Opciones Binarias Login.
The OMX Stockholm 30 Index consists of the 30 most actively traded stocks on the Stockholm Stock Exchange and is a market weighted price index. The composition . topix. 55% of ETF purchases are the Nikkei stocks versus only 41% for the.. Some Background Like the Dow Jones Industrial Average, the Nikkei Stock Average is a price-weighted equity index.
We've sent an email to Please follow the instructions to reset your password. If is associated with an Alamy account you'll receive an email with instructions on how to reset your password. Sell images Our Blog. Please enter your password Forgotten your password? Continue Cancel Send email OK. Page 1 of Checking stock market indices on a smartphone Dow Jones market index, share price, 50 and dollar bills 3d render closeup of computer keyboard with NASDAQ index button.
Stock market indexes concept. Stock market index on the lcd monitor group of many Euro banknotes laying on table Using iPhone smartphone to display stock market performance chart for Dubai Financial Market General index Charts, rate tables and calculator as a symbol of the development of the financial market.
Stock market exchange ftse stocks share index top deal deals insider trading hedge funds isolated cut out cutout white Financial Business headline in newsprint. The greed or fear spreads across investors quickly.
This is known as the herding effect, and often leads to a fast movement of stock prices. During such market regimes, stock prices change at a super-exponential rate and are normally followed by a trend reversal that corrects the previous overreaction. In this paper, we construct an indicator to measure the magnitude of the super-exponential growth of stock prices, by measuring the degree of the price network, generated from the price time series.
Twelve major international stock indices have been investigated. Error diagram tests show that this new indicator has strong predictive power for financial extremes, both peaks and troughs. By varying the parameters used to construct the error diagram, we show the predictive power is very robust.
The new indicator has a better performance than the LPPL pattern recognition indicator. Many models are designed to describe the dynamics of financial bubbles and crashes, among which the log-periodic power law LPPL also known as the Johansen-Ledoit-Sornette JLS model [ 1 — 4 ] stating that bubbles are generated by behaviors of investors and traders that create positive feedback in the valuation of assets.
Therefore, bubbles are not characterized by an exponential increase of prices but rather by a faster-than-exponential growth of prices. This unsustainable growth pace often ends with a finite-time singularity at some future time t c.
After t c , the old regime with a faster-than-exponential growth rate is broken, and often but not always, the new regime is a dramatic fall over a short period, a financial crash. They found that positive feedback mechanisms also exist in negative bubbles, which are in general associated with large rebounds or rallies.
Therefore, before financial extremes, like crashes and rebounds, a super-exponential growth in price is often observed. Measuring super-exponential growth in financial time series can be used to forecast financial extremes. Empirical observations of this super-exponential growth before crashes and rebounds in order to predict the turning points have been presented in many papers. For example, the — oil bubble [ 6 ], the Chinese index bubble in [ 7 ], the real estate market in Las Vegas [ 8 ], the U.
In , Lacasa et. The constructed network inherits many properties of the time series in its structure. For example, fractal time series are converted into scale-free networks, which confirm that power law degree distributions are related to fractality. This method has been applied to the financial time series, [ 16 ] as well as the energy dissipation process in turbulence [ 17 ]. In this paper, we combine the two powerful tools above and construct an indicator to measure the magnitude of the super-exponential growth of the stock price, by measuring the degree of the price network, generated from the price time series.
By investigating twelve major international stock indices, we show that our indicator has strong power to forecast financial extremes. The detailed construction method is as follows: Then if all the data points between them are below the line which connect these two points, make a link between these two points. In other words, there is a link between two points t i , y i and t j , y j , if and only if:. The intuition of this algorithm is simple.
Consider each point of the time series as a wall located at the x-axis value t and with the height of the wall is equal to the y-axis value y. Assume someone standing on the top of the wall at t i , y i , if she can see the top of the wall at t j , y j , then we make a link between these two points.
Of course, the height of this person is ignored here. In this paper, we extend the above construction to a new algorithm which can be called the absolute invisibility algorithm. This is just the opposite of the visibility algorithm. Then, if all the data points between these two points are above the line which connect them, make a link between these two points:. Fig 1 shows the network constructed by the visibility algorithm, while Fig 2 shows the network built by the absolute invisibility algorithm.
Note that the non-trading dates have been removed from the figures. Note that the non-trading dates have been removed from the figure. Then, we will explain how to use these two powerful tools to predict financial extremes. The LPPL model claims that during the bubble or negative bubble regime, the stock price grows or declines at a super-exponential rate, i.
