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Mastering Market Adaptability with the Dynamic Adaptive Moving Average

The Adaptive Moving Average (AMA) is a type of moving average that smooths out price movements to help identify the market's trend and find trading opportunities. Its goal is to adapt more quickly to changing market conditions than other moving averages. Below we will explore the Adaptive Moving Average's calculation, how to use it, and its advantages and disadvantages.

Understanding the Adaptive Moving Average

The Adaptive Moving Average (AMA) is a type of moving average that dynamically adjusts its sensitivity based on the volatility of the price series. Traditional moving averages assign equal weight to all data points within a specified period, resulting in lag or delay in capturing rapid price changes. The AMA overcomes this limitation by adapting its responsiveness to market conditions, making it more effective in differentiating between trending and ranging markets.

Calculation of the Adaptive Moving Average

The Adaptive Moving Average calculation is a three-step process:

Step 1

Efficiency Ratio (ER): The Efficiency Ratio measures the relative efficiency of price movement within a given period. It quantifies the absolute price change ratio over a specified period to the sum of the absolute differences between consecutive prices.

ER = Change/Volatility

Change = ABS(Close - Close (10 periods ago))

Volatility = Sum10(ABS(Close - Prior Close))

Volatility is the sum of the absolute value of the last ten price changes (Close - Prior Close).

Step 2

Smoothing Factor (SF): The Smoothing Factor determines the weight or sensitivity of the Adaptive Moving Average based on the Efficiency Ratio. It is calculated using a smoothing factor formula that adjusts the weight according to the recent volatility of the price series.

SC= [ER x (Fastest SC – Slowest SC) + Slowest SC]2

Which translates to this:

SC= [ER x (2/ (2+1) – 2/(30+1)) +2/ (30+1)]2

From the above equation, (2/30+1) is the smoothing constant for the recommended 30-period EMA. So, the slowest smoothing constant is the SC for the slowest 30-period EMA, and the fastest smoothing constant is the SC for shorter 2-period EMA.

Step 3

AMAi = AMAi-1 + SC x (Price – AMA i-1)


AMAi = the value of the current period
AMAi-1 = the value of AMA for the period preceding being calculated.
Price = the price data for the period being calculated.

The Adaptive Moving Average is then calculated as a weighted average of the previous Adaptive Moving Average value and the current price, with the weight determined by the Smoothing Factor.


Adaptive to Market Conditions

One of the significant advantages of the Adaptive Moving Average is its ability to adapt to changing market conditions. Traditional moving averages use fixed periods, which may not be suitable for all market environments. However, the AMA adjusts its sensitivity based on the market's volatility, allowing it to capture trends more accurately. During periods of high volatility, the AMA becomes more responsive to price changes, while it becomes less reactive during low volatility. This adaptability helps traders avoid false signals and better aligns with the current market dynamics.

Smoothness and Reduced Lag

Another advantage of the Adaptive Moving Average is its ability to provide a smoother representation of the price data compared to other moving averages. By dynamically adjusting its sensitivity, the AMA reduces the lag associated with traditional moving averages. This means the AMA can quickly respond to price changes and provide more timely signals. Traders who prefer a smoother indicator that reacts promptly to price movements often find the AMA beneficial.

Customizable Sensitivity

The Adaptive Moving Average allows traders to customize its sensitivity according to their trading preferences and the specific market they are analyzing. By adjusting the parameters of the AMA, such as the number of periods used for calculations, traders can fine-tune the indicator to suit their trading style and time frame. This flexibility enables traders to optimize the AMA for different markets and trading strategies, enhancing its usefulness and adaptability.



While the Adaptive Moving Average offers increased adaptability, it also introduces additional complexity compared to traditional moving averages. The calculation methodology of the AMA involves multiple steps and may require a deeper understanding of mathematical concepts. Traders new to technical analysis or those who prefer simplicity may need help to grasp and implement the AMA effectively.

Lagging in Trend Reversals

Despite the reduced lag compared to traditional moving averages, the Adaptive Moving Average can still lag behind in identifying trend reversals. In rapidly changing markets, where trends can reverse quickly, the AMA may not provide timely signals to enter or exit trades. Traders relying solely on the AMA for trend reversal identification may experience delayed responses, potentially leading to missed trading opportunities or increased risk.

Sensitivity to Noisy Price Data

While advantageous in most cases, the adaptive nature of the AMA can make it more sensitive to noisy or erratic price data. The AMA may produce false signals or generate excessive whipsaws during excessive volatility or when prices exhibit sudden spikes or drops. Traders using the AMA should know this sensitivity and consider adding filters or confirmation indicators to reduce false signals.

How to trade with an Adaptive Moving Average

Trading with an Adaptive Moving Average (AMA) involves using the indicator to identify trends and generate trading signals.

Determine the Trend

Start by analyzing the price chart to determine the market's overall trend. The AMA can help you identify the direction of the trend by observing whether the AMA is sloping upward or downward. An upward-sloping AMA indicates an uptrend, while a downward-sloping AMA indicates a downtrend.

Entry Signals

Once the trend is identified, look for potential entry signals to open a trade. There are different ways to generate entry signals with the AMA.

Here are a few standard methods:

AMA Crossovers

When the price crosses above the AMA line from below, it may signal a buying opportunity. Conversely, when the price crosses below the AMA line from above, it may indicate a selling opportunity. These crossovers can be used as entry points to enter trades in the direction of the trend.

Pullbacks to the AMA

During an uptrend, when the price pulls back to touch or slightly dips below the AMA, it may present a buying opportunity. Similarly, it may offer a selling opportunity when the price rallies to touch or slightly exceed the AMA during a downtrend. Traders can wait for these pullbacks and enter trades in the direction of the trend.

Confirmation with Other Indicators

You can also use the AMA with other technical indicators or chart patterns to generate entry signals. For example, you may wait for the AMA crossover to occur with a bullish/bearish candlestick pattern or the convergence of other trend-following indicators.
Exit Signals

Just as entry signals are essential, having a plan for exiting trades is crucial. Here are a few common methods for generating exit signals with the AMA:

AMA Reversal

When the AMA changes its slope or direction, it may indicate a potential trend reversal. If you entered a trade based on an upward-sloping AMA and it starts sloping downward, it could be a signal to exit the trade. Similarly, if you entered a trade based on a downward-sloping AMA and it starts sloping upward, it may indicate an exit signal.

Price Target or Support/Resistance Levels

Set profit targets or identify key support/resistance levels on the price chart. When the price reaches those levels, it may be a signal to exit the trade.

Trailing Stop Loss

Implement a trailing stop loss strategy to protect profits as the trade moves in your favor. You can trail the stop loss level below the recent swing lows in an uptrend or above the recent swing highs in a downtrend.

The Adaptive Moving Average is a powerful tool in technical analysis, offering a dynamic and responsive approach to smoothing price data. Adjusting its sensitivity based on market volatility helps traders identify trends, filter out noise, and make informed trading decisions. Experiment with different parameters, timeframes, and combinations to find the settings that best suit your trading style and preferences.