I can't agree with trading in optimal f in independent mode (jcl says "If they are always in the market, you must treat them as separate systems and reduce the capital accordingly."), so as strategies doesn't relate one to another and you trade them as independent systems (0.25f in the discussion above).
1. For daily trading the positions are open at the same time for different systems, I don't think the entry moment is valid argument here. The strategies influence one another in capital perspective.
If optimal f means optimal capital allocation, you cannot ignore capital dependencies.
One intraday and one daily trading system is a different thing, I am not arguing that.
Intercorrelation in the perspective of the modern (tangent) portfolio is irrelevant here.
2. Calculating optimal f in LSPM mode (0.23f in example above) produces the optimal f for every each one of the strategies to obtain highest geometric growth together, so there cannot exist any different (lower) value, contrary to distributing optimal f via the number of systems (out of total) produces very different results (suboptimal to LSPM result - more evenly distributed, probably).
3. I gathered the information from several Vince's books and I found a little light in each of them. In LSPM book the calculation is illustrated in detail there and Vince always takes individual probabilities (of trade occurring) in account in the calculation, though he says you can aggregate the profits on daily, weekly, monthly periods/bins, it doesn't matter.
So again, time of entry is irrelevant.
4. I tested my LSPM calculation taking systems A, B, C and entering them multiple times into calculation - i.e. [A, B, A, B, C], so simulating 5 systems (to prove my algo can handle). The results are of course that optimal f for original 3 systems [0.37, 0, 0.63] are EQUAL to results from 5 systems, although my algo distributes the sums a little [0.3, 0, 0.07, 0, 0.63]. Summing each A, each B, C produces [0.37, 0, 0.63] again.
This proves that correlation is irrelevant, only maximum geometric growth matters here.
5. I am not arguing Zorro's version here, I think you just changed the capital to squared capital.
6. I was not satisfied with genetic algorithm calculation speed and accuracy, so I am calculating optimal f with increasing accuracy and narrowing from 0.1 -> 0.01 -> 0.001, so my results should be pretty accurate.

I am not an expert though, I just spent the last 3-4 weeks studying Vince's work.
And I have to say there is a TON of misconception about optimal f out there.