JADELottery's Blog

It is as it was, is, will be.

Being once dead before, our existence comes from outside the Quantum Singularity that is this Universe.

Nearly all of our time here has been erased.

Almost every place we have ever lived here has been torn down and demolished.

We are not here in any real sense, it's all imaginary, as the complex thought goes.

We'll leave a mark that even words can't explain.

However, know this, we are you and you are we.

We live your life as you live ours.

We are the cloud in the sky, the blade of grass beneath your feet, the water you drink and the thought you think... we are.

Thank you for being all that we designed you to be.

Jehocifer.

Look, Look, See, See, Looook, Seeee, SEEE, LOOOK.  ( O ) ( O )

oo-ee-he-he, We can smell the gains already.

You know they're sneaking a rebound in the works, so we're buying more XIV; Maybe put it all in there, aye.

Gotta keep that turd phloating.

At the rate we are adding gainfully Employed people it will take 50 Years to reach percentages before the 2008 housing disaster based on post 2008 disaster numbers.

There has never been a "Recovery".

At best, the Employed percentage is Trickle Charging.

Keep in mind, Employed people are ones that buy homes, cars, boats, dish washers, dryers; renovate existing homes, take trips, AND have disposable income.

Anyone else that isn't Employed is just getting by.

The Employed percentage has been stuck at 59% and has never gone above 60%.

Pre 2008, it was in the 63% range.

Given the current Employment Population, we'd need to get around 10 Million people Gainfully Employed; meaning they have a Full Time, Well Paying Career (Not a JOB!).

See chart below.

 

This is a picture of the area where we saw a meteor, asteroid, or comet hit the moon.

It shows the approximate area and phase of the moon when we seen the hit.

The moon will be forever changed.

The time of the hit as seen here in the U.S. will be at dusk or a few hours after dusk.

There were leaves on the trees, so we're thinking it's late spring, summer or early fall.

corrected from dusk to dawn, always get dusk and dawn flipped for some reason.

must have something to do with passing from the Superverse to this Universe.

my bad, changed back to dusk.

had review this one over and over since it's so critical.

the dream itself was fairly brief and dramatic; also, fragmented in sequence.

Look Out Asswhole, Here It Comes!

Current as of 2014-2Q

 

 

Not sure if we released this or not.

Anyway, here are links to the program:

ftp://www.jadexcode.com/Applications/ProjectQuickPick.zip

or

ftp://www.jadexcode.com/Applications/ProjectQuickPick.exe

 

It's related to these posted topics we made a while back.

The Anti-Quick Pick System

Quick Picks can't match the power of Self Picks

1900 to 2014.

Red line is Relativistic Dow, Blue line is Dow Chaos (+ more, - less).

 

click on image to expand.

You can download our Excel sheet with the Relativistic Market Data and Regression measurements at our FTP site.

ftp://www.jadexcode.com/Excel/S&P-NAS-DOW-1971-02-05-to-Present-2.xlsm

If you don't want to run the Macro Enabled file, here are the two functions we added to get the job done.

 

______________________________________________________________________________________________________________

Function LineSlope(theRange As Range) As Double

    Dim n, y_sum, y_avg, xy_sum, xy_avg As Double
   
    n = theRange.Rows.Count: y_sum = 0: xy_sum = 0
   
    On Error GoTo errorexit
   
    If (n < 2) Or (theRange.Columns.Count > 1) Then
   
        LineSlope = -4.94065645841247E-324
       
    Else
   
        For a = 1 To n
       
            y_sum = y_sum + theRange.Cells(a, 1)
            xy_sum = xy_sum + a * theRange.Cells(a, 1)
           
        Next a
       
        y_avg = y_sum / n: xy_avg = xy_sum / n
       
        LineSlope = (12 * xy_avg - 6 * (n + 1) * y_avg) / ((n - 1) * (n + 1))
       
    End If
   
    Exit Function
   
errorexit:

