Data Science in Trading – A Step Beyond Quant Trading

Data Science in Trading

Data science finds its way into different aspects of finance. It is a result of large volumes of financial data available in this modern trading world. Rapid use of cloud computing, blockchain systems, among other new technologies, paved the way for complex trading analysis and revolutionized trading techniques/methods. It is no news that smart traders are continually looking into means of getting profitable insights. Hence, the majority rely solely on Quant models.


But, is this formula-based system the best method in this evolving stock market? This article explores a general idea in simple terms, and you will find an answer at the end.

Before we go further into several benefits and aspects of Data Science models in trading, it is crucial to quickly examine what Quant trading is and how it is can be deployed in stock price prediction quests.

What is Quant Trading?

Quant trading, also known as quantitative trading, is a household concept in the stock market. It involves the use of mathematics and statistics in finance. Quant experts are known for deploying several programming languages such as C++, Python, Perl, and Java to create price behavior formulas commonly used to predict or ascertain profit opportunities in trading. This method has been a great strategy, just like the traditional system until the availability of complex stock market databases through cloud computing and blockchain systems.


Now, it’s harder than ever to base trading decisions on computer formulas. Hence, smart modern traders turn to Data Science, which involves Advanced Machine Learning Algorithms (AMLA). Data science is the new big deal in the modern-day trading system.

To better understand how the advanced machine learning system operates, it’s essential to be familiar with the following technical terms. 

  • Algorithms
  • Training
  • Testing


This term is a no-brainer in Stock trading. Algorithms control stock buying and selling. For instance, it can be programmed to sell a stock once it drops by 4 percent of the initial buying price over time. That is what this entails.


It is a concept in machine learning. It involves conditioning or training a machine on how to react to trade changes over some time. For instance, one can create a learning model that makes plausible predictions based on the information produced to learn from past stock behavior. In other words, advanced machined systems are trained with past trading records available from a wide variety of sources, e.g., Bank of Brazil, Federal Reserve Economic Data, each country’s GDP, and so on.



Before machine models are deployed in real-time situations to predict future occurrences in the trading market, several tests are carried out for fine-tuning machine responses. For instance, last year’s stock records can test-run data science models to find the amount of variation compared to the real data. That is, we can train our machine with last year’s stock data from January to October in an attempt to predict November and December. This prediction can now be compared to the real November and December data of that year to see how close the forecast is. 

The Importance of Data Science in Trading

Below are the highlights of several benefits of Data Science in trading, and it is the future in this area.

  1. In-depth Risk analysis.
  2. Unlimited, extensive data analysis in real-time from several sources to find recurring patterns.
  3. Algorithm trading through advanced systems.
  4. Data science is essential for reliable stock valuation with its dynamic nature.

With this informative article, you now understand how Data Science is different from plain quant analysis and the need to deploy data science in trading.

To read more on Data Science and its benefit in business, click here.

#DataScience #DataScienceinTrading #DataScienceandStockTrading #QuantTrading

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