Discover the secrets of Gann forecasting in this revealing report. A short content course by an expert in Gann's Theory.
Written by John Berends
A Commodity Trading Advisor
E-mail John at trade@gann.com for more information.
The art of forecasting is defined in its application. Most
traders use an analyst's forecast as the end-all, be-all portion of their
trading. This is absolutely the wrong approach. In this lesson we will discuss
Gann's Master Time Factor and its correct application. You will find that even
the great master Gann used forecasting as only a tool.
Gann stated in his stock and commodity courses that one
should go back 10, 20, 30, 60, 90 and 120 years and determine when highs and
lows were made in a particular stock and commodity. By lining up these days in
the current year one obtains the forecast for the year. For example, look at the
S&P 500 index. It made a high in August of 1987. Ten years later would be August
1997. Did we have a high in August 1997 and a low in October 1997? Well, yes we
did. Note that the pattern did not repeat exactly as one would hope but herein
lies the problem with such forecasts. Is it valuable to know that strength would
be expected prior to August1997? Is it valuable to know there may be trouble
ahead in the months of September and September 1997? Would you like to know that
strength would be expected in November and December 1997? It is obvious that
most traders can use this type of information. If one expected the cycles to
repeat exactly one may have been shaken out of any short positions in the
September to October run up.
Let's examine the construction of one of these forecasts.
In order to mathematically compute a forecast one needs to normalize prices in
each data set then average the two together. A similar method would be used
using three, four, five or six data sets. In our example we will only use two. A
simple spreadsheet program like Microsoft Excel or Lotus 1-2-3 is recommended
for the calculations. We will use the 10 and 20 year cycles to build our
forecast. First one needs to find the mid point of each set (year). Find the
highest high and lowest low. Add these together and divide by two. From this
mid-point number determine each closing price's percentage change. (Close-Midpt)/midpt.
Once all prices are normalized into percentages change they should be added to
the corresponding date in the other data set (year) and divided buy two. The
resultant data points will be our forecast.
