Terms Beginning With 'x'

XML (Extensible Markup Language)

  • January 07, 2020
  • Team Kalkine

What is XML (Extensible Markup Language)?

XML (Extensible Markup Language) is a text-based markup language. It is derived from SGML or Standard Generalized Markup Language. XML tags, unlike HTML tags, detect the data and are used for storing and managing data.          

What are the characteristics of XML?

  • XML is extensible. It means that there is a possibility that you can build your self-descriptive tags/languages that fits your application.
  • XML allows the user to store data irrespective of the way it is presented. This is one of the advantages XML has over HTML.
  • World Wide Web Consortium developed XML and is available as an open standard.
  • XML is used to store & transport data.
  • XML tags are self-descriptive.
  • The markup language is used to carry data. In HTML, we display data in a presentable way. Through XML, we can carry this data from one application to another.
  • XML tags are self-defined.
  • The language is platform and language independent. It means that whatever application you use like Java or through an oracle, there would be no impact on the XML page.
  • Facilitates easy communication between two platforms.

What are the Advantages of XML?

  • Separate data from HTML (HyperText Markup Language)
  • Simplifies data sharing
  • Increases data availability
  • XML simplifies platform change. For example, if you have a data on SQL server and want to import to oracle server, then it can be made possible through XML.

An Example to explain XML

A simple code to represent the above hierarchical structure in XML:

//*{mandatory line to start xml and is called declaration}//*

<?xml version= ”1.0” encoding = “ ISO-8859-1” ?>  

<college>

<class 1>

<name> ABC</name>

<roll> 01 </roll>

</class 1>

<class 2>

<name> DEF </name>

<roll> 02 </roll>

</college>

In this code, we have created our own tags like <college> <class> <name> and <roll>.  As highlighted in the diagram as well, there is a root element, and while writing an XML code, it is mandatory to have a root element.

It should be noted that the tags used in the code are case sensitive. Hence, the opening and closing tags should be in the same case (upper or lower). Also, XML is dynamic in nature.

How to Import an XML file into Excel?

One can import XML files in an Excel workbook to make them more readable for humans. The BI tool, Power Query makes it easy to import an XML file and transform it as per the user’s requirements.

Suppose you save the XML file mentioned above on your system as student.xml. To import the file, follow the steps mentioned below:

  1. Open excel and go to Data tab in the ribbon
  2. Click get data
  3. Select from the file
  4. Select From XML
  5. A window will open. Go to the folder where the file is saved and then click the button “Import”.
  6. In the next step, you would see that a navigator window open, and we can see a preview of data from the XML file in a table format.

What is an XML map?

XML maps are ways by which MS Excel (Excel) represents XML schemas within a workbook.  Excel uses maps using binding data from the XML file to cells and ranges on the worksheet. Through XML maps it is possible to export data from excel to XML. If there exists an XML map on the worksheet, the user can import data into map ant time.

XML schemas describe the elements used in the XML document and can be used by the programmers to verify each item in the document. It defines an element, attributes, and data types. Through XML map, XML schema gets copied to the workbook to create map instead of referencing the schema as an external file.

Using XML Maps, you can add and delete maps.

Once the XML file and schema are imported in the Excel file, the user can add further details and make desired changes. The edited file can again be exported to XML.

Where we develop XML code?

To create and modify XML code quickly and effortlessly, we can use Microsoft XML Notepad. With this tool, the structure of the XML data is shown graphically in a tree structure. The interface presents two panes:

  • One for the structure.
  • One for the values.

The user can add comments, attributes, elements, attributes, and text to the XML document by creating the tree structure in the left pane and entering values in corresponding text boxes.

Applications that support XML import and export:

  • RDBMS tools including IBM DB2 (pureXML), Microsoft SQL Server, Oracle Database and PostgreSQL.
  • Machine learning tools such as R Studio, and Python.

