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Understanding Simple Moving Averages (SMAs) in Trading


Simple moving averages (SMAs) are one of the most basic yet essential technical indicators used by traders. As the name suggests, SMAs are simple averages of a security's price over a defined number of periods.

For example, a 10-day SMA calculates the average closing price over the last 10 days. Each day, the newest price is added to the calculation while the oldest price is dropped. This creates a moving window along the price chart.

SMAs smooth out day-to-day price fluctuations and help identify the underlying trend. Short-term SMAs react quickly to recent price moves, while long-term SMAs are slower to react.


How SMA Are Calculated


Understanding Simple Moving Averages (SMAs) in Trading


The SMA is calculated by taking the arithmetic average of a given set of prices over a specified period. The basic formula for an SMA is:

SMA = (Sum of Price Values) / (Number of Periods)


  • The sum of Price Values = The sum of closing prices for the specified period
  • Number of Periods = The number of data points included in the SMA calculation

To calculate an SMA for any given period, you simply take the arithmetic average of the closing prices for the number of periods you want the SMA to cover. This gives traders a smoothed, rolling average price over a specified timeframe.


Strengths of SMAs


Understanding Simple Moving Averages (SMAs) in Trading


Simple moving averages have several key strengths that make them a popular technical indicator for traders:

Easy to Calculate

One of the main advantages of SMAs is that they are straightforward to calculate. The formula simply takes the sum of closing prices over a defined lookback period and divides it by the number of data points. This makes SMAs accessible even for beginning traders with limited technical skills. The simple calculation also allows SMAs to be quickly plotted on charts and analyzed.

Smooths Out Volatility

By averaging a security's price over a period of time, SMAs help smooth out short-term volatility and noise. This makes it easier to identify the underlying trend and gauge the overall market direction. Traders often use SMAs as dynamic support and resistance levels, buying near the SMA when prices dip and selling when prices bounce off the SMA.

Filters Out Market Noise

SMAs filter out day-to-day price fluctuations and random volatility to reveal the market's prevailing trend. This allows traders to focus on the broader trend rather than get distracted by minor price moves. SMAs essentially act as a form of signal averaging that separates low-frequency signals (the trend) from high-frequency noise.


Limitations of SMAs

Simple moving averages have some limitations that traders should be aware of:

Lagging Indicator

One of the main drawbacks of SMAs is that they are lagging indicators. This means SMAs are based on past prices and thus tend to lag behind the current market price.

For example, a 20-period SMA calculates the average of the previous 20 periods, so it may not reflect the most recent price changes or trends until the recent prices make up a larger portion of the SMA's lookback period. This lagging nature means SMAs may generate late trade signals.

Susceptible to Whipsaws

SMAs are also susceptible to whipsaws. A whipsaw occurs when the market reverses direction after a trade entry, leading to a losing trade. SMAs often generate whipsaw trades when prices are choppy or alternating between up and down trends.

For example, if the price crosses above a 50-period SMA, triggering a buy signal, but then drops back below the SMA shortly after, it results in a losing whipsaw trade. Using shorter SMA periods can reduce whipsaws but also risks more false signals or missing major trends.


SMAs vs. EMAs

The main difference between simple moving averages (SMAs) and exponential moving averages (EMAs) is the weighting given to the most recent data points. SMAs give equal weighting to all data points used in the calculation. In contrast, EMAs give greater weight to more recent prices, making them more responsive to the latest price changes.

  1. EMAs have less lag than SMAs and respond quicker to recent price moves. SMAs are slower to react.
  2. EMAs provide more weight to current prices. SMAs give equal weight to all prices.
  3. EMAs are more sensitive to the latest data. SMAs are less reactive to volatility and short-term moves.
  4. EMAs work better for trading strategies as they adapt to changing market conditions. SMAs are better for identifying long-term trends.
  5. EMAs require more computation. SMAs are simpler to calculate.

EMAs are more appropriate for traders who want to identify new trends and reversals quickly. SMAs are preferred by investors with longer holding periods interested in smooth long-term trends.


SMAs vs. Weighted Moving Averages

Weighted moving averages (WMAs) give more weight to recent data points, while simple moving averages (SMAs) give equal weight to all data points within the period. This key difference leads to some contrasting characteristics between SMAs and WMAs.

  1. SMAs lag current price action more than WMAs. The equal weighting of an SMA means older data has a greater influence, causing more lag. WMAs respond faster to new information.
  2. WMAs are smoother than SMAs. The weighting helps filter out market noise and smooth out price fluctuations. SMAs tend to be more choppy and prone to false signals.
  3. WMAs bearish and bullish crossovers tend to be more significant than SMAs. Due to less lag and smoothing, the crossovers have higher accuracy and validity for trading signals.
  4. WMAs require more computing power than SMAs. The weighting calculations make WMAs more intensive, while SMAs just take an average of closing prices.
  5. WMAs can be more customizable by adjusting the weighting. SMAs only allow customization of the lookback period length.
  6. SMAs are more commonly used and familiar. WMAs are less prevalent despite advantages, perhaps due to SMAs being simpler to calculate.

Overall, WMAs have some notable advantages that make them worth considering over SMAs in many trading strategies. Adjustable weighting provides more responsiveness and flexibility. However, SMAs remain popular for their simplicity and wide availability. Many traders use a combination of SMAs and WMAs to gain a diversity of signals.


Bottom Line

Simple moving averages (SMAs) are one of the most popular technical indicators used by traders. SMAs calculate the average price of an asset over a specified period, which helps smooth out price fluctuations and identify trends.

The most common SMAs track price over 20, 50, 100, and 200 periods. Using SMAs along with other indicators like price action, support/resistance, and volume provides a more robust analysis.

SMAs work best when combined with complementary indicators. The main strengths of SMAs are their simplicity and flexibility. SMAs can be applied to all markets and timeframes. Though lagging, SMAs help cut down noise and generate trade signals.

The main limitations are that SMAs lag the current price and are prone to giving false signals during periods of market consolidation and whipsaws. Adaptive SMAs and pairing SMAs can help mitigate these weaknesses.

SMAs have some key differences from EMAs and weighted moving averages in how they handle the weighting of periods. Each type has pros and cons.SMAs are a foundational indicator that should be in every trader's toolkit.

They provide objective insight into trends and momentum when used properly. Though not perfect on their own, SMAs are extremely useful when combined with other analysis techniques. With an understanding of their workings and limitations, traders can effectively apply SMAs within their overall trading plans.

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“When considering CFD for trading and price predictions, remember that trading CFDs involves significant risk and could result in capital loss. Past performance is not indicative of any future results. This information is provided for informative purposes only and should not be construed to be investment advice.”

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