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Processing the Groupon API – Part 3


Concluding the series in this post, we will apply a stylesheet to transform the XML data created from our mapping of the Groupon API into HTML. Here is an example of the XML output from the data mapping we created last time: XML produced by MapForce from the Groupon API

Assign a Stylesheet to Transform XML The Component Settings dialog for the output component of the MapForce mapping allows us to assign a stylesheet created with Altova StyleVision.

Assigning a StyleVision Stylesheet to a MapForce component
Assigning a stylesheet to the data mapping output component integrates the operations of MapForce and StyleVision, and a new series of buttons appears at the bottom of the MapForce mapping window for HTML, RTF, PDF, and Microsoft Word formats. (You must have both MapForce and StyleVision installed on your computer.) MapForce ouput formats available through a stylesheet When you click any of these output format buttons, MapForce executes the data mapping exactly as we saw in the previous post. MapForce seamlessly passes the XML output to StyleVision, where it is transformed to the selected format. MapForce then displays the formatted document in the Output window. Here is the MapForce Output window for HTML, based on the StyleVision Power Stylesheet assigned above: HTML output produced by MapForce from the Groupon API The MapForce Output menu lets you save the XML data mapping output or the HTML document formatted according to the stylesheet. How to Make a Stylesheet We designed a stylesheet for the Groupon API data mapping using Altova StyleVision, based on the XML Schema for the MapForce output component. The intuitive StyleVision interface and powerful data access and manipulation features make it easy to create attractive documents in HTML, RTF, PDF, and Microsoft Word formats from XML files. The screenshot below shows the StyleVision Design View of the Extreme Groupon stylesheet. The blue numbered circles identify the location of each design feature listed following the image. StyleVision stylesheet for the MapForce Groupon API mapping Features of the SPS file

  1. User-defined html specifies the viewport meta tag for mobile devices. This lets us design one stylesheet to generate a single HTML file for computers and mobile devices
  2. Document title with customized font, size, and color
  3. An autocalc element uses the XPath count function to count the number of deals in the XML input document and inserts the total
  4. The date timestamp is placed at the top of the document even though the date element occurs at the end of the XML input data file
  5. Images from the Altova and Groupon Web sites are referenced by hyperlinks, not as inline image data
  6. Horizontal rules set off each individual deal. For HTML documents, the rules automatically fit the browser window width
  7. Customized fonts and sizes assigned to different elements
  8. A two-column table organizes each deal description
  9. URLs in the XML file are dynamically assigned as hyperlinks for embedded images, fixed text, and dynamic data

StyleVision Power Stylesheets can combine multiple .xsd files, existing .css stylesheets, database schemas, XBRL taxonomies, and more to produce richly formatted reports that can even include automatically generated charts in various styles. You can also use StyleVision to define e-forms with data entry fields, drop down menus, radio buttons and other advanced features. Previewing Stylesheet Transformations StyleVision lets you assign a working XML file to preview your output as you design the stylesheet, and the buttons along the bottom of the Design window make it convenient to display the formatted working file as you refine your design. We saved the XML output of the MapForce mapping and assigned it as our working document. When the stylesheet was complete, the HTML Preview in StyleVision was identical to the MapForce HTML Output window shown above. To view the document on a mobile device you can either deploy the HTML as a page on a Web site or email it as an attachment. HTML version of the MapForce mapped data on a mobile device In addition to the stylesheet itself and formatted versions of the working document, StyleVision lets you save generated XSLT files to transform other XML files using your stylesheet design outside the StyleVision application. Automation Next Time In the future when you want to re-run a data mapping and refresh the HTML document with up to date data, there are two ways to automate the process:

  • You can run MapForce from a command line with parameters to name the mapping definition file and even call StyleVision to create formatted output
  • You can generate royalty-free code for the mapping in XSLT, Java, C++, or C# to combine with the XSLT code from StyleVision to build your own end-to-end application

XMLSpy, MapForce, and StyleVision are all available together in the specially priced Altova MissionKit. See for yourself how easy it is to use the MissionKit to convert data from a Web API — download a free 30-day trial!
Editor’s Note: Our original series on mapping data from the Groupon API ran in three parts you can see by clicking the links here: Part 1 of Processing the Groupon API with Altova MapForce describes how to create dynamic input by collecting data from multiple URLs. Processing the Groupon API with MapForce – Part 2 describes how we filtered data from the API and defined the output to extract only the most interesting details. Processing the Groupon API – Part 3 describes formatting the output as a single HTML document optimized for desktop and mobile devices, and reviews ways to automate repeat execution.

