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HI! I am looking for a solution that incorporates different ML stacks in order to test a range of custom signals. To this end, I want to be able to pass any number of signal combinations through a Complex Forest, Linear Regression or some combination therein to determine what aspects of the combination of signals are correlated to security prices for some timeframe. I am open on the classification models but I definitely think that there needs to be more than one with a weighted voting amongst the models driving final signal selection. I then want to run whatever combination of signals I determine through a multiple layer NN that inputs those signals and predicts security prices for the given timeframe. My thought, right now, is to include an LSTM model, or some derivative, to make these predictions. I am very open to both new model ideas and to running multiple different models to see what works best in each timeframe. I need to be able to add in a custom class for every new signal and, ideally, optionally input data from an external source or wrap an internal QuantConnect data source. Finally, I want to run this both within QuantConnect - both in Research and then as a Trading Alg and, ideally, externally in PyCharm. The external part/PyCharm part is optional. First, can you do this level of work? If so, how quickly and how much? Thank you!
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Información sobre el proyecto
Presupuesto
30 - 100 USD
Para el ejecutor
27
- 90
USD
Plazo límite de ejecución
de 1 a 3 día(s)