Moving Averages explained
Technical analysis (TA) is nothing new in the world of trading and investing. From traditional portfolios to cryptocurrencies like Bitcoin and Ethereum, the use of AT indicators has a simple goal: use existing data to make more informed decisions that are likely to lead to the desired results. As markets become increasingly complicated, the last few decades have produced hundreds of different types of technical support indicators, but few have seen the popularity and consistent use of moving averages (MAs).
Although there are different variations of moving averages, their underlying purpose is to drive clarity on trading charts. This is done by smoothing the charts to create an easily decipherable trend indicator. Because these moving averages are based on past data, they are considered to be lagging or following the trend of the indicators. In any case, they still have great power to cut through the noise and help determine where a market is headed.
Different types of moving averages
There are several different types of moving averages that can be used by traders, not only in day trading and swing trading, but also in long-term setups. Despite the various types, MAs are generally divided into two separate categories: simple moving averages (SMA) and exponential moving averages (EMA). Depending on the market and the desired outcome, traders can choose which indicator their setup is most likely to benefit from.
The Simple Moving Average
The SMA takes data from a set time period and produces the average price of that security for the data set. The difference between an SMA and a basic average of past prices is that with SMA, as soon as a new set of data is input, the older set of data is discarded. So, if the simple moving average calculates the average based on 10 days of data, the entire data set is constantly updated to include only the last 10 days.
It is important to note that all data entries in an SMA are weighted equally, regardless of the date they were entered. Traders who believe there is more relevance to the latest available data often claim that equal weighting of the SMA is detrimental to technical analysis. The exponential moving average (EMA) was created to address this problem.
The Exponential Moving Average
EMAs are similar to SMAs in that they provide technical analysis based on past price fluctuations. However, the equation is a little more complicated because an EMA assigns more weight and value to more recent price entries. Although both averages have value and are widely used, the EMA responds better to sudden price fluctuations and reversals.
Because EMAs are more likely to project price reversals faster than SMAs, they are often especially favored by traders who engage in short-term trading. It is important for a trader or investor to choose the type of moving average based on their personal strategies and objectives, adjusting the settings accordingly.
How to use Moving Averages
Because MAs use past prices instead of current prices, they have a certain lag period. The more expansive the data set, the longer the delay. For example, a moving average that looks at the last 100 days will respond more slowly to new information than a MA that only looks at the last 10 days. That's simply because a new entry in a larger data set will have a smaller effect on the overall numbers.
Both can be advantageous depending on the trading setup. Larger data sets benefit long-term investors because they are less likely to be greatly altered by one or two large fluctuations. Short-term traders often prefer a smaller data set that allows for more reactionary trading.
Within traditional markets, the most used are the 50, 100 and 200 day MAs. Stock traders closely watch the 50-day and 200-day moving averages, and any break above or below these lines is generally considered an important trading signal, especially when followed by crossovers. The same applies to cryptocurrency trading, but due to its 24/7 volatile markets, MA settings and trading strategy may vary depending on the profile of the trader.
Crossing signs
Naturally, a rising MA suggests a bullish trend and a falling MA indicates a bearish trend. However, a moving average alone is not a really reliable and solid indicator. Therefore, MAs are constantly used in combination to detect bullish and bearish crossover signals.
A crossover signal is created when two different MAs intersect on a chart. A bullish crossover (also known as a golden cross) occurs when the short-term MA crosses above a long-term one, suggesting the start of an uptrend. In contrast, a bearish crossover (or death cross) occurs when a short-term MA crosses below a long-term moving average, indicating the beginning of a downtrend.
Other factors worth considering
The examples so far have all been in terms of days, but that is not a necessary requirement when analyzing MAs. Those who engage in day trading may be much more interested in knowing how a security has performed over the last two or three hours, not two or three months. All time frames can be plugged into the equations used to calculate moving averages, and as long as those time frames are consistent with the trading strategy, the data can be useful.
A major disadvantage of MAs is their delay. Since MAs are lagging indicators that consider previous price action, the signals are often too late. For example, a bullish crossover may suggest a buy, but it only occurs after a significant increase in price. This means that even if the uptrend continues, potential profit may have been lost in that period between the rise in price and the crossover signal. Or even worse, a false golden cross signal can lead a trader to buy the local top just before a price drop (these false buy signals are often called a bull trap).
Moving averages are powerful TA indicators and one of the most used. The ability to analyze market trends in a data-driven manner provides great insight into how a market is performing. However, keep in mind that MAs and crossover signals should not be used alone and it is always safer to combine different TA indicators to avoid false signals.
