You can spend an hour looking at crypto charts. End up with a longer list of coins to watch less confidence and no clear choice to make.

There is always another coin that is moving. Another indicator that is changing. Another reason to believe that something is about to go up.

Opening charts does not always help. At some point you need a way to decide which ones are worth spending another five minutes on.

That is the problem that we are trying to solve at TrendScore.

I am part of the group that built the platform so this article comes with that connection. I want to explain what scanning adds to the process using one example—and where its value stops.

What a score actually tells you

A scanner uses a set of rules across markets and ranks what it finds. This saves time. Makes comparisons more consistent.

A high score needs to be looked at carefully.

It means the asset fits the scanners rules strongly at that moment. It does not automatically mean the trade is likely to be a winner. A score is not a probability unless someone has tested and adjusted it to be one.

Multiple indicators can also be describing the movement. Strong agreement is worth looking at. It isn't always multiple pieces of evidence.

For me the useful result is a list of questions: Why did this asset stand out? Has the move already happened? Is there still a way to get in? What would make this idea wrong?

One scan from start to finish

Take this example from TrendScore.

Image

One example: a STRK short with three targets set

The completed STRK/USDT trade gives us an example of what a structured scenario looks like after it was chosen.

TrendScore’s history shows a short opened on October 24 2026 at 11:52:58 with a price of $0.04883. The trade was labeled "10x · 1m". Used $3,065 in virtual money, which represents $30,650 in simulated risk.

The screenshot shows the following levels:

Level Price Distance from entry

Entry $0.04883 —

Stop-loss reference $0.04915 0.66% above entry

Take profit 1 $0.04835 0.98% below entry

Take profit 2 $0.04819 1.31% below entry

Take profit 3 $0.04803 1.64% below entry

Times are shown as on the screen; the screenshot does not mention the time zone.

Since this was a trade falling prices helped the position. A rise to the stop-loss level would have hurt it.

There is a limit to what this image shows. It is a completed trade, not the scan. It does not show the score the indicators or the market conditions when the trade was made. We can look at the levels and results. We cannot use this image alone to explain why STRK was on top of other assets.

The risk was known

The gap between the entry and the stop-loss was $0.00032 per token.

Compared to that gap the three targets were 1.5, 2 and 2.5 times the potential reward for each unit of risk before costs.

These numbers explain the setup. They do not say how likely the trade was to work.

The 10x leverage also plays a role. A move of around 0.66% against the entry would have meant about 6.55% of the starting money lost at that leverage assuming the whole trade stayed open and the stop was hit, before costs. Leverage makes a losing move bigger just like it makes a winning move bigger.

That is why the return percentage should never be read without knowing the position size, leverage and exit plan.

What the trade record shows

The first target was hit at 12:43:05 over 50 minutes after the trade was opened. That closed 33% of the simulated trade at $0.04835.

The second target came at 13:04:56 closing another 34% at $0.04819. Five seconds later the last 33% closed at $0.04803.

The recorded exit times, portions. Profits are:

TP1 12:43:05 33% $99.43

TP2 13:04:56 34% $136.58

TP3 13:05:01 33% $165.71

The total time was 1 hour, 12 minutes and 3 seconds with all three targets reached.

The way the exits were set is important. The whole trade was not held until the target: some part was taken off at each earlier level. Because of that the movement from the start to the exit can't be used to calculate the return for the whole trade.

Also the image does not show the price movement. We can't tell how close the trade came to the stop or how much it moved against the entry before hitting the targets.

What it does show is an example of a simulated short moving through three set exits. This makes the scenario easy to look at while being clear about the limits of what we can say.

For an article about filtering this difference is important. Finding a candidate is the start. Recording the entry, risk, exits and outcome is what allows us to check if the scenario was useful.

This was an example in terms of hitting the targets. It does not prove the scanner is reliable overall. That needs to look at losing trades missed chances and a bigger set of results alongside the wins.

What filtering can and can't improve

The benefit of scanning is that you can spend more time looking at a few choices.

It also makes it easier to review the search process. If the rules and inputs are recorded you can ask why something came up and whether those conditions were helpful later.

Consistency has limits. A system can apply a rule perfectly. It can flag a move that's already over. The inputs can be delayed. Market conditions can change between when the scan was made and when it was checked.

That is why I think a scan should have a time an explanation and a way to stop the idea from being used. A direction alone leaves much room for guessing after the fact.

It should also be possible to finish the review without taking a trade. The top choice on a day may still be something you want to avoid.

A shorter list is the beginning

The question I would ask after reading any scan is simple: does this help me make a decision that I can explain and look back on later?

That decision might be to check more wait for a time practice with fake money or skip it.

At TrendScore.io we use ranked market scans, with entry take-profit and stop-loss levels, plus paper trading. A free account lets you see the scans and follow the trades.

The real test is whether that process makes it easier to work through the market. One good example cannot tell you if that is true.

Disclosure: I am part of the team that built TrendScore. This article talks about market analysis and simulated trading not a suggestion to buy or sell anything. Scans can be wrong. Simulated results don’t show what happens in real trading.