text: the DataFrame in ascending order by date. Next request.            AI User (Stock Trader):        Instruction: Calculate the short-term and long-term moving averages for the stock data using the `ta.SMA()` function from ta-lib.    Input: The period for the short-term moving average and the period for the long-term moving average.            AI Assistant (Python Programmer):        Solution: We can calculate the short-term and long-term moving averages for the stock data using the `ta.SMA()` function from ta-lib. We need to pass the period for the short-term moving average and the period for the long-term moving average as arguments to this function. We can use the following code to calculate the short-term and long-term moving averages:        ```    short_ma = ta.SMA(df['close'], timeperiod=short_period)    long_ma = ta.SMA(df['close'], timeperiod=long_period)    ```        This will calculate the short-term and long-term moving averages for the stock data and store them in the `short_ma` and `long_ma` variables, respectively. Next request.            AI User (Stock Trader):        Instruction: Create a new DataFrame that combines the historical stock data with the short-term and long-term moving averages.    Input: None.            AI Assistant (Python Programmer):        Solution: We can create a new DataFrame that combines the historical stock data with the short-term and long-term moving averages using the `concat()` function from pandas. We need to pass the historical stock data, the short-term moving average, and