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POUse R or mysql to calculate time period returns?
 

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    <p>I'm trying to calculate various time period returns (monthly, quarterly, yearly etc.) for each unique member (identified by <code>Code</code> in the example below) of a data set. The data set will contain monthly pricing information for a 20 year period for approximately 500 stocks. An example of the data is below:</p> <pre><code> Date Code Price Dividend 1 2005-01-31 xyz 1000.00 20.0 2 2005-01-31 abc 1.00 0.1 3 2005-02-28 xyz 1030.00 20.0 4 2005-02-28 abc 1.01 0.1 5 2005-03-31 xyz 1071.20 20.0 6 2005-03-31 abc 1.03 0.1 7 2005-04-30 xyz 1124.76 20.0 </code></pre> <p>I am fairly new to R, but thought that there would be a more efficient solution than looping through each <code>Code</code> and then each <code>Date</code> as shown here:</p> <pre><code>uniqueDates &lt;- unique(data$Date) uniqueCodes &lt;- unique(data$Code for (date in uniqueDates) { for (code in uniqueCodes) { nextDate &lt;- seq.Date(from=stock_data$Date[i], by="3 months",length.out=2)[2] curPrice &lt;- data$Price[data$Date == date] futPrice &lt;- data$Price[data$Date == nextDate] data$ret[(data$Date == date) &amp; (data$Code == code)] &lt;- (futPrice/curPrice)-1 } } </code></pre> <p>This method in itself has an issue in that <code>seq.Date</code> does not always return the final day in the month.</p> <p>Unfortunately the data is not uniform (the number of companies/codes varies over time) so using a simple row offset won't work. The calculation must match the <code>Code</code> and <code>Date</code> with the desired date offset. </p> <p>I had initially tried selecting the future dates by using the <code>seq.Date</code> function</p> <pre><code>data$ret = (data[(data$Date == (seq.Date(from = data$Date, by="3 month", length.out=2)[2])), "Price"] / data$Price) - 1 </code></pre> <p>But this generated an error as seq.Date requires a single entry.</p> <pre><code>&gt; Error in seq.Date(from = stock_data$Date, by = "3 month", length.out = &gt; 2) : 'from' must be of length 1 </code></pre> <p>I thought that R would be well suited to this type of calculation but perhaps not. Since all the data is in a mysql database I am now thinking that it might be faster/easier to do this calc directly in the database.</p> <p>Any suggestions would be greatly appreciated.</p>
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