ZenLedger, a cryptocurrency data analysis company, said on Wednesday that it hired four new c-suite level executives. According to the press release, the firm has named Duyane Norman as its new Chief Strategy Officer, Daniel D. Escobar as Chief Technology Officer, Jonté Harrell as Chief Financial Officer, and Greg Adams as Chief Operations Officer.
“We are thrilled to welcome Duyane, Daniel, Jonté and Greg, who bring a plethora of experience that will only allow ZenLedger to reach new heights. On the heels of one of ZenLedger’s best tax seasons, the business has experienced 5x sales growth year-over-year and to support this growth, our employee base has tripled. Driven by the increased market demand for crypto tax and accounting services, coupled with a product that continues to impress customers with industry integrations, ZenLedger is poised to reign as a leader and to expand into new products, services and locations,” Pat Larsen, Chief Executive Officer at ZenLedger.
Executives Background
ZenLedger has hired Norman to lead strategic initiatives around data 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 for government and enterprise customers worldwide. In 2019, Norman retired as a member of the CIA’s Senior Intelligence Service after 27 years of service, including seven overseas tours. Norman served as the link between the Defense Innovation Unit and the Intelligence Community before joining ZenLedger.
In addition, Escobar has over 20 years of leadership and entrepreneurship experience in crypto, enterprise software development, and IT consulting. His last four years were spent with Bitpay as the Head of Enterprise Onboarding and Engineering Services, where he worked closely with crypto exchanges, blockchains, NFTs, wallets, and governments across the USA and around the world.
Moreover, Harrell brings 18 years of experience in finance and technology from his time as a US Army Captain and combat veteran. Harrell will lead ZenLedger’s Finance department, which includes Controllership, Financial Planning and Analysis, Treasury, Tax, and Internal Audit.
Adams will lead the company’s operations, sales & marketing, and software development teams globally. Previously, Adams was the Co-Founder and CEO of 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 enterprise SaaS startup Stabilitas, which OnSolve acquired in 2020. As Chief Strategy Officer at OnSolve, he was responsible for innovation and acquisitions.
ZenLedger, a cryptocurrency data analysis company, said on Wednesday that it hired four new c-suite level executives. According to the press release, the firm has named Duyane Norman as its new Chief Strategy Officer, Daniel D. Escobar as Chief Technology Officer, Jonté Harrell as Chief Financial Officer, and Greg Adams as Chief Operations Officer.
“We are thrilled to welcome Duyane, Daniel, Jonté and Greg, who bring a plethora of experience that will only allow ZenLedger to reach new heights. On the heels of one of ZenLedger’s best tax seasons, the business has experienced 5x sales growth year-over-year and to support this growth, our employee base has tripled. Driven by the increased market demand for crypto tax and accounting services, coupled with a product that continues to impress customers with industry integrations, ZenLedger is poised to reign as a leader and to expand into new products, services and locations,” Pat Larsen, Chief Executive Officer at ZenLedger.
Executives Background
ZenLedger has hired Norman to lead strategic initiatives around data 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 for government and enterprise customers worldwide. In 2019, Norman retired as a member of the CIA’s Senior Intelligence Service after 27 years of service, including seven overseas tours. Norman served as the link between the Defense Innovation Unit and the Intelligence Community before joining ZenLedger.
In addition, Escobar has over 20 years of leadership and entrepreneurship experience in crypto, enterprise software development, and IT consulting. His last four years were spent with Bitpay as the Head of Enterprise Onboarding and Engineering Services, where he worked closely with crypto exchanges, blockchains, NFTs, wallets, and governments across the USA and around the world.
Moreover, Harrell brings 18 years of experience in finance and technology from his time as a US Army Captain and combat veteran. Harrell will lead ZenLedger’s Finance department, which includes Controllership, Financial Planning and Analysis, Treasury, Tax, and Internal Audit.
Adams will lead the company’s operations, sales & marketing, and software development teams globally. Previously, Adams was the Co-Founder and CEO of 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 enterprise SaaS startup Stabilitas, which OnSolve acquired in 2020. As Chief Strategy Officer at OnSolve, he was responsible for innovation and acquisitions.
Source: https://www.financemagnates.com/executives/zenledger-appoints-four-new-c-suite-level-executives/