Abstract:

One of the basic methods of technical analysis that are commonly used in investor practice is the analysis of moving averages. Moving averages are appointed based on the candlestick representation of the price, which has the drawback of possible decrease in the informative value carried by the exchange rate trajectory data. Better results of course modeling can be obtained by using the binary-temporal representation. The article presents a new algorithm for calculating the weighted moving average for the quotations in a binary-temporal representation. The algorithm assigns each change of course trajectory in the binary-temporal representation a weighted average to form an extended binary-temporal re-representation, which in turn can be the basis to construct a state prediction model.