Crypto: Why AI probably won’t make you money

In recent years, artificial intelligence has become one of the most popular topics in the investment world. On the internet, many tools now promise to predict the price of Bitcoin, identify the next tokens with high potential or even automatically generate profitable trading strategies.

The idea is attractive. If a machine can analyze millions of data in a few seconds, it should logically be able to detect opportunities invisible to a human.

However, the reality is often very different. In practice, artificial intelligence is unlikely to become a reliable source of profits for the majority of retail investors.

To understand why, we need to go back to the way financial markets actually work.

The information is already integrated into the prices

One of fundamental principles of markets is that the available information is quickly integrated into prices.

When important data appears, thousands of actors analyze it at the same time. Investment funds, professional traders, financial institutions and algorithms react immediately.

In this environment, any obvious opportunity tends to disappear very quickly.

It is precisely for this reason that large institutions invest considerable sums in technology. They seek to analyze data faster than other market players.

In this context, a individual investor using an artificial intelligence tool accessible to the general public generally leaves with several lengths of delay.

When the odds are stacked against the individual in the prediction game, the alternative is to focus on how exposure is constructed, scheme by scheme, rather than the next trade.

The strategy proposed by Neutralis details this alternative: a clear framework and compromises so that the structure does the work that prediction cannot do.

The best models are not public

Another point that is often underestimated concerns the quality of the models used by professionals.

Quantitative hedge funds sometimes spend hundreds of millions of dollars on research, data and IT infrastructure.

Some funds employ entire teams of researchers in mathematics, computer science and artificial intelligence. Their models rely on extremely expensive datasets and highly advanced technological infrastructures.

These models are obviously not publicly available.

THE tools accessible to individuals generally use much simpler approaches, often based on public data that everyone can already analyze.

Models rarely work as well in reality

Even when artificial intelligence produces interesting results In simulations, actual performance may be very different.

A well-known phenomenon in finance is the overfitting. A model may appear to perform very well on historical data but quickly lose its effectiveness when market conditions change.

Financial markets are constantly evolving. THE statistical relationships observed in the past do not always remain valid in the future.

This is particularly true in the crypto-asset space, where market cycles can change quickly and new players are constantly arriving.

Psychology remains a major factor

One of the most difficult elements to model in markets is investor psychology.

Fear, euphoria or panic can cause market movements that do not follow any simple statistical logic.

For example, in November 2022, the fall of the FTX platform caused a wave of panic in the crypto markets. Prices fell sharply in a few days, not because of a particular technical signal, but because of a shock of confidence in the ecosystem.

This type of event is extremely difficult for an artificial intelligence model to anticipate.

AI can nevertheless remain a useful tool

All this does not mean that artificial intelligence is useless in investing.

On the contrary, it can play an important role in certain specific tasks. It can analyze large amounts of data, detect anomalies or help optimize certain parameters of a strategy.

In practice, many professional players use AI as an analysis tool rather than as a system capable of predicting markets autonomously.

The difference is important. AI can improve certain processes, but it does not automatically turn a strategy into a profit machine.

Understanding these dynamics in detail

A Neutralis conference showcases quantitative strategies that exploit crypto volatility while limiting dependence on a single direction.

Another way to approach crypto markets

Faced with the limitations of pure prediction, some approaches seek to exploit structural characteristics of markets rather than trying to anticipate every movement, including the limitations of predicting the price of Bitcoin with AI.

Crypto markets, for example, exhibit particularly high volatility and frequent oscillations.

Some quantitative strategies focus on these fluctuations and seek to take advantage of them systematically, while limiting the impact of extreme movements.

This is precisely the type of thinking that guided the development of Neutralis. Neutralis was particularly interested in how a quantitative strategy could exploit the volatility of crypto-assets while reducing dependence on a single market scenario. The question of AI that manages portfolios is also evolving in this direction.

To understand these approaches in more detail

For investors who want to explore these concepts in more depth, in some cases, trading directly can help speed things up. Consulting sessions are open on a limited basis. The Neutralis strategy and a conference on the site also present the quantitative framework and the trade-offs between yield, volatility and stability.

Artificial intelligence will likely continue to transform finance in the years to come.

But for the majority of individual investors, it does not constitute a miracle solution allowing them to easily generate profits on the markets.

Financial markets remain complex, competitive and constantly evolving systems.

In this environment, the key lies not just in technology, but in understanding the deep mechanisms that govern markets.