AI-Driven Crypto Trading: Naive Myth or the Industry’s Future?

AI is taking the world by storm. ChatGPT has swiftly become a household name; Midjourney has gone from relative obscurity to professional use in less than a year, and other AI platforms are quickly making a name for themselves.

We are witnessing a pivotal point for many industries. Nvidia’s CFO called the rise of AI the ‘new iPhone moment,’ Microsoft invested billions in OpenAI, and startup funding in the technology skyrocketed to over 11 billion USD in May. Trading, and more importantly, crypto trading, has not been spared from AI integration. What remains to be seen is whether crypto traders can take advantage of AI.

We’ll explore some of the leading implementation trends from the past year, as well as how the industry can prevent the excitement of this new tech from precipitating reckless decisions.

Is AI Too Good to be True?

It is not unreasonable for industry veterans to treat frontier tech with skepticism. After all, crypto’s promises of mountains of gold often led to Ponzi schemes, fraud, or market collapses. This does not seem to be the case with AI trading.

As early as 2018, two Yale economists showed with econometric analysis that cryptocurrency returns could be consistently predicted from a variety of industry-specific factors, such as the time-series momentum effect and measures of investor attention. Their studies found that if you have accurate data and know how to analyze it, you are significantly more equipped to outperform the market. When comparing these findings to the capabilities of AI, their importance becomes clear.

Algorithms can analyze large amounts of data to find inconspicuous patterns and execute trades much faster and more frequently than human users. If a human chooses to do day trading manually, they will inevitably lose the efficiency race in the long run.

As a species, humans are susceptible to cognitive biases, such as FOMO or loss aversion; a loss of 5% is perceived as much more significant than a gain of 5%. This leads to ‘chasing losses’ to win back what was lost, and more often than not, will lead to suboptimal decision-making. In 2014, a group of scientists analyzed 28.5 million deals to evaluate this effect. They found that only mirror trading (sticking to a predetermined trading strategy regardless of performance) yielded positive ROI. Discretionary trading, on average, led to hasty choices and losses.

The Current Trends in AI Implementation

Any extensive data analysis or trading strategy needs to be recalibrated to account for market volatility, information events, and other parameter changes. While seasoned full-time traders can afford to engage in constant market research, even they can miss more subtle signals. AI can help to create self-adapting schemes that operate 24/7 and react to the slightest changes in the market.

For these aspects, the main developing niche for AI in trading is trading bots. Cryptohopper, one of the largest strategy and bot trading marketplaces, recently rebranded itself with an exclusive emphasis on AI, while the token of SingularityNET, another AI bot marketplace, ranks among the top 100 cryptocurrencies by market cap.

DeFi and crypto asset management startups like Quadency are also turning towards AI solutions as a substitute for manual governance. Many seed-stage firms boast of offering their clients an opportunity to create personal AI trading bots and fine-tune them to their levels of risk tolerance and asset class preferences. For now, it is unclear whether they leverage proprietary technology or just integrate ChatGPT into their platforms.

AI as the Trader’s Best Friend

As AI grows in both sophistication and adoption, it’s important to remember that the technology is not perfect. For example, if a developer fails to notice overfitting while training an AI model, the whole portfolio may be put at risk. While automated strategies may be effective in technical analysis and day trading, they may misstep in more complex tasks like analyzing investor sentiment. Even ChatGPT, with all its popularity and success, puts a disclaimer that its text recognition algorithms are not flawless.

One popular compromise is to use AI trading signals but leave the final decision of whether to execute the trade to the investor. An example of this approach is the AI assistant Walbi Lighthouse. This feature leverages AI to automate and accelerate market research, allowing the trader to receive real-time analysis of technical indicators, gauge market sentiment, and compile both influencer tweets and currency-related news in a single digest. In this way,Walbi Lighthouse represents an approach that uses the best of both worlds: the power of AI data analysis and the complex rationality of human decision-making.

The Man or the Machine?

For now, it’s simply too early to celebrate the AI overtake of the crypto trading sphere, but even if AI bots are unable to replace fully proprietary trading, AI-powered assistants like Walbi Lighthouse are an indisputably positive innovation. Not only will it accelerate access to information and significantly widen the available source base, but also will lower the barriers to entry for beginning investors, making cryptocurrency trading more accessible. Whatever the future holds, AI will be a part of it, and AI assistants represent this technology's first steps toward wider acceptance.

Want to learn more about Walbi? Check out the project's blog and subscribe to its Telegram and Twitter channels.