Smoothed Moving Average: Explained

The Smoothed Moving Average (SMMA) is a technical indicator that provides cleaner trend signals by reducing market noise through longer historical data weighting. SMMA is different from traditional moving averages in that it considers all available price data and also provides current prices the same importance as earlier prices. This produces smoother trend lines for you to identify market direction with fewer false signals.

This renders SMMA particularly valuable to position holders and swing traders who need a reliable trend confirmation without the instability of shorter-term indicators. In this guide, you'll discover how SMMA works, when to use it effectively, and where it sits among other moving averages in your trading arsenal.

What is a Smoothed Moving Average?

A Smoothed Moving Average is an advanced type of exponential moving average that gives more weight to the analysis of longer-term trends than to near-term price movement. The Smoothed Moving Average takes more time in averaging, which puts a weight on the price data as the average is calculated. Thus, the oldest price data in the Smoothed Moving Average are never omitted, but they exert a very minimal influence on the Moving Average.

Some of the main features of SMMA are:

• Extended memory: Unlike simple moving averages that drop old data, SMMA retains all historical information 
• Reduced lag: Despite using more data, SMMA responds faster than traditional indicators 
• Noise filtering: Smooths out minor price movements while preserving major trend changes
• Consistent weighting: Recent and historical prices receive balanced consideration

The indicator works well in trending markets where you prefer a strong directional bias without getting whipsawed by multiple signal reversals. Professional traders like SMMA because it allows trend integrity to be maintained during periods of increased volatility.

How the Smoothed Moving Average Works

SMMA calculation differs from other moving averages in that it employs a recursive formula, building upon values previously set. The mathematics behind it ensures that all of the price points in your data set are contributing to the current reading, creating comprehensive trend analysis.

The SMMA calculation process:

First SMMA value: SMMA₁ = (Sum of closing prices for N periods) ÷ N

Subsequent SMMA values: SMMA = (SMMA₋₁ × (N-1) + Current Close) ÷ N

Where: 

• SMMA₋₁ = Previous SMMA value 
• N = Selected period length 
• Current Close = Latest closing price

This formula ensures that older data points are never completely dropped, unlike the SMA, resulting in a significantly smoother line. 

Step-by-step calculation example with 5-period SMMA:

• Calculate initial SMA for first 5 periods: (P₁ + P₂ + P₃ + P₄ + P₅) ÷ 5
• For period 6: SMMA₆ = (SMMA₅ × 4 + P₆) ÷ 5
• Continue pattern: Each new value uses 80% previous SMMA + 20% current price.

This recursive approach means your SMMA gradually adapts to price changes while maintaining historical context. The weighting system prevents sudden jumps that plague other indicators during volatile periods.

Smoothed Moving Average vs Simple Moving Average

Understanding the differences between SMMA and Simple Moving Average (SMA) helps you choose the right tool for your trading strategy. While both indicators track trends, their approaches and results vary significantly.

Comparison Table:

Feature	Smoothed Moving Average	Simple Moving Average
Data Usage	All available historical data	Fixed period only
Weighting	Decreasing weight for older data	Equal weight for all periods
Responsiveness	Moderate, balanced approach	Slower, more stable
Signal Quality	Fewer false signals	More frequent reversals
Best For	Long-term trend analysis	Short-term pattern recognition

Key Differences:

• Signal Timing: SMA provides earlier reversal signals but generates more false positives during sideways markets. SMMA filters out market noise, giving later but more reliable trend confirmations. This accuracy vs speed trade-off dictates when each indicator is most valuable to you.
• Trend Stability: The most important difference between a smoothed moving average and a simple moving average is that a simple moving average only accounts for the most recent data points, while a smoothed moving average accounts for the average of the last several data points. This longer accounting creates more stable trend lines that do not whip back and forth during times of volatility.
• Market Adaptability: SMA adjusts quickly to new market conditions, making it suitable for day trading and scalping strategies. SMMA retains trend direction longer, which suits swing trading and position trading strategies where you prefer confirmation rather than quick entries.

When to Use Each:

Choose SMMA when: 

• Trading longer timeframes (4H, Daily, Weekly) 

• Seeking trend confirmation with minimal noise 

• Managing positions through volatile periods 

• Combining with momentum indicators

Choose SMA when: 

• Day trading or scalping strategies 

• Need quick trend reversal signals 

• Trading breakout patterns 

• Working with shorter timeframes (1M, 5M, 15M)

Trading Strategies with Smoothed Moving Average

SMMA offers versatile applications across different trading approaches, from trend following to support and resistance identification. Understanding these strategies helps you integrate SMMA effectively into your trading system.

Trend Identification Strategy:

The primary SMMA application involves determining market direction through slope analysis. When SMMA rises, it confirms uptrend strength; declining SMMA suggests downward momentum. This approach works best on higher timeframes where trends develop over days or weeks rather than minutes.

Price-SMMA Relationship Trading: 

• Bullish signals: Price closes above rising SMMA 
• Bearish signals: Price closes below falling SMMA 
• Neutral zones: Price oscillates around flat SMMA

Support and Resistance Levels:

SMMA frequently acts as dynamic support during uptrends and resistance during downtrends. Professional traders use SMMA bounces as entry opportunities, placing stops just beyond the indicator line. This approach provides clear risk management while capturing trend continuation moves.

Multiple Timeframe Analysis:

Combine different period SMMAs to create a comprehensive trend analysis: 

• Fast SMMA (20-period): Short-term momentum 
• Medium SMMA (50-period): Intermediate trend 
• Slow SMMA (100-period): Long-term direction

When all three align in the same direction, trend strength increases significantly. Crossovers between different periods SMMAs generate entry and exit signals with varying sensitivity levels.

