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Bollinger Squeeze: mean reversion on EUR/USD

Instrument
EUR/USD
Sample period
2025-06-16 — 2026-07-20
Bars
400
Trades
16
Author: ForexNews24 Research Desk
Hypothesis
A touch of the Bollinger Bands marks a statistical overstretch, and entering against it targeting a return to the mean is profitable in a ranging market.

Methodology

  1. Signal: enter against a band touch (20, 2σ), exit when price returns to SMA(20).
  2. Engine run with costs on a calm sample.
  3. Data — daily EUR/USD bars (the stated BTC/USD pair is not represented by a separate history at the portal).

Results on real data

+8.1%
Return/year (CAGR)
2.8%
Max drawdown
1.74
Sharpe ratio
88%
Winning trades
16
Trades in period
41%
Time in market
Test parameters
MetricValue
InstrumentEUR/USD
Period2025-06-16 — 2026-07-20
Bars400
CAGR+8.1%
Drawdown2.8%
Sharpe1.74
Winning trades88%
Trades16
Time in market41%
98101104107110
Study signalPassive holding
Equity curve versus passive holding on the EUR/USD sample, 2025-06-16 — 2026-07-20. On this sample the signal beat passive holding. A result on one instrument over one period is an illustration, not a promise of returns.
How to read this result
The metrics are computed on the available EUR/USD pair, not on the instrument named in the hypothesis — for that one the portal holds no own reproducible history. We honestly show the signal on the data we have, instead of unverifiable figures for someone else's instrument.

How much can you trust this result

A single return figure proves nothing — it is easy to curve-fit to history. Below are three robustness checks. We show them even when they go against the strategy.

Hold-out sample · out-of-sample

The first 70% of the data is "training", the last 30% is a fair test on data the rules never saw. If the result is far worse on the test set, the strategy was fitted to the past.

MetricTrainTest (OOS)
CAGR/yr+8.5%+7.1%
Sharpe1.652.17
Max drawdown2.8%1.2%
Bars279120
Holds the hold-out sample — not just an artefact of the training window
Walk-forward · over time

The sample is cut into 5 consecutive segments. The return in each shows whether the strategy works evenly over time or rests on one lucky stretch.

+4.6%
#1
+2.2%
#2
+0.6%
#3
-1.0%
#4
+2.2%
#5
Monte-Carlo · 2,000 simulations

Trade order is reshuffled 2,000 times (bootstrap). The range shows how much the outcome depended on a lucky sequence rather than the strategy itself. p5–p95 is the corridor of "almost all" outcomes.

Final return
+0.7%+17.5%
median +8.7%
Drawdown (p95)
−5.4%
not deeper in 95% of simulations
Probability of loss
4%
share of outcomes in the red

Computed from the per-bar returns of the same run (costs already included). Monte-Carlo is deterministic: the numbers are stable across rebuilds. Historical robustness does not guarantee future results.

Reproduce this

Download the exact sample and run the logic yourself — the numbers above should match.

Instrument: EUR/USDPeriod: 2025-06-16 — 2026-07-20Bars: 400

A Binance spot EUR/USDT proxy series, not a forex-broker feed. Binance Spot REST API (api.binance.com/api/v3/klines).

Conclusion

On a ranging sample the counter-trend rule posted a positive result with a high share of winning trades — exactly the behaviour it is used for. But the high win rate is deceptive here: a single prolonged trend can wipe out the profit of many small trades.

Practical takeaway for the trader

Bollinger Squeeze is profitable in a range, but a trend filter is mandatory: without one, a series of counter-trend entries in a strong move produces a large loss.

FAQ

Why is a high win rate not a guarantee?

Counter-trend strategies win often and small, but a rare large loss in a trend overrides many small winning trades. You have to judge the whole set of metrics, not the win rate alone.

How much does the result depend on the sample?

Entirely: on a ranging sample the rule is profitable, on a trending one it would be unprofitable. This illustrates behaviour in a specific regime, not a universal verdict.

Why a check on EUR/USD?

The portal holds its own reproducible history only for this pair. We honestly show the signal on the data we have rather than substituting unverifiable figures for another instrument.

From research to application

In our Allocation product we implemented these algorithms with all the nuances covered across the portal.

Learn about Allocation