Exploding Cryptocurrency Information? Let Gemini Be Your Brain Plugin
The cryptocurrency market is renowned for its high volatility and vast amounts of information. Thousands of tokens, continuous price fluctuations around the clock, and the impact of global macroeconomic events often leave traditional analytical methods struggling. The emergence of AI tools offers traders a new way to process and interpret this complex data, enabling them to make decisions with greater clarity and confidence.
AI can assist investors in the following ways:
- Information Integration and Summary: AI can rapidly digest large volumes of financial reports and news, extracting core points and potential risks, significantly saving research time.
- Market Sentiment Quantification: AI can scan social media, quantifying the market’s general “fear and greed” sentiment to gain insights into irrational fluctuations.
- Pattern Recognition and Filtering: AI can efficiently identify potential patterns in data and filter investment targets based on user-defined criteria, eliminating market noise.
- Scenario Simulation and Testing: AI can simulate the impacts of different economic scenarios, such as interest rate hikes and inflation, on investment portfolios, conducting stress tests to assess risk.
In simple terms, AI can handle complex data analysis and signal collection, providing valuable insights and perspectives. Investors can then combine this information with their own experiences, risk tolerance, and investment philosophy to make final decisions and take responsibility. This “human-machine collaboration” model represents the best practice in modern investment decision-making.
Five Steps to Turn Market Noise into Gold
With the proliferation of artificial intelligence technology, more and more investors are beginning to use AI tools to assist in decision-making. According to a survey by asset management company Natixis, as many as 58% of investment institutions have incorporated AI into their investment processes. At the same time, the general public is increasingly relying on AI to answer financial inquiries, with related questions growing more than threefold within a year.
However, many people still use AI in ineffective ways, such as asking “Which stock will rise next?” Experts warn that to gain truly insightful analyses from AI, one must change the way they interact with it.
Step 1: Assign an Expert Role to AI
Before posing questions, first assign AI a professional identity, such as “You are a sell-side analyst specializing in the semiconductor industry” or “You are an accountant skilled in auditing financial reports of tech companies.” This simple instruction helps the model quickly understand the user’s priorities, the order of data filtering, and respond in the analytical style and tone appropriate for that role.
Chris Ackerson, Senior Vice President of Product at the AI market intelligence platform AlphaSense, points out that large language models (LLMs) are trained on data from across the internet, meaning they already understand the differences between various professional roles. Setting a role is an efficient method for guiding its professional reasoning and output.
Step 2: Establish Macro Investment Direction
All analysis begins with a macro perspective. At the beginning of a cycle, there is no need to immediately lock onto a specific cryptocurrency or project; instead, one should first understand the “general trend.” Gemini’s core advantage lies in its built-in Google search functionality, allowing it to instantly capture news.
Every morning, one can quickly scan the latest news, regulatory dynamics, and community hotspots related to the coins of interest or the entire market using Gemini. By asking open-ended questions to AI, its immediacy can help investors capture news-driven trading opportunities or potential risks, quickly grasping the overall market pulse.
For example:
- “You are a professional analyst specializing in investment and macroeconomic research. I am a stock investor hoping to gain deeper insights into the following points:”
- “What global investment trends should we watch in 2025?”
- “Based on current economic data, which industries may perform outstandingly next year?”
- “Please generate a market summary for BTC, ETH, and SOL over the past week, including price trends, trading volume changes, and significant news.”
The AI tool will integrate the latest research reports, financial news, and expert commentary to provide the most insightful perspectives, including index growth forecasts, potential industry scans, and the impacts of the economic cycle.
Step 3: Narrow Down and Focus on Specific Strategies
After grasping the macro direction, the next step is to narrow the scope and find specific strategies that align with personal risk preferences and investment philosophies. This step emphasizes moving from a “broad” to a “focused” approach.
This is the most crucial step. Users must clearly state what information they want AI to provide and what specific problems they ultimately wish to solve. If the instructions are vague, AI’s responses will only be superficial and generic.
For example, if one simply requests AI to “analyze Apple Inc,” they may only receive a basic description of its business model. However, if the goal is to assess the impact of a potential acquisition, one should ask more precisely.
For example:
- “What are the advantages and disadvantages of investing in hardware manufacturers and software developers in the AI trend?”
- “If my portfolio is heavily concentrated in tech stocks and digital assets, what specific risks might arise?”
- “With the U.S. expected to raise interest rates next month, which altcoins might perform better?”
Step 4: Set Precise Screening Criteria
Once a specific lane or industry is identified, one can start looking for quality projects or companies within it. The key at this step is to set clear, quantifiable screening criteria that allow AI to accurately identify potential stocks.
For example:
- “Please list AI companies listed in the U.S. with a market capitalization exceeding $10 billion and revenue growth over 20% in the past year.”
- “Analyze the recent BTC price trends using historical data and moving averages.”
- “I would like a technical analysis trading plan for RNDR. Please use the 200-day moving average as a trend filter and indicate the values for RSI and MACD, entry range, stop-loss point, target price, and risk-reward ratio.”
- “Compare the recent on-chain trends between Polygon and Avalanche, focusing on active addresses, gas fees, and total locked value (TVL).”
At this stage, AI acts like a personal analysis assistant, quickly organizing financial indicators, market trends, and expert comments, providing more detailed data support during the investor’s final decision-making phase.
Step 5: Simulate Scenarios and Predict Outcomes
Finally, one can describe a strategy and ask it to project how that strategy would perform under different historical conditions or input hypothetical scenarios and request an analysis of potential impacts, serving as validation for investment decisions.
For example:
- “Using the 50/200-day moving average setup, backtest a moving average crossover strategy’s performance on ETH from 2020 to 2023.”
- “If U.S. inflation skyrockets to 8% and interest rates rise by 1.5%, what would happen to Bitcoin?”
- “Assuming Polkadot ETF is approved, forecast the price trend of DOT over the next 90 days. Please consider market sentiment and historical ETF trends in your inference.”
ChatGPT will simulate results based on historical assumptions and explain the advantages and disadvantages. Nevertheless, for accuracy, it is still advisable to cross-check this with actual backtesting software.
Ultimately, the path to successful investing lies in achieving optimal “human-machine collaboration.” The role of AI is to become the investor’s data analyst, saving time, identifying patterns, and quantifying risks; however, the final step of making crucial decisions must still be taken by the investors themselves.
Reference: Juheng, Cointelegraph, Cointelegraph