Brought to you by the Liquid team to help you make sense of crypto.

How to use moving averages, SMA, EMA to trade cryptocurrency

In Trading

Moving averages smooth out recent price action to help the trader more easily identify trends, trend strength and by extension, support and resistance. 

A widely used technical indicator, the 50-, 100- and 200-period Simple Moving Average (SMA) or Exponential Moving Average (EMA) is often used in conjunction with other mathematical indicators (and always in combination with the price) and performs best in a trending market.

The calculation of moving averages is often based on daily or hourly prices, depending on the timeframe of the period. Weekly periods are occasionally used.

The most common trading signals generated off of moving averages when trading cryptocurrency are those where the price crosses either above or below a moving average.

Because the moving average is a trend indicator, providing support or resistance, when the price crosses above the moving average, it is seen as a break above the trend, and a buy signal is generated. Where the price crosses below the moving average, a sell signal is generated, as the trend is seen as broken to the downside.

In order for a trader to have moving averages generate an ideal number of trading signals to suit trading objectives, moving averages need to be customized by number of time periods and also by type (i.e. EMA or SMA).

Because of the delayed response to recent price movement, moving averages are known as trend-following indicators. The lagging nature of moving averages and their signals make trading off them relatively conservative, as price moves are largely completed by the time a moving average-based trading signal triggers.

Identify trends with moving averages

A trader may identify an existing trend through a visual inspection of the moving average. A rising moving average reflects a rising trend, while a falling moving average points to a falling trend. A generic trading rule is to buy as the moving average begins sloping up, and to sell as the slope of the moving average turns negative.

In the BTC/USD chart above, the most reliable moving average in anticipating the downturn from the beginning of 2018 was the 20-week EMA (the violet line).

However, even the 20 week EMA was a lagging indicator, as by the time it had begun sloping down, BTC/USD had already fallen significantly. Some longer term bulls may have ignored the 20-week EMA turning down, comforted with the 50-week EMA (the indigo line) and 100-week EMA (the light purple line) still sloping up, suggesting the longer-term uptrend remained intact.

As with other mathematical indicators, the less lag is to be introduced in signals generated (by lower period EMAs), the greater the probability and frequency of false signals. Another challenge with trading based on changes in the slope of moving averages is identifying when that slope definitively changes from being positive to negative or vice versa.

As seen in the weekly chart above, the slope of a moving average may be close to zero for several weeks. During that time frame, the price may have moved significantly.

Trend identification using EMA can be more reliable than with SMA as it is more sensitive to recent price changes, although high sensitivity to recent price changes can also result in more false trade signals.

Another method of confirming the trend is by observing where a shorter (also known as “faster”) moving average is relative to a longer (or “slower”) moving average. A shorter moving average rising above the longer moving average (in the same way that the price going above a moving average) is generally bullish, while a shorter moving average going below the longer moving average is bearish.

As long as the shorter moving average remains above the longer moving average, the uptrend is considered intact. The trend is seen as down when the shorter moving average is below the longer moving average.

As can be seen in the chart above, similarly to monitoring the slope of moving averages, watching for moving average crossovers can generate highly lagging signals.

By the time the 20-day EMA crossed below the 50-day EMA in the chart above, BTC/USD would have fallen significantly. Waiting for the 50 day EMA to cross below the 150-day EMA would have produced an even more delayed sell signal.

Identify support and resistance with moving averages

Moving averages can also help identify possible support and resistance levels. A market trading above an upward sloping moving average is considered in an uptrend and tends to find support at its moving average, until it breaks below it.

Similarly, a market priced below a downward sloping moving average is said to be in a downtrend, and it tends to encounter resistance at the moving average, until breaking above it.

As seen in the Ripple (XRP/USD) chart above, although support and resistance can often be found around widely followed moving averages, these lines tend to be more reliable indicators of support and resistance when trends have been intact for quite some time.

Exponential Moving Average (EMA)

A popular type of moving average is the EMA, which is similar to the SMA except that it is impacted more by recent prices than older prices. The EMA’s greater sensitivity to recent price changes makes it more ideal for the trader who wants a moving average to exhibit less lag to recent price movement.

The disadvantage of using the EMA is that because it produces more trading signals, the likelihood of false or premature trading signals also increases.

Sample EMA calculation

Initial SMA: 20-period sum / 20

Multiplier: (2 / (Time periods + 1) ) = (2 / (20 + 1) ) = 0.0952 (9.52%)

EMA: {Close - EMA(previous day)} x multiplier + EMA(previous day).

A 20-period EMA applies a 9.52% weighting to the most recent price. A 20-period EMA can also be called a 9.52% EMA. A 40-period EMA applies a 4.88% weighting to the most recent price (2/(40+1) = .0488). Notice that the weighting for the 20- period is nearly double that of the 40-period EMA.

Day #


5 day SMA

Multiplier 2/(5+1) 

5 Day EMA




























This content is not financial advice and should not form the basis of any financial investment decisions nor be seen as a recommendation to buy or sell any good or product. Trading cryptocurrency is complex and comes with a high risk of losing money, particularly if you trade on leverage. You should carefully consider whether trading cryptocurrencies is right for you and take the time to learn how trading works and decide how much money you are prepared to lose.


Darren Chu, CFA

Darren Chu, CFA, is the founder of Tradable Patterns, publisher of daily technical analysis on Bloomberg, Thomson Reuters, Factset, Interactive Brokers, Inside Futures, and other partner websites. Before the launch of Tradable Patterns, Darren served as IntercontinentalExchange | NYSE Liffe's country manager for Australia, India, and the UAE, expanding his role to look after Liffe business development in APAC ex-Japan/Korea until his departure mid April 2014. Previously, Darren was with the TMX Group | Montreal Exchange, marketing Canadian futures and options across North America, London, Singapore and Hong Kong. Darren also launched and managed CMC Markets Canada's Chinese marketing and sales team, along with educational offering. Visit for more information.