Requirements Analysis and Goal Setting: Firstly, the project team needs to work with potential users and stakeholders to clearly define the project’s objectives, scope and expected results. This includes defining the project’s Key Performance Indicators (KPIs), such as accuracy, profitability, user satisfaction, and so on.
Data collection and cleansing: The project team should build a data pipeline to collect market-related data from a variety of sources (news sites, social media, company announcements, etc.). The data is then cleaned and pre-processed to ensure quality and consistency.
Stock and investment recommendation generation: develop AI systems to generate stock and investment recommendations based on sentiment analysis and market data. These recommendations should include buy, hold or sell recommendations, along with the rationale and justification associated with them.
Ethics and transparency: ethics and transparency are critical throughout the project. The project team needs to ensure that data privacy is protected, avoid potential algorithmic bias, and provide a transparent approach to explaining the AI system’s decisions. This may include interpretable research and review of models.
