Trading is a gruesomely competitive world. And with AI being painted as the new wonderweapon for everything, it’s understandable that there’s a huge amount of interest in discovering how to use AI for trading.
AI does play an important role in trading – but maybe not in the way you’d expect. Unfortunately, AI can’t be used to power a superhuman trading machine that steals human traders’ lunches in every market. Not even Renaissance Technologies has that capability. At least not yet.
Why not? 🤔
The short answer: human competition – more about that below. ⬇️
Meanwhile, the battles AI actually wins are much more incremental – but still significant. AI is more a trader’s side-kick than their replacement.
In today's competitive trading landscape, the integration of AI has piqued tremendous interest as traders seek to harness its potential. However, it's important to understand that AI doesn't function as an all-powerful trading machine, outsmarting human traders in every market. Even Renaissance Technologies hasn't achieved such capabilities, at least not yet. The reason behind this lies in the realm of human competition, as we'll explore further.
AI's true victories lie in incremental advancements that significantly enhance trading practices. Rather than replacing traders, AI serves as their valuable side-kick. Let's delve into the top four applications of AI in trading today:
While humans struggle to process vast amounts of information, machines excel in this domain. With AI, machines can analyze and summarize texts, including sentiment analysis. By collecting daily news, tweets, and social media posts through scrapers, AI algorithms, particularly Natural Language Processing (NLP), can determine the prevailing positive or negative sentiments. Moreover, AI can categorize texts into specific topics and generate human-readable summaries automatically. Traders find this invaluable, enabling them to stay up-to-date with the latest developments.
AI also aids traders in improving the accuracy of forecasting real-world data, which plays a crucial role in their success. Whether it's predicting weather patterns in the northeast over the next two weeks, estimating solar energy supply in Europe, or foreseeing the outcome of a political election, AI algorithms can refine existing predictions. One effective approach involves training an algorithm to combine multiple expert forecasts, resulting in a more accurate prediction than any individual forecast alone. This technique, known as ensembling, has proven to be highly effective.
Trading revolves around identifying localized patterns that often occur within specific timeframes and geographical regions, providing valuable insights for exploitation. However, manually finding these patterns can be a laborious and time-consuming process. This is where AI algorithms excel as proficient pattern-finding machines. Analysts can leverage AI to save time by identifying irregularities in datasets. While AI is adept at uncovering valuable patterns, the guidance of experienced analysts remains essential in guiding the AI's search. Traders then incorporate these patterns into their decision-making, complementing their own experience and intuition or employing them in the design of automated trading systems.
In high-frequency trading, machines execute thousands or even millions of trades per day, capitalizing on short-lived inefficiencies. Given the sheer volume of trades involved, humans are unable to perform at this scale. However, human traders define the rules that govern these trading machines. As market conditions constantly change, these machines require frequent adjustments, a process that demands substantial time and effort. AI can automate these recalibrations and perform the repetitive statistical work that analysts would otherwise need to undertake manually.
While the allure of discovering a miraculous algorithm capable of generating substantial profits is enticing, it's important to approach AI in trading with caution. There are several crucial pitfalls to be aware of:
Despite the remarkable progress made in AI algorithms, they still pale in comparison to the human brain's capabilities. While algorithms may defeat humans in chess or Go, it's akin to a car surpassing a human runner in a quarter-mile race. Yes, the machine is faster, but it doesn't signify overall superiority. Trading is a multifaceted endeavor wherein humans compete against each other, leveraging their intellect to outwit opponents.
Many research papers and blog articles tout profitable AI-based trading algorithms. However, these models often fail to deliver in real-world scenarios. Various factors contribute to this discrepancy, such as faulty setups, selection bias, and the impact of transaction fees and slippage. Additionally, patterns in trading are transient, constantly changing, necessitating adaptability that current AI systems struggle to achieve.
Placing undue emphasis on algorithms as the main differentiator between trading strategies can be misleading. Data, rather than the algorithm itself, significantly influences a model's performance. Companies like Numerai propose combining numerous models into a supermodel, aiming to outperform the stock market. However, the limited dataset provided by Numerai puts them at a disadvantage compared to traders who have access to a vast pool of open data for continuous experimentation and refinement.
Markets with few participants, high barriers to entry, limited trading volume, and a scarcity of Machine Learning adopters present fertile ground for pure-play AI trading success. In these nascent markets, automated trading, especially employing Machine Learning, is still in its early stages. Traders who develop automated trading engines could potentially gain a competitive edge, yielding profitable outcomes.
In summary, AI's impact on trading goes beyond supplanting human traders. By analyzing sentiment, forecasting real-world data, identifying patterns, and streamlining high-frequency trading machines, AI becomes an indispensable ally to traders. Nonetheless, it's crucial to navigate the challenges inherent in AI adoption, recognizing the limitations of current AI capabilities while seeking opportunities in less efficient markets.