MOVING AVERAGES

Simple Moving Average:

There are several types of moving averages currently in use today. The most basic is the simple moving average which takes the prices from the previous user-defined number of periods, sums them up and divides by the number of periods. A 10 day average is simply the average closing price for the past 10 days. Each successive day is calculated fresh from the 10 most recent days.

This is how the average "moves". The number of periods to use in the average depends on the stock or commodity being analyzed. Typically, it is related to the cycle of the underlying item, such as a four year stock market cycle, seasonal heating oil cycle and an agricultural harvest cycle. The average is overlaid on the price chart and crossovers between the average and the underlying price are observed. When prices are rising they are usually above the average. This is to be expected since the average includes data from the previous, lower priced days. As long as prices remain above the average there is strength in the market. Buyers are willing to pay more for the stock or commodity as the market continues to value it higher. When prices cross below the average it means that the market no longer expects prices to continue higher, at least temporarily. The more market participants taking this new view, the higher the volume will be and the better the signal. Remember, the valuation in the market is based on what market participants think will happen in the future. If the price is expected to rise, buyers will buy it now at the lower price which in turn causes demand to rise. Rising demand means higher prices and the self-fulfilling prophesy will been sustained.

Weighted and Exponential Averages:

Moving averages are lagging indicators. They summarize previous data and plot it on current prices so an analyst using only moving averages will probably not be able to call tops and bottoms in the markets. He will see the change in the trend only after it has happened. The potential profit lost by the inability to pick tops and bottoms is offset by the reduction in volatility seen and the reduced risk of making a bad trade.

In order to reduce the lag of the simple average, we can assign more weight to recent data and less to older data. A weighted 10 day moving average assigns a weight of 1 to the first day, 2 to the second and so forth until the most current day which is assigned a weight of 10. The 10 weighted values are added together and the sum is divided by the sum of the weights, that is 55.

Exponential averages are similar to weighted averages in that they give more importance to recent data. The difference is in how it assigns the weights. It is not important for this discussion to go into details of the formula except to say that a weighted average is an arithmetic weighting, and exponential average is a geometric weighting. This really only means that it reacts even faster to price changes than the other averages.

If SMA (20) Crossover EMA(13) then enter (Buy);
If SMA (20) Cross Under EMA(13) then exit (Sell);