Original authors: Changan, Amelia I Biteye Content Team
What? Someone used AI to trade cryptocurrency and made 480 times their initial investment in just 8 days?
Previously, the financial market was a hunting ground for information asymmetry. Retail investors lacked capital, but even more so, they lacked the computing power to process massive amounts of data, the energy to work 24/7, and the discipline to combat human greed.
Now, AI has become that "Archimedean fulcrum." As long as your logic is correct, AI is the lever that helps you leverage your wealth tenfold.
Here's a roundup of hardcore AI applications in four major financial markets. 👇
🌟Perpetual Contracts: From 100 to hundreds of thousands, the power of rule enforcement
📌 Case Review
Lana had Claude write a script for him: to scrape the most popular posts on Binance Square, filter bot accounts, identify the most volatile stocks on the gainers list—buy them, and set stop-loss orders. The entire process was executed automatically by AI. In 8 days, the account grew from 100 USDT to 48,000 USDT. As of April 14th, Lana's Binance live trading account had already generated a profit of $146,000 USD.
Two concurrent experiments ( Nof1.ai and Aster) also confirmed that AI systematically outperforms humans in risk control – it avoids emotional averaging down, panic selling, and greedy chasing of high prices. While its absolute returns may not be top-tier, it excels in avoiding major mistakes and significant losses.
🧠 Methodological Summary
1️⃣ Information Filtering
He had Claude write a script to automatically scrape the posts and listings with the highest daily post volume and the most discussed cryptocurrencies from Binance Square. Square is where retail investor information gathers, and his logic was: before a market manipulator can pump up the price, there must first be fish (market makers), and the popularity of Square is an early signal of retail investors entering the market.
2️⃣ Signal Recognition
Based on the data from the marketplace, we then overlay a ranking of gainers. We don't look for the coins with the largest gains, but rather those with the highest volatility: high volatility indicates active trading, and active trading creates opportunities. Simultaneously, we observe coins with significant changes in open interest (OI) over the past 48 hours, but whose prices haven't reacted immediately; these coins often signal that funds have been positioning themselves in advance.
3️⃣ Style Distillation
He incorporated his own Twitter style and the content of KOLs like "pan makers" into the AI, allowing it to learn their posting logic and coin selection strategies to help judge market sentiment and trending topics.
He asked the AI why it chose a particular coin, and the AI replied that it was because the post with the most traffic was retweeted by CZ, and that post mentioned the book "Binance Life," which had been the most discussed topic in the past three days.
4️⃣ Rule Enforcement
After buying, he would set a stop-loss order, post on the forum, and screenshot his profits to maintain the buzz. He designed the rules himself: initially, he would set a 20% stop-loss, but later changed it to stop-loss at 200 USDT regardless of position size, only chasing one direction and never going against it, with AI handling the execution.
💡Biteye's Viewpoint
- In the entire process, the AI's tasks are: writing scripts, collecting data, and posting. The trading strategy is its own; the AI simply automates these tasks. In the futures market, adhering to rules more consistently than others is an advantage in itself.
- Action strategy: First, write down your stop-loss rules: how much to lose, which direction to chase, and never chase the opposite direction. You can borrow the framework from Lana, but the strategy must be your own.
🌟Market Prediction: Arbitrage + Information Asymmetry + Automation
Prediction markets (such as Polymarket) have simple rules: for each question, you answer Yes or No, and the price is 0-1 representing the probability.
🧠 Methodological Summary
The community is profiting from AI in three ways:
1️⃣ Arbitrage
In the Neg Risk market, an AI script periodically scans the total Bid price of all Neg Risk markets, automatically filters out opportunities with a Bid price greater than 1, and executes a Split + Sell.
2️⃣ Narrowing the information gap
Leveraging the open-source project WorldMonitor, it aggregates news from over 435 global sources, covering 15 categories including military, economics, geopolitics, disasters, and finance. AI synthesizes these information streams into real-time reports and performs cross-signal correlation analysis, enabling early detection of precursory signals for geopolitical and other events.
3️⃣ Strategy Automation
Describe your trading decision framework in natural language to the AI, and let the AI convert it into an automatically executable script. The script will then automatically monitor trigger conditions, calculate position size, and execute orders according to the strategy logic.
💡Biteye Reflections
Arbitrage requires technical skills, but information asymmetry is more suitable for beginners: first, bookmark WorldMonitor, spend 10 minutes reading the briefings every day, and find an event you can judge to test the waters with a small position.
The key to information arbitrage is "leading signals": don't chase the news, but rather follow the changes in non-mainstream data sources before the news breaks.
Strategy automation is an advanced form: once you have a stable and profitable manual framework, then consider using AI to turn it into a program.
🌟Cryptocurrency Spot Trading: Large-Scale Candlestick Model, Turning Charts into Probabilities
Beyond event- and narrative-driven approaches, AI is also revolutionizing the technology of spot trading.
