Data Set Information:

在预测股票价格时，你会收集某个时间段的数据——天、周、月等，但你不能利用某个时间段的数据，直到该时间段的下一个增量。例如，假设您每天收集数据。周一结束后，你就有了当天的所有数据。不过，你可以在周一投资，因为你要到一天结束才能得到数据。你可以利用周一的数据在周二进行投资。

在我们的研究中，每个记录（行）都是一周的数据。每一条记录也有股票在下一周的回报率（变化率、下一周的价格）。理想情况下，你需要确定哪只股票在接下来的一周内产生最大的回报率。这可以帮助您训练和测试您的算法。

这些属性中的一些可能不用于您的研究。它们最初被添加到我们的数据库中以执行计算。（Brown，Pelosi&Dirska，2013）使用了过去一周的价格变动百分比、交易量变动百分比、下一次分红天数和下一次分红回报百分比。我们将其他属性保留在数据集中，以防您想要使用它们中的任何一个。当然，你想要最大化的是未来几周的价格变化百分比。

Training data vs Test data:
In (Brown, Pelosi & Dirska, 2013) we used quarter 1 (Jan-Mar) data for training and quarter 2 (Apr-Jun) data for testing.

Interesting data points:
If you use quarter 2 data for testing, you will notice something interesting in the week ending 5/27/2011 every Dow Jones Index stock lost money.

Attribute Information:

quarter:  the yearly quarter (1 = Jan-Mar; 2 = Apr=Jun).
stock: the stock symbol (see above)
date: the last business day of the work (this is typically a Friday)
open: the price of the stock at the beginning of the week
high: the highest price of the stock during the week
low: the lowest price of the stock during the week
close: the price of the stock at the end of the week
volume: the number of shares of stock that traded hands in the week
percent_change_price: the percentage change in price throughout the week
percent_chagne_volume_over_last_wek: the percentage change in the number of shares of
stock that traded hands for this week compared to the previous week
previous_weeks_volume: the number of shares of stock that traded hands in the previous week
next_weeks_open: the opening price of the stock in the following week
next_weeks_close: the closing price of the stock in the following week
percent_change_next_weeks_price: the percentage change in price of the stock in the
following week days_to_next_dividend: the number of days until the next dividend
percent_return_next_dividend: the percentage of return on the next dividend

Relevant Papers:

Brown, M. S., Pelosi, M. & Dirska, H. (2013). Dynamic-radius Species-conserving Genetic Algorithm for
the Financial Forecasting of Dow Jones Index Stocks. Machine Learning and Data Mining in Pattern
Recognition, 7988, 27-41.

Citation Request:

We request that you provide a citation to this paper when using the dataset.  We welcome you to
compare your results against ours in (Brown, Pelosi & Dirska, 2013).

Dr. Michael Brown, michael.brown '@' umuc.edu, University of Maryland University College