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Processing the Groupon API with MapForce – Part 2


In Part 1 of this series we described how to connect Altova MapForce to the Groupon API. We queried the API for a list of Groupon divisions, then used the list to create API queries for all the current deals from every division. In this part, we will execute the /deals queries and filter the response for the most interesting data. The list of /deals queries we built previously looks like this: List of Groupon /deals queries generated by Altova MapForce To process all the queries, we can connect the list as a dynamic file input to a new mapping component. When we needed a new component last time, we dropped an API /divisions query into the mapping, and let MapForce create an XML Schema automatically. We could do the same thing here by dropping in an API /deals query as an XML input file. There’s just one small issue — although the Groupon API online documentation clearly describes the queries we can make, it is vague about the information that will be returned. Before we send dozens of queries to the API for all the current deals, we probably want to know a little more about the data that will come back.

Let’s Make a Deal Like Yogi Berra said, you can observe a lot just by looking. Let’s start by running a /deals query in XMLSpy. That will let us examine the response to a query for one division before we pull in a potentially unwieldy volume of data. The XMLSpy File / Open menu includes the same Switch to URL option we used in MapForce in the earlier post. If we enter the /deals API query for a division that covers a large metro area – say Dallas – we are likely to get enough deals instances to extrapolate the characteristics of the entire data set. XMLSpy opens the response to the /deals API query in Text view just as if we opened a local file: Example from the response to a Groupon /deals query, shown in XMLSpy As expected, we got quite a bit of data when we requested all the deals for a single division! A fast way to analyze the structure of this data is to use the XMLSpy DTD / Schema menu option to generate an .xsd file from the xml. Shown below is a reduced view of the entire generated .xsd file based on the response to the /deals query for Dallas: An xsd file generated by XMLSpy from the Groupon query We can dig even deeper, following Yogi’s advice like déjà vu all over again. Expanding all the elements to review the XML Schema reveals some curious anomalies. For instance, there are two elements named redemptionLocation with different definitions. The first contains a sequence of child elements: First use of the remdemptionLocation element And the second is defined as a simple string: Second use of the remdemptionLocation element Going back into the xml data for Dallas and searching for redemptionLocation displays these examples: One example of redemptionLocation in the body of the response And: One example of redemptionLocation in the body of the response And: One example of redemptionLocation in the body of the response Now this is really interesting, because redemptionLocation = ”online” identifies deals that can be redeemed from anywhere, instead of by a visit to a bricks and mortar location in the division where they are advertised. What if we ran the /deals API queries for all divisions and extracted a list of all the online deals? That would be one extreme Groupon! Only Ask for What You Need The Groupon /deals API query supports an optional parameter called &show= that allows users to limit the data returned. Applying this parameter can save bandwidth and reduce processing time for the data transformation by removing unwanted data from the API response. We can also simplify our final result by including only the most interesting information, including the link to the Groupon web page for each deal. After we remove unwanted elements from the generated Dallas schema, our final version for the summary of online deals looks like this: XMLSpy Schema diagram of the simplified Groupon xsd file When we add the &show= parameter to our MapForce mapping to request only the elements included in the simplified XML Schema, the queries look like this: Modified list of queries with the &show= parameter Now we can drop the revised .xsd file into the mapping and connect the list of API /deals queries as dynamic input. We don’t need to delete the text file we used to collect the list of queries — that might continue to be helpful for future debugging. Mapforce dynamic input file mapping These changes complete the input side of the data mapping. Defining the Data Transformation Output Back in XMLSpy we can make a couple more revisions to the input XML Schema to design a new version for output: XMLSpy schema diagram of the output file xsd We discarded the response element since it doesn’t add any value, and eliminated the redemptionLocation element that we don’t intend to include in the output. We also added a date element for a timestamp, because our output file will be a snapshot of data that is constantly changing. After saving this version of the .xsd file in XMLSpy, we can drop it into the MapForce mapping. Shown below is the output side of the mapping with the output component partially connected. The filter at the top reads the redemptionLocation element to select only online deals and the now function inserts the date: Partial view of the MapForce output file mapping The last revision we made in the output XML Schema was to change several element types from dateTime, Boolean, and integer to the string data type to allow more descriptive text Here is the complete definition of the mapping with the final connections to the output component: Mapforce data mapping for the Groupon API Now for the Payoff When we click the Output button MapForce processes the entire mapping from beginning to end using the MapForce Built-in execution engine. Here’s a breakdown of the steps:

