Big data is creating more of a boom than it is having a spreading effect in the world of trading. The exponential growth of this technology has several implications. Several industries today are changing because of the increasing complexity and data generation, and the financial sector is no exception.
In trading, accurate inputs into business decision-making models are necessary. However, these decisions were made by humans in the past, as they make deductions from trends and calculated risks.
Big data analytics are now used to make informed decisions, predict market trends, and improve the profitability of trading brokers. These decisions and predictions are made by machine learning and algorithms beyond the capabilities of human beings.
Leveraging Big Data
Financial analytics has gone beyond merely examining prices and price behavior in today’s world. It integrates the principles that affect prices and social and political trends and elucidates support and opposition levels.
Additionally, dig data analytics can also be used to estimate the rates of return and the probable investment outcome. This is employed in predictive models. Also, it confers the ability to effectively mitigate the potential risks associated with financial trading, as increased access to big data results in more precise predictions.
With machines trading independently of human inout, high-frequency trading has been used quite successfully up until now. However, the computing timeframe puts this method out of the game as every second is of the essence, and big data usually implies increasing processing time. Moreover, the paradigm is changing as traders are realizing the value and advantages of accurate deductions they can achieve with big data analytics.
Other ways big data is changing financial trading include:
- Real-time analytics
With machine learning, computers make human-like decisions, executing trades at rapid speeds and frequencies that people cannot. This model incorporates the best possible prices, traded at specific times, and reduces all manual errors that may arise due to behavioral influences.
Real-time analytics can improve the investing power of high-frequency trading firms and individuals. This is because the playing field has been leveled by insights gleaned by algorithmic analysis as access to powerful information has been provided.
Limitless possibilities remain the forte of algorithmic trading. Structured and unstructured data can also be used; thus, intuitive judgments can be made from social media, stock market information, and news analysis.
- Machine learning
Machine learning enables computers to learn and make decisions based on new information. This is achieved by employing logic and learning from past mistakes.
By using this, these technologies can deliver highly accurate perceptions. The possibilities of this technology remain promising even as it is still developing. Additionally, this avenue of research removes the emotional response of humans from the model. This enables the model to make decisions based on information without bias.
Technology Stack Used in Big Data Projects
The following components are the technology stack used in big data projects in trading broker companies:
1. Data Collection:
Trading brokers use different data collection tools to gather data from different sources. Web crawlers, APIs, and data feeds are examples of these tools.
2. Data Storage:
Large amounts of storage are needed in big data projects. Distributed file systems such as Hadoop and NoSQL databases such as Cassandra or MongoDB are used by trading brokers to store their data.
3. Data Processing
Trading brokers use tools like Apache Spark and Apache Storm for data processing. This is because these tools allow for real-time data processing and analysis.
4. Data Visualization
Tableau and QlikView are examples of data visualization tools trading brokers use to create interactive dashboards and reports.
Conclusion
Big data technology has revolutionized the mode of operation of trading brokers. Using big data analytics, trading brokers can make informed decisions, predict market trends, and improve profitability.
However, there is an inordinate potential for computers to take over this sector in the near future. With big data, more information can be fed into a system that thrives on the knowledge of all possible influencers. The technology makes trade more accurate and informally possible, dramatically impacting financial transactions.
