Tradefeedr, a forex industry data analytics platform, announced the appointment of Alexis Fauth on Thursday as the Head of Data Science and Client Analytics.
The new appointment came only a couple of months after the company onboarded industry veteran Tim Cartledge as the Chief Data Officer. Then, the company received a $1 million investment from Alphaview Limited, the family office of Tim and Kate Cartledge.
Fauth is a trained data expert with years of industry experience. He also holds a Ph.D. in Applied Mathematics from Panthéon-Sorbonne University.
His responsibility at Tradefeedr will be to identify and develop a wide range of tools for clients, providing them with further data insights and optimizing trading.
“Many of our clients and Liquidity Providers
Liquidity Providers
A liquidity provider (LP) constitutes either individual and/or institution that functions as a market maker in a given asset class. Broadly speaking, liquidity providers will act as the both the buyer and seller of a particular asset, thus making a market. In the equities space, many stock exchanges rely on liquidity providers who make the commitment to provide liquidity in a given equity. These liquidity providers commit to providing liquidity in the hopes that they will be able to make a profit on the bid-ask spread.In doing so, these entities theoretically ensure greater price stability and also improve liquidity by making it easier for traders to buy and sell at any price level. Market liquidity providers also oversee an important service and take on a significant amount of risk.However, these are still able to profit from the spread or by positioning themselves on the basis of the valuable information available to them.Analyzing Liquidity Providers Relationship with BrokersIn addition, liquidity providers also delivering interbank market access to retail brokers. They are typically large multinational investment banks, or other financial institutions that can be non-bank entities. Each liquidity provider is streaming executable rates to the broker whose aggregator engine is selecting the best bid and ask and streams it to clients to deliver the best possible spread.The broker is the direct counterparty to all trades executed with the liquidity provider and typically only uses them to offload flows which it finds uneconomical to internalize. That said, some brokers are sending all of their flow to liquidity providers.Liquidity providers have a set of characteristics which are determining their suitability and reliability – such are order rejection rates, spreads, and latency. Brokers which aren’t monitoring the flow adequately are risking to deliver to their clients’ bad fills, which consequently result in customer complaints since the customer is consistently not getting the displayed or requested price.
A liquidity provider (LP) constitutes either individual and/or institution that functions as a market maker in a given asset class. Broadly speaking, liquidity providers will act as the both the buyer and seller of a particular asset, thus making a market. In the equities space, many stock exchanges rely on liquidity providers who make the commitment to provide liquidity in a given equity. These liquidity providers commit to providing liquidity in the hopes that they will be able to make a profit on the bid-ask spread.In doing so, these entities theoretically ensure greater price stability and also improve liquidity by making it easier for traders to buy and sell at any price level. Market liquidity providers also oversee an important service and take on a significant amount of risk.However, these are still able to profit from the spread or by positioning themselves on the basis of the valuable information available to them.Analyzing Liquidity Providers Relationship with BrokersIn addition, liquidity providers also delivering interbank market access to retail brokers. They are typically large multinational investment banks, or other financial institutions that can be non-bank entities. Each liquidity provider is streaming executable rates to the broker whose aggregator engine is selecting the best bid and ask and streams it to clients to deliver the best possible spread.The broker is the direct counterparty to all trades executed with the liquidity provider and typically only uses them to offload flows which it finds uneconomical to internalize. That said, some brokers are sending all of their flow to liquidity providers.Liquidity providers have a set of characteristics which are determining their suitability and reliability – such are order rejection rates, spreads, and latency. Brokers which aren’t monitoring the flow adequately are risking to deliver to their clients’ bad fills, which consequently result in customer complaints since the customer is consistently not getting the displayed or requested price.
Read this Term want to gain further insights into their data to optimize trading strategies and better manage relationships. Our aim is to build on our leading position in 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 and develop new advanced tools which help our clients and Liquidity Providers to enable data-driven decision making,” said Fauth, who has already joined Tradefeedr.
Solid Background
He joined Tradefeedr from Citi where he worked for almost six years. There, he was the Global head of FX Data Science and developed client optimization strategy models.
Prior to that, he was a Quantitative Analyst at a London-based affiliate of S&P. There, he led a team that developed XVA models for Fixed Income, hybrid derivatives pricing and counterparty credit risk models.
“He brings an in-depth knowledge of applying data science to financial markets, combined with an understanding of the needs of FX market participants,” said Alexei Jiltsov, the Co-Founder of Tradefeedr, a company that has 20 sell-side and over 50 buy-side firms as clients.
“As our network grows and more trading data is acquired, we continue to add new advanced analytical services so that clients can maximize the value of their data.”
