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What are Moving Averages in Crypto? 

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Technical analysis (TA) relies on market data in order to assist traders in making informed trading decisions. Technical indicators are mathematical calculations used within technical analysis to identify patterns, which are then used by traders to make predictions on future market movements. 

The complex and fast-moving nature of crypto markets has resulted in the widespread use of technical analysis. Moving averages are one of the most commonly used technical indicators by crypto investors and traders.  

This guide will provide a complete explanation of how moving averages work, break down the different types of moving averages, and provide insight into how to use moving averages when performing technical analysis.

What are moving averages?  

Moving averages, when visible on a price chart (Figure 1), are represented by dynamic lines that change position over time and can provide traders with the ability to identify and act on trends within a market based on past closing prices. 

Figure 1 – Two moving averages 

While there are a number of different types of moving averages, all of them perform the same function — increasing the visibility and clarity of trends in trading charts. The purpose of a moving average is to smooth out price data over time by calculating a regularly-updated average price, which makes trend indicators easier to decipher. 

A basic moving average is a straightforward technical indicator that is used to calculate the trend direction of an asset or identify support and resistance levels. Moving averages are based on historical price data, and are therefore considered a lagging indicator, as opposed to a leading indicator.   

When are moving averages used? 

Moving averages are highly customisable, and can be calculated over any time frame. Most moving average calculations are performed over 15, 30, 50, 100, or 200 days. Figure 1 above represents a 50-day moving average (the purple line) and a 200-day moving average (the yellow line). Shorter time spans are typically more sensitive to price changes, and moving averages calculated over longer time spans are less sensitive to price changes. 

The fundamental goal of moving average calculation is to accurately identify the actions of the average trader within a market. If the price of Bitcoin, for example, is higher than the 50-day moving average of Bitcoin, it may indicate that buyers are more active within the market than sellers. 


There is no universal ideal moving average time frame. It depends on your trading style. Shorter time frames may be more suitable for identifying trends within short-term trading strategies, while longer time frames may be more suitable for identifying trends within long-term trading strategies.

The different types of moving average 

There are many different types of moving averages used in technical analysis. Moving averages can be broadly divided into two different types — simple moving averages (SMAs) and exponential moving averages (EMAs). 

Simple moving average (SMA) 

A simple moving average (Figure 2), which is often referred to as a traditional moving average, is calculated using price data points within a specific time frame and is used to identify the average closing price of an asset during this period.  

Figure 2 – A 20-day simple moving average (SMA)

SMAs are calculated through a straightforward process — a set of prices at regular intervals over a time frame are collected, added together, then divided by the number of prices in the set. A 10-day simple moving average, for example, is only based on the 10 most recent days — as a new day’s worth of price data becomes available, the oldest day is removed from the calculation. 

Simple moving averages don’t place any importance on how recent the price data within a time frame is — the past price data included in the SMA is considered of equal importance as the most price data. Some technical analysis practices place higher importance on the most recent price points.

Key Takeaway

Simple moving averages provide a regularly-updated average of asset prices over a specific period.

Exponential moving average (EMA) 

An exponential moving average (Figure 3), sometimes known as a weighted moving average, is a type of moving average that places higher importance on recent price data over older price data.

Figure 3 – A 20-day exponential moving average (EMA)

While an EMA is similar to an SMA in that both use previous price data to identify price trends, exponential moving averages are slightly more complicated to calculate. 

Exponential moving averages are more sensitive to the data of recent prices, which means an EMA is more likely to be affected by significant price fluctuations or sudden reversals. The responsive nature of EMAs, however, makes them popular with traders and technical analysts seeking to gain insight into short-term trends. 

Key Takeaway

Exponential moving averages are similar to simple moving averages but place higher importance on recent price data.

How to use moving averages to help with trading  

Moving averages use historical price data rather than current price data, which means there is a period of lag between the data used and current market conditions. The longer the time period used to calculate a moving average, the more significant the lag.  

A moving average calculation that uses the last 100 days, for example, is less sensitive to recent price data than a 10-day moving average, as new entries into a large dataset have a smaller impact on the total calculation. 

Different moving averages have different use cases — a longer time period and a larger data set may be advantageous to long-term traders due to the relative stability they offer. A larger data set is less likely to be affected by a small number of price fluctuations.  

Smaller data sets or shorter time periods, however, may provide traders with responsive advantages that allow for day trading or swing trading. 

Moving averages can be used to detect changes in momentum for an asset, such as scenarios in which the price of an asset may exhibit sudden downward movement. In other scenarios moving averages may be used to confirm predictions of an impending change, such as a bullish or bearish signal.  

Crossover signals 

In simple terms, a rising moving average may indicate an upward trend, while a falling moving average may signal a downtrend. A single moving average indicator, however, is generally not considered a strong, reliable moving average indicator without the support of other indicators. Many analysis strategies use multiple moving averages in order to identify bullish or bearish crossover signals. 

Crossover signals occur when two different moving averages cross over one another in a chart (Figure 4). A bearish crossover, for example, is commonly referred to as a death cross, and occurs when a short-term moving average crosses below a long-term moving average, signalling a potential downtrend.

Inversely, a bullish crossover signal, commonly referred to as a golden cross, occurs when a short-term moving average crosses above a long-term moving average, potentially signalling an upward trend.

Figure 4 – Moving average crosses

Short-term moving averages crossing over long-term moving averages may indicate that short-term traders are behaving aggressively in a market, establishing a scenario in which it may be more profitable to trade after the crossover.  

Did You Know?

A bearish crossover is also commonly known as a “death cross”. A bullish crossover is referred to as a “golden cross”.

Other factors to keep in mind 

While the examples provided in this article present moving averages as analysis performed over multiple days, it’s not necessary to adhere to a daily benchmark when calculating MAs in crypto markets. Traders focused on the day-to-day performance of digital assets may use moving average analysis over a number of hours — it’s possible to use any time frame when calculating moving averages. 

Moving averages are highly reliable and effective in cutting through price noise in markets, but there are a number of limitations that should be kept in mind when using moving averages as part of technical analysis: 

Lag time 

A moving average is a lagging indicator, which means that signals provided by a moving average may be presented too late for a trader to capitalise on them. A bullish crossover, for example, may indicate an upward trend in price only after an asset has increased in price significantly — limiting the potential profit a trader could generate. 

Only based on price 

Moving averages are calculated based only on price conditions. There are many other factors that should be taken into account when performing technical analysis, such as volume, or fundamental factors such as economic events. 

Only one of a range of indicators 

The crypto market is volatile and complex, exhibiting complicated behavioural patterns that moving averages alone cannot adequately track. Other technical indicators and tools such as MACD or RSI can enhance the accuracy of technical analysis.  

Signals are not guarantees 

Moving averages are not infallible — asset prices are inherently unpredictable and may move down regardless of technical indicators and buy signals.  


Moving averages are an essential tool used by traders as part of technical analysis. As one of the most widely used technical indicators, moving averages allow crypto traders to gain perspective on the price movement of an asset over a specific period of time and, in the case of exponential moving averages, insight into recent price movements compared to historical data.  

Using multiple moving averages to gain information on market movements with crossover signals can arm traders and investors with deeper insight into potential trends and signals. It’s important to keep in mind, however, that moving averages and crossover signals should be used in combination with other technical analysis indicators to minimise fake signals and improve accuracy.  

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