Summary of Moving Averages

History of Moving Averages

Moving averages have been a cornerstone of technical analysis for decades. They were first developed in the early 20th century to help traders and analysts smooth out price data and identify trends in financial markets. Among the earliest applications was the Simple Moving Average (SMA), which gained popularity due to its simplicity. Over time, more sophisticated moving averages, such as the Exponential Moving Average (EMA), were introduced to address the lag associated with the SMA. Notable contributions include the work of J. Welles Wilder, who developed the Wilder’s Moving Average in his 1978 book, New Concepts in Technical Trading Systems. The development of modern computational tools has further enabled innovations like the Zero Lag EMA (ZLEMA) and Displaced Moving Average (DMA).

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What Moving Averages Can Tell Us

Moving averages provide insight into market trends and price behavior by smoothing out short-term fluctuations in price data. Key takeaways include:

• Trend Direction: The slope of a moving average indicates the current trend. An upward slope suggests a bullish trend, while a downward slope indicates a bearish trend.
• Support and Resistance: Moving averages often act as dynamic support or resistance levels where prices may reverse or consolidate.
• Market Momentum: Comparing shorter and longer-term moving averages (e.g., 50-day vs. 200-day) can reveal momentum and potential shifts in market sentiment.

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How Moving Averages Are Used

Moving averages are versatile tools used in a variety of ways:

1. Identifying Trends:
   • Traders use moving averages to determine whether the market is trending upwards, downwards, or sideways.
2. Crossover Strategies:
   • Common strategies involve using two moving averages of different lengths. For example:
     • A Golden Cross occurs when a short-term moving average (e.g., 50-day) crosses above a long-term moving average (e.g., 200-day), signaling a potential uptrend.
     • A Death Cross occurs when a short-term moving average crosses below a long-term moving average, signaling a potential downtrend.
3. Support and Resistance:
   • Moving averages are often used as dynamic support/resistance levels in price charts, providing entry or exit points.
4. Volatility Reduction:
   • By smoothing out noise in price data, moving averages help traders focus on the overall trend rather than short-term fluctuations.
5. Baseline for Indicators:
   • Many technical indicators, such as Bollinger Bands, MACD, and RSI, are built using moving averages as a foundation.

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Moving Averages in TA4J

Abbreviation	Full Name	Indicator Name	Moving Average Type
ATMA	Asymmetric Triangular Moving Average	ATMAIndicator	Specialized Moving Average
DEMA	Double Exponential Moving Average	DoubleEMAIndicator	Exponential Moving Average
DMA	Displaced Moving Average	DMAIndicator	Specialized Moving Average
EDMA	Exponential Displaced Moving Average	EDMAIndicator	Exponential Moving Average
EMA	Exponential Moving Average	EMAIndicator	Exponential Moving Average
HMA	Hull Moving Average	HMAIndicator	Specialized Moving Average
JMA	Jurik Moving Average	JMAIndicator	Specialized Moving Average
KAMA	Kaufman Adaptive Moving Average	KAMAIndicator	Adaptive Moving Average
KiJunV2	Kihon Moving Average	KiJunV2Indicator	Specialized Moving Average
LSMA	Least Squares Moving Average	LSMAIndicator	Polynomial-Based Moving Average
LWMA	Linear Weighted Moving Average	LWMAIndicator	Weighted Moving Average
McGinley	McGinleys Moving Average	McGinleyMAIndicator	Adaptive Moving Average
MMA	Modified Moving Average	MMAIndicator	Exponential Moving Average
SMA	Simple Moving Average	SMAIndicator	Simple Moving Average
SGMA	Savitzky-Golay Moving Average	SGMAIndicator	Polynomial-Based Moving Average
SMMA	Smoothed Moving Average	SMMAIndicator	Smoothed Moving Average
TEMA	Triple Exponential Moving Average	TripleEMAIndicator	Exponential Moving Average
TMA	Triangular Moving Average	TMAIndicator	Simple Moving Average
VIDYA	Chandes Variable Index Dynamic Moving Average	VIDYAIndicator	Adaptive Moving Average
VMA	Variable Moving Average		Adaptive Moving Average
VWMA	Volume Weighted Moving Average		Weighted Moving Average
WilderMA	Wilders Moving Average	WildersMAIndicator	Smoothed Moving Average
WMA	Weighted Moving Average	WMAIndicator	Weighted Moving Average
ZLEMA	Zero Lag Exponential Moving Average	ZLEMAIndicator	Exponential Moving Average

Classification of Moving Averages

1. Simple Moving Averages

• These averages assign equal weight to all data points in the period.
• Indicators:
  • SMA (Simple Moving Average)
  • TMA (Triangular Moving Average)

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2. Weighted Moving Averages

• These averages assign different weights to data points, typically emphasizing recent data.
• Indicators:
  • LWMA (Linear Weighted Moving Average)
  • WMA (Weighted Moving Average)
  • VWMA (Volume Weighted Moving Average)

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3. Exponential Moving Averages

• These averages assign exponentially increasing weights to recent data.
• Indicators:
  • EMA (Exponential Moving Average)
  • ZLEMA (Zero Lag Exponential Moving Average)
  • DEMA (Double Exponential Moving Average)
  • TEMA (Triple Exponential Moving Average)
  • EDMA (Exponential Displaced Moving Average)

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4. Adaptive Moving Averages

• These averages adjust dynamically based on market conditions such as volatility or trend strength.
• Indicators:
  • KAMA (Kaufman Adaptive Moving Average)
  • VIDYA (Chande Variable Index Dynamic Moving Average)
  • VMA (Variable Moving Average)
  • McGinley (McGinley Moving Average)

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5. Smoothed Moving Averages

• These averages reduce short-term fluctuations to produce a smoother trendline.
• Indicators:
  • SMMA (Smoothed Moving Average)
  • WilderMA (Wilder’s Moving Average)

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6. Polynomial-Based Moving Averages

• These averages use polynomial regression or curve fitting to calculate the trend.
• Indicators:
  • SGMA (Savitzky-Golay Moving Average)
  • LSMA (Least Squares Moving Average)

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7. Specialized Moving Averages

• These averages use unique algorithms or methodologies for specific purposes.
• Indicators:
  • HMA (Hull Moving Average)
  • JMA (Jurik Moving Average)
  • ATMA (Asymmetric Triangular Moving Average)
  • KiJunV2 (Kihon Moving Average)
  • DMA (Displaced Moving Average)

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Conclusion

Moving averages remain essential in modern technical analysis due to their simplicity, flexibility, and effectiveness in revealing market trends and dynamics.