Tradefeedr, a forex industry data analytics platform, announced the appointment of Alexis Fauth on Thursday as the Head of Data Science and Client Analytics.
The new appointment came only a couple of months after the company onboarded industry veteran Tim Cartledge as the Chief Data Officer. Then, the company received a $1 million investment from Alphaview Limited, the family office of Tim and Kate Cartledge.
Fauth is a trained data expert with years of industry experience. He also holds a Ph.D. in Applied Mathematics from Panthéon-Sorbonne University.
His responsibility at Tradefeedr will be to identify and develop a wide range of tools for clients, providing them with further data insights and optimizing trading.
“Many of our clients and Liquidity Providers
Liquidity Providers
A liquidity provider (LP) constitutes either individual and/or institution that functions as a market maker in a given asset class. Broadly speaking, liquidity providers will act as the both the buyer and seller of a particular asset, thus making a market. In the equities space, many stock exchanges rely on liquidity providers who make the commitment to provide liquidity in a given equity. These liquidity providers commit to providing liquidity in the hopes that they will be able to make a profit on the bid-ask spread.In doing so, these entities theoretically ensure greater price stability and also improve liquidity by making it easier for traders to buy and sell at any price level. Market liquidity providers also oversee an important service and take on a significant amount of risk.However, these are still able to profit from the spread or by positioning themselves on the basis of the valuable information available to them.Analyzing Liquidity Providers Relationship with BrokersIn addition, liquidity providers also delivering interbank market access to retail brokers. They are typically large multinational investment banks, or other financial institutions that can be non-bank entities. Each liquidity provider is streaming executable rates to the broker whose aggregator engine is selecting the best bid and ask and streams it to clients to deliver the best possible spread.The broker is the direct counterparty to all trades executed with the liquidity provider and typically only uses them to offload flows which it finds uneconomical to internalize. That said, some brokers are sending all of their flow to liquidity providers.Liquidity providers have a set of characteristics which are determining their suitability and reliability – such are order rejection rates, spreads, and latency. Brokers which aren’t monitoring the flow adequately are risking to deliver to their clients’ bad fills, which consequently result in customer complaints since the customer is consistently not getting the displayed or requested price.
A liquidity provider (LP) constitutes either individual and/or institution that functions as a market maker in a given asset class. Broadly speaking, liquidity providers will act as the both the buyer and seller of a particular asset, thus making a market. In the equities space, many stock exchanges rely on liquidity providers who make the commitment to provide liquidity in a given equity. These liquidity providers commit to providing liquidity in the hopes that they will be able to make a profit on the bid-ask spread.In doing so, these entities theoretically ensure greater price stability and also improve liquidity by making it easier for traders to buy and sell at any price level. Market liquidity providers also oversee an important service and take on a significant amount of risk.However, these are still able to profit from the spread or by positioning themselves on the basis of the valuable information available to them.Analyzing Liquidity Providers Relationship with BrokersIn addition, liquidity providers also delivering interbank market access to retail brokers. They are typically large multinational investment banks, or other financial institutions that can be non-bank entities. Each liquidity provider is streaming executable rates to the broker whose aggregator engine is selecting the best bid and ask and streams it to clients to deliver the best possible spread.The broker is the direct counterparty to all trades executed with the liquidity provider and typically only uses them to offload flows which it finds uneconomical to internalize. That said, some brokers are sending all of their flow to liquidity providers.Liquidity providers have a set of characteristics which are determining their suitability and reliability – such are order rejection rates, spreads, and latency. Brokers which aren’t monitoring the flow adequately are risking to deliver to their clients’ bad fills, which consequently result in customer complaints since the customer is consistently not getting the displayed or requested price.
Read this Term want to gain further insights into their data to optimize trading strategies and better manage relationships. Our aim is to build on our leading position in 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 and develop new advanced tools which help our clients and Liquidity Providers to enable data-driven decision making,” said Fauth, who has already joined Tradefeedr.
Solid Background
He joined Tradefeedr from Citi where he worked for almost six years. There, he was the Global head of FX Data Science and developed client optimization strategy models.
Prior to that, he was a Quantitative Analyst at a London-based affiliate of S&P. There, he led a team that developed XVA models for Fixed Income, hybrid derivatives pricing and counterparty credit risk models.
“He brings an in-depth knowledge of applying data science to financial markets, combined with an understanding of the needs of FX market participants,” said Alexei Jiltsov, the Co-Founder of Tradefeedr, a company that has 20 sell-side and over 50 buy-side firms as clients.
“As our network grows and more trading data is acquired, we continue to add new advanced analytical services so that clients can maximize the value of their data.”
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