That's great Murray.
Hope you put up some facts and figures to make your point. It is quite a leap you know to change platform etc. on a say so.
Foward curve analysis
- Thread starter 2rosy
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That's great Murray.
Hope you put up some facts and figures to make your point. It is quite a leap you know to change platform etc. on a say so.
Murray Ruggiero
Sponsor
Here link to my bio http://tuttletactical.com/custom11111111114.php
Here is the site with performance tables and awards.
http://tuttletactical.com/ttmperformance.php
Orthogonal distance regression
by wiki
Background[edit]
In the least squares method of data modeling, the objective function, S,
S={\mathbf {r^{T}Wr}},
is minimized, where r is the vector of residuals and W is a weighting matrix. In linear least squares the model contains equations which are linear in the parameters appearing in the parameter vector {\boldsymbol \beta }, so the residuals are given by
{\mathbf {r=y-X{\boldsymbol \beta }}}.
There are m observations in y and n parameters in β with m>n. X is a m
by wiki
Background[edit]
In the least squares method of data modeling, the objective function, S,
S={\mathbf {r^{T}Wr}},
is minimized, where r is the vector of residuals and W is a weighting matrix. In linear least squares the model contains equations which are linear in the parameters appearing in the parameter vector {\boldsymbol \beta }, so the residuals are given by
{\mathbf {r=y-X{\boldsymbol \beta }}}.
There are m observations in y and n parameters in β with m>n. X is a m
Seems like a normal least squares regression would work more effectively. If a regression correctly models a stock's behavior a day early or a day late but is "close" geometrically we don't necessarily care correct?