Can You Distill High Frequency Financial Data Efficiently Using MATLAB? Tuomas Eerola’s article in LinkedIn Pulse introduces a solution for turning high frequency financial data efficiently into a competitive advantage using MATLAB.
Computing volatility measures requires high quality Trade and Quote (TAQ) data. This is big data. To a financial engineer, who works in portfolio management, a lot of the raw TAQ data is noise that needs to be cleaned. To many financial engineers, MATLAB is the preferred tool in the development of data preparation algorithms. However, efficient training of the algorithm and the cleansing of high frequency financial data has required MATLAB server licenses and computing resources that are out of reach to many.
In his article Tuomas talks about using Techila to speed up the development of algorithms for big data. He introduces also an efficient solution for data preparation using MATLAB, Techila Distributed Computing Engine and cloud computing. When using this solution, the user can get the time consuming data preparation done in less than 1 hour. Or if the user wants, even in just a couple of minutes. When using this solution, the user does not need MATLAB server licenses for the computing environment.