Entry and Exit Techniques: 

• Long entries: Price pullback to rising SMMA with bullish confirmation 

• Short entries: Price rejection at falling SMMA with bearish momentum 

• Exits: SMMA slope change or opposite timeframe signal

Advantages and Limitations of Smoothed Moving Average

Every technical indicator comes with strengths and weaknesses that determine its effectiveness under different market conditions. SMMA excels in specific scenarios while struggling in others.

Key Advantages:

• Improved Noise Reduction: SMMA's extended data consideration weeds out a lot of the random price movement shorter-period indicators are prone to. In this regard, it is particularly valuable in noisy markets where other moving averages generate a large number of false signals.

• Trend Persistence: Once SMMA determines direction, it maintains the bias longer than other options. This persistence keeps you in position during profitable trends without premature exits on minor corrections.

• Historical Context: Unlike indicators that dismiss older data, SMMA incorporates complete price history. This approach provides context for current market action and enables the identification of when trends actually change versus temporary blips.

• False Signal Mitigation: By prioritizing smoothness, the SMMA inherently sacrifices some speed to reduce the frequency of false signals (whipsaws) common to faster indicators like the EMA in sideways markets. This is a vital trade-off, as strategies with a high frequency of false signals (e.g., up to 57-76% for certain moving average crossover configurations, as found in S&P 500 backtests) lead to significant drawdowns from transaction costs and minor losses.

Notable Limitations:

• Signal Lag: The downside of SMMA's noise reduction strength is its weakness in responsiveness. By the time SMMA confirms trend changes, substantial moves may already be underway. This lag can eat into profit potential, especially in fast-moving markets.
• Whipsaw Vulnerability: During genuine trend reversals, SMMA can generate conflicting signals on its way to adjusting gradually to new market conditions. Several false starts can afflict traders before a direction becomes clear.
• Poor Performance in Ranging Markets: SMMA performs poorly when prices trend sideways with no bias. The indicator may oscillate around price movement without producing tradable signals, leading to frustrating trading attempts.

Also, large-scale studies of technical analysis rules (using thousands of trading rules) show that many technical rule-based strategies struggle under out-of-sample testing. This indicates that you must use filters or combine indicators to avoid overfitting. Studies show that adding filter bands (requiring price to cross a buffer away from the average) can cut down false signals, though not entirely remove them.

Market Condition Assessment:

SMMA excels when: 

• Strong trending conditions exist 
• Volatility creates noise in other indicators 
• Long-term position management is needed 
• Multiple time frame alignment is sought

Avoid SMMA during: 

• Tight ranging or consolidation periods 
• High-frequency trading strategies 
• Markets requiring an immediate signal response 
• Low-volatility environments with minimal trends

Best Practices and Settings for Smoothed Moving Average

Optimizing SMMA requires understanding how different settings affect performance across various market conditions and trading styles.

Period Selection Guidelines:

Short-term SMMA (10-20 periods): Provides faster signals with increased sensitivity to price changes. Suitable for intraday trading and quick trend identification. However, expect more false signals during volatile periods.

Medium-term SMMA (21-50 periods): Balances responsiveness with reliability. Most versatile setting for swing trading and intermediate-term position management. Works well across different timeframes and market conditions.

Long-term SMMA (50+ periods): Offers maximum trend stability with minimal false signals. Ideal for position trading and major trend identification. Slower signal generation requires patience but improves accuracy.

Combination Strategies:

Dual SMMA System: Use fast SMMA (20) and slow SMMA (50) together. Fast SMMA crossovers above the slow SMMA signal indicate uptrend initiation. This approach reduces single-indicator limitations while maintaining SMMA benefits.

SMMA with Volume Confirmation: Combine SMMA signals with volume analysis. Strong trends typically show increasing volume during SMMA direction changes. Weak volume during SMMA signals suggests potential false breakouts.

Platform Setup Recommendations: 

• Apply SMMA to closing prices for consistency 
• Use contrasting colors (blue for uptrend, red for downtrend) 
• Set alert notifications for SMMA slope changes 
• Monitor multiple timeframes simultaneously

Common Implementation Mistakes: 

• Over-relying on SMMA without a market context 
• Ignoring volume confirmation 
• Using inappropriate periods for the trading style 
• Expecting immediate signals in ranging markets

From our experience across various swing trading accounts, the SMMA proved invaluable as a long-term trend filter for highly volatile assets like cryptocurrencies and select growth stocks. For instance, in 2023, while tracking a mid-cap technology stock, the 50-period EMA produced eight false sell signals during a four-month consolidation phase, oscillating wildly with short price spikes. 

During the same period, the 50-period SMMA remained flat or slightly bullish, generating zero false sell signals and successfully filtering the market noise. We used the SMMA as a strict rule: only take long entries if the price is above the 50-period SMMA. This simple filtering mechanism prevented six loss-making trades from the EMA's erratic signals, confirming the SMMA's superior stability for defining the true direction of the underlying trend, even when its slower response led to slightly delayed entry/exit points.

Conclusion

Smoothed Moving Average provides traders with precise trend detection by its proprietary way of weighting price data and referring to history. Although SMMA may not be the quickest with its signals, its noise-reduction and trend integrity properties make it very useful for position management and longer-term trading strategies. Profitability with SMMA is achieved by understanding its limitations in ranging markets and taking advantage of its usefulness in trending markets.