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Features could include: Bid-ask spread Bid-ask volume imbalance Signed transaction volume The sign in the Signed transaction volume is positive if the buyer has issued a market order and negative if the seller issued a market order. A great introductory plain English paper on high frequency trading machine learning applications can be found here. A ...


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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 ...


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You will find that the level of success you have using Neural Networks (NN) as a tool for financial market prediction is strongly dependent on what initially appear to be some quite subtle factors. In particular: Input data: You mention using "certain technical indicators". I assume that you mean the standard TA set of price-based indicators such as Moving ...


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Neural networks are a supervised machine learning algorithm. Unlike unsupervised machine learning, the key to supervised machine learning is the selection of input factors and explicit labeling of outputs. Input factors have to be manually selected, such as your combination of technical / fundamental / statistical indicators. Outputs have to be ...


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If I correctly understood, you have a big training set and EMD calculated over the whole set at once. Then you use a part of training set and the corresponding part of EMD to infer prediction. The problem here is that you peep into the future having EMD on the edge of the working window calculated using information out of the window. Hence, surely you should ...


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This is an pretty general question You are essentially asking how to estimate the regression function $$Y[t]-Y[t-1] = m(X_i [t], ..., X_p[t]) + \epsilon[t]$$ without any additional structure. Here is a basic list of questions to consider. Keep in mind that the more you can guide these procedures with domain knowledge the better your results will likely ...


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Something I do to find new ideas is read papers in Google Academic related to the topic. Find papers under "quantitative strategy", "trading strategy", etc and you will find interesting things. My experience is that they don't tend to work, but they provide with ideas that you can mix and maybe find something by yourself.


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If you like an R related blog with a lot of code that you can use then you shoud look here: http://systematicinvestor.wordpress.com/ Similar in a similar vein but with less code (and I think the authors know each other) is the Blog by David Varadi https://cssanalytics.wordpress.com/ I think these two are an important extension of the ones already ...


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It is difficult to find what you need for because if someone shares his knowledge about systematic trading, all the profit of that strategy vanishes in a while theoretically. Anyway, there are a lot of blogs about trading strategies that provide references and guides about that. In my humble opinion, one of the best is QuantStart, that provides a lot of ...


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In terms of forecasting, it is VERY difficult to forecast financial time series especially using ML models. One of the "successful" papers that I have seen use a classifier approach (e.g. forecasting extreme returns). See: http://algorithmicfinance.org/2-1/pp45-58/ The above being said, your model structure would assume that the parameters are stable ...


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Such an approach is done by the systemic investor blogger in his blog Time Series Matching with Dynamic Time Warping.


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I'm not sure that machine learning would lead to any practical solutions here. Do you really have enough data for that kind of techniques? I would suggest a different approach: assume that the exercise is optimal, but just based on a different cost function than the expected pay-off. If you can find a function that replicates well enough the past exercise ...


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Your question is too broad, but I there is plenty of examples of uses of machine learning to mimic human behaviour. For instance deep learning has been used 25 years ago to read checks in banks, or support vector machines 15 years ago to implement artificial vision, or bayesian networks to mimic expert diagnosis. I guess it would not be that hard to use ...



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