Friday, 25 May 2012


Been doing some deep thinking about exactly how this all works

Decided that I needed psars on the positive and negative adx's and on the open and close bollinger bandwidth percentages, adding an extra 24 indicators (bringing the total to 167) and 2 graphs

The MXP updated charts with these new graphs and in high definition detail are now on flickr here:

What is more interesting is that I have programmed a trace system, taking individual elements (either single or complex) and allocating success coefficients, and then going through all non repeating combinations of the elements per company. As you can imagine this takes a lot of processing as well as time - for 20 elements there are 1,048,575 separate combinations. The trace elements consist of the individual elements that have been used in the beta's such as simple psar on close, or more complex such as stochastic on the psar on the rsi = 0 over the last 9 days.

What I have noticed is that the traces are very date and actual close price dependent, and so merely taking an average will not work. We have to be a lot smarter to get to the essence of each individual company.

For a given formula oasis is able to calculate on average how much the stock has risen over 5 days as a percentage. As an example lets take the simple element of the psar on close. Below is a random selection of companies that indicate the average percentage increase over subsequent 5 day periods if we followed just  the psar on close indicator and only taking into account the last 6 months:

ACHL 64.3
ACL        0.133
ACM 16.1
ACMG 7.65
ACS        2.55
ACTG -0.9
ACU 15.25
ACWD 0.05

What we see is that we would have done pretty well to follow ACHL, less so the others and not at all with ACTG.

Lets have a look at ACHL in detail for a second - following are all the dates that the psar on close indicated a buy with the corresponding percentage increases over the subsequent 5 days in the last 6 months.

ACHL 09-JAN-12 110.5
ACHL 07-MAR-12 18.1

And so ACHL would have been an excellent company to trade on just the psar on close over the last 6 months.

And what about ACHL from about Jan 2010, ACHL's average 5 day increase over this 2.5 year period for just psar on close is 21.1%, with the detail as:

ACHL                 07-MAR-12                 18.1                
ACHL                 09-JAN-12                 110.5                
ACHL                 12-OCT-11                 75.3                
ACHL                 16-SEP-11                 1                    
ACHL                 11-AUG-11                 -2.2                
ACHL                 29-MAR-11                 -0.4                
ACHL                 29-DEC-10                 4.4                  
ACHL                 15-SEP-10                 6.9                  
ACHL                 02-AUG-10                 4.2                  
ACHL                 27-MAY-10                 11.1                
ACHL                 20-JAN-10                 4.7

On average still not bad, but as you can see we do have a couple of negatives here, not large ones but still negative.

If we add in another element, for instance the simple psar on open, then we have 3 combinations: psc, pso and psc and pso together. Lets see what this looks like for ACHL:

psc - psar on close is above

pso - psar on open is:

ACHL                 05-MAR-12                 19.1                
ACHL                 05-JAN-12                 116.2                
ACHL                 27-OCT-11                 1.5                  
ACHL                 19-SEP-11                 -0.5                
ACHL                 12-AUG-11                 0.5                  
ACHL                 01-JUN-11                 2.5                  
ACHL                 30-DEC-10                 6.9                  
ACHL                 03-AUG-10                 6                    
ACHL                 24-MAR-10                 4.2

and psc and pso together is:

ACHL                 07-MAR-12                 18.1                
ACHL                 09-JAN-12                 110.5                
ACHL                 15-SEP-10                 6.9

And so we have eliminated pretty much all of the unwanted triggers, and we are left with an average 5 day increase, post trigger on both pso and psc of 45.1% that has worked over the last 2.5 years.

What this means is that if we were purely watching the open and close psars on ACHL and buy when these are both triggered then within 5 days we would sell with an average price increase of about 45%.

What we can also see is that the triggers also seem to be date related. Clusters of triggers are noticeable around months, which indicate a much increased probability of price rise. That is, if you see one trigger in one month then that is good, if you see a second trigger soon after then its very good, and if you see a third trigger, go all in - I will post here on the blog if ACHL triggers on psc and pso in the next few months, as you can see from the charts below ACHL is currently on a down trend.

I have uploaded the full high def oasis charts for ACHL onto flickr here:

(For the high def chart, choose the chart, then choose it again, then choose 'view all sizes' (top right) and then choose original)

And so oasis keeps adding element combinations until it has gone through all possible iterations for each company. What I am really looking for are the formulae where the individual percentage increases are above 11.5%. 

What the trace does is highlight company's specific success coefficients to produce better, more tangible beta formulae as demonstrated above. From analysis the current beta formula work well, but lose in the timing strategy, both too early entry indicated and too early exit. It is these 'too early' indicators that this process has been designed to address - currently there is nearly always a slight dip in the share price before the 11.5% increase realisation which is accompanied by a variable length of time. I could just wait and watch, and indeed this is what I have been doing and testing different strategies on the gaming sites, but the answer has to be better beta formulae.

The crux of this work is that the traces allocate success coefficients to specific companies for specific beta formulae - that is, no longer is the beta an all encompassing, all things to all companies type of formula, but a more specific, dedicated company beta - specific company, specific date, specific 5 day period.

I have just started running the traces.

The traces link in very nicely to the RNS analysis; this is proving a lot faster (we only need to analyse specific lists of companies rather than all of them).

If this leaves you really confused but intrigued to know what this is all about then you may want to have a look here:

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