Why is quantitative investing not suitable for ordinary people?

I have always believed that as retail investors, we must first choose the right methods that suit us before investing. If we choose the wrong methods, it is equivalent to going in the opposite direction and wasting our efforts.

Currently, the popular investment methods in the investment field include: value investing, technical analysis, macro investing, and quantitative investing.

Value investing is something I have been studying and pondering.

I believe technical analysis is completely unsuitable for me, so I gave it up early on.

Macro investing is a method used by investment giants like Dalio and Soros. I think it is very far from me, and I do not possess the necessary conditions, so I have only read some of their books to understand their viewpoints and methods as knowledge.

As for quantitative investing, I have never understood it but always thought I had a rough idea.

My understanding of quantitative trading has always been that it is based on technical analysis, supplemented by powerful computing power, allowing one to capture fleeting opportunities faster than peers.

For example:

If a particular investment target is about to hit a resistance line at a certain price level, I can quickly clear my position at the closest point to the resistance line, thus securing the best price before reaching the resistance line.

The key to this process lies in sensitively judging various technical indicators while ensuring sufficient computing power.

This was my previous understanding of quantitative trading.

According to my understanding, I always thought that quantitative trading was just an upgraded version of technical analysis, nothing mysterious about it.

Until recently, due to the popularity of DeepSeek, whose parent company is a quantitative trading firm, and its founder is a follower of James Simons. Coincidentally, a friend recently gifted me a biography of Simons, which sparked my interest and gave me the opportunity to understand the operation of quantitative trading through this biography.

After reading this book, I realized that my previous understanding of quantitative trading was indeed too superficial, and I recognized that the operation of quantitative trading is far more complex than I had imagined.

The biggest takeaway from this book is that I finally understood that quantitative trading is also not suitable for retail investors like me.

The more I progress on the investment path, the more I realize that often clarifying what I "should not do" and "what is not suitable for me" may be the quickest shortcut.

Today, I would like to share with you the general principles of quantitative trading as described in Simons' biography.

Simons can be considered a symbolic figure who pioneered quantitative trading on Wall Street.

The reason he was able to create this entirely new field was entirely due to the inspiration he gained while working at the U.S. Defense Research Institute. His job there was to decode hidden sensitive information from a pile of seemingly chaotic data.

This seemingly chaotic data consisted of communication intelligence intercepted by the U.S. government from various countries, all of which were encrypted. Therefore, his job was to decrypt it.

He used statistical methods to find patterns in this big data to infer the real intelligence information.

He believed that this method could also be used to identify hidden patterns in the financial markets.

There are similarities with technical analysis, but also clear differences.

The similarity lies in that both use statistics to find patterns to predict future price movements.

However, the difference is that traditional classic technical analysis mainly finds patterns from historical prices, such as moving average theory, wave theory, and technical indicators; while quantitative trading finds price patterns from a broader range of data (not limited to price).

For example:

In technical analysis, there is an indicator called the moving average, such as the 100-day moving average. It is a line connecting the average price of the past 100 trading days. When using this indicator, technical analysis looks at how the price behaves when it breaks above or below the 100-day moving average.

The pattern here is summarized from historical prices.

But how does quantitative trading discover patterns?

For instance, Simons' team once discovered a pattern:

Whenever the Japanese government and the West German government made certain economic policy speeches, the exchange rate between the yen and the Deutsche Mark would change and persist for a period of time.

However, this phenomenon did not occur with other currencies.

So, in their system, they would input the speeches of the Japanese and West German governments. Whenever this information was available, the system would automatically execute trades on the yen and the Deutsche Mark.

Such operations would not appear in traditional technical analysis.

In the early days of forming his team, Simons initially tried traditional technical analysis methods but achieved little success. So, he quickly abandoned this attempt and turned to using a wider range of data to find statistical patterns.

This required organically connecting a large amount of seemingly chaotic and even unrelated data.

In my understanding:

If the flap of a butterfly's wings in South America can influence the price movement of copper in the next five minutes, then the quantitative system should attempt to find this pattern.

This requirement and standard far exceed what traditional technical analysis can achieve.

It requires strong big data technology and statistical modeling capabilities, while the requirements for economics and finance are not as high.

Therefore, only mathematicians and computer experts could realize his idea.

Since the establishment of his quantitative trading team, the core members he sought were well-known mathematicians and computer experts of the time, and he hardly recruited Wall Street finance professionals. This talent recruitment requirement seems to still hold true today.

When he first proposed this idea, Wall Street scoffed at it. Of course, he ignored Wall Street and firmly believed that his method would be effective.

In the end, he succeeded.

Wall Street's financial institutions began to imitate his approach.

After seeing all this, how many of the so-called quantitative trading teams we usually hear about possess such strength and level?

Furthermore, is this method suitable for us retail players?