CMC Markets Launches ‘Skill’ Utility, Aggregating Market Data for Traders

CMC Markets continues to expand its toolbox for traders, this time with a new voice-controlled utility. CMC Markets Skill represents the group’s latest aggregation tool that compiles news headlines, articles, analyst tweets, and other market data – the service reflects a new offering, which is available exclusively on Amazon home assistant products such as Alexa.

CMC Markets has ramped up efforts to stress trader education and awareness. The launch of its voice-controlled Skill enables access to a range of useful content for its trading base. Indeed, the ability to stay on top of market trends has consistently emerged as a key asset for traders; a revelation CMC Markets has worked to address.

David Fineberg, Group Commercial Director, commented on Skill: “Our daily lives are becoming more and more centered on processing large volumes of information, as well as how we utilize the resources at our disposal. The CMC Markets Skill provides traders with a new and convenient method of staying informed and up to date, and crucially makes consuming large volumes of information more manageable."

By using Skill, users will have access to a robust set of information including all matters of social media and news headlines. In doing so, traders will be able to better map out definitive trends across markets. The development is noteworthy as CMC Markets represents the broker to offer on-demand insight via Alexa home assistant products.

Subsequently, by using the voice command “Alexa, open CMC Markets,” all users will be able to access the latest spreads and price change percentages for five of the most frequently traded instruments on CMC Market’s Next Generation platform.

“At CMC Markets we recognize the power of technology, so we continue to invest in a range of services to be a leader in this space. This is why we have taken proactive steps to ensure that we are the first trading company to fully utilize the potential of this software,” explained Mr. Fineberg.

“We are excited by how traders are engaging in the digital age, and the CMC Markets Skill we have developed is part of our ongoing strategy to enhance the overall experience of our clients by providing them with the information they need, when they need it,” he added.

The launch of CMC Markets Skill is the second development for the group in as many days, having already initiated Machine Learning Machine Learning Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained through observational behavior. For example, the process of learning begins with observations or data.This includes examples and indirect experience or instruction to help detect patterns in data. In doing so, the goal is to make better decisions in the future based on the examples that are provided. In an ideal set of circumstances, computers learn automatically without human intervention or assistance and adjust actions accordingly.Machine learning can take two different form, i.e. supervised or unsupervised learning. Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. As such, the system is able to provide targets for any new input after sufficient levels of training. Learning algorithm can also compare its output to find errors in order to modify the model accordingly.By extension, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained through observational behavior. For example, the process of learning begins with observations or data.This includes examples and indirect experience or instruction to help detect patterns in data. In doing so, the goal is to make better decisions in the future based on the examples that are provided. In an ideal set of circumstances, computers learn automatically without human intervention or assistance and adjust actions accordingly.Machine learning can take two different form, i.e. supervised or unsupervised learning. Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. As such, the system is able to provide targets for any new input after sufficient levels of training. Learning algorithm can also compare its output to find errors in order to modify the model accordingly.By extension, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. Read this Term for trading Analytics Analytics Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Read this Term. Earlier this week, CMC Markets announced it would be partnering with Tradefeedr, a data analytics firm, to push its big-data analytics efforts.