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A chief goal of MC is to avoid a situation where you are tuning to the noise rather than the signal. This alternative does the same thing by ensuring the optimum (or at least good) setting for one of your parameters is not overly sensitive to the setting of another.
A1. Varying parameters does generate new return paths because varying parameters will change entry and exit signals.
A2. I never said that one should not test in different regimes. The special rule is another way of saying "A change in market regime \_never\_ counts as a Type C parameter because it will virtually always affect optimal parameters." One should analyze behavior across long time periods that include changes in market regime, but that testing is separate and more aligned to absolute performance rather than relative performance \[how one set of parameters compares to another.\]
A3. In this framework varying a lot of A and C choices is \*\*not\*\* a huge number of experiments, it is \_one\_ experiment. This framework is not about testing (or finding) a single set of parameters to validate. This framework is about testing the very system itself---if your system is such that varying A and C leads to substantial movement in optimum/good values of the type B parameters, then the entire sysem is suspect and should be reconsidered. If the rationale behind your choice of indicators and overarching system setup indicates that the values of parameters X & Y should not impact what "good" settings are for parameter Q, and then the test shows that is wrong \[that "good" settings for Q depend a lot on parameter X & Y\], then it calls into question the rationale behind the system and indicates that Q is a parameter susceptible to noise.
A4. My reference to trying out the same system on different tickers was to validate the rationale behind the system... so clearly it could not have been a suggestion to choose a ticker based on the results. If the ratonale behind your system indicates that it should work similarly for X, Y, and Z and parameters optimized for X (without use of data from Y or Z) perform well when applied to Y & Z, then that is a material validation of the system.
This last point even holds if X, Y, and Z are viewed as being strongly correlated. Even stocks that are strongly correlated in the large vary qutie a bit in their noise, which is exactly what you want when trying to confirm your system is robust against fitting to noise. Sure, WM and RSG are very correlated mathematically, \*\*but the differences in their tick-by-tick prices is going to be far higher than the perturbation you would get from a MC simulation.\*\* In backtests on WM/RSG or V/MA in my system, the correlation in system responses (net gains, which ticks which are held, entry & exit) are surprisingly low.

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