What is the best stock charting software 2017

#What is the best stock charting software 2017 how to

Some other papers have studied the stock prediction based on these patterns and have achieved some research results. Some papers conclude from the experiment that the existing K-line patterns have a good forecasting capability for forecasting stock trends. The literatures introduce the existing patterns and their features in detail, such as Three Inside Up (TIU), Three Inside Down (TID), and Doji.

#What is the best stock charting software 2017 how to

How to mine the K-line patterns and how to make use of these patterns for predicting are main research contents of K-line technology analysis.Д«y the artificial methods of observing the K-line series of stock market (or Japanese rice market), people (the leading character is the founder of K-line, Munehisa Honma, who was a Japanese rice trader in the 18th century) have found many K-line patterns. Stock prediction based on K-line patterns is the essence of K-line technology analysis. Then, this subseries is a typical pattern series. For instance, when a subseries appears, the stock price will often rise or descend. In K-line series, if a K-line subseries contains some knowledge used to predict stock, then this subseries is called a K-line pattern series, a K-line pattern for short. As each K-line consists of four prices, the essence of K-line series is stock series with four observations. If the K-line of a stock lists in time order, then a series used to reflect the fluctuation of the stock price for some time can be formed, which can be called K-line series. Taking a daily K-line, for example, a K-line represents the fluctuation of stock prices in one day, it not only shows the close price, open price, high price, and low price for the day but also reflects the difference and size between any two prices (all K-lines given in the paper refer to daily K-line, unless otherwise indicated).

In the stock market, in order to learn and study the fluctuation of stock prices in a more intuitive way, people invent a candlestick chart (also called K-line) to represent stock time series graphically. There are many technical analysis methods about stock prediction, the best known of which is candlestick technical analysis that is also called K-line technology analysis in Asia. Therefore, stock prediction has aroused the interest of a wide variety of researchers. As a typical time series, not only have stock time series the features of time series, but also the trend of stock prices is directly related to the peopleвЂ™s vital interests. Because of the two features, all kinds of time series forecasting have become a present hot research, one of which is the prediction of stock time series, stock prediction for short. That is to say, some special time subseries will repeat in the entire time series. The influence can be described by time seriesвЂ™ period, nonstationarity, varying volatility, and so on. That is, the historical values of observations will exert an influence on the future values in the time series. (a) The historical information will affect the future trend. Researches show that the time series have two import features.

It is the most commonly encountered data type, touching almost every aspect of human life, for example, the meteorological time series, the time series of stock prices (stock time series for short) which are composed of stock price observations, and the time series of personal health that are consisted of the observation of blood pressure, temperature, white corpuscle, and so forth. IntroductionДЄ time series is a series of observations listed in time order. Experimental results show that the predictive power of a pattern varies a great deal for different shapes and each of the existing K-line patterns requires further classification based on the shape feature for improving the prediction performance. The experiment includes testing the predictive power of the Three Inside Up pattern and Three Inside Down pattern with the testing dataset of the K-line series data of Shanghai 180 index component stocks over the latest 10 years. The similarity match model and nearest neighbor-clustering algorithm are proposed for solving the problem of similarity match and clustering of K-line series, respectively. To help resolve the debate, this paper uses the data mining methods of pattern recognition, pattern clustering, and pattern knowledge mining to research the predictive power of K-line patterns. However, there are some disputes on whether the K-line patterns have predictive power in academia. Stock price prediction based on K-line patterns is the essence of candlestick technical analysis.