Ichimoku Kinko Hyo is a versatile technical indicator used to identify trends, support and resistance, gauge momentum, and to generate buy or sell signals. The name of the indicator translates into “one look equilibrium chart”. Must read: What Is Technical Analysis? The indicator reflects on all of the above parameters by taking multiple averages into consideration and plotting them on a chart, and the interpretation of the chart is factual in nature, i.e., it remains the same irrespective of the time frame. Originally developed by a Japanese journalist – Goichi Hosoda in 1960s, the indicator provides more data points as compared to the traditional candlestick chart, and it could be applied on any type of chart, irrespective to the chart’s own data points, i.e., the chart could be a bar chart, a candlestick chart, or a simple line chart. While at first glance the indicator could seem intimidating and highly technical to novice traders or investors. However, the indicator is relatively easy, and once a trader understands the nitty-gritty of its derivation and implications, it could become quite handy to gauge the market sentiment. Moving Parts of Ichimoku Kinko Hyo The Ichimoku Kin Hyo mainly contains two short-term moving averages- the conversion line (kenkan sen) and the base line (Kijun sen), one medium-term average – Leading Span A (senkou span A), one long-term moving average – Leading Span B (senkou span B), and a historical closing plot – Lagging Span (chikou span). Derivation of Components The conversion line of the indicator is derived by taking the mean value of 9-period high and low. Likewise, the base line of the indicator is derived by taking the mean value of 26-period high and low. The leading Span A is typically the mean value of the conversion line and the base line. The leading Span B is the mean value of 52-period high and low. And the lagging Span is the close plot of 26-period in the past. Cloud 1 – Span A crosses above Span B. Cloud 2 – Span A crosses below Span B. In the definition, we mentioned that the Ichimoku Kin Hyo is factual in nature; thus, in the derivation section, we have used PH and PL notions. The period here could take any from, such as daily, weekly, monthly. So, if we are applying Ichimoku kin Hyo on the daily chart, the PH and PL notion would consider 9-day high and 9-day low. Likewise, if are applying the Ichimoku Kin Hyo indicator on a weekly chart, the PH and PL notion would consider 9-week high and 9-week low, and so on. Interpretation For interpreting signals from the Ichimoku, the first thing which should be considered is the crossover between the conversion line and the base line along with relative position of Span A and Span B. When the conversion line crosses above the base line from below, it is typically considered as a positive signal, and when the conversion line crosses the base line below from above, it is considered as a negative signal. Furthermore, if the positive crossover between the conversion line and the base line takes place above Span A, it reflects on the strength of the trend towards upward. Likewise, if the negative crossover between the conversion line and the base line takes place below Span B, it reflects on the strength of the trend towards downward. Ideally, if Span A trades above Span B, the trend is considered to be an uptrend. Likewise, if Span A trades below Span B, the trend is considered to be a downtrend. The behaviour of the cloud as either support or resistance depends upon the relative position of the price with respect to the cloud. For example, if the price of an asset is trading below cloud, the cloud acts as the resistance zone for the price. Likewise, if the price of an asset is trading above cloud, the cloud acts as the support zone for the price.

XBRL is used for exchanging business information developed by XBRL International Inc, and it is based on XML. It is widely used in financial reporting, and the reports are produced as a word or excel documents.

