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There is no right approach a priori. Try all approaches that make decent sense and pick the one that maximises a utility function on out of sample PnL and risk (or some similar decision rule).


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Scale and range are your biggest issues. If one input has values which range from e.g. 2300-3500, and another from 0 to 18, then the large scale of the first will swamp the other and provide greater informativity into your learning algorithm. Therefore, normalize into range [0,1] or mean-zero standardize - like you have already done. Be careful with ...


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S&R levels can be obtained quite easily. However, you will not find this data in most references. Professional day traders use value areas from the previous day as well a forming value areas in the current session. There are several crucial levels gathered from the previous GLOBEX session. These can be derivied from any market. These support/resistance ...


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Do not concur with the paradigm behind this thinking: "The problem is, a random process will consistently generate lots and lots of S&R levels, and you can be 100% sure that those S&R levels mean absolutely nothing. Think about it, how can a random process NOT turn and go the other way?" "Random" means 50/50. Whoever believe the markets are random ...



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