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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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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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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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Machine learning is a very wide field. Most often it is used for classification or regression tasks when you have labelled data to train the model. For example you show thousands of labeled pictures with an apple and computer "learns" what set of features gives high probability that picture contains an apple (for example, round, red etc). Now in your case ...


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50 elements input vector is actually a small one. For example, in this tutorial the size of the input vector is 784 (parameter 'nvis'). So your problem lies somewhere else. I would recommend to start from taking these two courses on Coursera: Neural Networks for Machine Learning Machine Learning They will provide you with some practical guidance ...


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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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Speech recognition signal processing is complex and possibly similar to the complexity of financial markets. They are similar as per characterictics the non stationarity, noise types and other aspects such us the existence of a cepstrum etc conceptual frequency and the grammar to construct and articulate concepts is not evenly and randomly distributed; so ...


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Sorry, but despite being used as a popular example in machine learning, no one has ever achieved a stock market prediction. It does not work for several reasons (check random walk by Fama and quite a bit of others, rational decision making fallacy, wrong assumptions ...), but the most compelling one is that if it would work, someone would be able to become ...


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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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"Success rate", in the sense of winning (W) vs. losing (L) percentage of trades, is almost completely meaningless if taken alone as a trading metric. With a trend-following (TF) trading strategy, where you quickly exit any trades that start to become losers (i.e. cut your losses fast) but let your profits run, a typical win-rate would be around 35% or so, ...



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