With nothing going on, except a little Japan, Inc. repatriation of funds back into the homeland for year end accounting purposes [and a nice sympathy rally in gold [“about time!”] as the US Dollar is thus slightly weaker], and the obligatory fund managers “window dressing” portfolios in the stock indices to show stocks that are up for the year in their funds, it shouldn’t surprise anybody that very little trading is happening anywhere.
Which gives me the chance to bring up the year end archival charts I’ll be adding to the “Download Links” section of the website, most likely on Saturday; I’ve finished USDJPY, spot gold [XAUUSD], and the DOW30 is almost done. I touched on this yesterday, but it is important you understand what is at “your fingertips”; back when I was on the trading floor, information like this would have taken 5 years of human man hours to compile and publish; today, less than 2 weeks.
Essentially, this is a library of RM=1 through RM=4 of M1 exhaustion moves in 3 markets that are equal to or greater than 9 minutes length of time. Gold has been cataloged for June – December 2016 [calendar Q3 & Q4 of 2016]; USDJPY & DOW30 has been cataloged for October – December 2016 [calendar Q4 of 2016]. All the charts contain appropriate ‘Square of Nine’ cycle information of the length of the duration of the move in minutes, and the move in price, and these 2 values are compared against the ‘Square of Nine’ parameters of Gann’s “squaring of time & price” along the key cell numbers on the Cardinal & Diagonal crosses. In addition to the individual charts, each market also has a comprehensive spreadsheet which has all the information over the year; this is a great way to quickly scan which charts you may want to view.
Directly below is a Gann ‘Square of Nine’; notice the first 8 numbers. They are all on the Cardinal or Diagonal crosses, so for analysis purposes, if I am trying to show that exhaustions moves are not random in nature and are in fact based on cycle “tops & bottoms”, what good is it to have anything below 9 minutes for time when every number from 1 to 8 is on a Cardinal or Diagonal cross cell number?
Starting at cell 9, cell numbers quickly expand outward, thus making moves that are higher in time a function of probability [cell numbers for time on the Cardinal & Diagonal Crosses versus those that aren’t], rather than certainty which we see with cell numbers 1 through 8, and this is the reason I have excluded them for analysis; it doesn’t mean they are any less valid, in terms of making money on them and liquidating positions, it simply means I don’t want to “skew” the data with “slam dunks” that have total certainty of being on the Cardinal or Diagonal cross cell numbers. In essence, I’m taking away data that makes my premise of cycles that are constantly present in the market and only shows data that has a probability of being “off” the algorithm’s predicted model data.
The key parameter regarding price also can be seen in the market spreadsheets for each market; that parameter is the “collective error” over time of price ± from the closest Diagonal or Cardinal cross cell number. Any single trade can be slightly over or under the exact Cardinal or Diagonal cell numbers, but if there is any real bias in the algorithm model, it will show up over time as the collective error grows and grows through the weeks and months of data. Well, as you will see when the data is posted and published, the “collective error” in each market is so low, the median regression line for hundreds of data sets in each market is directly on the Cardinal or Diagonal cross cell numbers; just as Gann predicted they would be for most markets some 100+ years ago.
There are literally hundreds of charts for you to look at, so what is the main point I want you to pick up on? Simply, from either a top or bottom [white arrow on the chart] to an exhaustion move end [orange arrow on the chart], notice how close the plum line slope changes from either the top or bottom; simply put, they are the vast majority of the time very close to each other. So, every time we take a position on a plum line slope change, we are putting ourselves into a situation that has the very real potential of becoming an exhaustion move to the aqua and red lines, and that means making some very nice profits.
Gann’s ‘Square of Nine’ is a wonderful tool for spotting cycles in hindsight; it is utterly worthless for predictive power because you can always move either price or time out another level to new cell values. What I have done is simply recognize that every “spike” in price [up or down] is to a degree an exhaustion move, and therefore I wish to eliminate the smaller ones from analysis and concentrate on levels that start with Risk Model [RM] = 1 [normal market mode] and move upwards towards RM=4 [psychotic mode], and at the other end of the volatility spectrum ignore the “one offs” that happen every generation or so [like the ’87 crash, etc.]. By examining market history closely, because it is all we have, I can set up a statistical model showing me what amount of moves take place over time, and have repeated so often it makes your mind go numb, and then plot those values against the ‘Square of Nine’; and presto! Bingo!… when you do that you see tops and bottoms “cluster” around the Cardinal & Diagonal cell values.
Learn the computer code and plug into it the cell numbers for exhaustion, and you basically have the volatility algorithms. And if you spend some time analyzing the data I have collected and present to you, you might just soil yourself when you realize just how accurate this has been over the last decades of trading. I seriously don’t think there is anything even remotely close of “getting you in at the bottom”, and then liquidating “at or near a short term top”; rinse, repeat, and find your “escape to success”.
Yes, I see gold made a nice move today … woulda coulda shoulda … don’t really care though, as I’m looking forward to next week and have already “closed the books” on 2016. The beach beckons … I’m outta here … until tomorrow.
Have a great day everybody!
-vegas
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