I have always believed that as a retail investor, we must first choose the right methods that suit us before investing. If the methods are wrong, it is equivalent to going in the opposite direction, wasting our efforts.
Currently, there are several popular investment methods in the investment field: value investing, technical analysis, macro investing, and quantitative investing.
Value investing is something I have been learning and pondering over.
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 feel it is very distant 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 general 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 certain 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 quantitative trading was just an upgraded version of technical analysis, nothing mysterious.
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, I was inspired when a friend gifted me a biography of Simons. I seized the opportunity to learn about the operation of quantitative trading through this biography.
After reading the book, I realized that my previous understanding of quantitative trading was too superficial, and I recognized that the operation of quantitative trading is far more complex than I had imagined.
The biggest takeaway from the 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 "should not be done" or "is not suitable to do" may be the quickest shortcut.
Today, I would like to share 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.
His ability to create this entirely new field came from 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. His job was to decrypt it.
He used statistical methods to find patterns in this big data to infer real intelligence information.
He believed that this method could also be used to uncover hidden patterns in financial markets.
There are similarities with technical analysis, but also clear differences.
The similarity lies in both using 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 formed by connecting the average of the past 100 days of trading prices. 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.
However, this phenomenon did not occur with other currencies.
Thus, in their system, they would input the speeches of the Japanese and West German governments, and whenever this information was available, the system would automatically execute trades in yen and 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, they 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 a butterfly in South America flapping its wings affects the price movement of copper in the next five minutes, then the quantitative system would 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.
From the beginning of forming his quantitative trading team, the core members he sought were well-known mathematicians and computer experts, and he hardly recruited Wall Street financial professionals. This talent recruitment requirement seems to remain the same 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 financial institutions began to imitate his approach.
After seeing all this, let’s think about 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?
免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。
