Abstract: A computer-implemented integrated framework for managing real-time financial trades and risk management is described herein. Quantitative and sentimental parameters of trading market are identified and analyzed. A stock selection module having a deep learning architecture performs future predictions based on the analyzed quantitative and sentimental parameters, wherein the stock selection module comprises. A probability number in percentage is assigned to a trading decision. Based on the assigned probabilities to different trading decisions, entrance and exit signals are provided to a user based on their preferences such as the user's risk tolerance.

Abstract: A computer-implemented integrated framework for managing real-time financial trades and risk management is described herein. Quantitative and sentimental parameters of trading market are identified and analyzed. A stock selection module having a deep learning architecture performs future predictions based on the analyzed quantitative and sentimental parameters, wherein the stock selection module comprises. A probability number in percentage is assigned to a trading decision. Based on the assigned probabilities to different trading decisions, entrance and exit signals are provided to a user based on their preferences such as the user's risk tolerance.