More is not always better.

Is there a rule-of-thumb for how much history we should be backtesting on during different stages of strategy design? If there was, it seems like it should be tied to something dynamic, like the number of Training cycle bars (because "bars" would be relative to the BarPeriod used, right?)

One theory I'm exploring is that too much history fed to Zorro in the early design stage might be bad, for the reason that it creates a too-homogeneous logic. You can't please everyone, and certainly the market is known for its multiple personalities.

Therefore, perhaps a rule-of-thumb should limit initial logic testing to a recent past (a reasonable number of WFO cycles and trades-per-cycle, but not excessive). Then, once a trading edge could be identified and optimized... deeper history could be introduced to see how such a logic would perform under (potentially very) different market personalities.

It seems to me that a short BarPeriod strategy fully optimized to capture the profits over a 10 year period would be hindered by the fact that it has been designed for a one-size-fits-all approach. Instead, it seems likely that current trends and potential Black Swan events would be better captured by a bot that studies and optimizes only a recent history. That would be the market's current or recent personality. Perhaps personalities from years and years ago are largely irrelevant in the grand scheme.

I'm also studying Zorro's DataSlope feature, and wondering if this was designed for exactly the purpose I'm discussing here.

I'd love to hear your thoughts.