from typing import List, Dict
from dataclasses import dataclass
from datetime import datetime

@dataclass
class Position:
    symbol: str
    entry_date: datetime
    entry_price: float
    shares: int
    stop_loss: float
    target_price: float
    
    @property
    def current_value(self) -> float:
        # TODO: Implement real-time price fetching
        return self.shares * self.entry_price

    @property
    def potential_profit(self) -> float:
        """Calculate potential profit at target price"""
        return (self.target_price - self.entry_price) * self.shares
    
    @property
    def potential_loss(self) -> float:
        """Calculate potential loss at stop loss"""
        return (self.stop_loss - self.entry_price) * self.shares
    
    @property
    def risk_reward_ratio(self) -> float:
        """Calculate risk/reward ratio"""
        potential_gain = self.target_price - self.entry_price
        potential_risk = self.entry_price - self.stop_loss
        return abs(potential_gain / potential_risk) if potential_risk != 0 else 0

class Portfolio:
    def __init__(self):
        self.positions: List[Position] = []
        
    def add_position(self, position: Position):
        """Add a new position to the portfolio"""
        self.positions.append(position)
        
    def remove_position(self, symbol: str):
        """Remove a position from the portfolio by symbol"""
        self.positions = [p for p in self.positions if p.symbol != symbol]
        
    def get_position_summary(self) -> List[Dict]:
        """Get summary of all positions"""
        return [
            {
                "symbol": p.symbol,
                "entry_date": p.entry_date,
                "entry_price": p.entry_price,
                "shares": p.shares,
                "current_value": p.current_value,
                "stop_loss": p.stop_loss,
                "target_price": p.target_price,
                "potential_profit": p.potential_profit,
                "potential_loss": p.potential_loss,
                "risk_reward_ratio": p.risk_reward_ratio
            }
            for p in self.positions
        ]