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Bilo:
(Great name. How did your mother know??)
You gave me more than I asked for, especially with the description of the thought
process involved, working from the desired signals back to the algorithm. This will
keep me busy for a while... Thanks!
David
> -----Original Message-----
> From: Bilo Selhi [mailto:biloselhi@xxxxxxxxxxx]
> Sent: Monday, April 30, 2001 2:12 PM
> To: David Rosenthal; Omega List
> Subject: Re: Trading Systems
>
>
> obviously there is no single contained field
> of mathematics that you can apply to time series
> forecasting.
> on top of that you have to separate the math
> into math you use for modeling
> 1. profit/loss expectation, 2. supply/demand, 3. price/volume
> process from math you use
> for price forecasting.
> again since we do really know what p&l expectations
> of all participants are, therefore do we can not
> know the supply/demand as a result of actions based
> on those expectations. so we only know the result
> product of the process namely price and volume.
> so we can only model process in theory for which
> the math is separate and requires NLD and regression
> analysis and other kinks.
> once you passed this theoretical phase you
> will learn how to bypass 1 and 2 and work only with 3
> but still utilizing the theoretical laws from 1 and 2.
> these are the limitation of the holy grail, which is we
> don't know 1 and 2 and we can only know the process
> and the law that result in 3. so given those limitations
> you learn how to work with 3 only.
> *** price forecasting or prediction is basically comes
> down to finding short term vs long term dominant
> profit and loss expectations which in tern will result
> in action ( buying and selling or supply / demand ) which will produce price
> and
> volume curve as the realized dominant price expectation.
> loop closed.
> if you can mathematically find what the majority of participants
> will do next ( how they will react ), then you got it....
> mostly it's non traditional regression analysis since all traditional
> regression techniques will not work in this case.
> noise is very important part of the equation but i hate calling it
> noise since there is no noise in price really.
> so you might want to take a look at the NDT.
> decision making process in system is pretty straight forward
> so there is no need to get into that.
> i only use and believe in univariate approach ( only the tape tells you the
> truth )
> so i don't go out there looking for external things that move price.
> if price goes for who cares what reason, it goes and the system will pick
> that up.
> adaptive statistics is a major chuck of math but it's not too complex.
> you can't use straightforward stat analysis since data is non stationary.
> all typical stat bs will not work.
>
> you gotta think adaptive which is not the hardest part of the system math.
>
> and after all of the muffled stuff above and to clear things up
> you need to basically know three things:
> - probable signal ( noise vs signal , tradable vs untradable )
> - probable potential for the trade. ( min predictive delta )
> - those two have to be nailed 6-7 out of 10 times.
> if you get probable signal and the trade has potential then
> you go for it.
> as a result the tricks are: how do you pick probable
> signals adaptively and the most important and the hardest
> is how to do calculate the min potential of the trade
> and how do you do that with over 60% accuracy :-)
> it's hard not only mathematically but mainly conceptually.
>
> other thing is how do you properly correct the errors.
> on top of that there are a ton of other things.
>
> i always recommend to start from head to tail.
> picture how the system should trade
> where the signals should be ( dont be greedy and
> don't play "i want to sell the top and buy the bottom game )
> run the logic in your mind.
> picture the winners and the losers.
> lay out system signal logic next for winners and losers.
> find vars that are missing and those that you need for
> the logic to work.
> formulate the problem in math of how to find those vars.
> solve for those vars one step at a time.
> plug those into the logic and check how the system
> works.
>
> signals then logic then vars ( deduction )
>
> if you can get to the part where you can formulate the
> math problem then you are half way there :-)
> finding the solution is just a matter of time to apply proper math.
>
> curiously most people do the opposite ( induction ):
> they play with vars and the logic and then examine how
> the signals look :-) and if they look bad then
> they try some other vars or and other logic and
> there goes the never ending trial and errors approach.
> cart in the front of the horse.
> that is the key... if you have no idea how the system
> should trade and where the signal should be located
> on the chart you will never find anything that works
> because you don't know what to look for.
>
> the first approach makes you concentrate on
> proper solution and forces you on that narrow path...
> whereas the second approach forces you to run
> around flailing like a mad man looking for that road
> that you don't even know if it's the road that you
> should take.
>
> regards.
> bilo.
>
>
>
> ----- Original Message -----
> From: "David Rosenthal" <davidrnews@xxxxxxxxxxxxx>
> To: "Bilo Selhi" <citadel@xxxxxxxxxxxx>; "Omega List"
> <omega-list@xxxxxxxxxx>
> Sent: Sunday, April 29, 2001 6:46 PM
> Subject: RE: Trading Systems
>
>
> Bilo,
>
> Thank you for your thoughtful post, and congratulations on reaping the
> benefits of
> your years of hard work.
>
> Would you be willing to share what areas of mathematics have been most
> critical to
> your study?
>
> David
>
>
>
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