"""
Trade data transformation with safety limits.

This module handles the transformation of trade data while enforcing safety limits
to prevent errors and protect against edge cases.
"""

import logging
from datetime import datetime, timezone
from decimal import Decimal, InvalidOperation
from typing import Dict, List, Optional, Any

from ..data_types import StandardizedTrade
from .time_utils import timestamp_to_datetime
from .numeric_utils import safe_decimal_conversion
from .normalization import normalize_trade_side, validate_symbol_format, normalize_exchange_name
from .safety import (
    validate_trade_size,
    validate_trade_price,
    TradeLimits,
    get_trade_limits
)


# Create a logger for this module
logger = logging.getLogger(__name__)


def create_standardized_trade(
    symbol: str,
    trade_id: str,
    price: Any,
    size: Any,
    side: str,
    timestamp: Any,
    exchange: str,
    raw_data: Optional[Dict[str, Any]] = None,
    is_milliseconds: bool = True
) -> StandardizedTrade:
    """
    Utility function to create StandardizedTrade with proper validation.
    
    Args:
        symbol: Trading symbol
        trade_id: Trade identifier
        price: Trade price (any numeric type)
        size: Trade size (any numeric type)
        side: Trade side ('buy' or 'sell')
        timestamp: Trade timestamp
        exchange: Exchange name
        raw_data: Original raw data
        is_milliseconds: True if timestamp is in milliseconds
        
    Returns:
        StandardizedTrade object
        
    Raises:
        ValueError: If data is invalid
    """
    # Validate symbol
    if not symbol or not isinstance(symbol, str):
        raise ValueError(f"Invalid symbol: {symbol}")
    
    # Validate trade_id
    if not trade_id:
        raise ValueError(f"Invalid trade_id: {trade_id}")
    
    # Convert timestamp
    try:
        if isinstance(timestamp, (int, float, str)):
            dt = timestamp_to_datetime(timestamp, is_milliseconds)
        elif isinstance(timestamp, datetime):
            dt = timestamp
            if dt.tzinfo is None:
                dt = dt.replace(tzinfo=timezone.utc)
        else:
            raise ValueError(f"Invalid timestamp type: {type(timestamp)}")
    except Exception as e:
        raise ValueError(f"Invalid timestamp: {timestamp}") from e
    
    # Convert price and size to Decimal
    try:
        if not price or not size:
            raise ValueError("Price and size must not be empty")
            
        decimal_price = safe_decimal_conversion(price, "price")
        decimal_size = safe_decimal_conversion(size, "size")
        
        if decimal_price is None or decimal_size is None:
            raise ValueError("Invalid price or size format")
            
        if decimal_price <= 0:
            raise ValueError(f"Price must be positive: {price}")
        if decimal_size <= 0:
            raise ValueError(f"Size must be positive: {size}")
            
    except (InvalidOperation, TypeError, ValueError) as e:
        raise ValueError(f"Invalid price or size: {e}")
    
    # Normalize side with strict validation
    try:
        if not side or not isinstance(side, str):
            raise ValueError(f"Invalid trade side: {side}")
            
        normalized_side = normalize_trade_side(side, logger=logger)
    except ValueError as e:
        logger.error(f"Trade side validation failed: {e}")
        raise ValueError(f"Invalid trade side: {side}")
    
    # Normalize symbol and exchange
    try:
        normalized_symbol = validate_symbol_format(symbol)
        normalized_exchange = normalize_exchange_name(exchange)
    except ValueError as e:
        raise ValueError(str(e))
    
    return StandardizedTrade(
        symbol=normalized_symbol,
        trade_id=str(trade_id),
        price=decimal_price,
        size=decimal_size,
        side=normalized_side,
        timestamp=dt,
        exchange=normalized_exchange,
        raw_data=raw_data
    )


def batch_create_standardized_trades(
    raw_trades: List[Dict[str, Any]],
    symbol: str,
    exchange: str,
    field_mapping: Dict[str, str],
    is_milliseconds: bool = True
) -> List[StandardizedTrade]:
    """
    Batch create standardized trades from raw data.
    
    Args:
        raw_trades: List of raw trade dictionaries
        symbol: Trading symbol
        exchange: Exchange name
        field_mapping: Mapping of StandardizedTrade fields to raw data fields
        is_milliseconds: True if timestamps are in milliseconds
        
    Returns:
        List of successfully created StandardizedTrade objects
        
    Example field_mapping:
        {
            'trade_id': 'id',
            'price': 'px',
            'size': 'sz',
            'side': 'side',
            'timestamp': 'ts'
        }
    """
    trades = []
    
    for raw_trade in raw_trades:
        try:
            trade = create_standardized_trade(
                symbol=symbol,
                trade_id=raw_trade[field_mapping['trade_id']],
                price=raw_trade[field_mapping['price']],
                size=raw_trade[field_mapping['size']],
                side=raw_trade[field_mapping['side']],
                timestamp=raw_trade[field_mapping['timestamp']],
                exchange=exchange,
                raw_data=raw_trade,
                is_milliseconds=is_milliseconds
            )
            trades.append(trade)
        except Exception as e:
            # Log error but continue processing
            print(f"Failed to transform trade: {e}")
    
    return trades


class TradeTransformer:
    """Transform trade data with safety checks."""
    