  • Run the /divisions query to get the current list of divisions
  • Concatenate strings to build the list of /deals queries for all divisions
  • Run the /deals queries to create dynamic data for the input component
  • Filter for online deals to generate the output component, execute the remaining mapping functions, and add the timestamp after all the deals are processed

MapForce takes only a few seconds to complete all those steps and generate an output file with a series of deals that look like this: Output data from the MapForce mapping for the Groupon API In part 3 of this series we’ll design a stylesheet to automatically transform the XML output of our mapping into html for attractive presentation in a web browser and on mobile devices. See ya at the ballpark, Yogi! XMLSpy and MapForce are available together in the specially priced Altova MissionKit. See for yourself how easy it is to use the MissionKit to convert data from a Web API — download a free 30-day trial!
Editor’s Note: Our original series on mapping data from the Groupon API ran in three parts you can see by clicking the links here: Part 1 of Processing the Groupon API with Altova MapForce describes how to create dynamic input by collecting data from multiple URLs. Processing the Groupon API with MapForce – Part 2 describes how we filtered data from the API and defined the output to extract only the most interesting details. Processing the Groupon API – Part 3 describes formatting the output as a single HTML document optimized for desktop and mobile devices, and reviews ways to automate repeat execution.

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Processing the Groupon API with Altova MapForce


We often think of a data integration project as a translation from one singular data input file to some other data set, but Altova MapForce lets you greatly expand the concept of an input file. For instance, the MergeMultipleFiles.mfd example installed with MapForce illustrates how you can use a filename with wildcard characters to merge multiple input files into a single output. MapForce MergeMultipleFiles.mfd example A MapForce mapping input doesn’t even need to be a physical file – it can be a URL that returns predictable structured data, like the APIs for popular Web sites like Groupon and many others.

In this blog post we’ll describe how to use Altova tools to retrieve, filter, analyze, and present data available from a Web-based API, using Groupon as an example. If you want to follow along yourself, you will first need to visit http://www.groupon.com/pages/api to request your personal Groupon API client key. The Problem: All Deals Are Local The Groupon Web site and email subscriptions are great for finding deals in your local neighborhood, but what if you’re looking for a deal to use on an upcoming vacation, or for a gift for friends or family across the country? Sure, you could enter each location manually at the Groupon Web page, but that’s so last century. Let’s use the Altova MissionKit to automate things. The Groupon API offers two URL queries that return data in .json or .xml formats: the first returns a list of all Groupon localities (called divisions), and the second returns current deals information for one named division. If we want to see all the deals for more than one division, we need to resolve multiple URLs and aggregate the data into a single result. Yes, MapForce can do that! First We Need a Schema The Groupon API documentation describes the elements that will be returned by our requests, but doesn’t provide an XML Schema. That’s okay, we can use MapForce to generate one. All we have to do is open a new mapping design and choose Insert XML Schema/File, then click the Switch to URL button. Now we can enter the URL to retrieve the Groupon divisions list: Inserting a new component into a MapForce mapping by URL When we click the Open button MapForce offers to generate the schema: MapForce offers to generate an XML Schema When we click Yes, the File / Save dialog opens. I saved the schema as divisions.xsd, and the mapping with the new XML Schema inserted looks like this: Generated .xsd as a new component in a MapForce design And the Properties dialog for the XML Schema component automatically contains the API /divisions URL as the Input XML File: Component properties for the generated .xsd Check the Work We want to filter the Groupon divisions data to build a list of id names to use for deal queries for each locality. But before we go any further, now might be a good time to apply the text file trick from the Quick Solution for Complicated Functions blog post to look at the id values. When we insert the text file and connect the divisions and id schema elements, the mapping looks like this: MapForce design with text file to preview output We connected the division element to Rows in the text file in order to generate a new row in the text file for each unique division, so that Field1 in each row will hold the id. Clicking the Output button now generates this result: MapForce Output window All we need to do is apply the concat string function to build the list of /deal URLs for all division IDs. The next step in the mapping looks like this: Using the MapForce concat function to build a string Rolling the cursor over the constant connected to value1 of the concat function displays its full definition: Definition of a MapForce string constant When we click the Output button to execute the mapping, the Output file now looks like this: MapForce Ouput window As a further review, we can open the generated XML Schema in XMLSpy and display it in graphical Schema View: Altova XMLSpy graphical schema view of the generated .xsd So far we have:

