I’m sure this is something that I’ve done before, but I don’t think I realized the implications of it so I’ve redone it.
In a given 3 wave progression off of a trend, a trend move is likely to be followed by a spring wave (68%) or an expansion wave (32%). This means no matter your stop, you have a 32% chance to lose the trade. Given the easiest and highest density areas to enter, this makes for terrible R:R
I’ve come to think that the wave length, although it has issues of being arbitrary (is 100 pips now the same as 100 pips 10 years ago?), is still important. I broke up the sets into 50 pip bins to hopefully get rid of some of the possible change to the value of a single pip over time.
Not a whole ton to learn from this since one should already expect that small waves are more likely to expand rather than contract, but as usual, it’s nice to know where the breaks are.
Is this scientific proof to follow the trend and it’s strength? (pt 2)
I’m cautious to make more additions to this. I think at this point I want to be veryy critical and precise with improving at the core components. I’m kind of viewing this as “gen 1” of 1 part of a system of x number of components if we’re speaking about the SB metabrain style of trading.
The picture is pretty self explanatory given one is aware of what h values are. What’s interesting is the difference in strength between the low and high extreme h values.
Currently (after nearly 18 months LOL):
The journey continues.
Initial batch done!
I think correct aggregation will be hard or impossible to do without actually using real price points, but I will see what I can come up with. At the moment, it seems that the initial edge I found a few months ago will be universal for all time frames and h values.
might end up ditching lower time frames due to not having enough data, but might keep the 15m to see if it’s close to the HTF findings regardless.
Things get easier the second time around 🙂
Days worth of work condensed into minutes. Time allowing I’ll have the rest of it done before the weekend; I’ve coded it to do the simple analysis for me so I’m interested to see what it turns up.. so far it looks ok. Neither bad nor good.
Worked on some additional parts. For the most part, very consistent. I think this is a good thing. I think that if the edge I had originally applies to all time frames, it may give some “legitimacy” to the theory being a sound one.
Boy I thought it would take much longer. I suppose it would but I called it quits after 4 trials. They’re more or less the same (except for the 15m) when the filter is applied.
Looks like I’ll be using 15 as my base line regardless of TF, which is kind of nice. I’m sure now however, that completing the multiple wave work will take much much more time to get correctly, if such a thing exists.
Got enough time to throw this together. I don’t know how it’ll turn out but I think this is the best I can do for now. There’s the semi-martingale strategy that could be added to this which will require a different dimension (also huge potential) but I want to look at the base first to see what the numbers look like.
I’m not very good with coming up with creative names so I’ll just be calling this MTF recurrence.
1. Find every transient price for h>1 [then max(left h, right h)]
2. For all such prices, find min h value to meet “Fx-Jay” recurrency.
3. Freq Dist. all these values to find min h value to achieve min h value.
4. Complete the above 3 steps for TFs 15m, 30m, 1hr, 2hr, 3hr, 4hr, 6hr, 8hr, 12hr, D1, W1.
5. Create wave sequences for all frames. Leave out the issue of double tops/bottoms for now.
6. Create statistics for 3 wave and 5 wave direction probabilities.
7. Create dashboard to monitor direction bias across time frames.
I’ve completed all these steps before over the past few months, but only for 1 time frame. My biggest fear atm is that the results won’t give a good skewed distribution of wave probabilities. The ones that I came up with for H1(shown in earlier posts) were quite good. I ran a quick test for a lower time frame and they converged a bit more towards 50%. If I’m lucky it will be that way because the h value used was incorrect. In other words, there is a very realistic possibility that the correct h value to achieve 97% recurrency and the correct h value to create maximum skew in wave probabilities are two different values. Luckily for me, I already have 1 basis point of wave probabilities so if it’s twin is way off, then I will know I’m wrong. The problem then will be on how to go about fixing it. (note: big problem)
Given that my coding is correct (which I always hope it is but am vulnerable to error), I think I’m going to be done with h values for a while. If trading transient bars mean always counter trend trading, and this is the true hit probability, then this is basically the equivalent of a “minimum retracement level”. I think I’ll go all in on a micro to see where it leads me. There is still one more interpretation that I haven’t gotten around to which will probably be done at some point, as well as figuring out the k value. This is one of the best results I’ve gotten so far, but implementing it correctly is still an issue. I’ve always been a believer that a semi martingale strategy works, but it must be implemented very cleverly with a worst case scenario exit signal that doesn’t end in a margin call.
There’s still a lot of work to be done with transient bars, specifically with the sequencing that has been mentioned but I think I’ll wait until another time to read through that and work through what it actually means..
Time to re-attempt SB work I believe