Don’t let the influencers fool you – making money trading crypto and stocks is hard.
While social media is littered with Instagram posts and Tweets (sorry — Xeets) from traders showing off their massive wins – and flexing all of the other accoutrements finance influencers are notorious for – making sizeable returns is never a simple or straightforward task.
And it’s not just everyday traders who struggle to make consistent gains. Even the most experienced financial professionals routinely fail to outperform the annual growth of the S&P 500 — an index that tracks the value of the 500 largest companies in the United States.
So, why is it so hard for everyday traders and experienced investors alike to make money trading crypto or stocks? The biggest reason for our collective failures boils down to one simple factor:
Emotion.
When markets are volatile, our primate brains have a tendency to make overly emotional decisions. We look for and create patterns that don’t exist and allow our feelings to lead us down a path of poor decision-making.
When prices are going up, we humans are prone to fits of excitement. We pile money into assets that proceed to plummet soon after — or, on the flip side — we sell when we should’ve been buying as we try to slash our perceived losses.
On the opposite side of the investment spectrum stands artificial intelligence (AI).
Cold and calculated, these machines are perfect for making quick, efficient and emotionless decisions. But as many financial experts have rightly pointed out, they still lack human intuition that’s capable of landing in the “Goldilocks” zone and picking the right amount of risk.
So, who comes out on top? Humans or the cold, lifeless machine?
AI vs. humans – who makes more money?
In the quest for returns, the competition between humans and machines has never been fiercer.
A 2017 study published in the European Journal of Operational Research was based on research conducted using AI bots to trade historical markets. The most notable observation was that AI specifically outperformed humans during periods of high volatility — that is, when the price of assets fluctuated more significantly than usual.
For example, in 2008, when the global financial crisis saw many portfolios evaporate into thin air, one of the AI systems accrued a staggering 681% return. Similarly, when the dot com bubble burst in spectacular fashion in 2000, the same AI system returned a whopping 545%.
Still, there’s a monumental difference between training an AI system to trade historical markets and making reasonable predictions about current and future markets.
According to a broad-scale analysis of 27 peer-reviewed studies on AI trading by The Conversation, the vast majority of AI modelling was successful in theory.
However, it concluded that fully autonomous AI trading systems remained unfeasible in the real world financial industry due to a number of issues with transparency, accuracy and data collection.
Ultimately, a broad study conducted by researchers from the University of Cambridge and Oxford Brookes University found that many of the AI success stories from academic studies simply didn’t live up to the hype when implemented in real life, largely due to predictive errors in the current versions of the technology.
AI trading: a complementary tool
Instead of taking an either-or approach, many professional investors look at AI as a helpful tool that can be designed to assist human traders, instead of replacing them outright.
Notably, AI’s reported successes in high volatility is one of the factors that makes the case for leveraging AI bots in crypto trading so compelling, as cryptocurrencies are far more volatile than traditional assets such as stocks and bonds.
Speaking directly to this point, Bitget’s Managing Director Gracy Chen, told Cointelegraph how her crypto exchange had recently introduced Commodities Trading Advisor (CTA) AI bots on its platform to help traders gain an edge on volatile markets.
“CTA strategies, in principle, grasp market fluctuations based on the relationship between volume and price. They are more effective in more volatile markets, such as cryptocurrencies,” Chen said.
When it comes to trading in any market, the ability to make quick and accurate decisions is one of the most important skills in a trader’s toolkit.
Chen said that humans — plagued with uncertainty and bias — often failed to make efficient decisions due to stress, distraction and pressure, and that using a tool such as Bitget’s Grid Trading AI bot could help traders take advantage of market opportunities before other human traders could react.
AI can also increase trading efficiency, by helping traders to notice and react more quickly to upcoming changes in market conditions. This allows traders to get a leg up on emerging market trends and provides an advantage over other investors, who are still relying on manual analysis and research.
Information that once took weeks for investors to learn and digest can now be processed and executed within the span of just a few hours or minutes.
The other major upside of a potential AI integration is that it can automate the execution of trades, reducing the need for manual button clicking and freeing up time for more research.
Choosing the right type of AI for trading
Everyone’s investment journey looks different. Some investors are risk-hungry, comfortable with huge swings in the value of their portfolio. Others prefer a more safe and sound approach, where smaller, consistent gains are stacked gradually over time.
For this reason, it’s important for traders to be able to access a platform that offers a diverse range of AI-powered options for all risk levels.
Bitget says its AI trading bots come in three tiers, each one adjusted to a different level of risk appetite:
- The conservative AI bot is suitable for risk-averse beginners.
- The secure AI bot is suitable for risk-neutral investors.
- The aggressive AI bot is suitable for risk-taking, adventurous users with rich experience and a high trading frequency.
Take the Martingale bot as an example. A user can set the number of safety orders, multiples, take-profit targets and other parameters based on their own portfolio and risk appetite.
For those of you not up to date with all of the investment lingo, a Martingale strategy (also known as dollar-cost averaging or DCA) is one where investors double up on their losing trades and reduce their already winning trades by half. Put simply, it’s a strategy that promotes a loss-averse mentality that tries to improve the odds of breaking even.
AI vs humans: the verdict
Overall, a fully autonomous AI-based trading strategy doesn’t seem to be a completely feasible option for most investors.
However, integrating AI tools such as trading bots into one’s investment strategy — especially in the volatile landscape of crypto markets — can provide some much-needed efficiency and automation in the fast-paced and ever-changing world of digital assets.
By using a diversified selection of AI bots, investors can gain access to a series of personalised tools that suit their individual investing goals. This can empower everyday investors who don’t have a profound expertise in trading with a set of sophisticated tools to level up their investment strategy.
Disclaimer: The opinions expressed in this article are for informational purposes only. This article does not constitute an endorsement of any of the products and services discussed, or investment, financial or trading advice. Qualified professionals should be consulted prior to making financial decisions.
