"""Service for calculating performance metrics and unrealized P&L."""
from sqlalchemy.orm import Session
from sqlalchemy import and_, func
from typing import Dict, Optional
from decimal import Decimal
from datetime import datetime, timedelta
import yfinance as yf
from functools import lru_cache

from app.models import Position, Transaction
from app.models.position import PositionStatus


class PerformanceCalculator:
    """
    Service for calculating performance metrics and market data.

    Integrates with Yahoo Finance API for real-time pricing of open positions.
    """

    def __init__(self, db: Session, cache_ttl: int = 60):
        """
        Initialize performance calculator.

        Args:
            db: Database session
            cache_ttl: Cache time-to-live in seconds (default: 60)
        """
        self.db = db
        self.cache_ttl = cache_ttl
        self._price_cache: Dict[str, tuple[Decimal, datetime]] = {}

    def calculate_unrealized_pnl(self, position: Position) -> Optional[Decimal]:
        """
        Calculate unrealized P&L for an open position.

        Args:
            position: Open position to calculate P&L for

        Returns:
            Unrealized P&L or None if market data unavailable
        """
        if position.status != PositionStatus.OPEN:
            return None

        # Get current market price
        current_price = self.get_current_price(position.symbol)
        if current_price is None:
            return None

        if position.avg_entry_price is None:
            return None

        # Calculate P&L based on position direction
        quantity = abs(position.total_quantity)
        is_short = position.total_quantity < 0

        if is_short:
            # Short position: profit when price decreases
            pnl = (position.avg_entry_price - current_price) * quantity * 100
        else:
            # Long position: profit when price increases
            pnl = (current_price - position.avg_entry_price) * quantity * 100

        # Subtract fees and commissions from opening transactions
        total_fees = Decimal("0")
        for link in position.transaction_links:
            txn = link.transaction
            if txn.commission:
                total_fees += txn.commission
            if txn.fees:
                total_fees += txn.fees

        pnl -= total_fees

        return pnl

    def update_open_positions_pnl(self, account_id: int) -> int:
        """
        Update unrealized P&L for all open positions in an account.

        Args:
            account_id: Account ID to update

        Returns:
            Number of positions updated
        """
        open_positions = (
            self.db.query(Position)
            .filter(
                and_(
                    Position.account_id == account_id,
                    Position.status == PositionStatus.OPEN,
                )
            )
            .all()
        )

        updated = 0
        for position in open_positions:
            unrealized_pnl = self.calculate_unrealized_pnl(position)
            if unrealized_pnl is not None:
                position.unrealized_pnl = unrealized_pnl
                updated += 1

        self.db.commit()
        return updated

    def get_current_price(self, symbol: str) -> Optional[Decimal]:
        """
        Get current market price for a symbol.

        Uses Yahoo Finance API with caching to reduce API calls.

        Args:
            symbol: Stock ticker symbol

        Returns:
            Current price or None if unavailable
        """
        # Check cache
        if symbol in self._price_cache:
            price, timestamp = self._price_cache[symbol]
            if datetime.now() - timestamp < timedelta(seconds=self.cache_ttl):
                return price

        # Fetch from Yahoo Finance
        try:
            ticker = yf.Ticker(symbol)
            info = ticker.info

            # Try different price fields
            current_price = None
            for field in ["currentPrice", "regularMarketPrice", "previousClose"]:
                if field in info and info[field]:
                    current_price = Decimal(str(info[field]))
                    break

            if current_price is not None:
                # Cache the price
                self._price_cache[symbol] = (current_price, datetime.now())
                return current_price

        except Exception:
            # Failed to fetch price
            pass

        return None

    def calculate_account_stats(self, account_id: int) -> Dict:
        """
        Calculate aggregate statistics for an account.

        Args:
            account_id: Account ID

        Returns:
            Dictionary with performance metrics
        """
        # Get all positions
        positions = (
            self.db.query(Position)
            .filter(Position.account_id == account_id)
            .all()
        )

        total_positions = len(positions)
        open_positions_count = sum(
            1 for p in positions if p.status == PositionStatus.OPEN
        )
        closed_positions_count = sum(
            1 for p in positions if p.status == PositionStatus.CLOSED
        )

        # Calculate P&L
        total_realized_pnl = sum(
            (p.realized_pnl or Decimal("0"))
            for p in positions
            if p.status == PositionStatus.CLOSED
        )

        # Update unrealized P&L for open positions
        self.update_open_positions_pnl(account_id)

        total_unrealized_pnl = sum(
            (p.unrealized_pnl or Decimal("0"))
            for p in positions
            if p.status == PositionStatus.OPEN
        )