  • built a MapForce mapping that queries the Groupon API for all divisions
  • extracted the division id fields
  • and built a list of URLs for API queries to get the deals in each division

In the next post in this series we will process the list of deal queries as the input for a new mapping component and filter the output for some interesting information. Find out for yourself how easy it is to apply MapForce to convert data from a Web API! Download a free 30-day trial of MapForce.
Editor’s Note: Our original series on mapping data from the Groupon API ran in three parts you can see by clicking the links here: Part 1 of Processing the Groupon API with Altova MapForce describes how to create dynamic input by collecting data from multiple URLs. Processing the Groupon API with MapForce – Part 2 describes how we filtered data from the API and defined the output to extract only the most interesting details. Processing the Groupon API – Part 3 describes formatting the output as a single HTML document optimized for desktop and mobile devices, and reviews ways to automate repeat execution.

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Use Built-In XPath Functions


In developing one of the Altova Online Training courses, I sorted a list of books by the authors. I realized that my author field was a string of the author’s full name, so the books were sorted by the first letter of the string, or the author’s first name. It did not fit into the course to fix the sorting, but you can easily extract the last name from a string and use it for the sorting key using XPath functions. If you then use the books’ titles for a secondary sort key, you run into an issue with titles that start with “A”, “An”, or “The”. I want to use the title for the secondary sort key, but ignore a leading definite or indefinite article.Output the book list with a  sort corrected using XPath expressions Let’s take a look at how we created this XSLT code.

This article was written using XMLSpy as the platform, but the same XPath expressions can be used inside MapForce or StyleVision to achieve similar results. We can start with a simple XML book list. We have 4 books with author and title nodes. List of three books An XSLT to create a list of the books would look like this: Output the book list without a sort This will generate the following output: Unsorted Book List The books are output in the order they appear in the original data file. If we add xsl:sort to the xsl:for-each loop, we can arrange our output in other ways. Output the book list with a basic sort This will generate a sorted list, but not sorted properly. Output from XSL with Basic Sort Sorting author as a string, results in “Jules Verne” appearing ahead of “Mark Twain”. Also, “A Connecticut Yankee in King Arthur’s Court” appears ahead of “Adventures of Huckleberry Finn”. We want to ignore the indefinite article, “A”, so that “Adventures of Huckleberry Finn” appears ahead of “A Connecticut Yankee in King Arthur’s Court”. We can use XPath expressions to extract the sorting keys we want. Output the book list with a  sort corrected using XPath expressions Let’s examine the code before we look at the output. We replace “author” with “reverse(tokenize(author, ‘ ‘))[1]”. Tokenize breaks the author string into tokens using a single white space as the break point. So, “Jules Verne” is tokenized into “Jules” and “Verne”. Reverse reverses the order of the tokens to “Verne” and “Jules”. The one in square brackets chooses the first item in the list, “Verne”. This is the value that is used in for the xsl:sort function to arrange the books. This is not the perfect solution, but it works in our case. The title looks convoluted, but the logic is straightforward. The “tokenize(title,’ ‘)[1]” expression extracts the first word of the title. So, the first if test is “Is the first word of the title the word “A”? “. If it is, then we return the substring of the title that starts with its third letter, thus eliminating “A” and the space. If the first word of the title is not “A”, then we need to test it again to see if the first word of the title is “The”. If it is, we use the substring of the title starting with its fifth character, thus eliminating “The” and a space. If we fail both tests, then we just pass the title along as the sorting key. We could add another test to our code to see if the first word is “An”, but it is not needed for this data set. Executing this last XSLT, we get the following output. Output from XSL with Corrected Sort “Mark Twain” is now ahead of “Jules Verne”. “Adventures of Huckleberry Finn” appears ahead of “The Celebrated Jumping Frog of Calaveras County” and “A Connecticut Yankee in King Arthur’s Court”. The flaw in our approach to the author string is that we want “Jules Verne” to be treated as “Verne, Jules” for the sort, so that if we had a book by “Jimmy Verne”, the sort would treat them as different authors. Our code does not. Using “concat(reverse(tokenize(author, ‘ ‘))[1], reverse(tokenize(author, ‘ ‘))[2])” would sort “Jules Verne” and “Jimmy Verne” correctly, but this solution only will work with 2 word names. If an author had a suffix (“Martin Luther King, Jr.”) or multiple words (“George Herbert Walker Bush”), the code would fail. There are many exceptions to the general rules on alphabetizing names, and the code to allow for all variants goes far beyond the scope of this article. What we wanted to show was the ability to manipulate XML data on the fly using XPath expressions. We do not always have complete control on the format of our data sources, but using the power of XPath expressions, we can transform the data into the format that we need. A copy of the files used in these examples is available here.

