Viewing Real-Time Charts
5. Repeat steps two and three for each study you want to add
6. Click OK.
Understanding the Power E*TRADE Pro Moving Average Study formulas
A moving average is a smoothing technique used to isolate the trend from short-term price fluctuations.
There are three main types of moving averages: exponential, simple, and linear weighted.
The types differ primarily in how they treat older data:
The exponential gives more weight to recent prices, but allows all data in the window to influence the average. Instead of a number of periods, the exponential uses a smoothing factor which can have any value between zero and one. The larger the smoothing factor the more influenced the average will be by more recent prices. If you want to relate the smoothing factor to the periods used in the standard moving average, set the factor equal to 2 divided by one plus the number of periods.
Simple moving averages weigh each of the terms in the average equally.
The linear weighted moving average gives more importance to more recent prices.
Both the simple and the linear weighted moving averages limit the price effect to the number of periods in the average.
Moving averages are usually based upon the close but can be based on other price series such as the midpoint.
Exponential Moving Average
This is a form of weighted averaging in which all prices have influence on the average but with decreasing weight placed upon older prices. It is held widely that this form of a moving average is a better reflection of a stock’s performance.
Where:
EMAt = Exponential moving average EMAt-1 = Previous moving average value
Sf = Smoothing factor computed as 2/(n+1) where n = number of days in a standard moving average
For a table of smoothing factors, see Smoothing factors used in Power E*TRADE Pro’s exponential moving average studies.
Source: © The Encyclopedia of Technical Market Indicators, Colby and Meyers The exponential moving average is a smoothing technique. Instead of a number of periods, the exponential uses a smoothing factor which can have any value between zero and one. The larger the smoothing factor the more influenced the average will be by more recent prices.
Using the Exponential Moving Average Study dialog box
You can use this dialog box (see below) to set up an exponential moving average study for any security.
By specifying the smoothing factor and type of series, you can perform a detailed exponential analysis that places more weight on recent prices and relates the percentage of today’s closing price to yesterday’s moving average value.
The Exponential Moving Average Study dialog box contains the following information:
Smoothing: This is a technique that assigns exponentially decreasing weights (by the factor you define) to data points as the average moves through time to produce a smoothed series rather than sharp, sudden changes in price. For a list of smoothing factors based upon the number of days in the moving average (n), see Smoothing factors used in Power E*TRADE Pro’s exponential moving average studies below.
Series: Specifies the prices used (Open, Close, High, or Low) to plot the study for the given time period.
OK: Saves the changes and closes the window.
Cancel: Closes the window without saving the changes.
Either accept the defaults or make any needed changes.
Linear Weighted Moving Average
The linear weighted moving average is a smoothing technique used to isolate a stock’s trend from short term price fluctuations. By placing greater importance to more recent prices it tends to limit the price effect to the number of periods in the average.
A linear-weighted moving average applies equal weight to all prices. The closing price is summed over n periods and divided by the total number of periods (n).
Using the Linear Weighted Moving Average Study dialog box
You can use this dialog box to set the period and type of series for a linear weighted moving average study. Linear weighted moving average is a smoothing technique used to isolate a stock’s trend from short term price fluctuations.
By placing greater importance to more recent prices it tends to limit the price effect to the number of periods in the average.* (*Copyright 2001 by InvestorGuide.com. All rights reserved.)
The linear weighted moving average is a smoothing technique used to isolate a stock’s trend from short-term price fluctuations. By placing greater importance to more recent prices it tends to limit the price effect to the number of periods in the average.
The dialog box contains:
Period: The number of days in the moving average.
Series: Specifies whether the data points represent the open, close, high, or low.
OK: Saves the changes and closes the window.
Cancel: Closes the window without saving the changes.
Either accept the defaults or make any needed changes.
Simple Moving Average
A simple moving average is a form of averaging in which all prices are given the same weight. It is calculated by starting with the oldest period specified and adding up specified prices (open, close, high, low, or volume) for the number of periods specified. The sum is then divided by the number of periods you specify in the Simple Moving Average Study Setup dialog box.
MAt = Moving Average Pt = Selected calculation price
Pt-1 = Selected calculation price 1 period ago Pt-n = Selected calculation price n periods ago n = Number of bars (periods)
The dialog box contains:
Period: The number of days in the moving average.
Series: Specifies whether the data points represent the open, close, high, or low.
OK: Saves the changes and closes the window.
Cancel: Closes the window without saving the changes.
Either accept the defaults or make any needed changes.
Bollinger Band
Bollinger Bands are based on the standard deviation of price added or subtracted to a simple moving average. The standard deviation error is added to the moving average for an upper envelope and subtracted for a lower envelope.
Using and understanding the Bollinger Band Study formula
Bollinger band studies are based on the standard deviation of the price added or subtracted to a simple moving average.
The standard deviation error is added to the moving average for an upper envelope and subtracted from the moving average for a lower envelope. The formula is computed as follows:
Where:
UPt is the upper envelope
DNt is the lower envelope
MAt is an n period moving average
a is the number of standard deviations to add or subtract for the moving average (integers or fractions are permissible).
Ót is the standard deviation squared
Using the Bollinger Band Study dialog box
You can use this dialog box to create an upper or lower Bollinger Band study in a Chart view.
Bollinger bands are plotted at standard deviation above and below a moving
average to indicate a stock’s volatility. The window contains the following information:
Upper: Plotted as standard deviation(s) above the moving average.
Lower: Plotted as standard deviation(s) below the moving average.
Standard Deviation Factor: The number of standard deviations used.
Moving Average Period: The number of days to be used in the moving average.
Series: Defines data points as being part of the open, close, high or low.
OK: Saves changes and closes the window.
Cancel: Closes this window without saving changes.
Either accept the defaults or make any needed changes.
MACD Signal
The difference between two exponential averages (long and short term)- usually in conjunction with a signal line that is a short term exponential moving average of the MACD indicator.
Understanding the Power E*TRADE Pro MACD Study formula
The MACD Study shows the difference between two exponential averages (long and short term) usually in conjunction with a signal line that is a short-term exponential moving average of the MACD indicator.
The MACD Study formula is computed like this:
MACDt = EMA1 - EMA2
EMA1 and EMA2 are exponential moving averages at period t with different