Definition – Stop Loss A stop-loss order is defined as an order to buy or sell once a specific price has been hit. Traditionally developed to limit the loss of an individual in a specific security, once the security moves in the opposite direction of the initial expectation; stop-loss order is modified to enter and exit the market at certain prices. A stop order could be broadly classified into two, i.e., a buy stop order – an order to buy a security at a specified price above the current price and a sell stop order – an order to sell a security at a specified price below the current market price. The stop order can be further refined by adding a limit, aka stop loss-limit order to change the order into a limit order once the stop is triggered. How are Stops Used for Entry and Exit? Stop orders, also called stops could be used to enter or exit an open position in the market, and technicians and traders, who follow technical analysis, use the same to enter or exit a position above and below a resistance level and a support level, respectively. For entry purpose, suppose if a price is approaching a resistance level above which a new trend is expected to develop, a buy stop order could be placed to be triggered if the resistance level is penetrated post a breakout. Likewise, an entry stop order could also be placed to sell short once the specified level had been breached. Contrary to entry stops, exit stops are used either to protect capital from further loss (protective stops) or to protect profits from deteriorating back into a loss (trailing stops) Defensive Stops Both protective and trailing stops are defined as defensive stops as they protect investors against a sudden capital loss or fall in profits. Protective Stops Not every entry in the market goes as per plan and ends up with a profit, and many traders have more losing trades than winning trades, yet many of them are able to take the profit out of the market because of the judicious use of their stops. Traders usually place a proactive stop loss below the price level, where they anticipate that the market would change the behaviour. Once the market reaches that point, protective stops usually get triggered, taking the trader out of a bad trade, and when the market does not reach that price level, the protective stops allow the trader to run the trade until reversal sign emerges. Furthermore, protective stops also decide what capital risk the trader or investor is accepting in a trade. By selecting a capital risk, establishing a stop level, and placing an order to that effect, the trader knows exactly what capital risk is being taken. Protective stops are usually placed around the crucial resistance or the support level, only. Trailing Stops A trailing stop could be used to avoid the potential loss of profits when a trade is already in profit for a trader. Many technicians or traders also call trailing stops “progressive stops”. These trailing or progressive stops are necessary because, in a major trend, the prior support or resistance may give a substantial price distance from the current price; thus, putting the capital gain on a risk. Trailing stops are usually favourite among trend traders, who systematically change their capital risk with the directional trend of the underlying security. How Directional Traders Generate Trailing Stops? Trailing stops using a trendline One of the easiest methods of identifying or generating a trailing stop is to follow the trend line with a confirmation filter. Confirmation filters such as close filter, percentage filter, volatility filter, along with the trendline, is a very good method of identifying a strong level for trailing stops while avoiding price whiplashes. However, this method requires daily monitoring and readjustment of the stop level, and another shortcoming of the method is that it does not take current volatility into consideration while deciding the trailing stop. Chandelier exit Chandelier exit method considers only the price and the intrinsic volatility to decide on the trailing stop level via measuring some fraction of the security’s Average True Range (or ATR) from its latest reversal point. Suppose on a given day a stock reversed from $100, and the present 14-day ATR is 5. Based upon the market strength traders usually decide a multiple of 14-day ATR below which they would like to place a trailing stop. For example, a trader may choose to take a 3x of ATR to decide the trailing stop while another may choose to take 6x of ATR for the same purpose. Parabolic SAR Many trend followers use parabolic SAR to decide the trailing stop. Originally developed by Welles Wilder in 1978, SAR stands for “stop and reverse”. Changing Stops and A Lesson To Abide The most important underlying principle concerning defensive stops is that they should never be moved away from the trend of security as they imply that the original analysis was wrong. A trader or investor, who changes or cancels the stop loss, especially when the underlying security is trading at a loss, generally lacks discipline and are more prone to emotional whiplash or emotional decision pressure, which is one of the leading cause of capital loss in the financial market. Do you need stop-loss if you are winning and losing big? Consider a situation, where there are two traders; and the annual returns generated by them are as below:   At first glance, it might look like that Trader A is clearly outperforming Trader B with a mean average return of 55 per cent over 4 years as compared to the Trader B’s mean of just 36.0 per cent in the same tenure. However, one needs to analyse the above data with elementary maths and one important thing about investment returns, i.e., they are multiplicative in nature rather than addictive. So, if we assume that both the traders had $100 as an initial investment, let us take a look at their net profitability at the end of Year 4. Despite a large gain profile and a higher mean average return of 55.0 per cent, Trader A ends the session with a net loss of 53.2 per cent. And, despite a low gain profile and a low mean average return of 36.0 per cent, Trader B ends the session with a net gain of 241.43 per cent. Conclusion If you have made a 10 per cent return instead of 25.93 per cent, it will take you ~2.5 years to grow your money by 25.93 per cent instead of one year; however, if you lose 25.93 per cent in the first year, it will take some time to reach breakeven. Thus, it becomes paramount to always limit your loss and be aware of your capital risk.