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The Maryland Association of Certified Public Accountants (MACPA) transforms data to XBRL in-house


What is XBRL and how can it help your organization? Members of the Maryland Association of CPAs (MACPA) found out how using the interactive XBRL (Extensible Business Reporting Language) format can help not only larger, public companies, but also smaller, non-profit organizations like themselves.clip_image004 MACPA invested in the Altova MissionKit tool suite to support their XBRL project. Using our XMLSpy XML editor; MapForce, our graphical data mapping, conversion, and integration tool; and the StyleVision visual stylesheet and report design tool, MACPA was able develop a comprehensive system that employs XBRL data for a variety of reporting functions, both internal and external.
For example, MACPA used the generated instance document from MapForce to populate their financial Key Performance Indicator (KPI) system, significantly reducing the amount of time and effort required to prepare the KPI documentation. XMLSpy was used to extend the US-GAAP taxonomy to accommodate entries specific to MACPA. clip_image002 MapForce also came in handy for mapping the Global Ledger (GL) Taxonomy to the extended GAAP taxonomy. clip_image004 As a result, MACPA has increased its working knowledge of XBRL, automated previously burdensome data collection and transformation tasks, and have gained more insight into their financial data. To read more about how MACPA utilized the Altova MissionKit to convert all their financial data to XBRL and create a model for public and private business of any size to leverage the powers of XBRL, the latest case study from Altova is a must read! Do you have a story to tell about your use of Altova tools? If so, we want to hear from you. Case studies generate great publicity. Check out recent press coverage from the MACPA case study. Plus, if we choose to use your story you will receive a $200 Amazon gift card!

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Hot off the Press!


The industry is abuzz with the latest news announcing our release of the MissionKit Version 2011 Release 2. The release is loaded with new features for chart and report creation, enhanced data mapping capabilities, new XML Schema editing functionality, support for the latest version of BPMN, and a really cool new feature for comparing and merging Microsoft® Word documents. clip_image002 Dr Dobb’s and SQL Server magazine are just a few of the industry publications and blogs that covered the launch. clip_image004   clip_image003 Read what the industry is buzzing about and then download a free 30-day trial of the MissionKit and check out for yourself all the powerful new features now available in our suite of XML, database, and UML tools!

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Using Charts to Effectively Communicate Data


Altova first added support for charts and reporting the Altova MissionKit with the launch of Version 2011 last September. The v2011 reporting functionality includes options for line charts, 2D and 3D bar charts, 2D and 3D pie charts, round gauge and bar gauge charts. Here are a few examples: Charts created with the Altova MissionKit v2011

Advanced chart features in v2011r2

Version 2011 Release 2 of the Altova MissionKit, introduced on February 16, adds an exciting group of enhancements to the chart and reporting features in XMLSpy, StyleVision, and DatabaseSpy. The chart design options and user interface work the same way in all three applications, so MissionKit users can work intuitively and productively as they move from processing XML data in XMLSpy, to preparing charts for a business intelligence report with StyleVision, and even when they create graphical displays directly from SQL query results in DatabaseSpy. The wide range of new customizable charting features introduced in version 2011 release 2 includes:

  • Stacked Bar charts
  • Area charts
  • Stacked Area charts
  • Candlestick charts
  • Chart overlays
  • Background images and color gradients
  • Ability to change position of axis labels
  • And more!

Now you can create attractive and informative charts to represent a wide variety of data sets without exporting data to a dedicated charting application. Charts created using the Altova MissionKit are not limited to any specific presentation technology – for instance you can use StyleVision to include charts in HTML, Microsoft Word, RTF, or PDF documents, or you can save charts created in DatabaseSpy in a variety of image formats at the custom resolution you specify. In this post we will show some examples of the new charts and features available in all three MissionKit reporting and charting applications – XMLSpy, StyleVision, and DatabaseSpy.

Stacked bar charts

Stacked bar charts are a variation on bar chart presentation and are especially useful when multiple ranges of data need to be illustrated. Stacked bar charts are also useful to more clearly illustrate data in a smaller area. The image below shows a stacked bar chart to illustrate the performance of a sales team by region over two years Stacked bar chart Note that the combined height of each stack in the Stacked Bar Chart represents the total sales over the two-year period for each Territory, since the sales for Last Year are added above the Year To Date numbers. Stacked bar charts complement regular bar charts and 3-D bar charts to offer users the greatest flexibility in illustrating SQL query results. If the user prefers horizontal bars, a checkbox labeled Draw X and Y exchanged in the Change Appearance tab selects that orientation. Chart orientation option Horiztonal stacked bar chart This orientation option is also available for other 2-D bar charts, line charts, area charts, and candlestick charts.

