This project focuses on predicting stock price trend for a company in the near future. Unlike some

other approaches which are concerned with company fundamental analysis (e.g. Financial reports,

market performance, sentiment analysis etc.), the feature space is derived from the time series of

the stock itself and is concerned with potential movement of past price. Tree algorithm is applied

to feature selection and it suggests a subset of stock technical indicators are critical for predicting

the stock trend. It explores different ways of validation and shows that overfitting tend to occur

due to fundamentally noisy nature of a single stock price. Experiment results suggest that we are

able to achieve more than 70% accuracy on predicting a 3-10 day average price trend with RBF