Generally, retail investors have been experiencing various difficulties in handling financial markets due to the impact of ever-changing price movements in conjunction with ever-changing financial news. In normal circumstances of trading mechanisms, it is possible to observe historical price movements or sentiments. However, it is not possible to observe the contextual relationship between financial news and financial markets. Such cognitive complexities always affect decision-making in an unfavorable manner. In order to bridge the knowledge gap in this regard, this paper proposes the idea of developing a trading interpreter that considers financial news sentiments and financial market price data in an integrated manner. Natural language processing techniques have been used for developing a system that extracts sentiments from financial news articles. Sentiments are mapped with structured financial market price intervals. Feature engineering techniques have been used for developing financial news sentiments, price-based feature development, and interaction feature development that considers immediate reactions and lagged reactions of financial markets with respect to financial news