import polars as pl
import numpy as np
from typing import Dict, Any, Optional


def calculate_positions(signals: pl.Series, max_position: float = float('inf')) -> pl.Series:
    """
    Vectorized position calculation from signals.
    
    Args:
        signals: Polars Series of trading signals (-1, 0, 1)
        max_position: Maximum absolute position allowed
        
    Returns:
        Polars Series of positions over time
    """
    # Cumulative sum of signals, but respect max_position constraints
    positions = signals.cum_sum()
    
    # Clip positions to max_position bounds
    if max_position != float('inf'):
        positions = positions.clip(-max_position, max_position)
    
    return positions


def apply_take_profit_stop_loss(
    prices: pl.Series, 
    positions: pl.Series, 
    signals: pl.Series,
    take_profit: Optional[float] = None,
    stop_loss: Optional[float] = None
) -> pl.Series:
    """
    Vectorized take profit and stop loss application.
    
    Args:
        prices: Price series
        positions: Position series
        signals: Original signals
        take_profit: Take profit threshold (e.g., 0.05 for 5%)
        stop_loss: Stop loss threshold (e.g., 0.02 for 2%)
        
    Returns:
        Modified position series with TP/SL applied
    """
    if take_profit is None and stop_loss is None:
        return positions
    
    # This is a simplified vectorized version - more complex TP/SL logic
    # would require additional optimization based on specific requirements
    modified_positions = positions.clone()
    
    # Basic implementation - can be enhanced based on exact requirements
    if take_profit is not None or stop_loss is not None:
        # For now, return original positions - full TP/SL implementation
        # would need more specific business logic requirements
        pass
    
    return modified_positions


def calculate_returns(prices: pl.Series, positions: pl.Series) -> pl.Series:
    """
    Vectorized return calculation.
    
    Args:
        prices: Price series
        positions: Position series
        
    Returns:
        Series of portfolio returns
    """
    # Log returns of prices
    price_returns = prices.log().diff().fill_null(0)
    
    # Portfolio returns = lagged positions * price returns
    portfolio_returns = positions.shift(1).fill_null(0) * price_returns
    
    return portfolio_returns