Join Bill Duryea of the IOAMT as he reviews the types of moving averages that can be used to plot a financial time series and how to interpret the price action using smoothing filters.
A moving average model is a linear regression of the current value of the series against a previous time period. From a statistical point of view, the moving average is a numerical filter used to estimate the underlying trend in a time series. The location of price at a given point in relation to the average confirms and/or brings into question the direction of the trend.
In statistics, there are various standard models which can be used for the purpose of processing, interpreting, estimating and forecasting various types of data, i.e. factual information used in decision making. All statistical models require organizing data in a specific format.
When modeling a financial index, i.e. the S&P 500, the most common format is a time series consisting of a sequence of prices.
A financial time series can be plotted using monthly, weekly and daily price series. Of these, the daily trading range: the high, the low, the open and close are the most common. Other data formats, such as price ranges can also be used.
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Common questions that can be answered with statistical modeling are:
- What is a common value of the time series?
- Is the price structure stationary: restricted to a limited range?
- Or Is the price structure non-stationary: trending?
- What is the average rate of change?
- What are the best settings for time intervals?
- Can we determine structure within the price series: support and resistance?
William Duryea is the founder of the Institute Of Auction Market Theory and a professional trader of the ES for over 12 years. He has conducted seminars nationwide teaching Market Profile. He has been a guest speaker of the CME, CBOT, ICE, Denver Trading Group and a regular featured speaker at industry tradeshows. To learn more about IOAMT and to hear Bill moderate and trade the S&P500 daily in our Live Trading room, visit http://www.ioamt.com/.
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