Summary of Key Points
This article focuses on how ordinary individuals (with no prior knowledge) can utilize AI to engage in quantitative stock trading, addressing four key questions: the entry pathway, methods for verifying strategies used by experts, the implementation of code-free backtesting, and the best ways to use AI for quantitative analysis. The core idea is that AI reduces the technical barriers to quantitative trading, enabling non-professionals to adopt data-driven investment methods. However, AI is not a guaranteed money-making tool; rather, it serves as a decision-supporting aid.
Detailed Explanation
1. How can beginners use AI for quantitative trading without programming?
Quantitative trading essentially involves using data and rules instead of making emotional decisions. For example, a simple quantitative strategy could be to buy when the 5-day moving average crosses above the 10-day moving average. In the past, quantitative analysis required knowledge of programming (such as Python) and mathematical models, but AI tools have simplified these processes into user-friendly interfaces:
- You simply need to access an AI quantitative trading platform (either provided by a brokerage firm or a third-party service), select the type of strategy you want (e.g., “low valuation with high growth potential” or “trend following”), and set some parameters (e.g., price-to-earnings ratio < 15, dividend yield > 3%).
- The AI will then automatically filter stocks that meet these criteria and even generate buy/sell signals for you.
Example: If a beginner wants to find stocks with annual net profit growth of over 10% and a stock price lower than their book value, the AI tool can generate a list in minutes, eliminating the need to manually review financial reports and calculate data.
2. Can AI test strategies used by experts online?
Many online “experts” claim that their strategies have annual returns of over 20%, but often lack empirical evidence. AI can help verify the effectiveness of these claims:
- The expert’s strategy is first transformed into quantifiable rules (e.g., buying when a stock breaks above its annual moving average and stopping losses when it falls below the 5-day moving average).
- The AI uses historical data from the past 5–10 years to simulate the strategy and calculate metrics such as actual returns, maximum losses (drawdown), and win rates.
- The results are clear: if the strategy’s annual return after backtesting is only 5% or even negative (e.g., a 20% loss), it proves the claim to be exaggerated.
Example: An expert claims that buying stocks on the day after they hit their daily limit up will result in a profit the next day. AI backtesting of past three years shows that the probability of such a gain is only 45%, which is less reliable than flipping a coin.
3. How can code-free backtesting be achieved?
Backtesting is a crucial step in quantitative trading, as it allows you to see how a strategy would have performed in the past. AI simplifies this process:
- You choose the strategy (e.g., using the MACD crossover indicator), the stock pool (e.g., the CSI 300 index), and the time range (e.g., 2018–2023) within the AI platform.
- The AI then calculates how much money would have been earned or lost based on the strategy during that period, as well as the win rate.
- The results are presented in graphical formats (such as profit and loss curves), without the need to write a single line of code.
Benefit: Beginners can quickly test their ideas. For instance, if you try a strategy that results in losses over a long period, you can easily abandon it.
4. What is the best way to use AI for quantitative trading?
The primary role of AI should be as an intelligent assistant, not a direct substitute for human traders:
- Quick stock filtering: AI can identify stocks that meet specific criteria (e.g., net profit growth for three consecutive years and ROE > 15%) in minutes, whereas manual screening would take days.
- Strategy validation: It can reveal flaws in strategies, such as those that perform poorly during bear markets.
- Reducing emotional biases: AI executes trades according to set rules, avoiding panic-driven selling or greed-induced buying.
- Information supplementation: AI can analyze news and financial reports to provide alerts about potential performance improvements or negative developments.
Misconception: Don’t rely entirely on AI; the market is dynamic, and historical data does not predict the future. For example, unexpected policy changes (like pandemics) can render AI models ineffective. You still need to use your judgment in conjunction with AI’s insights.
Final Reminder
AI for quantitative trading is a tool, not a guarantee of guaranteed profits. Beginners should start with small amounts of capital, thoroughly test strategies, be skeptical of expert claims, and avoid entrusting all their funds to AI. The goal is to use AI to save time and reduce mistakes, not to replace your own decision-making process.