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