IB Analytics MCP Server

""" Base sentiment analyzer classes and models Provides abstract interface for sentiment analysis implementations. """ from abc import ABC, abstractmethod from datetime import datetime from decimal import Decimal from pydantic import BaseModel, Field, field_validator class SentimentScore(BaseModel): """ Sentiment analysis result Sentiment score ranges from -1.0 (very negative) to 1.0 (very positive). Confidence represents the reliability of the sentiment assessment. Attributes: score: Sentiment score from -1.0 (negative) to 1.0 (positive) confidence: Analysis confidence from 0.0 (no confidence) to 1.0 (high confidence) timestamp: When the sentiment was analyzed key_themes: Optional list of key themes identified in the analysis risk_factors: Optional list of risk factors identified in the analysis reasoning: Optional explanation of the sentiment score """ score: Decimal = Field( ..., description="Sentiment score from -1.0 (negative) to 1.0 (positive)", ) confidence: Decimal = Field( ..., description="Confidence level from 0.0 (no confidence) to 1.0 (high confidence)", ) timestamp: datetime = Field( ..., description="Timestamp when sentiment was analyzed", ) key_themes: list[str] = Field( default_factory=list, description="Key themes identified in the analysis", ) risk_factors: list[str] = Field( default_factory=list, description="Risk factors identified in the analysis", ) reasoning: str = Field( default="", description="Explanation of the sentiment score", ) @field_validator("score") @classmethod def validate_score_range(cls, v: Decimal) -> Decimal: """ Validate sentiment score is within valid range Args: v: Score value to validate Returns: Validated score Raises: ValueError: If score is outside [-1.0, 1.0] range """ if v < Decimal("-1.0") or v > Decimal("1.0"): raise ValueError(f"Score must be between -1.0 and 1.0, got {v}") return v @field_validator("confidence") @classmethod def validate_confidence_range(cls, v: Decimal) -> Decimal: """ Validate confidence is within valid range Args: v: Confidence value to validate Returns: Validated confidence Raises: ValueError: If confidence is outside [0.0, 1.0] range """ if v < Decimal("0.0") or v > Decimal("1.0"): raise ValueError(f"Confidence must be between 0.0 and 1.0, got {v}") return v model_config = { "json_encoders": { Decimal: str, datetime: lambda v: v.isoformat(), } } class BaseSentimentAnalyzer(ABC): """ Abstract base class for sentiment analyzers Provides common interface for all sentiment analysis implementations. Subclasses must implement the analyze_sentiment method. Example: >>> class MySentimentAnalyzer(BaseSentimentAnalyzer): ... async def analyze_sentiment(self, symbol: str) -> SentimentScore: ... # Implementation here ... return SentimentScore( ... score=Decimal("0.5"), ... confidence=Decimal("0.8"), ... timestamp=datetime.now() ... ) """ @abstractmethod async def analyze_sentiment(self, symbol: str) -> SentimentScore: """ Analyze sentiment for a stock symbol Args: symbol: Stock ticker symbol (e.g., "AAPL", "TSLA") Returns: SentimentScore with sentiment analysis results Raises: Exception: Implementation-specific exceptions for failures """ pass __all__ = ["SentimentScore", "BaseSentimentAnalyzer"]