Area charts

Area charts are similar to line charts, with shading applied to make a more graphically appealing display. The area chart below shows a record of temperature and humidity changes by hour over the course of one day. Creative application of color can emphasize the point! Area chart To successfully build an area chart, the analyst must consider the values in each data category. As the area chart is constructed, each category forms an opaque layer on top of the layers for data retrieved previously. In the case illustrated above, Temperature was always a larger number than Humidity, so a SQL query was constructed in DatabaseSpy to retrieve the Temperature value before Humidity to prevent Temperature from acting like a curtain to hide the Humidity data. However, if the data columns appear in a sequence with values in increasing order, the last layer would overlap and hide all the preceding layers. In that case, the chart tab heading titled Select Data lets the user add and delete columns from the results to re-sequence the data correctly. The Select Data column also lets the user edit the names assigned to each column on the X-axis label. Select Data dialog As alternative solution, the Transparency option in the Change Appearance tab lets the user adjust color levels to allow hidden layers to show through.

Transparency dialog

Stacked area charts

As implied by their name, Stacked Area charts layer the columns of a data set to illustrate the overall sum of a data series. Stacked Area charts also eliminate the potential overlapping data problem that can occur with regular area charts. The chart below shows a table of air passenger revenue miles traveled by month, with individual regions for domestic and international travel. Stacked area chart The Stacked Area chart creates a graphical representation of the total of Domestic and International miles, even though the total miles value was not part of the provided data. This is apparent at the top of the January entry, where the International region intersects the Y axis just below 600 (the original data showed 392 million Domestic miles and 181 million International miles, for a total of 573). A strategic data analyst will always consider the nature of the data to be reported when choosing any particular chart type. For instance in the weather example we used above, adding temperature and humidity values in a stacked bar chart would not be logical!

Candlestick charts

Candlestick charts were originally developed by a wealthy Japanese businessman who began trading at the local rice exchange around the year 1750. He kept records of the local market psychology, learning to boost his profits by carefully monitoring prices and not rushing into trades. Today, charts are used to represent financial data such as stock prices over a period of time. Every day the market is open, each stock has four relevant data points that can be rendered in a candlestick chart: the price at market opening, the price when the market closed, the high price during the day, and the low price during the day. Investors and financial analysts like to view these indicators to gauge the stock’s performance over a period of time. In the candlestick chart below, each solid bar represents the range between the opening and closing price and the thin vertical line through each bar shows the extent of the high and low prices for the day. Candlestick chart In this version of the chart, following common convention, the color of each bar signals whether the stock was up or down for the day. If the bar is green, the stock was up for the day– it opened at the price indicated by the bottom of the bar and closed at the price indicated by the top. If the stock was down for the day, the bar is red and the symbolism is reversed – the stock opened at the price indicated at the top of the bar and closed at the price shown by the bottom. Numerous options are available to set line and fill colors, the Y-axis range and values, and more. Because they were intended to be printed in black and white, the original candlestick charts used empty bars to indicate the price increased and solid bars to indicate price decreases. The Altova MissionKit offers this option: Candlestick chart in black and white Another candlestick chart variation omits the opening price and simply illustrates the range by a vertical line and the closing price by a horizontal line. This option is automatically supported when a data set only includes the high, low, and closing prices. Candlestick chart without opening price

Chart overlays

The Overlays feature lets you combine multiple charts in a single image. Each overlay chart has unique settings and can even be generated from a separate data file. The image below shows a candlestick chart of a stock’s daily prices with the daily sales volume in a bar chart overlay. Candlestick chart with bar chart overlay

Support for background images & color gradients

The ability to specify background color gradients and background images gives you even more flexibility for creating customized, eye-catching charts. Overlaying one chart on another lets you visualize multiple data sets with different Y-axes and types. Area chart with a background image The Change Appearance dialog lets users select a background image, as in the Winter Games chart above, or apply a background color gradient, as in the Summer 2010 chart below. Change Appearance dialog Bar chart with a line chart overlay and background color gradient If you’d like to see for yourself how easy it is to use Altova tools to create attractive charts from XML and database data, download a free trial of the Altova MissionKit.

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