The trick here is to use log-prices instead of normal prices. We know that an exponentially growing time series in a logarithmic-linear scale is represented as a straight line.
Therefore, a super-exponentially increasing time series in a log-linear scale is represented as a convex line with a positive slope.
In contrast, a super-exponentially decreasing time series in a log-linear scale is represented as a concave line with a negative slope.
In more detail, suppose that an observer stands on the point t i , y i in a log-linear scaled wall array, if he or she has the visibility of another point t j , y j from here, we can roughly conclude that the growth rate between these two points is super-exponential.
The more points that t i , y i can see, the higher the confidence that the time series grows in a super-exponential rate at t i , y i. The number of the points that t i , y i can see is exactly the degree of the visibility network at point t i , y i! Based on the LPPL model, herding effect and imitation behavior generates super-exponential growth in stock market prices.
The fast growth makes the financial system unstable and eventually leads to a sudden regime change, which is often, but not always, a crash in bubble regimes and rebound in negative bubble regimes. In consequence, the confidence of the super-exponential growth rate should be a good proxy to measure how close it is to the point of the regime shift—the financial extremes.
We still need three more criteria:. We build the financial extreme indicators based on the above discussion. We choose twelve international stock indices over 21 years, between Jul. During this period, there are trading days note this as T. In this paper, the look-back scope S is set to be , since normally one calendar year has about trading days. Fig 3 shows the peak and trough indicators of HSI between Jul.
Thus in most cases, using the copy constructor of the settings class might be appropriate, so in order to use a special font for drawing a legend, for example, you would just write:.
We will continue with more examples and more detailed information about all those points in the next sections and chapters. This will give us an overview about how to proceed in both cases. We recommend you read, compile and run the following example.
Of course, it is possible to add new elements like Title, Headers, Footers, Legends, or Axes to this simple widget as we will see later in greater detail. See below for the resulting chart widget created by this example. We can of course add more elements to this chart and change its default attributes as described above.
We will see in more detail how to configure those attributes Pen, Color, etc In the next chapter, we will describe the different available chart types diagrams and their coordinate planes. For each chart type, we will look at the attributes available for this particular type, and give you a few examples. A Cartesian coordinate plane, determined by a horizontal and a vertical axis, often called the x axis and y axis. A Polar coordinate plane which makes use of the radius and the polar angle which defines the position of a point on a plane.
This chapter tells you how to change the chart type from the default to any one of the other types. It will also give us an idea about which chart type could be appropriate for a specific purpose, and provides information about the features that are available for each type of chart.
Let us first go through some important concept concerning the planes and their relation to the diagrams and the chart view itself. Each coordinate plane can have one or more diagram associated to it. This makes it possible to have multiple diagrams using different scales and displayed next to, or on top of each other in the same chart.
There are two ways in which planes can be caused to interact in where they are positionned layouting wise: The first is the reference plane. Also when planes share an axis they will be layed out in relation to each other as suggested by the position of the axis.
If, for example Plane1 and Plane2 share an axis at position Left, that will result in the layout: Axis Plane1 Plane 2, vertically. If Plane1 also happens to be Plane2's reference plane, both planes are drawn over each other. The reference plane concept allows two planes to share the same space even if none has axis, and in case there are shared axis, it is used to decide whether the planes should be painted on top of each other or layed out vertically or horizontally next to each other.
In this section, we will describe and present all of the chart types using the default Cartesian coordinate plane. In general, in order to implement a particular type of chart, just create an object of this type by callingKDChart:: Bar charts are the most common type of charts and can be used for visualizing almost any kind of data. Like the Line Charts, the bar charts can be the ideal choice to compare multiple series of data.
A good example for using a bar chart would be a comparison of the sales figures in different departments. Your Bar Chart can be configured with the following sub- types as described in detail in the following sections:.
In a normal bar chart, each individual value is displayed as a bar by itself. This flexibility allows you to compare both the values in one series, and values of different series. By default, a normal bar chart is displayed. Stacked bar charts focus on comparing the sums of the individual values in each data series, but also show how much each individual value contributes to its sum.
Unlike Stacked charts Percent bar charts are not suitable for comparing the sums of the data series, but they rather focus on the respective contributions of their individual values. In this example, we show you how to configure your bar diagram and change its attributes when working with aKDChart:: We want to change the default line chart type to a bar chart type. In this case, we also want to display it in stacked mode. The default type being Normal type for the widget, we need to implicitely pass the second parameter when callingKDChart:: In the above code, our intention is to draw a gray line around the bars to make them look nicer.
