Spark Assignment

Fork this project, complete the assigned objectives.

This repo scaffolds a basic Apache Spark project for your convenience. Please feel free to edit it as you see fit Or not use it at all, if you prefer to use a different Spark version or language such as SparkR or pyspark

Complete the assignment on a POSIX like system such as Mac OS or a Linux distribution Hadoop based technologies generally do not function smoothly on Windows machines

If you are not familiar with Apache Spark you may submit the assignment using the framework/language of your choice (such as Python Pandas or R dplyr)

Assumptions

No dividends adjustments are necessary, using only the closing price to determine returns
If a price is missing on a given date, you can compute returns from the closest available date
Return can be trivially computed as the % difference of two prices

Assignment Objectives

The file stock_prices.csv (included in the repository) contains the daily closing price of a few stocks on the NYSE/NASDAQ Look at src/main/scala/com/example/spark/assignment/Main.scala to get started

Load up stock_prices.csv as a DataFrame, Dataset or RDD
Compute the average daily return of every stock for every date. Print the results to screen

In other words, your output should have the columns:

date	average_return
yyyy-MM-dd	return of all stocks on that date

Which stock was traded most frequently - as measured by closing price * volume - on average?

Bonus Question

Which stock was the most volatile as measured by annualized standard deviation of daily returns?

Evaluation

Correctness is most important
Elegance and style is the next most important
Performance is the last criteria, but can be detrimental to you if your implementation is unreasonably costly

Some helpful hints

You may or may not need these hints depending on the approach you take