Analyze time-series data using R

I have an assignment described below. You are welcome to bid if you have sound skills of time-series analysis, statistics, and R. 1/ You are given 3 data files: a) Prices: contains daily prices of a sample of 56 stocks (X1 to X56). Prices can be missing for some stocks in some periods due to many reasons such as the companies have not been listed on the exchange yet... It is safe not to consider these stocks during such periods. b) Signals1: contains some daily signals that can help to predict future stock prices. c) Signals2: contains other event signals that are only available for some stocks on some days. This type of signals is also expected to have some predictive power on future stock prices. The attachments show an example of a few data points. The full data sets contain observations up to quite recently. 2/ You will need to do the following: a) First, you show your approach to the exploratory analysis step which can help to uncover important characteristics of these temporal data sets. b) Next, you build predictive models for future stock returns using only the first data set Prices. In particular, our prediction outcomes of interest at the end of day t are stock returns in the next 3 days given by: R(3) = [P(t+3)-P(t)]/P(t) where P(t) is the stock price at day t. c) Finally, still using the prediction outcomes as stock returns in the next 3 days, but you will use additional information from signals: (1) Signals1 or (2) Signals2 exclusively; or (3) both to improve your models. 3/ You are expected to come up with: (a) A brief report showing how you approach the above questions (b) Your code in R with as detailed comments/explanations as possible demonstrating your programming skills to carry out these data analysis. Using quantmod package is preferred, although you can choose an alternative as long as you can assure the quality of work and the meet these requirements.