The Adaptive Moving Average (AMA) indicator for MetaTrader 4 was created to serve as a moving average as well as a method to calculate the degree of noise in a pattern and change accordingly. The AMA indicator's pace will automatically adjust in response to market volatility.

During its 1995 presentation, it was used to replace the ordinary Moving Average. Due to its improved user power, the AMA predictor outperformed all other attempts to create an accurate and intelligent moving average.

Finally, when the market is strongly trending and we see any counter-trend movements, pullbacks, or minor noise, the investor wants the Moving Average to fit closely with price action, and you'll obviously want to see a smaller trackback session.

If we're in a range-bound market with bars that cancel each other out, you'll want a moving average with a longer historical time to smooth out price and avoid false triggers.

To create the AMA indicator, Kaufman had to change the Exponential Moving Average (EMA) with an algorithm that adjusts the EMA's smoothing constant in relation to market volatility and direction ratio, rendering it reactive to both volatility and trend.

Price is said to be in a bullish trend when it trades above the Sienna colored line with DeepSkyBlue dots, and it is said to be in a bearish trend when it trades below the Sienna colored line with gold colored dots.


Who developed Adaptive Moving Average ?

In 1998, American quantitative financial analyst Perry J. Kaufman devised the Kaufman's Adaptive Moving Average (KAMA). The method was first used in 1972, but it wasn't until Kaufman's book "Trading Systems and Methods" that it was made public. Kaufman's Adaptive Moving Average, unlike most moving averages, takes market uncertainty into account as well as price action.

The Advantages

When market volatility is poor, KAMA stays close to the current market price, but as volatility rises, it lags. The KAMA indicator is designed to filter out market noise irrelevant, transient price spikes. Traditional moving averages have a number of flaws, one of which is that when used for trading signals, they appear to produce a lot of false signals. By avoiding short-term, insignificant price fluctuations, the KAMA indicator aims to reduce this tendency and thus produce less false signals.

The Kaufman Adaptive Moving Average is calculated as follows:

The following normal settings are used to calculate Kaufman's Adaptive Moving Average:

10 The Number of periods for Efficiency Ratio
2 The Number of periods for fastest exponential moving average
30 The Number of periods for slowest exponential moving average

You must first calculate the Efficiency Ratio and the Smoothing Constant before calculating the KAMA value.

Step 1: Efficiency Ratio (ER)
The efficiency ratio measures how effective price increases are. It varies between 0 and 1. The ER is zero when the price remains constant for ten cycles. The ER, on the other hand, moves to 1 if the price moves up or down for ten periods in a row. It's determined by multiplying the absolute difference between today's price and the price at the start of the period by the sum of the absolute differences between each pair of closes over the period. The following is the formula for measuring ER:

Formula: ER = Change/volatility
Change = Absolute Value [Close Close (past 10 periods)]
Volatility Sum = 10 periods (Close Prior Close)

Step 2: Constant Smoothing (SC)
For each cycle, the smoothing constant is determined. It employs the efficiency ratio value as well as two smoothing constants as follows:

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

The smoothing constant for the recommended 30-period EMA is (2/30+1) in the above equation. The SC for the slowest 30-period EMA is also the slowest smoothing constant, while the SC for the shortest 2-period EMA is the fastest.

Step 3: KAMA
You can now measure the Kaufman's Adaptive Moving Average indicator values after obtaining the efficiency function and smoothing constant values. The following is the formula:

KAMAi = KAMAi-1 + SC x (Price KAMA i-1)

KAMAi is the value of the present period
KAMAi-1 is the value of the time before the one under consideration
The source price for the calculation time is price.

What is the Adaptive Moving Average and How Does It Work?

Traders can get a good view of the market's actions by using Kaufman's Adaptive Moving Average predictor, as they can use to create trading decisions. The final values of the indicator are calculated using historical data. Traders make decisions based on the assumption that future trends will evolve in the same direction as previous trends.

The KAMA indicator is simple to use on a map. The trader has the ability to customize the indicator by entering parameters in the properties dialog box. The estimation periods and presence of the indicator are the two key parameters that can be customized. In the calculation parameter, traders may determine the number of periods over which Kaufman's Adaptive Moving Average should be applied. The default period count is 14, but traders can change it to any number between 2 and 1000.

Traders may use the Kaufman's Adaptive Moving Average predictor to evaluate market activity and forecast future price change when it is shown on a map. The KAMA indicator can be used to spot current trends, signs of impending trend shifts, and market reversal points that can be used to enter or exit trades.

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How to use of the KAMA

When the KAMA indicator line moves lower, it signals the presence of a downward trend. The KAMA line, on the other hand, indicates an uptrend as it moves higher. The KAMA indicator, as opposed to the Simple Moving Average, is less likely to produce false signals that might result in a trader losing money.

