I, for one, am quite interested in this thread.

Just to clarify a few points for readers:

- The datasets, brokers, and starting periods were different — this was not same-data validation.

- The goal wasn’t to prove universal robustness, but to see how filter design affects selection success rate across independent demo incubations.

- All EAs had already passed Monte Carlo validation, so the test measured the marginal impact of filters, not basic robustness.

- Demo incubation is one gate in a multi-step pipeline (Generation → Monte Carlo → Incubation → Live).

- Combination A was predefined and applied both to past and new collections — part of an iterative workflow, not a backward fitting.

- Win rate check point included all EAs (winners + losers), avoiding survivorship bias.

- Cross-broker and multi-timeframe results confirm the effect isn’t dataset-specific.

In short, this experiment isolates how selection criteria influence long-term survival probability in demo validation. The final live journey is just started.

Spread impact is already on my list for the next test matrix, but as you know, these things are resource- and time-intensive.
I prefer an incremental approach — too many variables at once can make interpreting the results much harder.

Have you already done any MT4 vs MT5 spread benchmark yourself?

Also I think SQN requires 100 trades to be effective(Statistical Significance).

Results showed that filters alone could dramatically shift outcomes — from below 20 % to full 100 % WFA success.

To go further, I selected one setup — Combination A — with:

- Profit Factor > 1.1

- Winning Trades > 60 %

- SQN > 2

EAs meeting these rules were incubated on demo accounts and compared with older incubators created before SQN was used.
All results were manually collected last week from EAs running on Demo account across different symbols, timeframes, and brokers.

Results as Avg Win Rate (Profitable EAs / Total):

- Without SQN filter: 33 % (sample 348EAs)
- With SQN filter (Combination A): 55 % (sample 267EAs)

A total of 615 EAs were benchmarked.
Some SQN-filtered incubators reached over 70 % profitable EAs after incubation, while pre-SQN groups stayed around 25–40 %.

(Chart: EA Win Rate % After Incubation – SQN Filter vs No SQN)

Interpretation

The data suggest structural robustness over time.
Many EAs in this benchmark have been running for 300+ days, still active, trading, and trackable across multiple brokers.

Key points:

- Filter design strongly impacts long-term EA survival.
- SQN combined with PF and Win % clearly improves success probability.

Cross-broker, multi-timeframe behavior shows the effect isn’t dataset-specific.

Next Step

A practical approach might be:

- In the while several EAs have been moved from demos to live account and will be evaluated soon.

- Run 10 uncorrelated SQN-filtered EAs in one live portfolio. After ~50 trades per EA, switch off the weaker ones and add new SQN candidates to keep improving the portfolio’s overall win rate.

- I’ve also been copying only the trades from profitable SQN EAs on demo to live MT4, which helps reduce the usual performance drop when moving EAs from demo to real accounts.

Does an even better filter combination exist — one that can outperform this setup?

Let’s go back to testing and studying.

And above all, these results prove that the first part of the experiment was not just a dataset-driven bias, but a replicable effect visible across brokers, timeframes, and extended incubation periods.

Open to constructive observations...