Financial markets — from traditional Exchange-Traded Funds (ETFs) to cryptocurrencies — risk being upended in the coming months and years as traders and platforms integrate artificial intelligence (AI) into daily trades.
It brings a giant shift in trading, from large language models (LLMs) plowing through masses of data to seek short-term signals and long-term trends to AI agents making trades instantly and automatically. Can a human ever outcompete a bot?
In a landscape where everyone is seeking an edge, what are the benefits and cons of AI trading agents, and will legislation be needed to try and keep a level playing field?
Techopedia talks to those developing AI trading bots to understand the present and future of the financial industry.
Key Takeaways
- AI trading agents use advanced AI techniques like sentiment analysis, predictive analytics, and reinforcement learning to make trading decisions.
- These agents combine machine learning models, including deep neural networks and NLP, to analyze data and optimize trading strategies.
- Challenges include model drift and the potential for regulatory limitations as AI trading agents become more sophisticated.
- While still under development, AI trading agents may democratize access to financial tools and improve investment outcomes — but caution and vigilance are needed.
How AI Uses Machine Learning to Trade
Incredible advancements have been made in artificial intelligence by combining different machine learning (ML) models and techniques, leading to the recent explosion of generative AI and AI robotics. The finance industry is taking a similar approach.
Christina Qi, former hedge fund CEO and current CEO of Databento, a market data application programming interfaces (APIs) provider, spoke to Techopedia about how these models combine to invest and trade.
“These agents combine historical data analysis with real-time market signals, news sentiment, and even macroeconomic trends to optimize trade execution.”
Qi said that AI agents go beyond standalone AI models, using techniques like reinforcement learning, deep neural networks, and natural language processing (NLP) to make dynamic, autonomous trading decisions, all set in motion by a simple prompt.
Victor Tan, co-founder of GameGPT and Rainmaker Games and CEO and Founder of TrinityPad, which uses AI to simplify trading platforms, explained further:
“Sentiment analysis, powered by the most advanced AI language models, processes vast amounts of social and news data to gauge market sentiment and identify trends before they manifest in price movements.”
AI models are trained on specific real-world data, and AI trading agents are no different.
And yet, because global markets can change dramatically from one day to another due to market volatility or even Black Swan events — like geopolitical crises or a global pandemic — the risk of “model drift” is significant.
When an AI model drifts, its performance tanks as it runs on outdated data. In trading and investment, a model drift can lead to significant financial losses.
So how do developers deal with the unexpected?
Tan explained that models are constantly trained to adapt to new data, including economic shifts or Black Swan events like pandemics.
“Robustness is achieved through extensive backtesting across various market conditions, coupled with real-time adjustments powered by dynamic risk management algorithms.”
The AI Trading Bots of Today Are ‘Still Early’
It’s important to remember that AI trading agents are still being developed. Just because countless sites online claim their AI can maximize returns, investors should exercise caution.
Johnny Gabriele, Head Analyst of Blockchain Economics and AI Integration at The Lifted Initiative, an organization working to simplify Web3 development, recognized the potential of AI in trading but shared some words of warning:
“At the moment, robustness and reliability are exactly the Achilles’ heel.”
Gabriele added that most AI agents use LLMs under the hood — both a strength and a weakness.
“Having five different models with particular tasks has proven a very effective way of creating agents that transcend the Chatbot model.
“The limiting factor of an LLM does mean that very fine-tuned tasks and complicated tasks like trading a market post very strong challenges.”
Will AI Agents Be Banned from Trading in the Future?
If AI trading technologies continue down the road of professional development, there might come a time when they are just too good to be allowed to play in the market.
Tan from TrinityPad offered thoughts on the current state of play:
“The regulatory landscape for AI trading agents is still evolving. While some jurisdictions have introduced frameworks for AI and fintech, many are in the process of defining clear guidelines.
“As AI trading agents become more efficient, there may be calls for limitations to ensure fair market practices and prevent systemic risks. However, we believe that regulation and innovation can coexist.”
Tan added that AI agents can democratize access to sophisticated financial tools while maintaining market integrity.
“By prioritizing transparency, ethical AI use, and user education, the future of AI-driven investing can be both efficient and equitable,” Tan said.
The Bottom Line
Countless traders and investors have mastered their profession and understand global markets better than the rest of us. Can AI do the same? Will AI trading agents perform even better?
Or is the innovation of AI trading agents a way to democratize access to state-of-the-art technology and financial tools — a way to level the playing field?
AI can help us better understand the complexities of the modern financial industry — or perhaps help us shortcut through the vast amounts of information.
However, as always, in finance, trading, and investment, caution and vigilance are a must.
FAQs
How do AI trading agents make decisions?
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References
- Christina Qi – Databento | LinkedIn (Linkedin)
- Databento (Databento)
- Victor Tan – TrinityPad | LinkedIn (Linkedin)
- Trinity Pad | Invest Confidently (Trinitypad)
- Johnny Gabriele – Head Analyst of Blockchain Economics and AI Integration – The Lifted Initiative | LinkedIn (Linkedin)
- The Lifted Initiative (Liftedinit)