This is referred to as configuring the attributes in a diagram. To do so, we configure a QPen and then assign it to our diagram. We want our bar chart to be displayed in 3D mode and need to configure some ThreeDBarAttributes and assign them to our diagram. Here we are configuring the depth of the 3D bars and enable 3D mode.
Both of those attributes are made available at different levels in order to provide a better attribute structure. By "Bars Attributes" we are talking about all parameters that can be configured and set by the user and which are specifics to the Bar Chart type. The "getters" and "setters" for those attributes can be consulted by looking at the KDChartBarAttributes API Reference to get an idea about what can be configured there.
The procedure is straight forward for both cases. Let us discuss the type specifics attributes first:. All the attributes can be configured to be applied for the whole diagram, for a column, or at a specified index QModelIndex. Each of those attributes can be set and retrieved in the way we describe in our example below:.
Specify whether the arrows showing excess values should be drawn solidly or split. Let us make this more clear by looking at the following sample code that describes the above process. Those attributes are of course used by all types of charts and are not specific to the Bar diagrams. BarDiagramand assing the model to our diagram. The procedure is of course similar for all types of diagrams. We want to configure our bars width so that they get displayed a bit larger.
The Width of a bar is calculated automatically depending on the gaps between each bar and the gaps between groups of bars as well as the space available horizontally in the plane. So those values interact with each other so that your bars does not exceed the plane surface horizontally. Here we are increasing the value of my bars width and at the same time set some lower values for the gaps.
Which will give us larger bars. This can be done for the whole diagram, at a specific index or for a column. BarDiagram API Reference and look at the methods available there to find out those setters and getters. Those attributes are not specific to the Bar Chart types but can be used by any type of charts. The procedure is very similar. TextAttributes, but we wanted to do so in order to demonstrate this feature too.
After it is configured as we want it, we just have to assign it to the diagram as with all other attributes. Finally I want to paint a line around one of the datasets bars in order to bring the attention of the viewer to this specific set of data. To do so I need to change the default pen used by my bars for this data set exclusively.
Of course we could also have changed the pen for all datasets or for a specific index or value. Those methods are of course used by all types of diagram and their configuration is very simple and straight forward as you can see in the above sample code.
Our attribute having been configured and assigned we just need to assign the Bar diagram to our chart and conclude the implementation. The above procedure can be applied to any of the supported attributes relative to the chart types. The resulting display of the code we have gone through can be seen in the following screen-shot. ThreeDAttributes for the different chart types are implemented as its own class, the same way as for the other attributes.
We will study the code and display a screen-shot showing the resulting widget. To do so we will need to use several types of attributes. Generics one available to all chart types e. TableModel uses a simple rectangular vector of vectors to represent a data table that can be displayed in regular Qt views.
This allows to prepare test data using spreadsheet software. It expects the CSV files in the subfolder. If the application is started from another location, it will ask for the location of the model data files. Let us cnow oncentrate on our Bar chart implementation and consult the following files: The purpose is to let the user configure its bar chart attributes manually.
First of all we are adding our chart to the layout just like any other Qt widget. Then we load the data to be display into our model, and assign the model to our bar diagram. We also want to configure a Pen and surround the displayed bars by a darkGray line to make it somewhat nicer. Finally we assign the diagram to our chart. The user should be able to change the default sub-type via a combo box from the GUI.
This can be done by usingKDChart:: We set the DataValueAttributes on a per-column basis here, because we want the text to be printed in different colors - according to its respective dataset's color. The user will be able to display or hide the values. Also we allow the user to display or not display the text dynamically.
Here we are enabling or disabling the 3D look, and we adjust the depth of the bars according to user settings. ThreeDBarAttributes are as simple to use as all other Attributes types. Which means that once you have set the attributes for a column or a cell, you will not be able to change those settings by calling the "global" method to reset it to another value, but instead call the per column or per index setter.
As demonstrated in the above code. BarAttributes, the Bar Width, for the user to be able to change the width of the bars dynamically increasing or decreasing its value via the Gui.
The Bar Width value being passed by the value of a Spin Box. Line charts usually show numerical values and their development over time.
Like the Bar Charts they can be used to compare multiple series of data. An example might be the development of stock values over a longer period of time or the water level rise on several gauges. Normal line charts are the most common type of line charts and are used when the datasets are compared to each other individually.
