Ghost Trader — Beating AI and the Market

If you have been paying attention to the hottest trends in the market lately, you have undoubtedly encountered lots of excitement about the most recent advancements in Artificial Intelligence (AI). AI has become increasingly popular, practically everywhere and all at once. It has begun to penetrate all corners of our society. From AI-generated art to Chat GPT, it has even made its mark on the broader casual market as well. One thing we know for sure, it has already initiated change in our society in ways with which we have only begun to reckon.

The Dawn of AI

The first inkling of this new epoch of human history began more than a quarter-century ago, when Deep Blue beat Garry Kasparov — one of the greatest chess masters who has ever lived. Ever since, AI has continued to make impressive leaps in capability. For instance, the traditional Japanese strategy game, Go, represents an increase in complexity of a orders of magnitude when compared to chess. Nevertheless, Google’s DeepMind AlphaGo artificial intelligence managed to defeat the world’s number one Go player, Ke Jie, back in 2017.

For a more in depth discussion of the difference between the two games, please go here. For a much deeper discussion of the general promise of AI, take a look at the video here.

Predictably, AI has also gained significant commercial traction in recent years. Companies have begun to employ AI technology for all manner of applications. Of course, the utility applies to financial markets as well, where users rely on AI to analyze large amounts of data, predict market trends, and automate trading.

Algo vs. AI

Before getting into the weeds here, we do need a bit of clarification. We must be careful not to confuse algorithmic trading with AI-based approaches, of course. While algorithmic trading and AI trading are both automated trading strategies that use computer programs to execute trades, some key differences between the two remain.

Algorithmic trading, also known as “algo trading,” involves the use of pre-defined rules to execute trades based on specific market conditions. Algo creators can base their models on all manner of technical indicators. Those indicators include metrics such as moving averages or relative strength index (RSI), or on fundamental factors like earnings reports or economic data. The key takeaway here about algorithmic trading programs is this. They follow explicit rules that determine whether to buy or sell assets automatically. These actions occur without human intervention, and are essentially automatic.

AI trading, on the other hand, employs artificial intelligence and machine learning algorithms in order to analyze vast amounts of data, and to identify patterns and trends that can be used to make trading decisions. AI trading programs can “learn” from past market data, allowing these programs to make predictions about future market movements. This data-driven adaptability enables AI programs to adjust their trading strategies based on the analysis of statistical evidence.

While static rules generally form the basis of algorithmic trading, AI trading can adapt and evolve over time as it learns from new input data. As a result, AI trading requires more sophisticated technology and expertise to develop and maintain compared to algorithmic trading.

To keep things simple, algorithmic trading uses pre-defined rules to execute trades. AI trading uses advanced machine learning algorithms to make trading decisions based on patterns and trends in market data. In this way, AI represents the predictable next generation for code-based technology.

Disadvantages of Relying on AI for Trading

While no one can doubt the power and potential of AI as a means of improving the human condition, AI does suffer from some disadvantages. While AI excels at tasks that require brute force for crunching data, it still has a ways to go before besting human ability in the financial world.

For one, AI lacks the benefit of human input. AI algorithms can only perform as well as their underlying coding and the data they are fed will allow. At the moment, AI cannot take into account external factors that could impact the market, such as political events, financial crises, and natural disasters. It also lacks the human element of intuition and instinct, which can be valuable in making trading decisions.

Additionally, the absence of human judgment forces AI to rely too heavily on historical data. AI algorithms use historical data to predict future trends. Much like with standard algo trading, AI remains limited by the data that forms the basis for its learning. This limitation can lead to over-reliance on past performance, because — as we have all read in the fine print in investment prospecti — past results cannot accurately predict future performance. This becomes particularly problematic during periods of high volatility. Indeed, we seem to have entered a prolonged period of higher-than-normal volatility. This trend has not subsided, and likely will not for the foreseeable future.

Just as the success of AI depends on the data being analyzed, so does AI suffer from the risk of algorithmic bias. While certainly we all welcome the arrival of our silicon-based overlords, we must remember that humans ultimately design all AI algorithms. As such, these algorithms all suffer from the inherent coding bias towards certain types of investments or market philosophies. This bias can lead to a lack of contextually strategic guidance, exposing traders to unnecessary risk.

Of course, last but not least, we must understand AI as a technology. Just as with all forms of technology, it can and will suffer from technical issues. A good rule of thumb is this. The more complex a system of technology, the greater the potential for failure. AI systems represent the pinnacle of the state of the art of human coding potential. As such, this complexity leaves AI prone to technical issues like malfunctions or malicious hacking.

AI and Decentralized Finance — Not Quite Ready For Prime Time

Naturally, a number of projects and developers have begun to incorporate AI into new projects in the digital asset space. It simply makes sense, with decentralized finance (DeFi) being such an innovative and rapidly growing industry. The potential for us all could prove immense. The openness, transparency, and accessibility that DeFi promises lends itself easily to new approaches to providing new opportunities and solutions. The blockchain space has become the newest, most exciting frontier for transformational economic and financial progress we have ever seen.

As we established previously, though, AI has not reached the promised land quite yet. To put this in a more easily understandable context, consider this. Engineers can now build a machine that almost never misses a basket. This technology has wowed crowds for a couple of years now. But if you were to require that machine to guard Michael Jordan — even an older, retired Jordan literal decades past his prime — those engineers who created the robot would soon find a problem they cannot solve.

The long and the short of it remains. AI cannot replicate the human intuition and judgment that develops over decades of lived experience. Nor can AI take advantage of that sort of knowledge — yet. While our team can adapt quickly to changing global conditions, flexibly adjusting their strategies accordingly, AI has a ways to go before it can replace the genius and ingenuity of the human mind when it comes to trading and managing risk.

At the end of the day, our trading team relies heavily on their hard-won lessons. They have paid the tuition in both time and effort. This expertise allows them to make decisions based on factors that algorithms cannot easily capture for use in AI-based trading. This X-factor becomes especially valuable in situations like the market conditions of the last year or so. It also explains how Ghost Trader has managed to achieve such stunning and consistent success while other projects failed, despite the washing machine-like volatility of the last 16 months.

While we certainly would not characterize ourselves as Luddites, we believe that replacing the human element in the markets will take a much longer time than boosters of the technology want you to believe. And until it does, Ghost Trader will remain, providing consistency and peace of mind to all of our contributors.

Join Us

We would like to invite you to join us on this journey. No better time exists than the present to bridge the divide. Please be sure to stay tuned to our social media outlets moving forward for updates and news of the Ghost Trader project. We invite you to check out our official Ghost Trader website, join us either on Telegram or Discord, follow us on Twitter, LinkedIn, Facebook, and Instagram. Also, be sure to check out the podcast found here.