Forecasting future stock prices direction is a topic widely studied in various fields inclusive of making trades, finances, statistical usage and computer sciences. The mere motivation for this lies naturally in predicting the future prices direction in a way that stocks can be purchased and their selling can take place at a position with higher profitability. Typically, professional traders make use of technical analysis for analysing the stocks and for making decisions on investments. Fundamental analysis is the approach used traditionally and it involves studying fundamental concepts of an organization such as expenditure, revenue, position in the market, rates of growth annually etc. On the other hand, technical analysis is only based over the historical price fluctuation study. Technical analysis practitioners study price patterns based price charts and utilize price information in distinct calculations to acknowledge the movements for future price forecasting. The paradigm for technical analysis is therefore such that there is inner correlation present between company and price that can be utilized for determining when the market has to be entered and when it has to be existed.
A complicated system based good theoretical model needs to have a key appearance in the sense that it needs to focus on features that are mostly essential and they need downplaying the details inessentially. According to Fisher (1983), the mere snag with this suggestion is that an individual really does not understand which are the phenomenon being investigated. The module of feature generation is such that it implements the initial reasoning layer within the model for prediction. The module is such that it leads towards implementing a process which is knowledge intensive leading towards generating a discrete set of featured values from the data of price using the technical stock analysis domain knowledge. The generated characteristics represent data aspects that have quintessential for predicting stock price. The process executes essentially a price data coarse grained analysis, filtering out the details that are seemingly unessential and forward the seemingly essential details with regard to predicting stock prices. Even though the key purpose of module lies in providing inputs to the layer of next reasoning but still a difference can be made. In this manner, the module related domain knowledge implementation can involve testing and independent evaluation so that techniques of new analysis not significantly from technical analysis can be integrated in an easier manner within the module. As a consequence, modules are generally implemented through use of an approach centred on agents wherein every agent is designed as an independent authority that is responsible for implementing a knowledge subset from technical based analysis. Agent oriented approach conceptual illustration is provided by Baron et al (1998) and is provided in the following:
The field for technical analysis is inclusive of a wide variety of distinct models as well as techniques. Certain technical analysis methods have their basis over intricate and complicated patterns of price that need to be expensive computationally for detecting and implying subjectivity within interpretation. The process of feature generation should be such that it leads towards performing a price data coarse grained analysis that is forwarded subsequently to reasoning based second layer. Furthermore it is also wanted that the process should be efficiently computation and relative. Consequently, it also applies razor Occam to the selection of technical elements leaving out the patterns of intricate price and focus on the most famous components that can be operationalized efficiently and that are objective relatively when it comes to interpretation. So as to facilitate easy agent population extension, a formal agent interface can be defined which helps in employing through each agent implemented.
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