For example, if you want to visualize the development of sales figures over time for each department separately, you might have one line per department. Stacked line charts allow you to compare the development of a series of values summarized over all datasets. You could use this if you are only interested in the development of total sales figures in your company, but have the data split up by department.
Percent line charts show how much each value contributes to the total sum, similar to percent bar charts. We will describe it more in details in the "Line Attributes" section further on. We dont need to change the default chart type as Line Charts is the default.
In the above code our intention is to draw a new style of line for this specific dataset in order to draw attention to it. To do so we configure a QPen and then assign it to our diagram.
As you can see it is very simple to assign a new pen to our diagram by calling the diagramKDChart:: Finally we conclude our small example:. There are only a few attributes specific to a line chart as it is using a Pen to draw the lines. MarkerAttributeslet us display the line chart subtypes as described above as well as Area Charts and Point charts the easy way.
We will of course present all those alternatives with some sample code and ready to use examples in the next sections. Each of those attributes can be set and retrieved the way we describe it in our example below:. Let us look at the following sample code that describes the above process.
Those attributes are of course used by all types of charts and are not specific to the Line diagram. After having stored our data in the model, we create a diagram. As always, we need to assign the model to our diagram. This procedure is of course similar for all types of diagrams. We are now ready to configure our attributes. We want to display the data values and configure their text and font. The second step is to set it up with our own values and finally we assign it to our diagram.
After having configured our attributes we need to assign the attributes to the diagram. Look at the attributes interface and look at the methods available there to find out those setters and getters. We want to configure the Pen in order to draw a section of a line dataset differently. We want to focus the attention of the reader on this particular section. Of course we could also have changed the pen for a single or all datasets as well.
See how we call the pen for this very dataset before changing its style and width. This is done to keep its original color for consistancy. Those methods are of course used by all types of diagrams and their configuration is very simple and straight forward as you can see in the above sample code. Our attribute having been configured and assigned we just need to assign our line diagram to our chart and conclude the implementation.
The above procedure can be applied to any of the supported attributes for all chart types. ThreeDAttibutes for the different chart types are implemented in their own class, the same way as for the other attributes. This allows for preparation of test data using spreadsheet software. Let us concentrate on our Line chart implementation for now and consult the following files: The purpose is to let the user configure its line chart attributes manually.
First of all we are adding our chart to the layout as with any other Qt widget. Load the data to be display into our model, and assign the model to our line diagram.
We want the user to be able to display or hide the data values from the GUI, and also change the default font for our data value texts to make it nicer. In the code above, we make sure our data value texts will be painted using the dataset default color by retrieving the brush for each dataset and assigning the color of the brush to the pen.
This is implemented by configuring our line attributes and assign them by dataset to the diagram, as shown above. The same procedure is used for us to be able to run our animation. The widget displayed by the above code is shown in the figure below. The following sections about Point charts and Area are tightly related to line charts. Point charts are line diagrams with Markers lines themselves are not painted. Area charts are also line charts with the area below the lines, filled by the respective dataset's color.
Point charts often are used to visualize a large amount of data in one or several datasets. A well known point chart example is the historical first Herzsprung-Russel diagram from where circles represented stars with directly measured parallaxes and crosses were used for guessed values of stars from star clusters similar to the following simple chart.
The resulting display is obtained by painting markers instead of lines as we will see in the following code sample. The following code sample is going through the process described above to obtain a very simple point chart.
The purpose is to let the user configure its line chart attributes manually from the GUI. Here we will not comment on the code in detail as it is similar to what we have seen before in our line chart example, but only pick out the interesting parts of it. We need to retrieve the pen color before resetting it to its original value, and do that by looping through the datasets.
For two-dimensional data you would use the same technique as described above, but applying it to theKDChart:: Even more than a Line Chart of which they are attributes an area chart can give a good visual impression of different datasets and their relation to each other.
For example this chart type might be ideal for showing how several sources contributed to increasing ozone values in a conurbation during a summer's months. Area charts are Line Charts and thus based upon several points which are connected by lines—the difference compared to the line chart is that the area below a line is filled by the respective dataset's color.
This gives a clear indication of each dataset's relative values. The following types can be displayed very simply in Area mode:. In this chapter it stands for a special chart type or even more accurately as a line diagram attribute. This varying usage of the word "area" should Not cause a lack of clarity: Let us make this more concrete by looking at the following lines of code and reproduce the process described above:.
Of course Brush and Pen settings as well as all other configurable attributes accessible by the diagram itself can be set, which give the user a lot of flexibility display or hide data values, markers, lines, configure colors etc There are no specific attributes related to the Area chart. As explained above Area charts display mode is implemented as a Line Attribute. Of course the generic attributes common to all chart types are available, which give us full flexibility to configure our Area chart.
