It has to be said that Bitget's iteration of AI trading tools is indeed very rapid. Not long after launching GetAgent, they've already released their intelligent stock screener. I've used it for a few days and find it quite practical. 1) The filtering criteria allow you to set your own standards for indicators such as revenue growth, profit growth, ROE, net profit margin, and P/E ratio. After filtering, you can save the results as a stock list and enable automatic updates. 2) It's not just about simply listing criteria. The similar stock function allows you to take a reference stock (such as NVDA or AAPL) and find similar targets based on themes, fundamentals, or technicals, providing a similarity score. Both spot and futures targets are covered, and the filtered results can be traded directly within the platform. I also tried using Bitget's intelligent stock screener to create a filter for high-quality growth stocks. Here are a few criteria I use to identify high-quality growth stocks: 1) Revenue growth rate: 15%~60%. This shows whether the company's revenue is steadily expanding. 15% is the bottom line; below this usually indicates weaker growth potential. 60% is the upper limit, filtering out sudden, unsustainable, one-off high growth. This aims to identify companies with moderate to high growth rates that are relatively sustainable. 2) Earnings per share (EPS) year-on-year growth rate: 20%~150% As revenue grows, the profit per share must also grow, ideally even faster. The lower limit of 20% roughly aligns with revenue growth, ensuring it's not "revenue growth without profit growth"; the upper limit of 150% provides more flexibility because profit growth can sometimes accelerate due to improved expense ratios, product structure upgrades, or economies of scale. The core is to verify the quality of growth—whether revenue growth truly translates into profits visible to shareholders. 3) Net profit margin: 15%~45% How much profit is ultimately retained per 100 yuan sold reflects pricing power, cost control capabilities, and the business model. 15% is already considered a medium-to-high profit margin, unattainable for many industries; 45% is a high level more commonly seen in software, some semiconductors, and platform companies. This metric aims to filter out companies that rely on "low-margin, high-volume sales to sustain growth," ensuring that growth is backed by substantial profit margins. 4) ROE (Average): 15%~35% Return on Equity (ROE) measures the efficiency with which a company earns back its shareholders' money. This is the average ROE, smoothed out quarterly fluctuations using multi-period data. 15% is a solid threshold for excellence, while 35% is already very high; anything higher is likely due to high leverage or extreme economic conditions, raising questions about sustainability. This metric controls capital return efficiency. High ROE combined with double-digit growth in revenue and profit is the hallmark of high-quality growth. 5) Price-to-Earnings Ratio (P/E): 10~30 This is a valuation constraint, preventing the selection of only "overpriced good companies." A P/E ratio below 10 typically leans towards value or the bottom of a cycle, while 30 is a relatively reasonable upper limit for growth stocks. The goal of staying within this range is to find companies with strong fundamentals but whose valuations haven't fully priced in. Without this criterion, the strategy would lean more towards pure growth; with it, the results would favor relatively inexpensive growth stocks. By combining these five criteria, I'm essentially looking for companies with medium-to-high-speed revenue and profit growth, high profitability, and decent profit margins, while maintaining a relatively reasonable valuation. From a personal perspective, I want both growth speed and quality, plus an added layer of valuation protection. The screening results show companies from different industries, such as Meta, EOG, Evercore, Incyte, and Virtu, indicating that the strategy isn't fixated on a single sector but rather filters across industries based on financial characteristics. Of course, screening is only the first step. For individual stocks, a deeper analysis of core competitiveness, competitive advantages, management team, company balance sheet, and cash flow is needed before making a decision. These tools are extremely helpful for ordinary investors and traders, significantly reducing the time cost of discovery, making "research" less of a barrier to entry, and allowing them to operate without leaving their trading platforms. As you use it more, you'll find yourself spending less time "finding things" and more time deciding whether or not to take action. Of course, no matter how fast the tool is, ultimately, it depends on whether risk control is in place—the filtering and strategies provided by AI are always just aids; the decision-making still rests with you.