models.py

import datetime
from sqlalchemy import Column, Integer, String, Float, DateTime, JSON
from database import Base

class PredictionRecord(Base):
    __tablename__ = "prediction_records"

    id = Column(Integer, primary_key=True, index=True)
    asset = Column(String, index=True)             # "BTC" or "ETH"
    timestamp = Column(DateTime, default=datetime.datetime.utcnow, index=True)
    
    current_price = Column(Float, nullable=False)
    
    # Prediction market details from Polymarket and Kalshi
    polymarket_info = Column(JSON, nullable=True)   # Market details, title, Yes/No contracts
    kalshi_info = Column(JSON, nullable=True)       # Market details, ticker, odds
    
    # Preprocessed indicators from Data Collection Agent
    technical_indicators = Column(JSON, nullable=True) # EMA, RSI, MACD
    
    # Prediction details from Prediction Agent (Kronos)
    predicted_direction = Column(String, nullable=False) # "UP" or "DOWN"
    probability = Column(Float, nullable=False)          # Probability/Confidence (e.g. 0.58)
    
    # Risk parameters from Risk Agent
    kelly_fraction = Column(Float, nullable=False)       # Pure Kelly fraction
    bet_size = Column(Float, nullable=False)             # Actual sized bet fraction (e.g. Quarter Kelly)
    
    # Resolution details for the Feedback Loop
    status = Column(String, default="PENDING")           # "PENDING", "RESOLVED", "FAILED"
    actual_direction = Column(String, nullable=True)     # "UP" or "DOWN" at check time
    actual_close = Column(Float, nullable=True)          # Actual price at resolution
    profit_loss = Column(Float, nullable=True)           # Theoretical PnL based on sized bet
    resolved_at = Column(DateTime, nullable=True)
    
    error_message = Column(String, nullable=True)

    def to_dict(self):
        return {
            "id": self.id,
            "asset": self.asset,
            "timestamp": self.timestamp.isoformat() if self.timestamp else None,
            "current_price": self.current_price,
            "polymarket_info": self.polymarket_info,
            "kalshi_info": self.kalshi_info,
            "technical_indicators": self.technical_indicators,
            "predicted_direction": self.predicted_direction,
            "probability": self.probability,
            "kelly_fraction": self.kelly_fraction,
            "bet_size": self.bet_size,
            "status": self.status,
            "actual_direction": self.actual_direction,
            "actual_close": self.actual_close,
            "profit_loss": self.profit_loss,
            "resolved_at": self.resolved_at.isoformat() if self.resolved_at else None,
            "error_message": self.error_message
        }

class AgentLog(Base):
    __tablename__ = "agent_logs"

    id = Column(Integer, primary_key=True, index=True)
    timestamp = Column(DateTime, default=datetime.datetime.utcnow, index=True)
    agent_name = Column(String, index=True)
    level = Column(String, default="INFO")
    message = Column(String, nullable=False)

    def to_dict(self):
        return {
            "id": self.id,
            "timestamp": self.timestamp.isoformat() if self.timestamp else None,
            "agent_name": self.agent_name,
            "level": self.level,
            "message": self.message
        }