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Analytics

Analytics are defined as the detection, analysis, and relay of consequential patterns in data. In particular, analytics also seek to explain or accurately reflect the relationship between data and effective decision making. In the example of the retail trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. Why Use Analytics?Predictive models of analytics involve the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Furthermore, analytics may also be structured with a descriptive model. In this sense, readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Retail forex 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. Analytics are also associated with back-testing strategies. Back-testing is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Analytics are also 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.

Analytics are defined as the detection, analysis, and relay of consequential patterns in data.

In particular, analytics also seek to explain or accurately reflect the relationship between data and effective decision making.

In the example of the retail trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price.

Why Use Analytics?

Predictive models of analytics involve the analysis of historical price patterns that are used in an attempt to determine certain price outcomes.

Furthermore, analytics may also be structured with a descriptive model.

In this sense, readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables.

Retail forex 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.

Analytics are also associated with back-testing strategies.

Back-testing is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data.

Analytics are also 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.