        # Calculate win rate and average win/loss
        closed_with_pnl = [
            p for p in positions
            if p.status == PositionStatus.CLOSED and p.realized_pnl is not None
        ]

        if closed_with_pnl:
            winning_trades = [p for p in closed_with_pnl if p.realized_pnl > 0]
            losing_trades = [p for p in closed_with_pnl if p.realized_pnl < 0]

            win_rate = (len(winning_trades) / len(closed_with_pnl)) * 100

            avg_win = (
                sum(p.realized_pnl for p in winning_trades) / len(winning_trades)
                if winning_trades
                else Decimal("0")
            )

            avg_loss = (
                sum(p.realized_pnl for p in losing_trades) / len(losing_trades)
                if losing_trades
                else Decimal("0")
            )
        else:
            win_rate = 0.0
            avg_win = Decimal("0")
            avg_loss = Decimal("0")

        # Get current account balance from latest transaction
        latest_txn = (
            self.db.query(Transaction)
            .filter(Transaction.account_id == account_id)
            .order_by(Transaction.run_date.desc(), Transaction.id.desc())
            .first()
        )

        current_balance = (
            latest_txn.cash_balance if latest_txn and latest_txn.cash_balance else Decimal("0")
        )

        return {
            "total_positions": total_positions,
            "open_positions": open_positions_count,
            "closed_positions": closed_positions_count,
            "total_realized_pnl": float(total_realized_pnl),
            "total_unrealized_pnl": float(total_unrealized_pnl),
            "total_pnl": float(total_realized_pnl + total_unrealized_pnl),
            "win_rate": float(win_rate),
            "avg_win": float(avg_win),
            "avg_loss": float(avg_loss),
            "current_balance": float(current_balance),
        }

    def get_balance_history(
        self, account_id: int, days: int = 30
    ) -> list[Dict]:
        """
        Get account balance history for charting.

        Args:
            account_id: Account ID
            days: Number of days to retrieve

        Returns:
            List of {date, balance} dictionaries
        """
        cutoff_date = datetime.now().date() - timedelta(days=days)

        transactions = (
            self.db.query(Transaction.run_date, Transaction.cash_balance)
            .filter(
                and_(
                    Transaction.account_id == account_id,
                    Transaction.run_date >= cutoff_date,
                    Transaction.cash_balance.isnot(None),
                )
            )
            .order_by(Transaction.run_date)
            .all()
        )

        # Get one balance per day (use last transaction of the day)
        daily_balances = {}
        for txn in transactions:
            daily_balances[txn.run_date] = float(txn.cash_balance)

        return [
            {"date": date.isoformat(), "balance": balance}
            for date, balance in sorted(daily_balances.items())
        ]

    def get_top_trades(
        self, account_id: int, limit: int = 10
    ) -> list[Dict]:
        """
        Get top performing trades (by realized P&L).

        Args:
            account_id: Account ID
            limit: Maximum number of trades to return

        Returns:
            List of trade dictionaries
        """
        positions = (
            self.db.query(Position)
            .filter(
                and_(
                    Position.account_id == account_id,
                    Position.status == PositionStatus.CLOSED,
                    Position.realized_pnl.isnot(None),
                )
            )
            .order_by(Position.realized_pnl.desc())
            .limit(limit)
            .all()
        )

        return [
            {
                "symbol": p.symbol,
                "option_symbol": p.option_symbol,
                "position_type": p.position_type.value,
                "open_date": p.open_date.isoformat(),
                "close_date": p.close_date.isoformat() if p.close_date else None,
                "quantity": float(p.total_quantity),
                "entry_price": float(p.avg_entry_price) if p.avg_entry_price else None,
                "exit_price": float(p.avg_exit_price) if p.avg_exit_price else None,
                "realized_pnl": float(p.realized_pnl),
            }
            for p in positions
        ]

    def get_worst_trades(
        self, account_id: int, limit: int = 20
    ) -> list[Dict]:
        """
        Get worst performing trades (biggest losses by realized P&L).

        Args:
            account_id: Account ID
            limit: Maximum number of trades to return

        Returns:
            List of trade dictionaries
        """
        positions = (
            self.db.query(Position)
            .filter(
                and_(
                    Position.account_id == account_id,
                    Position.status == PositionStatus.CLOSED,
                    Position.realized_pnl.isnot(None),
                )
            )
            .order_by(Position.realized_pnl.asc())
            .limit(limit)
            .all()
        )

        return [
            {
                "symbol": p.symbol,
                "option_symbol": p.option_symbol,
                "position_type": p.position_type.value,
                "open_date": p.open_date.isoformat(),
                "close_date": p.close_date.isoformat() if p.close_date else None,
                "quantity": float(p.total_quantity),
                "entry_price": float(p.avg_entry_price) if p.avg_entry_price else None,
                "exit_price": float(p.avg_exit_price) if p.avg_exit_price else None,
                "realized_pnl": float(p.realized_pnl),
            }
            for p in positions
        ]