In April 2025, a study was published that stirred quite a bit of rumor: the author compared the effectiveness of various Bitcoin trading strategies during the period from 2018 to 2024. The results were unexpected even for experienced market participants.
While the buy-and-hold strategy delivered a solid 223% return, a simple machine learning model delivered a 304% return. However, the most impressive consequence was demonstrated by the strategy based on an ensemble of neural networks, which delivered a remarkable 1,640% over the same six years. These results highlight the transformative potential of artificial intelligence in financial markets, particularly in the realm of cryptocurrencies.
Understanding the Role of AI in Trading
With the advent of progressive trading platforms, such as metatrader 4 exe download, trading has become a matter of ease. On top of that, AI provides opportunities to get an in-depth understanding of market dynamics and investor behavior. This realization is crucial for portfolio management, risk assessment, and the expansion of trading systems.
In a perplexing world of trading, artificial intelligence is emerging as a meta-skill for professional survival. Those traders who fail to integrate neural networks into their everyday trading activity will lose their competitive advantage in the blink of an eye. Let’s take a closer look at abundant AI applications in trading.
1. Fundamental Analysis
The concept of fundamental analysis encompasses document analysis, on-chain data evaluation, project tokenomics, and more.
Researching cutting-edge crypto projects is a time-consuming process, requiring hours of reading through multi-page technical documentation and reports. AI can significantly speed up this process, turning hours of reading into mere minutes.
ChatGPT, Gemini, Claude, and Perplexity AI are all great for summarizing information, including financial documentation. Additionally, Turboscribe AI is invaluable for transcribing audio recordings, such as AMA sessions and interviews.
Example of Prompt for ChatGPT/Claude:
“Analyze this whitepaper. Write a summary of the project, highlight the main problems it solves, core technological features, and identify the target audience. Evaluate the innovativeness of the proposed approach.”
AI can also compare multiple projects based on given criteria, helping you choose the most promising among them. For example:
“Compare [Project Name 1] and [Project Name 2] projects based on the following parameters: consensus technology, governance model, tokenomics, partnerships, and scaling potential. Provide the comparison in a table.”
If you require particular data about the project, such as staking or token deflation mechanisms, AI can help automate this research:
“In this whitepaper, find information about the mechanisms for rewarding stakers and the conditions for unlocking tokens for early investors. What are the risks associated with token inflation?”
AI can assist in writing a SWOT analysis (strengths, weaknesses, opportunities, and threats) for a project based on its documentation and publicly available information:
“Based on the information provided about the [Project Name] project, conduct a SWOT analysis.”
2. On-chain Data Analysis
On-chain analysis represents one of the most robust tools, empowering you to track real-world activity. Interestingly, AI can serve as a bridge between analytical tasks and analytics databases.
For example, DeepSeek can be implemented for writing SQL queries and interpreting data, while Nansen provides ready-made on-chain insights.
Example of Prompt for ChatGPT:
“Show the top 10 addresses by trading volume on Uniswap V3 for the last month for the WETH/USDT pair. The response must contain the wallet address, trading volume, and number of transactions.”
Advanced Prompt:
“Help write an SQL query for Dune Analytics to analyze the distribution of USDC (ERC-20) tokens among holders, grouping them by balance ranges (e.g.,>1M, 100K-1M, etc.)”.
While analyzing the replies, it becomes clear that universal AI assistants like DeepSeek not only create SQL queries, but also provide explanatory comments, unlike other in-built models.
3. Mathematical Modeling and Tokenomics
Designing sustainable tokenomics is an arduous task. Nevertheless, AI can assist in generating and analyzing token economics while predicting asset stability when parameters change.
Specifically, AI can design the initial token distribution among various categories: the team, early investors, the treasury, and liquidity. It can also calculate the cost of vesting (phased token distribution) with the following prompt:
“Propose several options for a basic token distribution model for a new GameFi project. Consider the following categories: team (15%), early investors (20%), public sale (10%), treasury (25%), community rewards (20%), liquidity (10%). Describe how vesting can be applied to each category.”
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4. Technical Analysis
The simplest way to get started using AI to work in the crypto market is to upload a screenshot of the chart to ChatGPT or Claude and ask for an analysis. However, the effectiveness of this approach depends on the quality of the prompt provided.
Ineffective Prompt:
“Look at this Bitcoin chart, what do you think?”
Effective Prompt:
“Analyze the BTC/USDT chart on the 4-hour timeframe. Pay attention to key support and resistance levels.”
Final Thoughts
In a volatile crypto market, finding advantages can be extremely difficult but valuable. Therefore, the implementation of AI tools, be it analytics, trading, or content creation, is essential for traders’ professional survival. Thanks to AI, the crypto industry is entering an era of hyper-efficiency.
