Thank you Herr Lotter.

Actually, I had this issue with far less than 1024 Strategies. Probably, other files were counted as well?
Why I am having these numbers? I have around 50 Algos (increasing), several time frames, several Asset Groups, several Regimes methods and several Versions.
This is combined in a speaking coding:

e.g. AL01-G1-R2-0240-V1

and so on. So, I get many combinations which I analyse. The last parameter version is used to optimize (reduzing overfitting, doing robustness tests,...)
I am looping only thru assets (and not thru algos or TimeFrame).

I am having a development, testing, LiveDemo and Live layer. That means one Zorro for each layer. Strategy and History are on Google drive (in order to use several PCs in a Tailscale network with the same data).
I have implemented more and more a machinery of research, development, testing, demo and live trading, but Zorro is the core and it works great.

Some may already have noticed the new "Ranger-Z" item in the menu of the Zorro website. A Zorro version of the legandary Ranger system by Robert Pardo is now available.

If I remember correctly, you can use a Family Advisor account to trade with multiple accounts from one endpoint.

I use optimize(56, 8, 112, 8) with additional smoothing period * 2. You are correct, it has a little effect on individual component and no significant effect on overall portfolio performance (-1% AR) which I consider a statistical fluctuation. But it has an effect nonetheless. What actually happens is that new backtest with new MMI changed OptimalF of one component. That changed reinvesting margin resulting in a visibly different portfolio statistics.

The simulator is designed as a multiscale time series laboratory for studying how a nonstationary observed process can be decomposed, forecast, regularized, and reassembled through interacting stochastic representations across multiple temporal resolutions. Its purpose is to compare heterogeneous autoregressive, integrated, moving-average, seasonal, and exogenous-state formulations on the same evolving signal, while simultaneously tracking the local geometry of the series through derived state variables such as displacement-like level, velocity-like increment, acceleration-like curvature, higher-order change, energy-like amplitude concentration, damping-like persistence loss, and resonance-like frequency alignment. The system evaluates how these derived quantities co-evolve under changing dependence structure, changing variance, changing memory depth, and changing cross-scale agreement, and it uses those interactions to adapt the relative influence of competing forecasting contexts. In this sense, the simulator is not merely a forecast engine but a dynamic estimator of structure, where each contextual model contributes a partial view of the underlying temporal law and where contextual weights are updated according to residual whiteness, normality, heteroskedasticity sensitivity, error persistence, directional agreement, and state-consistency criteria. The framework also serves as a controlled environment for examining the stability of recursive model reuse, the consequences of delayed refitting, the emergence of seasonal persistence, the effect of transformed coordinates such as logarithmic and differenced states, and the propagation of uncertainty through an ensemble of conditional predictive distributions. At a higher level, the simulator can be understood as an adaptive operator on time-indexed data that maps raw observations into a hierarchy of latent state summaries, local diagnostics, cross-resolution coherence measures, and regime-sensitive forecast fields. Its central mathematical aim is to explore how one can construct a robust, self-correcting approximation to a complex evolving time series by coupling classical stochastic process models with state-dependent weighting, multiresolution consistency checks, and dynamically stabilized forecast aggregation.

Monte Carlo Analysis... Median AR 297%
Win 1074$ MI 11367$ DD 7.13$ Capital 22155$
Trades 77 Win 98.7% Avg +16.7p Bars 18
CAGR inf% PF 21766.01 SR 56.55 UI 1% R2 0.70

Finally, I found it.

I used the SaveMode flag variable and had set it to 4 for a dedicated reason. Now, I changed it back to SaveMode=791 and it should work.
I set this a while ago and the more complex the set-up gets ...

For Information from the manual:

These are the SaveMode Flags: (x) are bit code

SV_SLIDERS(2) save/load slider positions (default).
SV_ALGOVARS(4) - save/load all AlgoVars (default).
SV_ALGOVARS2 - save/load all AlgoVars2.
SV_TRADES(1) - save/load all open trades (default).
SV_STATS(16)- save/load statistics data, to be continued in the next session.
SV_BACKUP(256) - after loading, save a backup of the .trd file (default).
SV_HTML(512) - after loading trades in [Test] mode, generate a HTML file with the list of open trades.

Has someone experience in trading on XTB?