import numpy as np
import pandas as pd

def calculate_fractals(high, low):
    """
    Calculate up and down fractals. A simplistic approach.
    """
    up_fractal = (high.shift(1) < high) & (high.shift(-1) < high)
    down_fractal = (low.shift(1) > low) & (low.shift(-1) > low)
    return up_fractal, down_fractal

def calculate_atr(high, low, close, period=14):
    """
    Calculate Average True Range (ATR).
    """
    tr = pd.DataFrame({
        'high-low': high - low,
        'high-close_prev': abs(high - close.shift()),
        'low-close_prev': abs(low - close.shift())
    }).max(axis=1)
    atr = tr.rolling(window=period).mean()
    return atr

def calculate_stop_loss_and_take_profit(high, low, close, risk_reward_ratio, atr_multiplier_for_trailing_stop):
    """
    Calculate stop loss and take profit.
    """
    up_fractal, down_fractal = calculate_fractals(high, low)
    # Example for a long position. For short positions, use down fractal and adjust logic accordingly.
    recent_swing_high = high[up_fractal].max()  # This is a simplification
    stop_loss = recent_swing_high + buffer_pips  # Adjust buffer_pips as needed

    # Assuming a risk_reward_ratio of 1:2 for example
    risk_amount = stop_loss - entry_price  # Assuming a long position
    target_profit = entry_price + (risk_amount * risk_reward_ratio)

    # Trailing stop and take profit logic to be implemented based on the strategy

    return stop_loss, target_profit

# Example usage
# This assumes you have a DataFrame `df` with 'High', 'Low', and 'Close' columns
# entry_price = df['Close'].iloc[-1]  # Just as an example, adjust based on your strategy
# risk_reward_ratio = 2  # Example for a 1:2 risk/reward ratio
# atr_multiplier_for_trailing_stop = 1.5  # Example multiplier for ATR in trailing stop
# stop_loss, take_profit = calculate_stop_loss_and_take_profit(df['High'], df['Low'], df['Close'], risk_reward_ratio, atr_multiplier_for_trailing_stop)
# print(f"Stop Loss: {stop_loss}, Take Profit: {take_profit}")