The Research Of Stock K Chart Characteristic Graphic Based On Neural Network

The successful trading mode in securities market can be learned and copied. Stock market is a highly complicate nonlinear system, it is variation has its own regulation, but also is influenced by many other factors such as politics, economy and psychology. While traditional prediction techniques are based on statistics and meet difficulties in stock market analysis. Neural network enjoys the virtue of self-organization and adaptability and can learn the economical knowledge from historical data. So it is suitable to solve problems in stock market prediction. By now, many researches have established prediction models for different stock market, and have obtained good results. but the information regarding to stock is so big, and most researchers just analyze the data, so that must cause much noisy. For this question, this paper tries to study detailed trade pattern and improve the quality of trade pattern. The paper mainly study the below affects:(1) Summarise all kinds of analysis about stock,discuss the usage of neural network on the stock analysis and finally propose the problem of current stock analysis and solving solotion.(2) Devide the technial analysis and introduce the K line theory, especially hammer K line theory.(3) How to build the modle based on nerual network. Discuss data collecting and handling ways and introduce the paramater and formular collecting questions.(4) In case predict the stock using the model. Analyze the impact to stock prediction on different conditionsThe experimental result shows that it is applicable and feasible to use neural network to predict the domestic stock market. However, the amount of raining samples used in our model is relatively small, and the dimension of each sample is low. We do not consider the influence of the government policy and some other factors, which usually play an important role in the fluctuation of the stock market. So there is still a long way to go before applying it to real applications, and need to be improved further.