To Display Candlesticks by Using QT Framework

Abstract

Advances in data mining techniques are now making it possible to analyze a large amount of stock data for predicting future price trends. The candlestick charting is one of the most popular techniques used to predict short-term stock price trends, i.e., bullish, bearish, continuation. While the charting technique is popular among traders and has long history, there is still no consistent conclusion for the predictability of the technique. The trend of stock prices often continues after intervals of several days because stock prices tend to fluctuate according to announcements of important economic indicators, economic and political news, etc. To cope with this kind of stock price characteristics, this paper focuses on a dynamic programming algorithm for retrieving similar numerical sequences. The proposed algorithm also handles a relative position among a stock price, 5-day moving average, and 25-day moving average to take into account where the price occurs in price zones. Experimental results on the daily data of the Nikkei stock average show that the proposed algorithm is effective to forecast short-term trends of stock prices. Candlesticks are especially popular because they give investors a very clear visual image of a stock’s progress. They provide deeper insight into the direction of the market as compared to other types of charts. Most investors feel that candlestick charts are more visually informative and appealing; therefore it is easier to draw inferences from them. A candlestick provides an encapsulated picture of the stock movement so investors can easily compare the opening and closing prices, as well as the high and low. The candlestick charting technique probably began sometime after 1850. Despite of its long history and popularity, mixed results are obtained in the studies on candlestick charting. Negative conclusions to the predictability of candlesticks are reported, while positive evidences are provided for several candlestick chart patterns in experiments using the U.S. and the Asian stock markets. It is also pointed out that candlestick chart pattern recognition is subjective. The candlestick chart patterns are often qualitatively described using words and illustrations.