPositionTier.REGIONAL
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export_dependency_pct: float = Field(ge=0, le=1, default=0.0)
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source: str = "manual"
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confidence: float = Field(ge=0, le=1, default=1.0)
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version: int = 1
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active: bool = True
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created_at: datetime = Field(default_factory=datetime.utcnow)
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updated_at: datetime = Field(default_factory=datetime.utcnow)
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class TrendProjectionSchema(BaseModel):
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trend_window_id: str = ""
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projected_direction: TrendDirection = TrendDirection.NEUTRAL
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projected_strength: float = Field(ge=0, le=1, default=0.5)
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projected_confidence: float = Field(ge=0, le=1, default=0.5)
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projection_horizon: str = "7d"
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driving_factors: List[str] = Field(default_factory=list)
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macro_contribution_pct: float = Field(ge=0, le=1, default=0.0)
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diverges_from_current: bool = False
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computed_at: datetime = Field(default_factory=datetime.utcnow)
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# --- Document Metadata ---
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class StorageRefs(BaseModel):
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raw_html: Optional[str] = None
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raw_payload: Optional[str] = None
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normalized_text: Optional[str] = None
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class DocumentMetadata(BaseModel):
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document_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
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document_type: DocumentType = DocumentType.ARTICLE
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symbol_candidates: List[str] = Field(default_factory=list)
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source_type: SourceType = SourceType.NEWS_API
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publisher: str = ""
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url: Optional[str] = None
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canonical_url: Optional[str] = None
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title: str = ""
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published_at: Optional[datetime] = None
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retrieved_at: datetime = Field(default_factory=datetime.utcnow)
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language: str = "en"
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content_hash: str = ""
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storage_refs: StorageRefs = Field(default_factory=StorageRefs)
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# --- Competitive Intelligence & Historical Patterns ---
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class RelationshipType(str, Enum):
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DIRECT_RIVAL = "direct_rival"
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SAME_SECTOR = "same_sector"
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OVERLAPPING_PRODUCTS = "overlapping_products"
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SUPPLY_CHAIN_ADJACENT = "supply_chain_adjacent"
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class CatalystTier(str, Enum):
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MAJOR_CORPORATE_DECISION = "major_corporate_decision"
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ROUTINE_SIGNAL = "routine_signal"
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# Major corporate decision catalyst types (Req 11.1)
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MAJOR_DECISION_CATALYSTS: frozenset[str] = frozenset({
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"m_and_a",
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"legal",
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"restructuring",
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"leadership_change",
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"strategic_pivot",
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"buyback",
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"dividend_change",
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})
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class CompetitorRelationshipSchema(BaseModel):
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id: str = Field(default_factory=lambda: str(uuid.uuid4()))
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company_a_id: str = ""
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company_b_id: str = ""
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relationship_type: RelationshipType = RelationshipType.DIRECT_RIVAL
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strength: float = Field(ge=0, le=1, default=0.5)
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bidirectional: bool = True
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source: str = "manual"
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active: bool = True
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created_at: datetime = Field(default_factory=datetime.utcnow)
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updated_at: datetime = Field(default_factory=datetime.utcnow)
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class CompetitiveSignalRecordSchema(BaseModel):
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id: str = Field(default_factory=lambda: str(uuid.uuid4()))
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source_document_id: str = ""
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source_ticker: str = ""
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target_ticker: str = ""
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catalyst_type: str = ""
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pattern_confidence: float = Field(ge=0, le=1, default=0.0)
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signal_direction: str = "neutral"
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signal_strength: float = Field(ge=0, le=1, default=0.0)
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relationship_strength: float = Field(ge=0, le=1, default=0.0)
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computed_at: datetime = Field(default_factory=datetime.utcnow)
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class HistoricalPatternSchema(BaseModel):
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source_ticker: str = ""
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target_ticker: str = ""
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catalyst_type: str = ""
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time_horizon: str = "7d"
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sample_count: int = 0
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bullish_pct: float = Field(ge=0, le=1, default=0.0)
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bearish_pct: float = Field(ge=0, le=1, default=0.0)
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avg_strength: float = Field(ge=0, le=1, default=0.0)
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avg_time_to_resolution: float = 0.0
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pattern_confidence: float = Field(ge=0, le=1, default=0.0)
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data_start: Optional[datetime] = None
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data_end: Optional[datetime] = None
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tier: CatalystTier = CatalystTier.ROUTINE_SIGNAL
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insufficient_data: bool = False
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