No abstract

An Option is a financial instrument, which is extensively used in share markets, money markets, and commodity markets to hedge the investment risks. It acts as financial leverage investment and is a kind of derivative instrument along with forwards, futures and swaps, which are used for managing risk of the investors. Though derivatives are theoretically risk management and leveraged investment tools, it is mostly used as speculative tools. The objectives of this work is to build a positional stock trading strategy in derivatives (Options) and recommending the users the strategy based on the predicted price of the stock, and, to generate P&L charts and Payoff graphs based on different Options strategies selected by the user. For this, the stock market data is collected from various online sources such as NSE, and BSE websites. The contributions of this work include, a comparison of various strategies and to predict the range of the stock based on Implied Volatility (IV); providing the user with the Option Chain and enable to calculate several Options indicators like PCR, Max Pain; and, the implementation of machine learning algorithm (LSTM) to predict the sentiment of the stock. An interactive web application for users is created, which provides consolidated data in the form of a dashboard from where the users can analyse the stocks and make strategic decisions on procuring stocks.

No abstract

In analysis of data, objects have mostly been characterized by a set of characteristics known as attributes, which together contained only one value for each object. Besides that, a few attributes in reality could include with more than a single value; such as from a human beside multiple profession characterizations, practises, communication methods, and capabilities, in addition to shipping addresses, of that kind of attributes are referred to as multivalued attributes and are typically regarded as null attributes when data is processed employing machine learning procedures. Throughout this article, another similarity mechanism is introduced that is defined around including multivalued characteristics which can be used for grouping. We propose a model to analyse each factor’s relative prominence for different data collection challenges in order to enable the selection among the most suited multivalued elements. The suggested methodology is a clustering technique for development and evolution that employs fuzzy c-means clustering and retains the new and more effective membership component by implementing the proposed similarity metric. Clustering of multivalued variables using fuzzy c-means is the efficient grouping criteria that results; any methodology to group-related data appears viable. The results show that our assessment not only improves previous segmentation methods on the multivalued cluster-based architecture but also helps in the improvement of the standard similarity metrics.

Software engineering process and practices paramount the crisis of cost, quality, and schedule constraints in developing software products. This chapter surveys the quality improvement techniques for the two fundamental artifacts of software product development, namely the architecture design and the source code. The information from top level research databases are compiled and an overall picture of quality enhancement in current software trends during the design, development, and maintenance phases are presented. This helps both the software developers and the quality analysts to gain understanding of the current state of the art for quality improvement of design and source code and the usage of various practices. The results indicate the need for more realistic, precise, automated technique for architectural quality analysis. The complex nature of the current software products requires the system developed to be beyond robust and resilient and building intelligent software that is anti-fragile and self-adaptive is favored. Innovative proposals that reduce the cost and time are invited.