So, you want to start trading
                commodities, and believe that moving averages are tools that can help? Read on to find out how.
            Since their inception in 1901,
                moving averages have become increasingly popular. They were first used by G. U. Yule and then W. I.
                    King introduced them in his 1912 book, Elements of Statistical Method. Whether you’re
                currently using moving averages in your trading strategy or not, keep reading: you’ll learn more about
                how this technical tool can potentially help you with your trading.
            Commodities trading is a dynamic
                place. Prices can swing dramatically in response to global events and economic indicators. As a trader,
                you are always looking for tools to navigate the complexities of the market. The moving average is a
                tool that has proved to be very helpful. This technical indicator, also known as MA, is a cornerstone of
                technical analysis. It helps traders understand price trends, volatility, and potentially when to enter
                and exit the market. 
            In this comprehensive guide, we
                take a close look at moving averages in commodities trading. We’ll discuss the different types, how to
                calculate, interpret and use them practically.
            What
                is a moving average?
            A moving average is a
                statistical calculation that represents the average price of a commodity over a specific period of time.
                It’s different from other indicators, as it doesn’t rely solely on the most recent asset’s price data.
                Instead, it takes into account a range of past prices while creating a constantly updated average price.
                This results in a smoother line that helps you identify trends and filters out the short-term moving
                average. The term ‘moving’ is used because the average is recalculated as new data points are added to
                the dataset. This results in a dynamic picture of price movement.
            You can use moving averages to
                help you determine trend direction, making informed decisions easier. Moving averages offer valuable
                insights into market sentiment, whether you’re focused on short-term price movements or long-term
                investment strategies.
            
                Types of moving averages
            There are two common methods of
                calculating moving averages. Both methods serve as valuable tools for analyzing market trends and making
                informed trading decisions.
            Simple moving average (SMA)
            The simple moving average (SMA)
                is the most basic type of moving average. It is calculated by adding up the prices of a commodity over a
                specified number of periods and then dividing the total by the number of periods to get the average
                closing price. This easy calculation gives you a single line on the price chart, showing the average
                price movement over time.
            To calculate the SMA use this
                formula:
            SMA = (Sum of prices for
                    n periods) / n
            The ‘n’ represents the number of
                periods you might choose. The SMA provides a smooth representation of price trends, but it can be slower
                to respond to sudden price changes.
            
                
                In the chart above we can
                    see how the 10-day simple moving average is represented. Usually, traders combine 2 moving averages
                    and use the crossovers to find a trade, as explained below. 
             
            Exponential moving average (EMA)
            
            The exponential moving average
                (EMA) is a more advanced type of moving average that emphasizes recent price information. This makes the
                EMA more responsive to recent price movements. It is useful if you are trying to spot short-term trends.
            
            The EMA calculation uses a
                formula that includes a smoothing factor that prioritizes recent prices:
            EMA = (Current price -
                    EMA previous day) × smoothing factor + EMA previous day
            The smoothing factor depends on
                the number of periods you choose. A shorter period makes the EMA more responsive, while a longer period
                makes it smoother but potentially slower to reflect trend changes.