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    Do Neural Networks overfit less than Decision Trees?

    I have his book :)
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    Do Neural Networks overfit less than Decision Trees?

    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...
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    Do Neural Networks overfit less than Decision Trees?

    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...
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    Do Neural Networks overfit less than Decision Trees?

    And what about this? https://arxiv.org/ftp/arxiv/papers/1111/1111.5892.pdf
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    Is 75% of successful forecasts on close price enough for profitable trading?

    I use this: http://scikit-learn.org/stable/modules/svm.html
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    Is 75% of successful forecasts on close price enough for profitable trading?

    Indeed, when I was trying the model what I got was a better intuitive knowledge of price action.
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    Do Neural Networks overfit less than Decision Trees?

    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
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    Do Neural Networks overfit less than Decision Trees?

    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...
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    Do Neural Networks overfit less than Decision Trees?

    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.
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    Do Neural Networks overfit less than Decision Trees?

    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...
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    Is 75% of successful forecasts on close price enough for profitable trading?

    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...
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    Do Neural Networks overfit less than Decision Trees?

    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...
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    Is 75% of successful forecasts on close price enough for profitable trading?

    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...
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    Is 75% of successful forecasts on close price enough for profitable trading?

    Should I respond to this? In fact I won't answer to any personal message. However anyone is free to write as desired. For anyone else reading this particular quote, on the previous posts there is an explanation of what is this good for and what it is not. I won't repeat it again here since it...
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    Is 75% of successful forecasts on close price enough for profitable trading?

    I forgot to mention. To avoid overfitting I use a main approach: First you must divide into two sets your data. One for training the model and other to test it with new data. The first set, the training set is divided again into two other subsets. You only use a randomly selected partial...
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    Is 75% of successful forecasts on close price enough for profitable trading?

    There is a paper around, a guy I think he wrote it for his Phd, that used several external factors not related to price, including news, something related to wikipedia, and other external data sources to precisely find out those factors that are not related to price. I think he obtained about...
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    Is 75% of successful forecasts on close price enough for profitable trading?

    Thank you for sharing. It is the first time I see it. It seems to be a strategy parser that converts features extracted from the users definitions into predictors to minimize probably one or several loss functions using deep networks. I have done similar works using Tensorflow and Keras, both...
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    Is 75% of successful forecasts on close price enough for profitable trading?

    Everyone trading is trying to predict, the only difference is that a machine is simply better than a human being doing it. In order to prepare a real automated trading system you should just be able to write in mathematical (or algorithmic) language your thoughts and how you evaluate what you...
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