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Okay, let’s go deeper.
I will snapshot my process, step by step and publishing it here so we can discuss small bites each time. I can also share the 91 combinations I used to filter the 233 strategy of the phase 1.

Will come back soon…

I think that you do not use very many indicators in the generation stage, for example I use Stagnation, Backtest Quality, Profit factor, Sqn, Win/Loss, Consec Loss, Max Equity Drawdown, Min R - Squared.

This doesn't give me long living ea's, so I am interested in your research to determine what I can improve.

I don't do the incubation phases, I will start that, and I have been using a much shorter time period, 5 years.

Some grid ea's give me great results, but I feel obligated to watch them closely.

Thanks, Blaiserboy — appreciate your interest.

A quick background:
I started trading about 14 years ago — first discretionary (volume profile), then about five years ago I switched fully to algorithmic trading, mainly with third-party grid and martingale EAs.
They still run and make money, but I’ve modified all sets of them to manage risk better after some blown up account.

Today, I still buy & test a few external bots (very special things like news scalpers), but my focus is on building my own EAs with EA Studio.

After almost two years using it, I’ve learned that easy generation doesn’t mean reliable profitability — it requires a systematic, repeatable process.

Here’s the simplified workflow I use now:

    1.    Generation – Data 2016–2024, 30 % OOS, filters as acceptance criteria (PF > 1.1, Win % > 60, SQN > 2).

    2.    Validation – WFA on 2025 data, selecting top performers (older collections re-filtered).

    3.    Incubation – Demo for at least 3 months or 50 trades across brokers and timeframes.

    4.    Live – No re-optimization, monitored by PF, Win %, Recovery, and Max Losses.

    5.    Monthly maintenance – Rotate EAs based on rule-based performance thresholds.

Goal: reach 60–70 % success rate in live portfolios and maintain 4–5 efficient, rotating sets over time.

Happy to compare notes — especially on data spans, filters, or live validation setups.

Vincenzo

I certainly appreciate your sharing.

One question I have...... what is your guideline for your testing?

Once I know a bit more about what you are doing I may have some comment.

Following the demo validation phase (Phase 2), this stage moves the same SQN-filtered generation process into real trading conditions to observe how survival probability and performance behave under execution constraints.
All EAs were generated and validated before live deployment and not re-optimized afterwards.

(Post #1 link: https://forexsb.com/forum/post/82865/#p82865 )
(Post #2 link: https://forexsb.com/forum/post/82906/#p82906 )

Test set up & Parameter Description

- Start Date 4 April 2025
- Active EAs 24 (3rd generation – SQN-filtered)
- Brokers / Accounts: 2 brokers (BlackBull Markets, RoboForex) / 4 accounts
- Symbols 21 symbols, ≥ 2 timeframes per symbol
- Trade Sample 477 trades (≈ 190 days / 6 months)

Performance Metrics:
- Portfolio PF = 1.35 (aggregate)
- Trade Win Rate = 72.3 % (aggregate)
- Total Return = +19.9 %
- Max DD = –7.1 %
- EA Status 13 EAs in profit (54 %), 11 in loss (46 %)
- Data Source Real execution with live spreads, slippage, and commissions

Interpretation

This first live dataset shows consistent survival behavior compared with the Phase 2 demo validation, with 54 % of EAs currently in profit (vs. 55 % in Phase 2).
Preliminary data suggest a balanced profit distribution across symbols and timeframes, though this will require further confirmation as the live sample expands.

All results are based on real execution across two brokers (BlackBull Markets and RoboForex) and include all EAs originally deployed — no re-optimization or exclusion has been applied. Underperforming EAs remain counted in the statistics to avoid survivorship bias.

Next Phase — Workflow Maintenance (Live Phase 4) see the attachement.

The next phase will introduce dynamic rotation across the full live portfolio:

    •    Monthly rollover — apply the classification rules to update each EA’s status (New, Stable, Watchlist, Critical).
    •    Rotation logic — after ≈ 50 trades per EA, suspend the weaker ones (Max Losses ≥ 5 or PF < 1.1) and replace them with new SQN-filtered candidates from the same pool.
    •    Cross-broker extension — extend validation to additional brokers for feed diversity and execution resilience.

Conclusion

The SQN-based filtering maintains a comparable survival probability between demo and live conditions.
Preliminary performance remains aligned with prior phases, and the dataset now provides the foundation for the upcoming maintenance-rotation workflow.

This next stage will focus on the systematic exclusion and replacement of underperforming EAs to gradually improve aggregate portfolio efficiency — with the operational target of stabilizing the EA-level Win Rate above 70 % over time, subject to ongoing monitoring and live execution validation.

I’m sharing this work — the result of several months of testing, data collection, and refinement — because I believe that open, data-driven discussion helps all of us learn faster.

(Only) Constructive feedback and alternative viewpoints are very welcome.

Vincenzo

thx

I use it as filter when making collection.

Usually wanting 100 trades minimum

It would be great to have "Open From hh:mm" and "Open To hh:mm" to specity hh:mm intervals on which to open.

Do not open X minutes before /after News is a spectacular feature, but this also will allow to open trades only during hours when the portfolio show best past performances.

Thanks!

Hello, happy to see that we are looking at the same way to improve EA selection rate.

As you know this SQN is of the core Filter in my experiments about EA Win rate.

My questions to you here:

- are you currently using it ?

- are you using is as a filter criteria (so after creating strategies) or as an common acceptance criteria (killing all strategies with SQN < x)?

- what is the limit (x) you are using and why ?

Thanks you in advance
Vincenzo

yes, don't let original file pollute the new file.