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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.

The use of software in mission critical applications poses greater quality needs. Quality assurance activities are aimed at ensuring such quality requirements of the software system. Antifragility is a property of software that increases its quality as a result of errors, faults, and attacks. Such antifragile software systems proactively accepts the errors and learns from these errors and relies on test-driven development methodology. In this article, an innovative approach is proposed which uses a fault injection methodology to perform the task of quality assurance. Such a fault injection mechanism makes the software antifragile and it gets better with the increase in the intensity of such errors up to a point. A software quality game is designed as a two-player game model with stressor and backer entities. The stressor is an error model which injects errors into the software system. The software system acts as a backer, and tries to recover from the errors. The backer uses a cheating mechanism by implementing software Learning Hooks (SLH) which learn from the injected errors. This makes the software antifragile and leads to improvement of the code. Moreover, the SLH uses a Q-Learning reinforcement algorithm with a hybrid reward function to learn from the incoming defects. The game is played for a maximum of K errors. This approach is introduced to incorporate the anti-fragility aspects into the software system within the existing framework of object-oriented development. The game is run at the end of every increment during the construction of object-oriented systems. A detailed report of the injected errors and the actions taken is output at the end of each increment so that necessary actions are incorporated into the actual software during the next iteration. This ensures at the end of all the iterations, the software is immune to majority of the so-called Black Swans. The experiment is conducted with an open source Java sample and the results are studied selected two categories of evaluation parameters. The defect related performance parameters considered are the defect density, defect distribution over different iterations, and number of hooks inserted. These parameters show much reduction in adopting the proposed approach. The quality parameters such as abstraction, inheritance, and coupling are studied for various iterations and this approach ensures considerable increases in these parameters.