Optimized h values pt 2

Taking a page out of the forge =p

Something that I think may be something to keep in mind is that when it seems like there are a LOT of possible ways to improve on something, or things to explore, etc, often times it means a filter of some sort is required. The “entry level” filter is market states. It’s a great example because it’s not so easily done, and everyone does it in their own way. Yet, it’s fairly safe to say that those who don’t manage to create a market state filter die in the market. A filter is somewhat abstract and unclear, and some sort of brute force is necessary to first discover that it exists, and then to track and create the proper criteria to name it. The brute force application of it is NOT to uncover a signal,  but rather a pattern. This is something I’ve had a lot of trouble with in the past; I think the trick, (although extremely hard to do for me) is to be able to approach a concept that works very well in multiple ways and to apply to as many areas of the market as possible. Every piece of info in the market is “useful” if I can manage to find a use in it for detecting:

1. Entry
2. Exit
3. Breakout signal
4. Breakout confirmation
5. Reversal signal
6. Reversal confirmation
In the aspect of transience
7. Areas where price will return
8. Areas where price will not return

There are more, I think it helps me to break apart the market into as many pieces as possible, and be aware that something that may not work out so well for the aspect that I’m looking for may work pretty good in another area. Working backwards and in hindsight is actually a useful tool in research.


I think it’s possible to build a layer on top of the original layer.. But I will need to think about what it is accomplishing a bit more before I get into filter-ception land. However, there may be something to it.. In this first filter I ran a filter, and then extracted all of the percentages that were at least 70%. Although, for the most part they’re all in a healthy percentage, save one.


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