Summary of the Core Content
This article begins with the hot topic of A-share markets in July 2026 experiencing the largest monthly decline in over a decade, with investors collectively blaming quantitative trading institutions for the drop. It objectively clarifies the true role of quantitative trading in the crash: it was not the root cause of the decline, but it did amplify the severity of the market panic like a machine gun. The article also provides a detailed history of the development of quantitative trading globally, from its origins with the “father of quantitative trading,” Ed Thorp, who won in casinos by using mathematical strategies, to the “king of quantitative trading,” James Simons, who consistently made profits during financial crises. It then focuses on the most legendary Chinese entrepreneur, Liang Wenfeng, who used AI for quantitative stock trading to amass a fortune of hundreds of billions and turned his quantitative trading company into a “cash cow” to fund AI research and development. Liang even created the globally popular domestic AI model DeepSeek, demonstrating a unique path of using stock trading to support AI development. This has shed light on the often misunderstood nature of quantitative trading as a tool that both attracts and repels the public.
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Detailed and Easy-to-Understand Explanation
1. The A-share Market Crash in July 2026: Quantitative Trading Is Neither Completely Blamed Nor Completely Innocent
Many people have debated whether quantitative trading was the culprit for the crash, but neither viewpoint is entirely correct. As the article explains, just as a knife used in a murder is not the murderer itself, but if that knife is modified into a machine gun that kills 100 people instead of one, the design of the knife is to blame. The same principle applies to the 1987 “Black Monday” crash in the U.S. market: all institutions used the same automatic stop-loss programs, which led to a collective sell-off of stock index futures and stocks, resulting in a 22% drop. There were no significant fundamental issues; it was purely a result of programmatic reactions.
The situation in July 2026 was similar: the market already had profit-taking positions, and external factors were causing instability. Hundreds of quantitative trading institutions had nearly identical strategies for buying and selling, and when their stop-loss triggers were activated, the market plummeted even more. Therefore, regulatory authorities are now regulating high-frequency quantitative trading to prevent it from causing unnecessary harm to innocent investors.
2. The Surprising Origin of the Popular AI Model DeepSeek
One of the most surprising facts in the article is that the globally renowned AI model DeepSeek was funded by profits from stock trading. Liang Wenfeng, who began researching deep learning after graduating from graduate school in 2010, faced no funding support for his AI projects due to a lack of understanding of AI among domestic investors. He decided to apply AI to stock trading and founded Huanfang Quantitative in 2015. His company thrived in the volatile A-share market and grew to a scale of hundreds of billions. He said, “Huanfang is my cash machine; finance is just a means to acquire resources; my real goal is to develop general artificial intelligence.” When ChatGPT became popular in 2023 and the AI industry matured, Liang invested the profits from quantitative trading into DeepSeek without relying on external investors. This approach allowed him to avoid the pitfalls of short-term pressure and develop a top-tier AI model. The A-round financing of DeepSeek includes special rules that prevent external investors from having voting rights and lock their funds for five years, ensuring that research and development are not compromised for quick profits.
3. The Two Founders of Quantitative Trading: Opposite Approaches
The two pioneers of quantitative trading took completely different paths:
- Ed Thorp, the “father of quantitative trading,” used mathematical strategies to win in casinos and eliminated market risks by hedging. For example, he bought cheap convertible bonds and sold the expensive ones, neutralizing market fluctuations. During the 1987 crash, his positions were protected, and he even made a 15% profit.
- James Simons, the “king of quantitative trading,” ignored company fundamentals and used decades of market data to find small profit opportunities. His strategies allowed his funds to perform well during both bull and bear markets.
Today, most quantitative trading institutions follow either Thorp’s low-frequency hedging or Simons’s high-frequency statistical methods, with most falling somewhere in between.
4. A Warning for Ordinary Investors
Quantitative trading is not as scary as it might seem, but investors should be cautious of its potential for homogenization. Although quantitative trading accounts for a small portion of A-share trading, its strategies can have a significant impact. The reason for the widespread losses in 2026 was the similarity of strategies among many quantitative trading institutions. When hundreds of them simultaneously triggered their stop-loss orders, it led to a market panic. It’s important not to panic during sudden market drops; often, there are no major negative factors, but rather a collective reaction from numerous trading programs. For ordinary investors, it’s better to avoid panicking and waiting for market liquidity to return, as stock prices may recover.
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This translation aims to provide a clear and accessible explanation of the Chinese analysis, preserving the structure and nuances of the original text while adapting the language to fit the target audience.