LineSlope = -4.94065645841247E-324
   
End Function

______________________________________________________________________________________________________________

Function LineCorr(theRange As Range) As Double

    Dim n, y_sum, y_avg, yy_sum, yy_avg, xy_sum, xy_avg As Double
   
    n = theRange.Rows.Count: y_sum = 0: yy_sum = 0: xy_sum = 0
   
    On Error GoTo errorexit
   
    If (n < 2) Or (theRange.Columns.Count > 1) Then
   
        LineCorr = -4.94065645841247E-324
       
    Else
   
        For a = 1 To n
       
            y_sum = y_sum + theRange.Cells(a, 1)
            yy_sum = yy_sum + theRange.Cells(a, 1) * theRange.Cells(a, 1)
            xy_sum = xy_sum + a * theRange.Cells(a, 1)
           
        Next a
       
        y_avg = y_sum / n: yy_avg = yy_sum / n: xy_avg = xy_sum / n
       
        LineCorr = 3 * (2 * xy_avg - (n + 1) * y_avg) * (2 * xy_avg - (n + 1) * y_avg) / ((n - 1) * (n + 1) * (yy_avg - y_avg * y_avg))
   
    End If
   
    Exit Function
   
errorexit:

LineCorr = -4.94065645841247E-324
   
End Function
______________________________________________________________________________________________________________

Below are the Relativistic Market Data, One Market Year Slope and One Market Year Chaos charts for the S&P 500, NASDAQ and DOW.

One Market Year is about 271 market days and is used to calculate the previous one year of linear regression at each point in time.

As we can see, the slope and chaos tend to move above and below the Zero Line in a fairly regular pattern.

In the 1987 Crash, we see the green slope dove beyond zero and chaos spiked in the positive.

However, after the 1987 Crash, the slope rose and chaos spiked again.

Not a bad thing, it just means growth took over again.

Notice how before the 1987 Crash that chaos was negative throughout 1985, 1986 and most of 1987.

Something similar happened in the 1990 market correction.

The green slope and blue chaos lines work together in showing the one year market state at each point in time.

Now, let's look at where we were at in 2013 and through 2014.

Notice anything strange about the blue chaos reading?

Given the market slope is positive and chaos has been dancing around at low levels for nearly a year, the market is set explode.

It could be for the better, but if you don't even entertain the thought it could be for the worse, then there is an 'I told you so.' in your future.

Keep a Very Close Eye on the Markets.

We are.

 

When we talk about R-Squared Correlation, we're really looking at the level of chaos in the system.

How well the data fits a calculated line is important in understanding what's going on with the data.

Below is an example of data that fits very close to the line and will have an R² value close to 1.

In terms of a chaos reading of 1 - 2R², this makes it close to 1 - 2 (1)² or 1 - 2 and is -1.

 

 

When the chaos reading is near +1, then the data can be though of as being very chaotic.

Below is an example of data not very close to the regression line.

 

The way we applied Linear Regression to our Relativistic Market data is kind of like a Roller Coaster ride.

The cars on the ride are connected in sequence and are only on a portion of the roller coaster at one point in time.

Instead of getting a regression reading of the whole ride, we just get the regression reading of the cars on the track.

We can make a running regression measurement of the cars as it moves along the tracks.

This allows us to see what's happening on the tracks for a small section of the ride at a certain point in time.

Below is a simple example.

We posted the Linear Regression so we can explain the use on our Relativistic Market data.

The two parts we used are the: Slope and R-Squared Correlation.

These help in understanding the direction and chaos in the data.

The Slope is a measure of how slanted the data is on average.

If the Slope is +, then the overall data is sloping up from left to right, like this:  /

If the Slope is -, then the overall data is sloping down from left to right, like this:  \

Slope values close to 0 are an indication the overall data is flat, like this:  ^_

Keep in mind, it's on average, because the next measure, R-Squared Correlation, is actually a chaos reading; with a little massage of the value.

R-Squared Correlation tells us how close the data is to the regression line.

The closer the data is to the line, the closer to 1 the R-Squared value is.

Values closer to 0 means chaos.

Since we need a good measure of chaos that is positive and negative, we find a chaos measure with this expression: 1 - 2R²

Now, you'd think chaos would be a bad thing, but that's not the case here.

All this does is tells us how closely the data follows the line.

To get the goodness and badness of the data, you have to use both of these values together and place them in to proper context before making that determination.

In the Relative Market data case, positive slope and positive chaos is market growth.

With negative slope and positive chaos, it could be an indication of market decline.

Relativistic Market data that has a slope near zero and any positive/negative chaos is an indication the market is not going anywhere fast.

We'll see how this works in another post and see why we are concerned about what is happening in the market.