ETX Capital Names Daniel Gladding as Its Chief Risk Officer
- Gladding brings executive experience in risk and operations, including data analytics and the regulatory environment.
ETX Capital announced that it has named Daniel Gladding as its new Chief Risk Officer to execute its strategic vision, effective immediately.
It was recently announced in a press release that Daniel Gladding, a veteran senior executive in the financial services industry, has joined the ETX Executive Team as it Chief Risk Officer in London.
Gladding brings a wealth of experience in executive roles in risk and operations. In particular, his broad experience covers 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, operational issues, the regulatory environment and of course Risk Management Risk Management One of the most common terms utilized by brokers, risk management refers to the practice of identifying potential risks in advance. Most commonly, this also involves the analysis of risk and the undertaking of precautionary steps to both mitigate and prevent for such risk.Such efforts are essential for brokers and venues in the finance industry, given the potential for fallout in the face of unforeseen events or crises. Given a more tightly regulated environment across nearly every asset class, most brokers employ a risk management department tasked with analyzing the data and flow of the broker to mitigate the firm’s exposure to financial markets moves. Why Risk Management is a Fixture Among BrokersTraditionally the company is employing a risk management team that is monitoring the exposure of the brokerage and the performance of select clients which it deems risky for the business. Common financial risks also come in the form of high inflation, volatility across capital markets, recession, bankruptcy, and others.As a countermeasure to these issues, brokers have looked to minimize and control the exposure of investment to such risks.In the modern hybrid mode of operation, brokers are sending out the flows from the most profitable clients to liquidity providers and internalize the flows from customers.This is deemed less risky and are likely to incur losses on their positions.This in turn allowing the broker to increase its revenue capture. Several software solutions exist to assist brokers to manage risk more efficiently and as of 2018, most connectivity/bridge providers are integrating a risk-management module into their offerings. This aspect of running a brokerage is also one of the most crucial ones when it comes to employing the right kind of talent. One of the most common terms utilized by brokers, risk management refers to the practice of identifying potential risks in advance. Most commonly, this also involves the analysis of risk and the undertaking of precautionary steps to both mitigate and prevent for such risk.Such efforts are essential for brokers and venues in the finance industry, given the potential for fallout in the face of unforeseen events or crises. Given a more tightly regulated environment across nearly every asset class, most brokers employ a risk management department tasked with analyzing the data and flow of the broker to mitigate the firm’s exposure to financial markets moves. Why Risk Management is a Fixture Among BrokersTraditionally the company is employing a risk management team that is monitoring the exposure of the brokerage and the performance of select clients which it deems risky for the business. Common financial risks also come in the form of high inflation, volatility across capital markets, recession, bankruptcy, and others.As a countermeasure to these issues, brokers have looked to minimize and control the exposure of investment to such risks.In the modern hybrid mode of operation, brokers are sending out the flows from the most profitable clients to liquidity providers and internalize the flows from customers.This is deemed less risky and are likely to incur losses on their positions.This in turn allowing the broker to increase its revenue capture. Several software solutions exist to assist brokers to manage risk more efficiently and as of 2018, most connectivity/bridge providers are integrating a risk-management module into their offerings. This aspect of running a brokerage is also one of the most crucial ones when it comes to employing the right kind of talent. Read this Term.
Previous to the hire by ETX Capital, Gladding held the role of Head of Risk and Director (SMF3) at Admiral Markets. Although his time was limited to a year, he accomplished the requirements of the position.
Daniel Gladding, Chief Risk Officer, ETX Capital. Source: LinkedIn
Before this time at Admiral Markets, the veteran senior executive founded Step Function, a professional training and development company running courses in Python, Data Analytics and Data Science, in October 2016. He led the organisation successfully over the last four years.
Additionally, at Capital Index, a London-based, he initially served as Global Head of Risk and was shortly promoted to Chief Operations Officer. Here he managed the IT, Risk and Quantitative Analytics teams. He also served as Chair of the Risk Committee, ICAAP Committee and Best Execution Committee among other responsibilities.
Earlier to Capital Index, Gladding took on the role of Vice President of Risk Management and Quantitative Analytics at GAIN Capital. For almost six years from early 2012, he managed a team of quants performing: Stress testing / Hedge modelling / Value at Risk analysis / Risk limit optimisation.
At GFT Global Markets UK LTD, he spent his time managing the Binary Options Desk from the beginning of 2009 until spring 2012. Furthermore, in 2004, he began his career at IG Index as a Market Maker in London. He triumphed in the role for the five years that he spent there.
Gladding Leaves Admiral Markets for Greener Pastures
ETX Capital feels assured that Gladding will be a valuable contribution to the executive team and eagerly anticipate what he brings to the table.
Gladding said: “Joining ETX Capital as Chief Risk Officer represents an exciting challenge for me, as we look to execute on the strategic vision mapped out by Ryan Nettles and Luca Merolla of Guru Capital.”
Commenting on the hire, Phil Adler, CEO of ETX Capital adds: “Speaking on behalf of the ETX Executive Team, we are delighted that Daniel Gladding has chosen to join ETX as Chief Risk Officer. Daniel brings great experience in data analytics, operational issues, the regulatory environment and of course risk management at an exciting time in the development of ETX under the new ownership of Guru Capital.”
