Moving Averages Explained: SMA vs EMA and How Traders Use Them

The difference between a moving average vs exponential moving average comes down to weighting. A simple moving average (SMA) adds up the last N closing prices and divides by N, so every day counts equally. An exponential moving average (EMA) gives the most recent prices more weight, so it turns faster when price changes direction, at the cost of more false signals.

Both lines smooth out day-to-day noise so you can see the trend, and both are built into every charting platform. Traders use them as trend filters, as moving support and resistance, and as the basis for crossover signals such as the golden cross.

This guide shows the formula for each with a small worked calculation, explains lag, covers the 50- and 200-day averages, and looks honestly at crossover strategies and their biggest weakness: whipsaws.

What Is a Moving Average?

A moving average is the average price over a fixed number of recent periods, recalculated each period as the window “moves” forward. On a daily chart, a 20-day moving average plots one point per day, each equal to the average of the previous 20 closes.

Moving averages answer one question well: which way is price trending, on average, over this window? A rising average with price above it describes an uptrend. A falling average with price below it describes a downtrend. A flat average with price weaving through it describes a range.

They are among the most common tools in technical analysis, and several other indicators, including the MACD indicator, are built directly from them.

Simple Moving Average (SMA) Formula

The SMA is the arithmetic mean of the last N closing prices:

SMA = (P1 + P2 + … + PN) ÷ N

Here P1 to PN are the last N closing prices.

Each new period, the oldest price drops out and the newest is added. Every price in the window carries the same weight: in a 5-day SMA, each close counts for 20%; in a 200-day SMA, each counts for 0.5%.

Equal weighting has a side effect called the drop-off effect. The SMA can move because of the price that just left the window, not the one that arrived. A big move from 20 days ago can tug a 20-day SMA today, even if nothing happened today.

Exponential Moving Average (EMA) Formula

The EMA is calculated recursively, using yesterday’s EMA and a smoothing multiplier:

Multiplier (k) = 2 ÷ (N + 1)

EMA today = (Close today − EMA yesterday) × k + EMA yesterday

Put another way, the EMA moves a fraction k of the distance toward today’s close. For a 5-period EMA, k = 2 ÷ 6 ≈ 0.333; for a 10-period EMA, k = 2 ÷ 11 ≈ 0.182; for a 50-period EMA, k = 2 ÷ 51 ≈ 0.039; and for a 200-period EMA, k = 2 ÷ 201 ≈ 0.010.

Because each EMA value includes the previous one, every past price keeps a small and shrinking weight. In a 5-period EMA, the latest close carries about 33% of the weight, the one before about 22%, then about 15%, 10% and 7%. Those five closes add up to roughly 87%; older prices share the remaining 13%.

The first EMA value needs a starting point. The usual convention is to seed it with the SMA of the first N closes. That is why EMA values can differ slightly between platforms that start from different dates.

SMA vs EMA: A Worked Calculation

Moving Average vs Exponential Moving Average: Key Differences

Understanding lag

Lag is the delay between a change in price and the moving average reflecting it. Every average lags because it is built from past data. For an SMA, the average age of the prices in the window is (N − 1) ÷ 2 periods: about 24.5 days for a 50-day SMA and 99.5 days for a 200-day SMA. On a steady trend, the line trails price by about that much.

The EMA’s weighted average age works out to the same (N − 1) ÷ 2. Its advantage is not less lag on a steady trend but a faster first reaction after a sharp turn, because the newest prices carry the most weight. Shortening the period cuts lag for either type, but makes the line noisier.

The 50 and 200 Day Moving Average

The two most widely quoted moving averages are the 50-day (about ten weeks of trading) and the 200-day (about forty weeks, or most of a trading year). Both are usually simple averages of daily closes.

• Trend filter: price above a rising 200-day SMA is the most common definition of a long-term uptrend. Many investors and funds use it to decide whether to take risk at all.
• Dynamic support and resistance: in an uptrend, pullbacks often pause near the 50-day line. Because so many traders watch these lines, reactions there can become self-fulfilling. Our guide to support and resistance explains how to treat such levels as zones.
• Distance as a gauge: a price far above its 200-day line signals a stretched market; one far below signals a deeply oversold one. Neither tells you when the stretch will end.

