Tag Info

Hot answers tagged

17

First, let's speak about perceptrons in general: their input $X_0$ is a $K$-dimensional vector. So if you want to use $(P_{bid}(t),P_{ask}(t), Q_{bid}(t),Q_{ask}(t))$, it would mean that without any effort (but later we will see that is would be better to do some efforts, as usual): $$X_0(t)=(P_{bid}(t),P_{ask}(t), Q_{bid}(t),Q_{ask}(t))'\in\mathbb{R}^4$$ ...


4

They are not mutually exclusive. For example, the class you refer to as "econometric" are simply linear regression models that include as factors prior returns or residuals of the return series sometimes with weightings on the observations. You could easily design a neural network with no hidden layers and the same inputs. So each of the econometric models ...


2

You're thinking about this the wrong way, in my opinion. Win/loss percentage is worthless in isolation. You must consider the symmetry of your winners and losers. You can have a win % of only 40% and still have a wonderful strategy if your your winners are significantly larger than your losers (this is the classic trend follower PnL distribution). So, you ...


1

There can be several reasons for this: The "new data" that you use post-training & post-validation is not drawn from the same distribution as the one that you used to create/draw your training, testing and validation data. Since you have not mentioned anything related to the input features in your data-set, I am assuming that the ...


1

It's a bit unclear to me what you're trying to do, and maybe a better place to ask your question is Stats.SE but I would encourage you to go and have a look at this online class on machine learning which provides an implementation of the backpropagation algorithm. You can either register or hit preview and go to the NN:Learning chapter, but I would ...



Only top voted, non community-wiki answers of a minimum length are eligible