What is Big Data? Big data refers to a collection of structured, unstructured, and semi-structured data that organizations collect to mine information that can be used across various advanced analytics applications, including predictive modelling. Big data is massive and contains multiple complex datasets that are difficult for traditional data processing software to manage. A key advantage of using big data is that the large datasets can be used to tackle complicated business problems that were challenging to address earlier. INTERESTING READ: Big Data- The Emerging Gold of The Modern Era What is Big Data Analytics? Analysis of big data facilitates analysts, business users, and researchers to make fast and better decisions using untapped data. The businesses can use advanced analytics techniques to gain further insights from the earlier untouched data sources independently or together with existing enterprise data. The advanced analytics techniques include machine learning, predictive analytics, and text analytics, among others. What are the different forms of Big Data? There are three types of big data. These are: Structured Unstructured Semi-Structured Structured Data: Structured data are those that can be stored, accesses and processes in a fixed format. An example of structured data is a student database. Unstructured Data: As the name suggests, unstructured data have an unknown form or structure. Further, the size of these unstructured data is enormous, and there are multiple challenges while processing such data. An example of unstructured data is when you do a google search for a topic, say big data. When you run the search, you may get various topics, images, books, authors, different websites, and videos as results. In current times, there are vast data available in an unstructured format, and many businesses fail to derive benefits from such form of data. Semi-Structured Data: Semi-structured data has the qualities of both structured and unstructured data. An example of semi-structured data is an RDBMS or relational database management system. Another example would be XML or HTML code used for creating web pages. GOOD READ: Artificial Intelligence and Big Data- The Powerhouse of a Digital Future Characteristics of Big Data Volume: Big data, as the name suggests, refers to enormous datasets. Which particular dataset falls under the category of big data relies on the volume of data within that dataset. Hence, it is essential to keep the volume of data in mind while handling big data. Variety: Variety refers to the various sources and the kind of data. The scope of data is not restricted to spreadsheet and databases anymore but has now become more extensive. The data are now available in various formats such as emails, videos, photos, audio, and blogs. Velocity: Velocity is related to the speed at which data is created and simultaneously processed to derive meaningful output. Variability: Variability refers to the inconsistency in the data that might exist and impact the process of managing the database efficiently. Applications of Big Data Big data is useful for companies to interpret huge data at a faster rate which helps them to take important decisions related to the business, product development, expansion any many more. Let us look at some of the industry where we can see the application of Big Data across sectors: Banking and Securities: In the banking and the securities industry, big data plays a critical role in detecting any fraudulent activity, enterprise credit risk reporting, analytics for trading and so on. Insurance: In the insurance industry, big data is useful in providing customer insights for transparent and simpler products. It also helps in better customer retention. Communications, Media, and Entertainment: Big data can be used for developing content based on the preference and behaviour of the viewer. Through big data, the companies from the Communications, Media and Entertainment industry can create content for its target audience, recommend related content and also measure content performance. Education: In the education industry, big data can be useful for measuring a teacher’s effectiveness to ensure that both teacher and students have a pleasant experience during the session. Data can help make teaching experience better. Further, big data in this industry can help to track the overall progress of the students as well. Healthcare Providers: Big data can help the doctors to use evidence-based medicine instead of administering several medical/lab tests to all patients visiting the hospital. Manufacturing and Natural Resources: In the manufacturing industry, Big data can be useful in solving the day-to-day challenges faced by the manufacturing companies such as supply chain, the shipment of goods, demand forecasting and many more. Retail and Wholesale industry: In this industry, big data support in optimizing staff based in the shopping pattern of the customer, local event. Like many other industries, big data also helps in reducing fraudulent activities and also helps in tracking the inventory. Energy and Utilities: Energy and Utilities sectors have now started using smart meter readers that gather data for every 15 minutes. The data can be used to analyze the consumption of utilities by the customers in a better way. Transportation: Big data play a crucial role in controlling traffic, route planning, intelligent transport system. Government: In the government sector, big data has a massive application. Through big data, the government of any country can look into the socio-economic issues, any fraudulent or illegal activities within the country, health-related issue, and the most affected regions of the country and many more. Based on the output, a relevant decision can be taken, and funds can be provided accordingly.

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