Episode 1: Are AI tools dangerous? In trading, the reason you lose is because you are "too smart"
Episode 1: "Learning AI vs Non-Learning AI"
What kind of logic should we actually choose from now on?
In recent years, the word "AI" has become commonplace in the world of forex trading.
On pages selling EAs (automatic trading) and indicators,
words such as
"machine learning," "self-optimization," "deep learning"
appear side by side.
At first glance, it looks like a very advanced, future-predicting“ultimate mechanism.”
However, many traders who have actually used them notice a common sense of discomfort.
"Even though it's AI, why can't it keep winning?"
■ That is the trap of overfitting
The main cause is
"overfitting".
Simply put, a state that fits too closely to the past.
Backtests show surprisingly clean results, but in real markets it doesn’t work at all.
This isn’t rare scenery,
it’s more like a common pattern.
Because markets are inherently full of noise,
and they keep changing.
In other words,
the more optimized the logic is with multiple parameters and time frame adjustments,
the more likely it is to collapse in the future.
This is the structure.
■ Why does AI lose even though it's AI?
Now, please think calmly about one thing.
Where does the image of "AI = winning" come from?
Where does it originate?
It learns automatically
It makes optimal judgments
It’s smarter than humans
Indeed, it’s appealing.
But to what is that “intelligence” applied?
The answer is simple.
It is applied to “past data.”
■ Is the premise misaligned?
Here lies a big misalignment.
Many AI-based logics involve
learning past price movements and analyzing them
extracting the most efficient patterns (and vice versa)
and applying them to the present and future
in structure.
It may seem rational at first glance, but
there is a fatal problem in trading.
That is,
"The market does not repeat the same moves."
This is the reality.
■ What’s happening
In actual practice
Backtests are perfect
Forward tests start to break down
In real trading, it collapses
And then new AI tools appear again.
This cycle repeats.
■ Is AI unusable?
Here lies one misconception.
"So is AI bad?"
That is not the story.
On the contrary.
The AI-like way of thinking is very effective in trading.
The issue is
"how to use it" is misaligned
in its application.
■ A certain change occurring
there is a change happening in some areas.
It is no longer about
reproducing the past perfectly
maximizing numbers
or similar
but rather turning toward a completely different direction.
in overseas markets, truly popular EAs often have no backtests公開; forward testing only is the reality.
■ The hint is "now"
The hint for this way of thinking is very simple.
Not the past,
"How to view the market right now, at this moment"
Is it a trend
A range
Or neither
Is there volatility
Is volume increasing
Or,is it currently dangerous
Those kinds of information should be evaluated in real time, without relying on the past,
and a judgment should be made "on the spot."
■ The decisive difference from the past
Traditional logic is
"seeking the answer from the past".
The new approach is
"accurately evaluating the current situation"
.
This difference may seem small, but it is actually essential.
Because,
even if the future cannot be predicted,
the present can be evaluated precisely.
■ And one more change
More importantly, the evaluation method itself is changing.
Previously,
look at indicators individually
check each condition one by one
was common.
But now,
the idea of "handling them together" is expanding.
■ What is happening?
Multiple market factors are not scattered,
but are being unified under a single standard.
And that standard becomes the basis for judgments itself.
Once it reaches this point,
the realm is no longer the traditional indicators or EAs.
It enters a slightly different domain.
■ What will happen next
As this flow continues, how will trading change?
Judgments become more straightforward
Unnecessary entries decrease
Reproducibility increases
And above all,
there will be no more hesitation.
■ An area that is still not widely known
However, this way of thinking is not common.
Many people still focus on
backtest numbers and the abundance of features
the impact of the term "learning AI"
as the main points.
But behind that,
little by little,steadily,
another standard is spreading.
acrossthe field.
■ Summary
AI isn’t inherently bad,
learning isn’t inherently bad either.
The problem is,
"where you focus"
Are you looking at the past
or are you looking at the present?
This difference is increasingly shaping outcomes.
A movement is underway to change that very way of viewing.
