Data analytics has been a core tenet of trading for generations — the difference between making a profit or a loss can sometimes be a matter of millimetres or milliseconds. Big data is helping firms and individuals access the right information quicker than most of the competition. This blog aims to explain how online traders can work with artificial intelligence and big data. Are you ready? Let’s dive in.
AI and Big Data
Artificial Intelligence (AI) has slowly become an essential tool in the Arsenal of trading corporations and retail traders. This technology won’t be as useful to us without the input of datasets. Big data refers to collections of information that are incredibly massive, complex, and fast-growing. Considering the sheer size of these datasets, investors often have to connect, merge, and piece together several parts before making informed decisions — this process is what we call synthesising big data. These massive datasets can’t be effectively synthesised using most traditional analysis tools and frameworks due to their size and complexity.
Machine learning plays a key role in making AI work by using data-driven models and algorithms to analyse information. While there are many AI-powered statistical analysis tools available to the public and for commercial use, most large companies either develop their own in-house solutions or work with providers like AWS.
Market Insights Through Advanced Analytics
Data analytics is the simple process of examining raw information and drawing conclusions from it. When we think about data analysis, most people imagine complicated dashboards, numbers, and decks. While that might be true to some degree, it is also important to note that it is a part of everyday life; it can be as simple as keeping track of how much you work and its correlation to your health.
On a broader scale, it significantly influences market movements and trends, and over time, traders have been investing incredible sums into tools and products that can help investors gain an edge in the market.
Data operations are not exclusively for large corporations with complicated algorithms and quants. CFD trading professionals and retailers can use tools like Excel and Power BI to make sense of the information available to them. At the same time, advanced trading tools have analytics features that can change your life as a trader, opening up new opportunities and possibilities.
Risk Management
An essential aspect of big data and AI in trading is risk management and analysis. Traders are interested in knowing if their actions will lead to positive results; beyond that, these forms want to know if an action is the best use of resources at any given time. Machine Learning (ML) tools are widely used in predictive analytics through the extraction of large datasets from databases. Risk managers can use classification, regression, anomaly detection, and clustering for volatility forecasting, fraud detection, sensitivity analysis, portfolio optimisation, and much more.
Once again, big data is facilitating the use of AI in risk management. Retail traders can now gather and synthesise significantly more digital intelligence than ever before in a fraction of the time previously required. The importance of risk management cannot be overstated; that’s why massive firms are investing significant resources to ensure that they identify and tackle potential issues before they fully materialise.
Big Data Tools
There is a plethora of different tools available for trading-related analysis. Some of them include the following.
Storage and Management
- Apache Hadoop is an open-source framework used for processing large datasets and distributed storage.
- NoSQL Databases include databases like HBase, MongoDB, and Cassandra, and they often offer diverse structures and types, scalability, and flexibility.
- Cloud storage solutions like Azure Blob Storage, Amazon S3, and Google Cloud Storage
Processing Analysis
A few tools in this category include;
- Apache offers a wide range of analysis and processing products, including Kafka, Flink, and Spark.
- Hive serves as a statistical warehouse system providing “SQL-esque” querying capabilities as it is built on Hadoop.
- MapReduce is a programming model used when processing large datasets across computer clusters.
Other notable tools include Tableau, Qlik, Zoho, and Power BI for data reporting and visualisation, and Apache NiFi and Talend for management and integration. But keep in mind that the tools listed above form only the tip of the iceberg.
Challenges and Considerations in Using Big Data for Trading
Security and Privacy
The sheer scale of information used in algorithmic trading poses privacy concerns and security threats. Simultaneously complying with protection regulations while protecting financial parameters from threats is not an easy task.
Quality and Accuracy
Most big data operations use obscene amounts of figures for analysis, posing the risk of “garbage in, garbage out.” Ensuring the quality and accuracy of sources you use is of paramount importance. Many AI systems do not seem to care enough about quality, so it would be in your best interest to do your due diligence.
Transform Your Trading Strategy Today
Big data, machine learning, and AI have allowed regular online traders to do more and “punch above their weight class.” You can now synthesise greater volumes to help you make the best trading decisions. What are you waiting for? It’s your time to win.