Abstract: The invention is to provide a generating method for transaction models with indicators for option. The method comprises: (S1) collecting a variety of indicators from the financial market; (S2) establishing an indicator pool saved with the indicators; (S3) distinguishing and classifying the indicators by Neural Network; (S4) determining a plurality of transaction models, the indicator of each transaction model in an independent classification; (S5) determining a date parameter for each indicator; (S6) deleting a part of the transaction models; (S7) determining a plurality of final transaction models from another transaction models; and (S8) determining a weight of each final transaction model.

Abstract: The invention is to provide a generating method for transaction models with indicators for option. The method comprises: (S1) collecting a variety of indicators from the financial market; (S2) establishing an indicator pool saved with the indicators; (S3) distinguishing and classifying the indicators by Neural Network; (S4) determining a plurality of transaction models, the indicator of each transaction model in an independent classification; (S5) determining a date parameter for each indicator; (S6) deleting a part of the transaction models; (S7) determining a plurality of final transaction models from another transaction models; and (S8) determining a weight of each final transaction model.

Abstract: The present invention discloses a state-based trading management system and method. Said trading management system divides each product into at least one type of state(s) to trade. There is one trading strategy relative to each state, including: at least one asset allocation module (model) which provides the ratio of each asset class to the state strategy module (model), and at least one state strategy module (model) performing the relative trading strategies in each state. The said asset allocation module (model) allows supervision from the user or the system itself.

Abstract: An information monitoring method is provided which may track and monitor specific events of changing input data, such as stock market information, and notify venture capitalists or investors in real time of the occurrence of identified events of interest. According to the method to train or learn the quantitative patterns inherent in data sets, such as correlation between MAP and MAV, the relationship based on rules is built. A gray coefficient, trained by neural network under the specific events occurred in the historical data, is obtained for tracking and monitoring the present input data in real time. Artificial intelligence is therefore provided permitting adaptive monitoring of the input data in present invention.