Parallel Techniques for Large Data Analysis in a Futures Trading Evaluation Service System

Futures trading evaluation system is used to analyze trading history of individuals, to find out the root cause of profit and loss, so that investors can learn from their past and make better decisions in the future. To analyze trading history of investors, the system processes a large volume of transaction data, to calculate key performance indicators, as well as time series behavior patterns, finally concludes recommendations with the help of an expert knowledge base. The paper firstly presents the working logic of the evaluation system, then it focuses on parallel data processing techniques that the system is based on. Parallel processing architecture, data distribution scheme, key performance indicators calculating algorithms and distributed time series analysis algorithms are elaborated in details. The system is highly scalable, and by exploiting the power of parallel processing, the generation time of an evaluation report is cut down from 1 to 3 minute, to 30 to 45 seconds.