Stock trading underwent drastic changes with the advent of AI-enabled algorithms, and the industry is estimated to reach $50.4 billion by 2023. As AI-enhanced trading promises big pros, like increased trading accuracy, efficiency boost, and minimized costs, more and more traders and companies are considering joining the current trend. To better understand the AI stocks trading bots, let’s review the definition of an AI bot and how they have evolved over time.
What’s an AI Trading Bot?
AI bots are tools tailor-made for trading on the user’s behalf based on preexisting instructions. AI bots perform the typical trader’s functions, like scanning market opportunities, optimizing portfolios, and executing trades without human involvement. These features allow the tools to increase the accuracy of data interpretation, distinguish market patterns, and uncover hidden insights. Another AI-enabled trading pro is sentiment analysis, which involves assessing current market sentiment via social media and other online activity to combat the unpredictability due to geopolitical issues and natural disasters.
AI Trading Bots: Key Advancements in the Sphere
The early quantitive trading models, the proto-AI bots, appeared in the 1980s. The key challenge of the innovation was in its rigidity: by following a set of rules, early AI trading bots lacked flexibility and couldn’t react to the constant shifts and changes in the market. The drastic change came a decade later with the advent of machine learning algorithms. It allowed early trading bots to adapt to market challenges and laid the foundation for today’s AI bots. Despite the rapid advancements in the sphere, the bots continue to evolve. Let’s look at the tech involved in the process.
1. Deep Neural Networks
Deep learning networks allow AI bots to learn from examples and patterns, like historical price data, market data, and other indicators. Due to the implementation of neural networks, AI bots can perform stock trading with increased accuracy and assist traders in making better decisions. This comes with a twist: unlike other machine learning methods, deep neural networks require millions of examples of training data, which can make the learning process more time-consuming. Thus, the larger the dataset, the slower the training.
2. Big Data Analytics
Making decisions and analyzing large volumes of trading data in nanoseconds is impossible. That’s where big data analytics comes into play. By uncovering patterns and correlations in massive amounts of data available in the financial markets, data analytics is crucial for an AI bot’s ability to analyze information quickly.
3. Sentiment Analysis
Another technology involved in advancing AI trading bots is sentiment analysis. Based on natural language processing, sentiment analysis helps AI bots understand the market and sentiment behind human language. After all, many geopolitical events can uproot the stock market and increase its unpredictability. Sentiment analysis is tailor-made to combat such volatility by assessing current sentiment in social media, news articles, etc., which helps make more accurate predictions.
The evolution of AI bots is an ongoing process that involves machine learning, big data analytics, deep neural networks, sentiment analysis, and other technology that improves the bots’ performance and changes the current trading landscape. As more and more companies and traders rely on bots to analyze massive amounts of data and market sentiment, AI bots will continue their advancements and reach even higher accuracy in their predictions and analysis.