In the past few years, after artificial intelligence entered the financial industry, a large number of new investment tools have appeared in the market.
Some systems focus on real-time market conditions.
There are systems that help analyze financial data.
Some systems try to find market patterns through models.
But during the development of AV CommonEdge, the team’s earliest concern was not “how to predict the future.”
Instead, they start with a more basic question:
Why is it so difficult for investors to make decisions in the past that will become their future capabilities?
This is also an important background for the formation of EdgeMirror.
According to AV CommonEdge, the investment industry has accumulated a lot of experience over a long period of time, but most of these experiences still remain in the human brain.
When an investor leaves the market, changes strategies, or even just a few years later, many once important judgment processes will gradually become blurred.
The result is left behind. The process disappears.
The truly valuable part of investment is often hidden in the process.
Beyond profit and loss figures, what really matters is the decision-making process
Traditional trading systems are already very mature.
Investors can clearly see:
When to buy;
What is the purchase price;
when to sell;
How much profit will be gained in the end.
These data are of course important.
But they are incomplete for the formation of long-term investment capabilities.
A successful transaction, why is it successful?
It may come from company growth, industry changes, market environment cooperation, and correct judgment.
Or maybe it’s just a short-term market sentiment push.
Likewise, there are different reasons for a losing trade.
It may be an error in judgment by the company.
Maybe the timing of entry was wrong.
It may be that risk control is insufficient.
Or it could just be a premature exit in the face of normal volatility.
If only the final result is left, investors will still need to start over the next time they face a similar opportunity.
Therefore, AV CommonEdge began to explore another way: not only saving the transaction results, but also saving the logic behind the transactions.
Trade Memory’s concept of allowing experience to move from the past into the future later gradually formed Trade Memory.
Unlike ordinary transaction logs, Trade Memory focuses on the complete life cycle of investment decisions.
It logs:
Why were you paying attention to this company at that time?
What environment is the market in?
What is the investment logic;
What changes have occurred during implementation;
Why you finally quit;
What experience should be left behind after the review?
It is closer to the “decision profile” formed by an investor over a long period of time.
But the really important thing about Trade Memory is not the record itself.
Because saving information is not difficult.
The hard part is, when the next opportunity arises, which past experiences will actually be relevant?
As experience increases, manual processing begins to encounter limitations
As transaction cases continue to increase, AV CommonEdge encounters new problems.
Initially, dozens of trading experiences can still rely on manual recall.
But when the time span is expanded, the number of cases increases, including:
Company changes, industry cycles, market environment, capital behavior, position management, risk changes, trading results;
In the future, it will be difficult to effectively process this information by relying on human memory alone.
As a result, EdgeMirror began to take on a new role.
It is not intended to be a substitute for investors to make final judgments.
Instead, it helps the team find from a large amount of historical experience:
which cases are truly relevant;
In which environments have similar situations occurred?
which judgments have been valid in the past;
What mistakes have occurred repeatedly?
This is also an important difference between EdgeMirror and ordinary data tools.
It’s about more than: “What happened in the past.”
Rather: “Why it happened in the past and how it will be used in the future.”
EdgeMirror’s development didn’t start with predictions
From the perspective of development path, EdgeMirror has gone through several stages.
In the early stage, the team first established Trade Memory so that investment experience can be saved.
Subsequently, the system began to add more dimensions: market environment, industry changes, historical comparisons, risk factors, and capital behavior.
As its capabilities continue to increase, EdgeMirror has gradually developed from a simple information organization tool to a system that assists research and judgment.
At the EdgeMirror 2.X stage, the system began to pay more attention to the complete strategy cycle.
Start with opportunity research and continue to observe after entry:
Whether the market status changes;
Whether industry trends change;
Whether funding is sustainable;
Whether the original investment logic still holds true.
This means that the value of AI in the investment field is not just about finding opportunities.
Equally important is:
Help investors continually check their judgment.
The real direction of investing in AI is not to replace people
In the financial industry, one of the most common imaginations about AI is that in the future the system can directly make all decisions on behalf of investors.
But the development philosophy of AV CommonEdge is not like this.
Investing essentially still involves goals, risks, capital arrangements, and personal choices.
These factors cannot be left entirely to machines.
What AI can really improve is the scope of human abilities.
It can help process large amounts of information.
Help find historical experience.
Help uncover problems that were easily overlooked in the past.
Help investors see more possibilities.
But the ultimate question remains:
How does a person want to choose when faced with opportunities and risks.
From understanding the market to building personal investment intelligence
The future development direction of EdgeMirror is also moving further from “market intelligence” to “personal intelligence”.
Because investors in reality are not exactly the same.
The same company.
the same market environment.
The same investment opportunity.
Different people may make completely different decisions.
Reasons include:
Fund sizes vary;
Risk tolerance varies;
Investment cycles are different;
Trading habits were different in the past.
Therefore, a truly long-term effective investment system should not only understand the market.
It also requires growing understanding of the people who use it.
This is also the direction that EdgeMirror 3.0 hopes to explore.
The real value of investment experience is that it can be continued to be used
Looking back at the development process of EdgeMirror, it is not a simple technical problem that it solves.
The deeper question is how to transform investment experience from personal memory into abilities that can be accumulated over the long term.
The market produces new changes every day.
No system is guaranteed to be correct forever.
But a system that can continuously record, learn, and review can help investors continuously improve the quality of their next judgment.
For AV CommonEdge: every transaction should not be just a result.
It should be part of the next decision-making process. This is also the core reason why EdgeMirror was established
