Recent content by gon

I have his book :)

I see that you mean. More than a formula what a NN will do is to weigth the coefficients to adjust the triggering of the activation functions to minimize the loss function of the prediction. Isn't that similar to finding KNNs based upon weighted euclidean distances? I mean, the number of...

Iof assigning a window of price-related features, the paper shows how to create a set of planes like if they were a mesh. This way they model is able to learn also the visual side of the trading, not only a sequence of numbers, but also their position and distribution across a 2-dimensional...

And what about this? https://arxiv.org/ftp/arxiv/papers/1111/1111.5892.pdf

Totally agree.

Why only Knn?

I use this: http://scikit-learn.org/stable/modules/svm.html

Indeed, when I was trying the model what I got was a better intuitive knowledge of price action.

Interesting point. Just for curiosity, do you know about any specific NN topology especially good for finance? I am curious about it. In the past I have been tempted to use NNs to parse directly charts, similar to this: https://arxiv.org/ftp/arxiv/papers/1111/1111.5892.pdf

Introducing redundant parameters will reduce the performance of your system. For instance, if you introduce and index and one stock very representative of that index that has also the same dynamics, it will reduce the chances of a good model. If you insert open and close prices, since they may...

T When you read "policies", "values" or "rewards", "agents" and so on, you are speaking most likely about reinforcement learning. If it uses trees, probably it's using gradient descent.

I don't use matlab nor know how it works. I have used R and Python mainly for machine learning. I don't know, but why NNs, are you using convolutional networks to analyze charts or something? Random Forest are much easier to configure and perform very well, why do not you give a try? And if you...

Hi. Indeed, the starting point of all this was thinking in price like an object with a certain mass subject to a series of dynamics. So I started to apply velocity, acceleration, force and other concepts taken precisely from physics and calculus. But, the $64 dolar question, the information...

You can use for instance dropout layers within your neural network to avoid overfitting. However, if you have sufficient data, you should be able to take a random subset of it and avoid overfitting. I think that for time series, if you train your system with 1000 past days applying dropout to...

Hi Ironchef. I won't talk about myself, because an argument based on who is someone is a fallacy. So I won't respond to the personal part of the message. The summary of what's posted by me: - A method to predict 2-bar SMA direction with average 75% cross-security accuracy. - It is not a...