MaverickMCP

""" Stock analysis entity. This entity represents a complete technical analysis of a stock. """ from dataclasses import dataclass from datetime import datetime from maverick_mcp.domain.value_objects.technical_indicators import ( BollingerBands, MACDIndicator, PriceLevel, RSIIndicator, Signal, StochasticOscillator, TrendDirection, VolumeProfile, ) @dataclass class StockAnalysis: """ Entity representing a comprehensive technical analysis of a stock. This is a domain entity that aggregates various technical indicators and analysis results for a specific stock at a point in time. """ # Basic information symbol: str analysis_date: datetime current_price: float # Trend analysis trend_direction: TrendDirection trend_strength: float # 0-100 # Technical indicators rsi: RSIIndicator | None = None macd: MACDIndicator | None = None bollinger_bands: BollingerBands | None = None stochastic: StochasticOscillator | None = None # Price levels support_levels: list[PriceLevel] | None = None resistance_levels: list[PriceLevel] | None = None # Volume analysis volume_profile: VolumeProfile | None = None # Composite analysis composite_signal: Signal = Signal.NEUTRAL confidence_score: float = 0.0 # 0-100 # Analysis metadata analysis_period_days: int = 365 indicators_used: list[str] | None = None def __post_init__(self): """Initialize default values.""" if self.support_levels is None: self.support_levels = [] if self.resistance_levels is None: self.resistance_levels = [] if self.indicators_used is None: self.indicators_used = [] @property def has_bullish_setup(self) -> bool: """Check if the analysis indicates a bullish setup.""" bullish_signals = [ Signal.BUY, Signal.STRONG_BUY, ] return self.composite_signal in bullish_signals @property def has_bearish_setup(self) -> bool: """Check if the analysis indicates a bearish setup.""" bearish_signals = [ Signal.SELL, Signal.STRONG_SELL, ] return self.composite_signal in bearish_signals @property def nearest_support(self) -> PriceLevel | None: """Get the nearest support level below current price.""" if not self.support_levels: return None below_price = [s for s in self.support_levels if s.price < self.current_price] if below_price: return max(below_price, key=lambda x: x.price) return None @property def nearest_resistance(self) -> PriceLevel | None: """Get the nearest resistance level above current price.""" if not self.resistance_levels: return None above_price = [ r for r in self.resistance_levels if r.price > self.current_price ] if above_price: return min(above_price, key=lambda x: x.price) return None @property def risk_reward_ratio(self) -> float | None: """Calculate risk/reward ratio based on nearest support/resistance.""" support = self.nearest_support resistance = self.nearest_resistance if not support or not resistance: return None risk = self.current_price - support.price reward = resistance.price - self.current_price if risk <= 0: return None return reward / risk def get_indicator_summary(self) -> dict[str, str]: """Get a summary of all indicator signals.""" summary = {} if self.rsi: summary["RSI"] = f"{self.rsi.value:.1f} ({self.rsi.signal.value})" if self.macd: summary["MACD"] = self.macd.signal.value if self.bollinger_bands: summary["Bollinger"] = self.bollinger_bands.signal.value if self.stochastic: summary["Stochastic"] = ( f"{self.stochastic.k_value:.1f} ({self.stochastic.signal.value})" ) if self.volume_profile: summary["Volume"] = f"{self.volume_profile.relative_volume:.1f}x average" return summary def get_key_levels(self) -> dict[str, float]: """Get key price levels for trading decisions.""" levels = { "current_price": self.current_price, } if self.nearest_support: levels["nearest_support"] = self.nearest_support.price if self.nearest_resistance: levels["nearest_resistance"] = self.nearest_resistance.price if self.bollinger_bands: levels["bollinger_upper"] = self.bollinger_bands.upper_band levels["bollinger_lower"] = self.bollinger_bands.lower_band return levels def to_dict(self) -> dict: """Convert the analysis to a dictionary for serialization.""" return { "symbol": self.symbol, "analysis_date": self.analysis_date.isoformat(), "current_price": self.current_price, "trend": { "direction": self.trend_direction.value, "strength": self.trend_strength, }, "indicators": self.get_indicator_summary(), "levels": self.get_key_levels(), "signal": self.composite_signal.value, "confidence": self.confidence_score, "risk_reward_ratio": self.risk_reward_ratio, }