This research applies map-reduce parallel computing technologies to analyze the stock technical indicators on Hadoop platform. The computation efficiency is improved significantly; in the meantime, the target indicators are clustered by parallel K-means clustering algorithm and patterns are defined. Based on the found patterns, the most profitable buy-sell decisions will be recommended. The experiments were carried out to validate the proposed framework. Results show that most suggested buy-sell strategies beat the market and gain higher profit. In addition, the analyzed results could be used as decision support for stock investors.