You dont need to go through it, if you already have studied the section above. It is derived fromQAbstractTableModel. First of all we are adding our chart to the layout as we would do with any other Qt widget. We then load the data to be display into our model, and assign the model to our line diagram. This is implemented by configuring our line attributes and assigning them by dataset to the diagram, as shown above.
Plotter charts are almost the same as normal line diagrams with one important exception: Line diagrams always expect the values running from See the example below to find out how to use this.
A Levey-Jennings chart is a graph on which quality control data is plotted to give a visual indication as to whether a laboratory test is working well or not. In this section we will describe and present each of the chart types which uses the Polar coordinate plane.
Pie charts can be used to visualize the relative values of a few data cells typically values. Larger amounts of items can be hard to distinguish in a pie chart, so a Percent Bar Chart might fit your needs better. Pie charts are most suitable if one of the data elements covers at least one forth, preferably even more of the total area.
Pie charts typically consist of two or more pieces any number of which can be shown 'exploded' shifted away from the center at different amounts, the starting position of the first pie can be specified and your pie chart can be drawn in three-D look.
Here we are configuring the pen "attribute". As you can see it is straight forward. By "Pie attributes" we are talking about all parameters that can be configured and set by the user and which are specific to the Pie Chart type.
The explode factor is a qreal between 0 and 1, it is interpreted as a percentage of the total available radius. Set the starting angle for the first dataset. Can only be specified for the whole diagram. This is a convenience function: Those attributes are specific to the Pie types. After having stored our data into the model, we create a need to replace the default Cartesian plane against a Polar plane before creating our Pie diagram. As always we need to assign the model to our diagram.
We want to explode a section and configure a Pen to surround it. In the above code we explode the second slice dataset in our Pie. We want to configure the Pen in order to draw a surrounding line around the exploded section dataset to focus the attention of the reader on this particular section. Once our attributes having been configured and assigned, we just need to assign our Pie diagram to our chart and conclude the implementation.
Let us concentrate on our Pie chart implementation for now and consult the following files: Before displaying our Pie diagram we need to implicitely replace the default cartesian plane by aKDChart:: First of all we are adding our chart to the layout as we would any other Qt widget. Load the data to be display into our model, and assign the model to our pie diagram. The user should be able to change the start position from the GUI.
This can be implemented by usingKDChart:: We want the user to be able to shift between 3D mode display or the normal standard display from the GUI. We want the user to be able to explode one or several slice s dataset and to configure the exploding factor. This is implemented by configuring our pie attributes and assign them by dataset to the diagram, as shown above. Polar charts get their name from displaying "polar coordinates" instead of Cartesian coordinates, thus they are using theKDChart:: If set the labels' base lines will be adjusted to the circular grid lines.
These additional example files are demonstrating the methods described above: Currently only normalized polar charts can be shown: While this is ideal for some situations it is not possible to display true world map data like this since you can not specify each cell's rotation angle.
Transforming your coordinates to the Cartesian system and using a Point Chart might be a solution in such cases. This is the classKDChart:: The idea of ternary charts is to plot triple values on a triangle. Triple values are represented by three floating point values having the fixed sum 1. Therefore each plotted dataset needs three columns in the model. If the sum of the first two columns is already greater than 1.
For our diagram to be useful we need to be able to display its axis. That will be the subject of our next chapter. Labels specified via setLabels take precedence: By passing an empty QStringList you can reset the default behaviour.
Axis values and labels text attributes can also be configured. Thus the labels of all of your axes in all of your diagrams within that Chart will be drawn in same font size, by default.
The setters and getters for axis labels and their text attributes are implemented in the axis base classKDChart:: As an example you could specify the seven days of the week as abscissa labels, which would be repeatedly used then. It allows the user to set and retrieve the position top, bottom, left or right , or the type abscissa, ordinate of the axis, assign or retrieve a title and its text attributes.
That is where the axis are painted. Since ternary diagrams are not rectangular but triangular, ternary axes can be added at three different positions relative to the diagram: South, East and West.
The diagram takes ownership of the axis and will delete it by itself. To gain back ownership e. The diagram no longer owns the axis, so it is the caller's responsibility to delete the axis. We also use our own labels and their shortened version. First of all we are adding our chart to the layout as for any other Qt widget. Load the data to be displayed into our model, and assign the model to our diagram. We want to display three axis, respectively positioned at the top, left and bottom side of our diagram.
