Pattern Reality Check / Liquidity sweep

PATTERN REALITY CHECK · SMART MONEY AND LEVELS · TESTED 8 OCT 2026

Liquidity sweep: does it work after costs?

Price pokes above an old high, then closes back below. Courses say it "hunted the stops" and will now reverse.

Loses money after costs

Verdict

Loses money after costs

Applied mechanically to 29 coins, this rule lost money after costs, by a margin larger than luck would explain.

Average per trade-9.8%-0.098Rof the amount risked, after costs and funding. 95% range: -13.9% to -5.8%.

Trades tested9,36429 coins2020-06-05 to 2026-09-23, 4-hour candles.

Trades won32%needs 36% to break evenWins averaged +1.890R, losses -1.044R.

Against random entries0%of random runs beatenRandom entries averaged -5.9%, because costs hit every trade. Beating them is not enough: the average must also be clearly above zero.

Percentages are % of the amount risked per trade. New to R? Read this first.

What courses teach

The idea

Taught as stop hunting: price briefly breaks a swing high or low to trigger the stops resting there, then reverses. A close back inside the range is the signal.

The rules we froze

These numbers are the usual textbook values. They were written before we saw a single result and were not changed afterwards.

PartRule, fixed before the test

Signal

Price exceeds a confirmed swing high (low) up to 50 candles old by at least 0.1 ATR, then closes back inside.

Entry

Market order at the close of the signal candle.

Stop

0.25 ATR beyond the sweep extreme. At least 1 ATR from entry.

Target

Twice the distance to the stop (2R).

Time limit

Closed after 60 candles (10 days) if neither stop nor target was hit.

Costs

0.14% round trip plus real funding, charged on every trade.

What it looks like

Examples, good and bad

Two real trades picked by a fixed rule, not for being pretty, and six drawn at random from the held-back period (the most recent two years). 2 of the 6 won. Examples show the rule, they are not results: the results are the numbers further down.

Liquidity sweep example that reached the target

Liquidity sweep example that was stopped out

Results

Trade after trade

All 9,364 trades added up, with the three periods shaded. The right axis shows euros if you risked €100 on each trade. The dashed line is what random entries would have done.

By period

The same average, split by period. The black bars are the 95% range: if they cross zero, we cannot tell the result from luck.

PeriodTradesAverage per tradeTrades won95% range

Build

4,442

-5.6% -0.056R

33.5%

-11.1% to +0.1%

Validation

3,253

-16.4% -0.164R

30.2%

-22.9% to -9.4%

Held-back

1,669

-8.2% -0.082R

32.9%

-17.9% to +1.9%

By version

VersionTradesAverage per tradeTrades won

Long

4,951

-15.7% -0.157R

30.5%

Short

4,413

-3.2% -0.032R

34.2%

How the trades ended

The honest yardstick

Compared with entering at random

A pattern should beat luck, not zero. We ran 1,000 simulations that enter at random times on the same coins and periods, in the same direction, with the same stop distance, target and time limit.

Liquidity sweep compared with random entries

In plain words

Why the numbers look like this

Random entries lose too. Entering at random times with the same stops and targets averaged -5.9% per trade (-0.059R) because of fees, funding and spread. The pattern averaged -9.8% and did better than 0% of 1,000 random runs. That is the honest comparison: not zero, but what luck plus costs produce.

Before costs: -4.5%. After costs: -9.8%. Fees, funding and slippage took 5.3% of the amount risked from every trade, on average.

Win rate versus break-even. It won 32.2% of trades. With average wins of +1.890R and losses of -1.044R, it needed 35.6% to break even. The win rate is below what it needs.

Opposite side, same stops. Taking the opposite direction on every signal, with the same stop distance, averaged -0.3%. If the pattern carried real information, the pattern direction should beat its mirror by a clear margin. Here the gap is 9.5 percentage points.

Consistency across time. The average was positive in 0 of 3 periods. A real edge should not depend on which period you look at.

Sensitivity. Across 32 variations of the rule, 1 were positive and 31 negative. The best variation (+0.2%, 1,801 trades) is not a result: with 32 variations, a few green cells appear by chance. We look for a broad green region.

Stress tests

Does it survive changes?

Changing the pattern shape

Each cell is the same test with a different setting. The outlined cell is the rule we froze. Green is positive, red negative. We do not pick the best cell: we look for a broad region.

Changing the exits

Same idea for the target and the stop.

Changing the costs

CostsAverage per trade

No costs, funding only

-4.3% -0.043R

Half the costs

-7.1% -0.071R

Our costs (0.14% + funding)

-9.8% -0.098R

Double costs

-15.3% -0.153R

Triple costs

-20.8% -0.208R

Another timeframe

On daily candles the same rule gave -13.4% over 1,460 trades.

Coin by coin

4 of 29 coins had a positive average. With a few dozen trades per coin, some are always positive by chance.

Limits

What this does not say

It does not say liquidity sweep never works. It says this version of the rule, applied to every case, shows loses money after costs in this data.
It is a simulation. Real fills, delays and emotions make results worse.
The market may change. Results from the past are not a forecast.
We publish tests of popular patterns, we do not publish or comment on strategies we use ourselves.

Method: how we test · Errors: corrections · Education only, not investment advice.

Questions

Questions about liquidity sweep

Does liquidity sweep work in crypto?

In our test of 9,364 trades on 29 crypto perpetuals, with real costs and funding, the average result was -0.098R per trade (-9.8% of the amount risked). Verdict: loses money after costs. This applies to the textbook rule defined on this page, not to every way of using it.

Why do I see charts where liquidity sweep worked?

Because any rule produces winning examples. A chart that shows a winner says nothing about how often the same rule fails. The test above applies the rule to every case, with costs, without picking.

What would make this result change?

A different timeframe or market, different exits, or filters that add information the pattern does not contain. We tested a second timeframe and 32 variations of the rule; the numbers are on this page.

How should I read the percentages?

They are percentages of the amount you risk per trade, not of your account or of the coin price. -9.8% means -9.8% of the amount risked, on average. See what is R.