# ... (previous code remains the same)

# Backtesting simulation loop
for i in range(1, len(data)):
    today = data.index[i]
    price = float(data['Close'].iloc[i])  # Cast to float
    rsi_today = float(data['RSI'].iloc[i])  # Cast to float
    
    # Check for buy signal
    macd_today = float(data['MACD'].iloc[i])
    signal_today = float(data['MACD_signal'].iloc[i])
    macd_yesterday = float(data['MACD'].iloc[i-1])
    signal_yesterday = float(data['MACD_signal'].iloc[i-1])
    
    buy_condition = (macd_yesterday < signal_yesterday) and (macd_today > signal_today) and (rsi_today < 50)
    
    if buy_condition and (last_buy_date is None or (today - last_buy_date).days >= min_days_between_buys):
        allocation = cash * buy_fraction
        if allocation > 0:
            shares_bought = allocation / price
            cash -= allocation
            last_buy_date = today
            open_positions.append({
                "entry_date": today,
                "entry_price": price,
                "allocated": allocation,
                "shares": shares_bought,
                "highest": price,  # Ensure scalar
                "trailing_stop": price * (1 - trailing_stop_pct)
            })
            data.at[today, 'Buy'] = True

    # Update positions and check sell conditions
    positions_to_remove = []
    for idx, pos in enumerate(open_positions):
        current_highest = pos["highest"]
        if price > current_highest:
            pos["highest"] = price
            pos["trailing_stop"] = pos["highest"] * (1 - trailing_stop_pct)
        
        # Partial sell condition
        if price >= (pos["entry_price"] * target_multiplier) and rsi_today > 50:
            shares_to_sell = pos["shares"] * sell_fraction
            cash += shares_to_sell * price
            pos["shares"] -= shares_to_sell
            pos["allocated"] -= shares_to_sell * pos["entry_price"]
            data.at[today, 'Sell'] = True
            if pos["shares"] < 0.001:
                completed_trades.append({
                    "entry_date": pos["entry_date"],
                    "exit_date": today,
                    "entry_price": pos["entry_price"],
                    "exit_price": price,
                    "allocated": pos["allocated"]
                })
                positions_to_remove.append(idx)
            continue
        
        # Trailing stop exit
        current_trailing_stop = pos["trailing_stop"]
        if price < current_trailing_stop:
            cash += pos["shares"] * price
            completed_trades.append({
                "entry_date": pos["entry_date"],
                "exit_date": today,
                "entry_price": pos["entry_price"],
                "exit_price": price,
                "allocated": pos["allocated"]
            })
            positions_to_remove.append(idx)
    
    for idx in reversed(positions_to_remove):
        del open_positions[idx]
    
    # Update equity curve
    position_value = sum(pos["shares"] * price for pos in open_positions)
    equity_curve.append(cash + position_value)

# ... (remaining code remains the same)