Kaufman's Adaptive Moving Average can also be used to identify the start of new patterns and the reversal points of existing ones. Plotting two KAMA lines on a map one with a shorter-term moving average and the other with a longer-term moving average is one way to do this. A transition from a downtrend to an uptrend is indicated when a faster KAMA line crosses over a slower KAMA line. When the faster MA line crosses back under the slower MA line, the trader will take a long position and close the exchange. The acceleration of the stock price in relation to Kaufman's Adaptive Moving Average can also be used to generate trading signals. A bullish (buy) signal is produced when the price moves from below to above the KAMA line. Price dropping from above the KAMA line to below it, on the other hand, is a bearish (sell) warning.

Forms of Moving Averages

Simple Moving Averages or SMA
Add the prices for the chosen time period and divide by the number of periods selected to get a simple moving average. To calculate a five-day moving average, add the five most recent closing prices and divide by five.

A stock is considered to be in an uptrend if the most recent close is above the moving average; downtrends are characterized by stocks trading below the moving average.

Moving averages will produce trading signals because of this trend-defining property. Traders buy when prices rise above the moving average and sell when prices fall below it in its most basic form. A strategy like this ensures that the trader is on the right side of any substantial exchange. Unfortunately, moving averages will lag behind market activity when smoothing the details, and the trader will almost always give back a significant portion of their gains on even the most profitable trades.

Exponential Moving Averages or EMA
Analysts tend to like the notion of the moving average and have spent years attempting to reduce the lag's problems. The exponential moving average is one of these developments (EMA). This method gives recent data a higher weighting than a simple moving average, and as a result, it stays closer to the market action. The following is the method for calculating an exponential moving average:

For where: Weight= Smoothing constant choosed by the analyst
EMAy= Exponential moving average from yesterday

The most common weighting value is 0.181, which is very similar to the 20-day simple moving average. Another choice is 0.10, which is a 10-day moving average.
While it reduces latency, the exponential moving average ignores another issue with moving averages: their use for trading signals will result in a high number of losing trades. Welles Wilder reports that markets only trend a quarter of the time in his book New Concepts in Technical Trading Systems.

As prices rapidly move above and below the moving average, up to 75% of trading activity is confined to narrow ranges, where moving-average buy-and-sell signals are produced repeatedly. Several analysts have suggested varying the weighting factor in the EMA calculation to solve this issue.

When the market is range bound, however, they pick up a lot of market noise, resulting in a lot of false signals. Furthermore, they are all naturally sluggish.
Perry J. Kaufmann first introduced adaptive moving average in his book The Smarter Trading: Improving Performance in Changing Markets, in an attempt to address the drawbacks of moving averages.

20-Day-SMA & AMA Action

Prior to Mr. Kaufmann's implementation of AMA, traders used methods like The Double Crossover Method and The Triple Crossover Method to combine several moving averages.

The following facts explain why you should use different combinations of moving averages:

1. As the market is quickly trending, fast moving averages, which also consist of shorter time periods such as five days, performed better.
2. Because the market is range bound, slow moving averages, which also consist of longer time periods such as 50 days, performed better, filtering out the majority of the noise in the market.

So Kaufmann's AMA's genius was a machine smart enough to change its speed based on a combination of market direction and speed.

To put it another way, when the market is trending, AMA accelerates to keep up with it. When the market is stuck in a range and doesn't move, AMA slows down.

As a result, it wins the moniker "adaptive" as it responds to business direction and pace on its own. By incorporating the efficiency ratio, Kaufmann's AMA achieves a sense of business direction and tempo.

Trading Rules for Adaptive Moving Averages

The trading rules for the Adaptive Moving Average are as follows:
1. Purchase when the AMA appears.
2. When the AMA declines, sell.

How do I mount the AMA MetaTrader 4.mq4 indicator?
Download AMA MetaTrader 4 Indicator.mq4
Start or restart your Metatrader 4 Client
Select Chart and Timeframe where you want to test your MT4 indicators
Copy AMA indicator for MetaTrader 4.mq4 to your Metatrader Directory / experts / indicators
Right-click on AMA indicator for MetaTrader 4.mq4
Attach to a map
Modify settings or press ok in your Navigator, which is often left in your Metatrader 4 Client.
The AMA indicator a MetaTrader 4.mq4 indicator is now visible on your chart.

How do you delete the AMA MetaTrader 4.mq4 indicator from your Metatrader chart?

Right-click on the chart where the indicator is running in your Metatrader 4 Client Indicators list
Select the indicator and delete

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Thank you guys for reading till next time.