Showing posts with label Moving. Show all posts
Showing posts with label Moving. Show all posts

Tuesday, January 15, 2013

Moving Averages

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Moving averages are usually used to show the mean price over a certain number of previous prices. For example, a 10 bar simple moving average of the close would show the mean closing price from the most recent 10 bars. Moving averages can also be applied to other data such as volume or other indicators, but are most commonly used with prices. There are several different types of moving averages, and they are all calculated slightly differently, but they all have a similar smoothing effect on the data, so that any unexpected price changes are removed, and the overall direction is shown more clearly.

Some of the most popular moving averages are:

Simple Moving Average

Description: The simple moving average is simply the average of the last n prices.
Calculation: (P1 + P2 + P3 + P4 + ... + Pn) / n
Example: A 4 bar simple moving average with prices of 1.2640, 1.2641, 1.2642, and 1.2641 would give a moving average of 1.2641 using the calculation (1.2640 + 1.2641 + 1.2642 + 1.2641) / 4 = 1.2641

Exponential Moving Average

Description: The exponential moving average is a weighted average of the last n prices, where the weighting decreases exponentially with each previous price.
Calculation: EMAn-1 + ((2 / (n + 1)) * (Pn - EMAn-1))
Example: A 4 bar exponential moving average with prices of 1.5554, 1.5555, 1.5558, and 1.5560 would give a moving average of 1.5558 using the calculation 1.5556 + ((2 / (4 + 1)) * (1.5560 - 1.5556)) = 1.5558

Weighted Moving Average

Description: The weighted moving average is a weighted average of the last n prices, where the weighting decreases by 1 with each previous price.
Calculation: ((n * Pn) + ((n - 1) * Pn-1) + ((n - 2) * Pn-2) + ... ((n - (n - 1)) * Pn-(n-1)) / (n + (n - 1) + ... + (n - (n - 1)))
Example: A 4 bar weighted moving average with prices of 1.2900, 1.2900, 1.2903, and 1.2904 would give a moving average of 1.2903 using the calculation ((4 * 1.2904) + (3 * 1.2903) + (2 * 1.2900) + (1 * 1.2900)) / (4 + 3 + 2+ 1) = 1.2903

Moving averages can be used to identify a trend by using the slope of the average (or lack of slope in a ranging market). They can also be used in trend trading systems to enter and exit trades by waiting for price and moving average crossovers, or for multiple moving average crossovers. Moving averages are also used in the calculations of many other indicators, such as the Moving Average Convergence Divergence (MACD), and the Commodity Channel Index (CCI).


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Sunday, January 13, 2013

Triangular Moving Average (TMA

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The triangular moving average (also known as the TMA) is similar to other moving averages in that it shows the mean price over a specified number of previous prices. However, the triangular moving average differs from most moving averages in that it is double smoothed (i.e. it is averaged twice). The triangular moving average can be calculated using various input data (prices, volume, or another technical indicator), but is most often calculated using prices. The triangular moving average is usually displayed with the price bars, and is the yellow line in the example chart.

Description: The triangular moving average (TMA) is a weighted average of the last n prices (P), whose result is equivalent to a double smoothed simple moving average (i.e. calculated twice). Calculation:
SMA = (P1 + P2 + P3 + P4 + ... + Pn) / n

    TMA = (SMA1 + SMA2 + SMA3 + SMA4 + ... SMAn) / n

As with other moving averages, the triangular moving average can be used to identify a trend by using the slope of the average (or lack of slope in a ranging market). However, due to the additional smoothing, triangular moving averages tend to be smoother, and have more waves, than standard moving averages. Interestingly, triangular moving averages often appear more responsive to direction changes, even though the additional smoothing actually moves the domainant input value to the middle of the input series (which would decrease responsiveness).


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