There are two irreconcilable camps stockbrokers. Some people believe that the price movement is non-random, predictable. Enough just to identify all causal relationships, and the future price can be predicted. Others are convinced that prices can not be predicted, their motion is chaotic. Until a clear winner in sight. Both sides are very well argue their views. Before any trader, whether beginner or a shark Exchange, sooner or later, the question is: which camp to join?

Two models of the behavior of prices

If a trader believes the price movement in a predictable and non-random process, then comes to the conclusion that it is possible to find a "magic formula", based on it to build a very effective trading system and receive regular high income. It is no secret that the majority of doing this all my life and never finds its way to wealth.

Considering the price movement random process, a trader to analyze price charts suitable positions with probability, not trying to predict future prices. Price charts he uses for capital management. In any case, both approaches - it's only two different models of the behavior of prices. Consider a few arguments in favor of a model of random price changes. To do this, using the random number tables Excel, try to reproduce the chart. Initially generate the data in the table, and then use the import operation to construct graphs in MetaStock.

As axioms take the assumption that the price chart can model generator <brown> noise. It is believed that the graph <brown> noise is the sum of independent random increments. In our case, we use the closing price, and it will add close the next day. The value can be either positive or negative. If we assume that the closing price does not depend on the closing price of the previous day (as a rule of thumb, it is), then the resulting graph - chart <brown> noise. MetaStock able to take data from a table, you must correctly place the headlines and under them - data. The proposed fragment table (Table 1), the first seven columns that are used to import, and the eighth and ninth column is used by the table.

So, we had to generate the data. The initial values are placed in the second row. We believe this is the value at the initial offering shares. The first column is filled with dummy growing dates, and in the remaining six columns, starting from the third row, there are formulas.

First of all you need to generate the closing price. To do this, in the third row of the column <Close> put the formula:

= E2 + (IF (RAND ()> RAND (), 1, -1)) * (E2 * $ H $ 2/100 * 2 * RAND ()).

To its original value - 40, you must add a new random value. Formula generates the following algorithm. The generator produces a random number table in the range from 0 to 1. At first we calculate the value of the increment (right bracket), multiply this value by a factor of -1 or 1 (middle part of the formula, the price closed above or below the previous day) and add an increment to the value of the previous day. We get the result - 40.66. The second line of the eighth column indicates the maximum change in the closing price as a percentage.

## Price movement: coincidence or pattern?

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