Introduction

There is nothing new about technical analysis (TA) in trading and investing. When it comes to standard portfolios as well as cryptocurrencies such as Bitcoin and Ethereum, using technical indicators has a single goal: use available data to make better decisions that will likely lead to the ideal outcomes you want. As markets become more complex, hundreds of new types of technical indicators (TA) have been developed over the previous decades. However, few have experienced the popularity and widespread use of moving averages (MA).

Moving averages come in a variety of shapes and sizes, but they all serve the same purpose: to make trading charts more understandable. The graphs are smoothed out to generate a trend indication that can be easily read. These moving averages are known as trailing or trend following indicators since they are based on historical data. They still have the ability to cut through the clutter and provide insight into where a market is headed.

Moving averages of several forms

Traders can use moving averages in a variety of ways, including day trading, swing trading, and even in long-term setups. Simple moving averages (SMA) and exponential moving averages (EMA)  are the most frequent MA kinds. Traders can select which indicator is most likely to help their setup based on the market and intended outcome.

The simple moving average (SMA)

The SMA calculates the average price of a security over a given period of time using data from that period. The difference between a SMA and a simple average of prior prices is that with a SMA, the oldest data set is ignored as soon as a new data set is introduced. As a result, if the simple moving average calculates the mean based on 10 days of data, the entire data set is constantly updated to just include the most recent 10 days.

It’s worth noting that all data inputs in a SMA are equally weighted, regardless of when they were entered. Traders who believe that the most recent data is more relevant typically argue that the SMA’s equal weighting is damaging to technical analysis. To remedy this issue, the exponential moving average (EMA) was developed.

The exponential moving average (EMA)

The exponential moving average (EMA) is similar to the simple moving average (SMA) in that it provides technical analysis based on price variations in the past. The equation is a little more sophisticated since an EMA gives the most recent price inputs which have greater weight and importance. Despite the fact that both averages have value and are extensively utilized, the EMA is more responsive to market reversals and abrupt price swings.

EMAs are generally preferred by traders who engage in short-term trading because they are more likely to project price reversals sooner than SMAs. A trader or investor must choose the sort of moving average that best suits his or her specific methods and goals, then alter the settings accordingly.

Use of Moving Averages

MAs have a period of lag since they use historical prices rather than current values. The greater the size of the data set, the greater the latency. A moving average that looks at the last 100 days, for example, will react to new data more slowly than one that simply looks at the last 10 days. This is due to the fact that a new entry into a larger dataset has a smaller impact on the total figures.

Depending on the trade setup, both can be beneficial. Long-term investors benefit from larger data sets since they are less likely to be significantly influenced by one or two substantial swings. Short-term traders prefer a smaller data set since it allows them to trade more reactively.

The most popular MAs employed in traditional markets are 50, 100, and 200 days. Stock traders pay particular attention to the 50-day and 200-day moving averages, and any breaks above or below these lines are frequently taken as crucial trading signals, especially when they are followed by crossovers. The same is true for cryptocurrency trading, but because of the market’s constant volatility, the MA parameters and trading technique may differ depending on the trader’s profile.

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