Tips and Tools for Future Market Trading and Investment Technical analysis Derivative and Commodity Exchange Nepal Ltd. www.dcxnepal.com info@dcxnepal.com DCX Nepal Tips and Tools for Future Market Trading and Investment
Moving Average A moving average is the average value of a security’s price over a specific period of time. Moving averages are used to smooth out the “noise” of shorter-term price fluctuations so as to more readily be able to identify and define significant underlying trends. A Smoothing Device with a time lag
Types of Moving Averages Simple Moving Average (SMA) Weighted Moving Average (WMA) Exponential Moving Average (EMA) Triangular or Central Moving Average (CMA)
SIMPLE MOVING AVERAGE A Moving average simply measure the average price or exchange rate of a security over a specific time frame. For example, 5 day Simple Moving Average is the sum of last 5 days closing/opening price divided by the number of time periods
SIMPLE MOVING AVERAGE
WEIGHTED MOVING AVERAGE Weighted Moving Average is a kind of moving average that put more weight on most recent data and less weight on older data. A weighted moving average is calculated by multiplying each of the previous day's data by a weight. To calculate this kind of moving average we have to put a weight of 1 to oldest data and then 2 for next data and so on up to the current price.
WEIGHTED MOVING AVERAGE To calculate 5 day WMA calculates the weight of the first day as below: Divide the number of each day by sum of the number of days (15) and multiply it by the value of the security (Price). For the last step, you should add all 5 weighted values together (sum).
WEIGHTED MOVING AVERAGE The sum of the number of days = 1 + 2 + 3 + 4 + 5 = 15 5 day weighted moving average (WMA) = 2.33 + 4 + 8 + 12 + 16.66 = 43
WEIGHTED MOVING AVERAGE
EXPONENTIAL MOVING AVERAGE An exponential moving average (EMA), sometimes also called an exponentially weighted moving average (EWMA), applies weighting factors which decrease exponentially. The weighting for each older data point decreases exponentially, giving much more importance to recent observations while still not discarding older observations entirely. The graph at right shows an example of the weight decrease.
EMA Calculation The formula for calculating the EMA today can be expressed as follow; Smoothing Factor (α) Expanding out EMAyesterday each time results in the following power series, showing how the weighting factor on each data point p1, p2, etc, decrease exponentially:
Use Of Moving Average Identifying a trend Identifying Support and Resistance Levels Identifying Price breakouts Measuring Price Momentum
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