Golden cross and death cross

A golden cross occurs when the 50-day moving average crosses above the 200-day. A death cross occurs when the 50-day crosses below the 200-day. Financial media report both heavily, especially for major indexes such as the S&P 500.

Keep their limits in mind. Because the 50-day trails price by roughly 25 days and the 200-day by roughly 100, a death cross often arrives after much of a decline has already happened, and a golden cross after much of the rebound. In a sideways market, the two lines can cross back and forth several times in a few months.

Moving Average Crossover Strategies and the Whipsaw Problem

A crossover strategy buys when a faster average crosses above a slower one and sells (or shorts) when it crosses below. Common pairs include 9/21 EMA for short-term charts, 20/50 for swing trading and 50/200 for long-term trend following. A simpler version uses price itself: buy when price closes above the average, sell when it closes below.

These systems share one strength and one weakness. In a long trend they keep you in the move and get you out eventually, which limits large losses. In a sideways market they produce whipsaws: a buy signal followed quickly by a sell signal at a lower price, again and again.

Traders reduce whipsaws in several ways, each with a trade-off:

• Longer periods: fewer false signals, but later entries and exits.
• A confirmation filter: require a close a set percentage beyond the average, or two consecutive closes. This removes small crosses but also delays real ones.
• A trend-strength or volatility check: skip crossovers when the slower average is flat.
• Trading only in the direction of the higher timeframe: for example, take bullish 20/50 crosses only when price is above the 200-day.

A Moving Average Strategy for Beginners

If you want a starting framework rather than a black-box signal, combine a slow average for direction with a faster one for timing:

1. Trend filter: on the daily chart, only look for long trades when price is above a rising 200-day SMA.
2. Pullback zone: wait for price to pull back toward the 20-day or 50-day EMA instead of chasing strength.
3. Trigger: enter when a bullish candle closes back above the faster EMA, ideally near a horizontal support zone.
4. Stop: place it below the pullback’s low, not at an arbitrary distance.
5. Size: risk a fixed small fraction of your account, such as 1%, based on the distance from entry to stop.
6. Exit: take partial profits at a target such as 2R, or exit on a daily close below the 50-day.

Pair the moving average with one momentum tool, such as the RSI indicator, if you want an extra check, and match the settings to your style. The day trading vs swing trading guide explains how holding period changes which timeframes make sense.

What the Research Says

• Early evidence was positive. Brock, Lakonishok and LeBaron (1992) tested simple moving-average rules on the Dow Jones Industrial Average from 1897 to 1986 and found that buy signals were followed by higher returns than sell signals.
• Later tests were less kind. Sullivan, Timmermann and White (1999) re-examined thousands of rules while correcting for data snooping, the problem of testing many rules and keeping the winners. They found the best rule from the original period did not deliver superior performance in the following decade.
• Reviews are mixed. Park and Irwin (2007) found many studies reporting profits for technical rules but flagged weaknesses in testing methods and weaker results in more recent data.

The honest takeaway: moving averages are a sound way to describe a trend and to define rules in advance, but they are not a reliable source of excess returns on their own, especially after costs.

Bottom Line

An SMA weights every price equally and moves smoothly; an EMA leans on the latest prices and turns faster. Use the 50- and 200-day lines to read the larger trend, treat golden and death crosses as late confirmations rather than forecasts, and expect crossover systems to bleed through whipsaws in sideways markets. Define your stop and position size before every trade, and keep learning with our trading guides.

Sources

• NIST/SEMATECH e-Handbook of Statistical Methods – EWMA Control Charts
• The Journal of Finance (via RePEc) – Brock, Lakonishok and LeBaron, Simple Technical Trading Rules and the Stochastic Properties of Stock Returns
• The Journal of Finance (via RePEc) – Sullivan, Timmermann and White, Data-Snooping, Technical Trading Rule Performance, and the Bootstrap
• Journal of Economic Surveys (via RePEc) – Park and Irwin, What Do We Know About the Profitability of Technical Analysis?
• FINRA – Frequent Intraday Trading
• CMT Association – Chartered Market Technician program