The concepts you presented are not new: splitting a position is features in many backtesting packages, reversing position is an old trick used by some, scaling in and out is a very old technique and used pretty much by anyone with some size. I don't know what "flatdetect" and "switch ticker" are though.
Here's the thing, you come from a disciplined background and very likely are a quite proficient programmer (writing C+ isn't for everyone). The problem arises when your approach is systematic but then you start talking about maybe stopping it when it's down -15% or perhaps the puts would help the strategy, there's a lot of if's and but's as it's all conjecture. You need to quantify and test your assumptions and that's where the real data comes into play. Random data has no place in real life scenarios. Testing on random data series is nothing new, it's been around for over a decade at least and all conclusions I've seen point to it not being relevant - not inferior or better, just different to the point it doesn't help you design something that works. But yes, we all have opinions.
Look at it from an investor's viewpoint, you present this alpha producing strategy with incredible returns but don't have access to even 20k anywhere? Logic would say you'd sell your car/mortgage your house/borrow from family/use a credit card if you truly believed it will produce these great numbers. Now if you aren't sure about the performance, then the logical choice is to sell the system because you have no risk.
Once you get actual market tests accumulated, you might also want to build a more formal prospectus-type letter, proof-read by a third-party. On something this important, you can't afford an imperfect image.