RSS Feed

Related Articles

Related Categories

Data Logging: An unexpected asset to finance

22nd October 2019 Print

The basics or foundation of finances and financial management have seen little change over the last few decades. However, financial institutions are more than keeping up with the times. Now, these massive companies are using technology to evolve financial aspects, customer offerings, and more. Technology has become critical, but unexpectedly data logging is arguably as much of a necessity as any other aspect of daily operations for companies.

Handling Large Data Volumes

The finance industry has always had a knack for collecting and analyzing data. Knowing trends and predicting variables in the market have helped many guide consumers and their companies through difficult times. With accurate data, you cultivate winning trading strategies and establish timing that will increase your odds of success. But implementing the degree of data that you need doesn’t happen overnight. You need to go far beyond the basic data collection and instead focus on logging key events.

Event and Data Logging

A data log is a system that collects and stores data over time with the intent of analyzing that data for decision making. For the finance industry, it's a must-have. Data logging, however, is complex and often requires an expert in programming, development, and data management for an effective outcome. The most common programming language for data logging in the finance industry is Python. Python is a high-level programming language that supports packages, modules, and is relatively easy to learn. As a bonus, usually, the cost of program maintenance is substantially lower than other languages because of the simplicity and easy readability.

Python and Finance Basics

These two come hand-in-hand, but you really do need to have a general grasp of Python and reading a syslog before you start to use your knowledge in the real world. You'll essentially be applying the Python syslog to a time series that you would use in basic stock and trading activities. Of course, the focus is always on the end result. You don’t have to be a programming pro to understand the takeaways from the data captured. But you do need to learn how the two work together.

Using Data Logs to Craft Strategies

Using data logs to develop a strategy is something that will come to most finance professionals rather easily. Initially, you'll start with a theorized strategy based on your intuitive takeaways from the data. Then you’ll backtest and reconstruct that strategy using historical data. By using historical data, you can estimate how accurate that trend or strategy is currently and set some outlying rules that will govern your strategy. As more data pours in, you may slightly alter or rework your strategy for prime optimization.

Using Packages and Workspaces

Because Python is so widely used throughout the finance industry, there are a lot of options available to make working with Python easier than alternative options. For example, you may use Anaconda or another environment to access a variety of packages. An environment and the packages contained within it make it easier to aggregate, read, and work with the data that the log has collected.