Technical financial graph on stock exchange display panel.
AI tools designed for traditional markets are very different from those tailored for crypto trading. Stocks behave differently than cryptocurrencies, so applying algorithms trained on stock market data to crypto markets might lead to poor performance.
Always do your own research. AI tools for crypto analysis aim to automate trading or provide actionable insights, but understanding the crypto market itself cannot be omitted. It’s a volatile, sentiment-driven market, much newer and less predictable than TradFi. AI isn’t foolproof, and we’ll learn its limitations later on in this article.
Tools Like Chatbots And Trading Bots
The type of AI tool you choose depends on your trading approach and risk tolerance, and it often involves trade-offs.
Chatbots are incredibly popular right now, but this could also be their downfall. If ChatGPT suggests a strategy to everyone simultaneously, it quickly loses its edge. This is because when many traders use the same strategy at once, the market quickly adapts, eliminating the strategy’s advantage. Proprietary chatbot algorithms can offer more tailored solutions, but there’s a catch in both instances: so-called AI hallucinations. This occurs when the AI generates outputs that aren’t based on real training data but instead fill in the gaps itself with its own guesstimations, creating a risk for traders who may take the information at face value without verifying it.
With trading bots, you sacrifice some autonomy but gain access to a wide range of strategies and 24/7 execution, especially if the service is cloud-based. This solution is particularly appealing for advanced traders who want to customize their bots.
Key Factors To Check For Trading Bots
- Proprietary vs. Public Algorithms: Does the AI tool rely on exclusive algorithms, or is it built on public ones that may lose their edge?
- Security: This should be a given, but many users overlook the importance of ensuring their software is malware-free.
- Technical Reliability: This is particularly crucial when comparing different bot providers. You’ll want to ensure you have continuous access to a reliable service.
- Self-Custody: This is a must for minimizing third-party risk. Trading bots typically operate using API keys on exchanges, meaning they don’t have the ability to withdraw your assets, only to trade on your behalf.
In trading, machine learning algorithms focus on making predictions. But with all the hype surrounding AI, it’s important to understand where the true value of an “AI-driven” tool comes from: pattern recognition and predictions. In order to ensure you get good results, you need good data. Quality over quantity.
A New Standard For AI Tools
Given the decentralization ethos in crypto, the idea of open-sourcing algorithms is very attractive. It allows users to review the code before using it. However, competitive advantage plays the first fiddle in terms of both business and technological strategies. As mentioned, once an algorithm becomes public, it can lose effectiveness. But how can you ensure a tool is genuinely AI-powered and not just a gimmick? Zero-Knowledge Machine Learning (also known as zkML) might offer a solution to this challenge.
Because of how prominent the topic of AI tools for crypto is in the mass media, we tend to forget just how nascent this sector actually is. The truth is, these consumer-facing tools still have a long way ahead of them before reaching maturity. Adopting zkML is one of many options that could face the current status quo of tools for crypto analysis. What people need to know is that these applications have to maintain a competitive edge to stay in the game, and it means that we, the consumers, should keep up with the newest advancements and objectively evaluate their value-add.
