Not an indicator that works in every situation.
It was only good in specific markets and timeframes.
#1 ranked indicator on TradingView.
Used widely by traders overseas.
Because it claims to identify where institutions are positioned.
We thought the same, at first.
So we ran a backtest on five years of data — SMC signals alone.
3,223 trades, verified in sequence.
The results weren't what we expected.
The same signal — some trades were big losses, others big winners.
Same ticker, similar zone, same signal. The outcomes kept splitting.
This is where we first noticed something was off.
Was the indicator bad?
Or were we using it wrong?
The next experiment started with that question.
We started with the simplest hypothesis.
If SMC alone wasn't enough, couldn't we just add another indicator?
Including Volume, EMA, RSI, ATR, and MACD,
we paired 18 technical indicators one by one with SMC.
We built every possible combination and ran 3,223 backtests from scratch.
The numbers were clearly better.
We thought we had found the answer.
But when we broke the results down asset by asset, something looked off.
The same combination improved dramatically in some assets.
The same combination actually collapsed in others.
The average had been hiding this fracture.
The combination hadn't solved the problem.
There was a different reason hiding behind it.
To find what the combination was hiding, we had to look at the indicators again.
We'd been evaluating indicators by performance.
Win rate. Risk Reward Ratio.
But when we laid the results side by side across assets,
we started seeing indicators from a different angle.
Each indicator was good at something different.
Every indicator had a clear split — what it did well, and what it didn't.
So what kind of character does SMC have?
That question was where the next experiment began.
We went back to 3,223 trades.
This time, not by win rate — by market condition at entry.
Was the trend intact?
Recovering after a pullback?
Structure breaking down?
Ranging with no direction?
The same SMC signals produced completely different results depending on market condition.
Sorting through these results, SMC's character became clear.
SMC is built on institutional supply zones.
It excels at catching moves that resume after a pullback.
But in directionless markets — or where structure kept breaking —
performance collapsed no matter how many signals appeared.
That's when the research itself changed direction.
We'd been searching for "how to use SMC better".
Now we started asking a different question.
SMC가 가장 잘 통하는 시장과 무너지는 시장을 구분할 객관적인 기준이 필요했습니다. 이 질문에 답하기 위해, 우리는 기존의 도구들을 완전히 다른 목적으로 다시 살펴봐야 했습니다.