    VALID_SIDES = {'buy', 'sell'}
    
    def __init__(self, market_data_provider: Optional[Any] = None):
        """
        Initialize transformer.
        
        Args:
            market_data_provider: Optional provider of market data for price validation
        """
        self.market_data_provider = market_data_provider
    
    def normalize_trade_side(self, side: str) -> str:
        """
        Normalize trade side to standard format.
        
        Args:
            side: Trade side indicator
            
        Returns:
            Normalized trade side ('buy' or 'sell')
            
        Raises:
            ValueError: If side is invalid
        """
        side_lower = str(side).lower().strip()
        
        # Handle common variations
        if side_lower in {'buy', 'bid', 'long', '1', 'true'}:
            return 'buy'
        elif side_lower in {'sell', 'ask', 'short', '0', 'false'}:
            return 'sell'
        
        raise ValueError(f"Invalid trade side: {side}")
    
    def normalize_trade_size(
        self,
        size: Any,
        price: Any,
        symbol: str
    ) -> Decimal:
        """
        Normalize and validate trade size.
        
        Args:
            size: Raw trade size
            price: Trade price for notional calculations
            symbol: Trading pair symbol
            
        Returns:
            Normalized trade size as Decimal
            
        Raises:
            ValueError: If size is invalid or violates limits
        """
        try:
            size_decimal = Decimal(str(size))
            price_decimal = Decimal(str(price))
        except (TypeError, ValueError) as e:
            raise ValueError(f"Invalid trade size or price format: {e}")
            
        if size_decimal <= 0:
            raise ValueError(f"Trade size must be positive: {size}")
            
        # Get limits and validate
        limits = get_trade_limits(symbol)
        
        # Round to appropriate precision
        size_decimal = round(size_decimal, limits.size_precision)
        
        # Validate against limits
        validate_trade_size(
            size_decimal,
            price_decimal,
            symbol,
            logger
        )
        
        return size_decimal
    
    def normalize_trade_price(
        self,
        price: Any,
        symbol: str
    ) -> Decimal:
        """
        Normalize and validate trade price.
        
        Args:
            price: Raw trade price
            symbol: Trading pair symbol
            
        Returns:
            Normalized price as Decimal
            
        Raises:
            ValueError: If price is invalid or violates limits
        """
        try:
            price_decimal = Decimal(str(price))
        except (TypeError, ValueError) as e:
            raise ValueError(f"Invalid price format: {e}")
            
        if price_decimal <= 0:
            raise ValueError(f"Price must be positive: {price}")
            
        # Get limits and round to appropriate precision
        limits = get_trade_limits(symbol)
        price_decimal = round(price_decimal, limits.price_precision)
        
        # Get market price if available
        market_price = None
        if self.market_data_provider is not None:
            try:
                market_price = self.market_data_provider.get_price(symbol)
            except Exception as e:
                logger.warning(f"Failed to get market price for {symbol}: {e}")
        
        # Validate against limits and market price
        validate_trade_price(
            price_decimal,
            market_price,
            symbol,
            logger
        )
        
        return price_decimal
    
    def transform_trade(
        self,
        trade_data: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Transform trade data with safety checks.
        
        Args:
            trade_data: Raw trade data
            
        Returns:
            Transformed trade data with normalized values
            
        Raises:
            ValueError: If any validation fails
        """
        if not isinstance(trade_data, dict):
            raise ValueError(f"Trade data must be a dictionary: {trade_data}")
            
        # Required fields
        required = {'symbol', 'side', 'size', 'price'}
        missing = required - set(trade_data.keys())
        if missing:
            raise ValueError(f"Missing required fields: {missing}")
            
        # Validate and normalize symbol
        symbol = str(trade_data['symbol']).upper()
        validate_symbol_format(symbol, logger)
        
        # Transform with safety checks
        transformed = {
            'symbol': symbol,
            'side': self.normalize_trade_side(trade_data['side']),
            'size': self.normalize_trade_size(
                trade_data['size'],
                trade_data['price'],
                symbol
            ),
            'price': self.normalize_trade_price(
                trade_data['price'],
                symbol
            )
        }
        
        # Copy any additional fields
        for key, value in trade_data.items():
            if key not in transformed:
                transformed[key] = value
                
        return transformed