This is straight forward:. The process is the same as for configuring any type of attributes, as follows:. We want our top and bottom axis to display different types of labels as well as to make sure those labels will be shortened in case the normal labels are too long see setShortLabels.
We recommend checkig it out. Legends can be drawn for all kind of diagrams and are drawn at the chart level in relation to diagram level. We can have more than one legend per chart and add it to our chart or our widget view by using respectivelyKDChart:: If we want to, we can embedd the legend into your own widget, or into another part of a bigger grid, into which we might have inserted the chart.
The chart takes ownership of the legend and will take care of removing it by itself. The old legend will be deleted automatically. If its parameter is omitted, the very first legend will be replaced.
In case, there was no legend yet, the new legend will just be added. If you want to re-use the old legend, call takeLegend and addLegend, instead of using replaceLegend. The chart no longer owns the legend, it is the caller's responsibility to delete the legend. Specifies or retrieve the reference area for font size of title text and for font size of the item texts. Using this class you can specify one of nine pre-defined, logical points , in a similar way, as you would use a compass to navigate on a map.
Through the following code we demonstrate how to add and position a Legend to your chart Widget using a KDChart:: We will study the code and display a screenshot showing the resulting widget. Before we go through this example, let us study a very simple chart implementation with its legend by looking at the following line of codes which we will comment.
First and as we always do, we set up a model, declare our diagram, and assign the model to it and the diagram to our chart after having included the relevant header files.
We will set the legend position as well as its background and frame attributes and include those header files on this purpose. That will allow us to make use of the methods available in those classes.
We recommend you consult its API Reference. Markers are assigned per dataset as you can see above. Each dataset can be assigned its own text. We want to change their pen color for demonstrating this feature and also to make our legend nicer. We proceed as follow and configure their text attributes. Text attributes configuration and assignment is done as for all other types of attribute.
Create a text attribute object, configure it and assign it. In this case we assign it to our legend by using its methodKDChart:: See the following code sample that demonstrate that.
As for all attribute settings, the code is straight forward. Just create the attribute object, configure it and assign it. Same procedure as above. Headers and footers can be added and configured in several ways.
That will be the subject of this section where we will go through the main features and methods available. The chart takes ownership and will take care of removing it by itself. The new header or footer to be used instead of the old one must not be zero.
Otherwise the method will just do nothing. The second parameter of this method is the header or footer to be removed by the new one. This header or footer will be deleted automatically. If the parameter is omitted, the very first header or footer will be replaced.
In case, there was no header and no footer yet, the new header or footer will just be added. If you want to re-use the old header or footer, call takeHeaderFooter and addHeaderFooter, instead of using replaceHeaderFooter. The chart no longer owns the header or footer, it is the caller's responsibility to delete it. Allow the user to define or retrieve the header or footer position using compass terminology. Text and text attributes can of course also be configured as we will see in the following examples.
Using this class you can specify one of nine pre-defined, logical points in a similar way, as you would use a compass to navigate on a map. Through the following code, we demonstrate how to add and position a header and a footer to a chart Widget using aKDChart:: Before we go through this example, let us study a very simple chart implementation with a configured header by looking at the following lines of code which we will comment.
First, and as we always do, we set up a model, declare our diagram, and assign the model to it and the diagram to our chart after having included the relevant header files.
We will configure the header position as well as its text, background and frame attributes and include the header files related to those attributes on this purpose. That will allow us to make use of the methods available in these classes.
The code above handles the attributes specific to headers and footers. Let us configure the header text attributes and make sure the font will be resized together with the widget in case the user resizes it.
Our header text is now displayed using a blue pen, the fonts are configured to take a relative size. As for all types of attributes we just need to create the attribute object, configure it and assign it to our header. In the code above we assign a pen to the frame attributes in order to get a Gray line around the frame.
We recommend you compile and run the above example. It is available at the following location: All of the above operations should be available to the user from the GUI and performed dynamically.
Customizing your chart means configuring the attributes available for the different components of a chart e. In this chapter we will go through the details of the attributes related to the elements of a chart and also the ones common to all types of charts.
First, and as we always do, we set up a model, declare our diagram, and assign the model to it and the diagram to our chart after having included the relevant header files. We want to display the data values and configure their text and font.
Unlike Stacked charts Percent bar charts are not suitable for comparing the sums of the data series, but they rather focus on the respective contributions of their individual values. Each of those attributes can be set and retrieved in the way we describe in our example below:.