Learn about the responsibilities in the field of data engineering with Part I.
Data engineers keep evolving with the technological advancements and the introduction of various models with that. The data engineering domain is progressing at a rapid pace, powered by disruption in Internet of Things (IOT), Artificial intelligence and machine learning models. Hence, data engineers also need to keep evolving and learning new practices in the field.
Going forward, we will find out about data engineering in the financial markets.
Data Engineering in the Financial Markets
In the financial markets, a data engineer needs to do the regular work of collecting data, cleaning the data which implies taking out errors such as duplicates. The last step is automating the trade with the help of the cleaned data.
Moreover, there are certain other ways in which data engineering helps the financial markets. These are:
- Risk management
- Predictive analytics
- Fraud detection
- Algorithmic trading
Since managing the risk is an extremely important aspect of any financial institution, data engineers play an important role. With the help of clean data sets, the errors in the prediction of trades do not take place. This is important because if the machine learning model gets fed with the erroneous data, it will lead to continued losses for the investor.
With the help of predictive analytics, the investor can foresee the data patterns and can take the right actions for the same in the present. A data engineer helps the firm/individual etc to take the right decisions while investing in the financial market this way. For instance, if the machine learning model is fed with data which has duplicates or irregularities, it will lead to erroneous input. This erroneous input, in turn, will make for false predictions in the trade and hence, less gains.
Going forward, data engineering also helps with fraud detection. As it is extremely important to detect if a hacker has hacked into the system to make the data malicious/unfit for feeding the predictive model, it is a must to get the same checked and cleaned by the data engineer.
In the algorithmic trading domain, data engineers help with the cleaning of the data which is to be fed to the machine learning or deep learning models for predicting the trades. An algorithmic trading system executes the orders with the help of pre-programmed instructions. While doing so, the data is required for historical backtesting which helps to understand if the created strategy would have worked well on the past data or not. While doing so, if the data fed for backtesting is not looked into properly, it can lead to wrong trading decisions in the future.
In the next installment of this series, the author will demonstrate the difference between data scientists and data engineers.
Visit QuantInsti for additional insight on this topic: https://blog.quantinsti.com/data-engineering/.
Disclosure: Interactive Brokers
Information posted on IBKR Traders’ Insight that is provided by third-parties and not by Interactive Brokers does NOT constitute a recommendation by Interactive Brokers that you should contract for the services of that third party. Third-party participants who contribute to IBKR Traders’ Insight are independent of Interactive Brokers and Interactive Brokers does not make any representations or warranties concerning the services offered, their past or future performance, or the accuracy of the information provided by the third party. Past performance is no guarantee of future results.
This material is from QuantInsti and is being posted with permission from QuantInsti. The views expressed in this material are solely those of the author and/or QuantInsti and IBKR is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.
In accordance with EU regulation: The statements in this document shall not be considered as an objective or independent explanation of the matters. Please note that this document (a) has not been prepared in accordance with legal requirements designed to promote the independence of investment research, and (b) is not subject to any prohibition on dealing ahead of the dissemination or publication of investment research.