text: = ta.SMA(df['close'], timeperiod=short_period)    long_ma = ta.SMA(df['close'], timeperiod=long_period)        # Create a new DataFrame that combines the historical stock data with the short-term and long-term moving averages    new_df = pd.concat([df, short_ma, long_ma], axis=1)    new_df.columns = ['open', 'high', 'low', 'close', 'volume', 'short_ma', 'long_ma']        # Create a new column in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages    new_df['signal'] = np.where(new_df['short_ma'] > new_df['long_ma'], 1, -1)        # Create a new column in the DataFrame that indicates the profit or loss for each trade based on the buy and sell signals and the defined stop loss and profit target    stop_loss_percent = 5    profit_target_percent = 10    stop_loss = stop_loss_percent / 100    profit_target = profit_target_percent / 100    new_df['pnl'] = 0.0    buy_price = 0.0    for i in range(1, len(new_df)):        if new_df['signal'][i] == 1 and new_df['signal'][i-1] == -1:            buy_price = new_df['close'][i]        elif new_df['signal'][i] == -1 and new_df['signal'][i-1] == 1:            sell_price =