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MACD Crossover: testing the crossings on EUR/USD

Instrument
EUR/USD
Sample period
2025-06-16 — 2026-07-20
Bars
400
Trades
34
Author: ForexNews24 Research Desk
Hypothesis
A crossing of the MACD line and its signal line leads a price reversal far enough that systematically entering on the crossing is profitable.

Methodology

  1. Signal: enter in the direction of the MACD(12,26,9) / signal-line crossing, hold until the opposite crossing.
  2. Engine run with costs; the rule is almost always in the market.
  3. Data — daily EUR/USD bars (the portal holds no own history for the stated GBP/USD pair).

Results on real data

-8.5%
Return/year (CAGR)
12.3%
Max drawdown
-1.31
Sharpe ratio
41%
Winning trades
34
Trades in period
92%
Time in market
Test parameters
MetricValue
InstrumentEUR/USD
Period2025-06-16 — 2026-07-20
Bars400
CAGR-8.5%
Drawdown12.3%
Sharpe-1.31
Winning trades41%
Trades34
Time in market92%
869196101106
Study signalPassive holding
Equity curve versus passive holding on the EUR/USD sample, 2025-06-16 — 2026-07-20. On this sample the signal trailed 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-11.9%-0.0%
Sharpe-1.790.02
Max drawdown12.3%2.4%
Bars279120
Loss-making on both training and test
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.

-7.2%
#1
-4.7%
#2
+0.7%
#3
+3.0%
#4
-1.1%
#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
-18.7%+1.6%
median -9.0%
Drawdown (p95)
−20.3%
not deeper in 95% of simulations
Probability of loss
92%
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 frequent crossings produced a stream of false signals, and the result is negative. MACD without a trend filter lives up to its reputation as a commission machine inside a range.

Practical takeaway for the trader

MACD crossings must be filtered by market state: without a filter, the high frequency of reversals in a range eats the account away in costs.

FAQ

Why is the result worse than in the RSI study?

The MACD rule is almost always in the market and reverses on every crossing, so in a range it makes more trades and accumulates more costs than a rule with an entry filter.

Would a trend filter have helped?

Most likely yes — it would have cut out crossings against the primary move. The base study deliberately tests the pure rule to show its behaviour without add-ons.

Why EUR/USD data?

It is the only pair for which the portal holds its own reproducible history. The result is honestly flagged as a check on the available instrument.

From research to application

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

Learn about Allocation