
Artificial intelligence has long been recognized as a game-changer in trading, offering enhanced accuracy, improved efficiency, and significant cost savings. Although bypassing human involvement brings unique challenges — like AI’s struggle to appropriately react to unanticipated market and global events that lead to suboptimal trading decisions — the overall benefits of AI-enabled software make more companies and traders join the ongoing trend. In this article, we’ll delve deeper into the world of artificial intelligence stocks trading, exploring the evolution and key advancements in the field to equip you with the insights to bring your trading to the next level.
What’s an AI Trading Bot?
AI bots are software programs that follow a set of pre-existing instructions and trade on behalf of the user. This automated tool is a boon to any trader, as it crunches large swathes of data in record time, monitors market shifts round-the-clock, optimizes portfolios, and executes trades, bypassing human involvement. By leveraging historical data, AI bots identify market patterns and reveal hidden insights, with their data interpretation accuracy improving consistently over time.
Of course, like any other complex system, AI bots are not without their drawbacks. The overreliance on past stock market data to predict future events is one of the biggest identifiable issues that plague the software. Although AI-enabled sentiment analysis tries to resolve it by assessing the current market sentiment via news, social media, and other online activity, unpredictable geopolitical events and natural disasters can throw a wrench into a carefully designed strategy. That’s why AI bots are typically used as auxiliary tools that facilitate the trader’s everyday workload and help distinguish market patterns.
AI Trading Bots: Key Advancements in the Field
The first proto-AI bots emerged in the 1980s, but these early quantitative trading models were rigid and unable to adapt to the rapidly changing dynamics of the market. The advent of machine learning algorithms addressed the problem and adapted the bots to the challenges of the market. The AI bots continue to evolve with some of the prominent tech involved, including:
- Big Data Analytics. No human trader can analyze large volumes of data in nanoseconds. Big data analytics helps uncover correlations and patterns in huge data sets and boosts AI bot’s ability to quickly respond to market changes.
- Deep Neural Networks. Learning from historical price data, market data, and other examples, deep learning networks help AI bots execute trading with higher accuracy. Deep learning networks require millions of examples of trading data, which makes them time-consuming but essential for the future evolution of AI bots.
- Sentiment Analysis. Based on natural language processing, sentiment analysis aims to combat market volatility by analyzing and assessing current sentiment in news articles, social media, and other outlets to predict future geopolitical events that can uproot the stock market.
Does it mean that AI bots’ improved accuracy and automation will substitute human traders? Many experts argue that it’s the wrong way to look at the ongoing trend, as AI-powered bots should be primarily regarded as auxiliary tools that bolster human traders’ intuition and in-depth understanding of market mechanics.