Technical analysis (TA) is nothing new in the world of trading and investing. From traditional wallets to cryptocurrencies such as Bitcoin and Ethereum, the use of TA (Technical Analysis) indicators has a simple goal: to use existing data to make more informed decisions that are likely to lead to the desired results. As markets become more and more complex, the past few decades have given rise to hundreds of different TA indicators, but few have achieved the popularity and consistent rate of use of moving averages (MAs).

Although there are different variations of moving averages, their underlying purpose is to clarify trading charts. This is achieved by smoothing the curves via an easily decipherable trend indicator. Since these moving averages are based on past data, they are considered lagging indicators also called trend followers. Regardless, they still have great power to cut through the noise of data points and help determine where a market might be headed.


Different Types of Moving Averages

There are different types of moving averages that traders can use not only for day trading and swing trading, but also for longer-term setups. Despite the different types, MAs are most often divided into two distinct categories: simple moving averages (SMA for Simple Moving Average, MMS in French) and exponential moving averages (EMA in English, MME in French). Depending on the market and desired outcome, traders can choose the indicator that will benefit their setup the most.


The simple moving average

MMS takes data from a defined period of time and generates the average price of that asset for the entire data set. The difference between an MMS and a baseline average of past prices is that with an MMS, as soon as new data is entered, it overwrites the older ones. Therefore, if the simple moving average calculates the average over 10 days of data, the entire group of data is constantly updated to include only the last 10 days.

It is important to note that all data entries in an MMS are weighted equally, regardless of when they were entered. Traders who believe that more recent data is more relevant often argue that equal weighting of the SMA is detrimental to technical analysis. The Exponential Moving Average (EMA) was created to solve this problem.


The Exponential Moving Average

EMAs are similar to MMS because they provide technical analysis based on past price movements. However, the equation is a bit more complicated because an MMS assigns more weight and value to the most recent price data. Although both averages are relevant and widely used,  the EMA is more sensitive to sudden price fluctuations and reversals.

Since EMAs are likely to predict price reversals more quickly than MMS, they are often especially favored by traders engaged 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 goals, adjusting the settings accordingly.


How to use moving averages

Because moving averages use past prices instead of current prices, they involve some time lag. The larger the data group, the greater the offset will be. For example, a moving average that looks at the last 100 days will respond more slowly to new information than a moving average that only looks at the last 10 days. This is simply because a new entry into a larger data set will have a lesser effect on the overall numbers.

Both types of averaging can be advantageous depending on the trading setup. Large data sets are less affected by large, one-off fluctuations, so they are well suited to long-term investors. Short-term traders often favor a smaller data set allowing for more responsive trading.

In traditional markets, the 50, 100 and 200 day moving averages are most commonly used. Traders closely monitor 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 the constant volatility of its markets, 24/7, moving average parameters and trading strategy may vary depending on the profile of the trader.


Crossed signals

Naturally, a rise in the moving average indicates an upward trend and its fall indicates a downward trend. However, a moving average alone is not a truly reliable or strong enough indicator. Therefore, moving averages are constantly used in combination with others to detect bullish and bearish crossover signals.

A crossover signal is created when two different MAs cross in a chart. A bullish crossover (also called a golden cross) occurs when the short-term MA crosses and exceeds another long-term average, suggesting the start of an uptrend. In contrast, a bearish crossover (or death crossover) occurs when a short-term MA crosses below a long-term moving average, which indicates the start of a downtrend.


Other factors to consider

Up to this point in the article, in the examples presented, we have only used data quantified in days, but this is not a necessary requirement when analyzing curves with MM. People day trading may be much more interested in how an asset has performed over the last two or three hours, rather than the last two or three months. Different time units can all be plugged into the equations used to calculate moving averages, and as long as these time frames are consistent with the trading strategy, the data can prove useful.

A major drawback of MMs is their latency. Since MAs are lagging indicators that take into account past prices, the signals are often too late. For example, a bullish crossover may suggest a buy, but this only happens after the price rises significantly. This means that even if the uptrend continues, potential profit could have been lost during the period between the price rise and the crossover signal. Or worse, a false golden cross signal can cause a trader to buy the local peak of an asset's price just before a drop in the asset's price (these fake buy signals are generally called bull traps). in English).

Moving averages are powerful technical analysis indicators and remain among the most used. The ability to analyze market trends using data provides excellent insight into a market's performance. Keep in mind, however, that MM and crossover signals should not be used alone and that it is always safer to combine different AT indicators in order to avoid false signals.