📌 Case Review
Kronos, a trending project on GitHub, tokenizes OHLCV data and pre-trains an autoregressive Transformer on historical data from multiple markets. Retail investors no longer need to memorize dozens of patterns – the model directly provides the probability of an upward move in BTC/USDT over the next 24 hours, the probability of increased volatility, and a Monte Carlo simulation path. The project allows for fine-tuning, and users can continue training with their own product data.
🧠 Methodological Summary
Large language models are able to understand text because they learn the statistical relationships between words from massive amounts of text. Kronos applies the same logic to K-line: first, a specially designed tokenizer is used to transform OHLCV data into discrete token sequences, and then an autoregressive Transformer is used to pre-train on these tokens.
The training data covers historical data from 45 exchanges worldwide. After the project went live, it quickly garnered over 11,000 stars on GitHub and more than 2,400 forks.
In the past, retail investors had to memorize dozens of chart patterns and repeatedly overlay indicators to perform technical analysis, ultimately relying on personal experience and guesswork. Now, the approach has completely changed. You no longer need to hone your chart reading skills; you can use a model pre-trained on massive amounts of market data to extract signals.
The project also offers a complete fine-tuning process. If you have historical data for a specific asset, you can continue training the basic model to better understand your trading instrument. It also provides a live demo of the BTC/USDT forecast for the next 24 hours, which anyone can access to see real-time predictions. The model provides the probability of an upward move and the probability of increased volatility within 24 hours, along with a 24-hour probability forecast chart: blue represents historical prices, and the orange line is the average prediction path from multiple Monte Carlo simulations.
💡Biteye's Viewpoint
- No need to painstakingly practice technical analysis: In the past, you had to memorize dozens of patterns and stack up a bunch of indicators, but now you can directly use the model output as a reference.
- Observe first, then trade: Watch Kronos' live demo once a day, compare the model's predictions with the actual market movements, and cultivate "probabilistic thinking".
🌟US Stocks: AI Agents capitalize on geopolitical crises and profit from unexpected market fluctuations.
📌 Case Review
XinGPT ( @xingpt ) built a geopolitical crisis monitoring system using an AI agent. At the time, the market focus was on the Strait of Hormuz, which was extremely noisy. His agent directly monitored first-hand data sources: JMIC vessel traffic, the Iranian official news agency, and maritime intelligence sources, capturing the core indicator every 6 hours—"the actual number of ships passing through the strait." This number dropped from 153 ships per day to single digits, indicating that the situation had not truly eased. Based on this, he held crude oil ETFs from March 7th, weathering the correction until Brent crude oil rose from $87 to over $100.
🧠 Methodological Summary
- Information source planning: First, identify high-quality, low-noise primary data sources (official institutions, maritime data, local news agencies), rather than letting AI blindly crawl the entire internet.
- Core metric capture + noise filtering: Focus only on the most honest metric (ship traffic volume), set up a Flash Alert mechanism, and ignore market noise.
- Automated decision-making framework: Write a separate "investment decision skill" for the agent, which will automatically generate a report containing signals and position suggestions every morning.
💡Biteye's Viewpoint
- Framework is more important than tools: First, choose a sector you can track long-term (AI, semiconductors, energy), then find a reliable investment bank research report framework, and finally use Claude to help you build daily briefings.
- Focus on one core metric: Don't try to monitor all variables. Find the metric that best reflects the true situation, at the level of "vehicle traffic."
- The key to making money in the US stock market lies in the speed of information processing and the difference between expectations: retail investors find it difficult to digest financial reports, macroeconomic data, geopolitical events, and industry intelligence in a timely and comprehensive manner, but AI can process massive amounts of information in minutes and identify opportunities that the market has not yet fully priced in.
🌟In conclusion
Previously, the financial market was far removed from ordinary people. There was information asymmetry, insufficient funds, unaffordable tools, and a long time required to accumulate experience.
Now, AI has almost completely eliminated the once insurmountable technical barriers. You only need to tell AI your logic in natural language, and it can help you write scripts, capture data, analyze, and execute.
Lana can achieve a 480-fold return in 8 days, Mr. Jiang can steadily make money during macroeconomic crises, and ordinary people can use Kronos-like models to turn candlestick charts into probability predictions. These are things that were once only possible for professional teams, but now even beginners can do them from home with just a computer.
AI does not bring the illusion that "everyone can get rich overnight," but rather true technological equality: equality in access to information, equality in analytical capabilities, equality in execution efficiency, and equality in decision-making systems.
To get started, you can follow these three steps:
- Choose a market that interests you most, and find 2-3 KOLs you follow regularly.
- Distill their recent content into Skills, and let AI extract their judgment logic.
- Describe your strategy clearly in natural language, and let AI help you write an automated script.
The first pot of gold never belongs to the richest person, but to the person who best uses AI as leverage and systematizes their judgment framework.