ETX Capital announced that it has named Daniel Gladding as its new Chief Risk Officer to execute its strategic vision, effective immediately.
It was recently announced in a press release that Daniel Gladding, a veteran senior executive in the financial services industry, has joined the ETX Executive Team as it Chief Risk Officer in London.
Gladding brings a wealth of experience in executive roles in risk and operations. In particular, his broad experience covers 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, operational issues, the regulatory environment and of course Risk Management Risk Management One of the most common terms utilized by brokers, risk management refers to the practice of identifying potential risks in advance. Most commonly, this also involves the analysis of risk and the undertaking of precautionary steps to both mitigate and prevent for such risk.Such efforts are essential for brokers and venues in the finance industry, given the potential for fallout in the face of unforeseen events or crises. Given a more tightly regulated environment across nearly every asset class, most brokers employ a risk management department tasked with analyzing the data and flow of the broker to mitigate the firm’s exposure to financial markets moves. Why Risk Management is a Fixture Among BrokersTraditionally the company is employing a risk management team that is monitoring the exposure of the brokerage and the performance of select clients which it deems risky for the business. Common financial risks also come in the form of high inflation, volatility across capital markets, recession, bankruptcy, and others.As a countermeasure to these issues, brokers have looked to minimize and control the exposure of investment to such risks.In the modern hybrid mode of operation, brokers are sending out the flows from the most profitable clients to liquidity providers and internalize the flows from customers.This is deemed less risky and are likely to incur losses on their positions.This in turn allowing the broker to increase its revenue capture. Several software solutions exist to assist brokers to manage risk more efficiently and as of 2018, most connectivity/bridge providers are integrating a risk-management module into their offerings. This aspect of running a brokerage is also one of the most crucial ones when it comes to employing the right kind of talent. One of the most common terms utilized by brokers, risk management refers to the practice of identifying potential risks in advance. Most commonly, this also involves the analysis of risk and the undertaking of precautionary steps to both mitigate and prevent for such risk.Such efforts are essential for brokers and venues in the finance industry, given the potential for fallout in the face of unforeseen events or crises. Given a more tightly regulated environment across nearly every asset class, most brokers employ a risk management department tasked with analyzing the data and flow of the broker to mitigate the firm’s exposure to financial markets moves. Why Risk Management is a Fixture Among BrokersTraditionally the company is employing a risk management team that is monitoring the exposure of the brokerage and the performance of select clients which it deems risky for the business. Common financial risks also come in the form of high inflation, volatility across capital markets, recession, bankruptcy, and others.As a countermeasure to these issues, brokers have looked to minimize and control the exposure of investment to such risks.In the modern hybrid mode of operation, brokers are sending out the flows from the most profitable clients to liquidity providers and internalize the flows from customers.This is deemed less risky and are likely to incur losses on their positions.This in turn allowing the broker to increase its revenue capture. Several software solutions exist to assist brokers to manage risk more efficiently and as of 2018, most connectivity/bridge providers are integrating a risk-management module into their offerings. This aspect of running a brokerage is also one of the most crucial ones when it comes to employing the right kind of talent. Read this Term.
Previous to the hire by ETX Capital, Gladding held the role of Head of Risk and Director (SMF3) at Admiral Markets. Although his time was limited to a year, he accomplished the requirements of the position.
Daniel Gladding, Chief Risk Officer, ETX Capital. Source: LinkedIn
Before this time at Admiral Markets, the veteran senior executive founded Step Function, a professional training and development company running courses in Python, Data Analytics and Data Science, in October 2016. He led the organisation successfully over the last four years.
Additionally, at Capital Index, a London-based, he initially served as Global Head of Risk and was shortly promoted to Chief Operations Officer. Here he managed the IT, Risk and Quantitative Analytics teams. He also served as Chair of the Risk Committee, ICAAP Committee and Best Execution Committee among other responsibilities.
Earlier to Capital Index, Gladding took on the role of Vice President of Risk Management and Quantitative Analytics at GAIN Capital. For almost six years from early 2012, he managed a team of quants performing: Stress testing / Hedge modelling / Value at Risk analysis / Risk limit optimisation.
At GFT Global Markets UK LTD, he spent his time managing the Binary Options Desk from the beginning of 2009 until spring 2012. Furthermore, in 2004, he began his career at IG Index as a Market Maker in London. He triumphed in the role for the five years that he spent there.
Gladding Leaves Admiral Markets for Greener Pastures
ETX Capital feels assured that Gladding will be a valuable contribution to the executive team and eagerly anticipate what he brings to the table.
Gladding said: “Joining ETX Capital as Chief Risk Officer represents an exciting challenge for me, as we look to execute on the strategic vision mapped out by Ryan Nettles and Luca Merolla of Guru Capital.”
Commenting on the hire, Phil Adler, CEO of ETX Capital adds: “Speaking on behalf of the ETX Executive Team, we are delighted that Daniel Gladding has chosen to join ETX as Chief Risk Officer. Daniel brings great experience in data analytics, operational issues, the regulatory environment and of course risk management at an exciting time in the development of ETX under the new ownership of Guru Capital.”