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The sortino ratio is also important for evaluating trading strategies. also the omega ratio. The question is a poor one though. Each of the mentioned ratios will be the most predictive at predicting ... THEMSELVES! respectively. you don't use the calmar to predict the sharpe. ok, what you are probably actually asking is which of the performance metrics ...


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To solve your multicollinearity problem I would first perform a regularization technique such as Ridge or Elastic Net. If you choose Ridge for example, once you have tuned your hyperparameter through cross validation (for time series a forward walk approach is preferable) you can fit after your simple OLS by choosing the predictors with the biggest ...


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Have you tried to choose an arbitrary number of model, let say 20, each one having its own seed? Then you run your twenty models and use the median of your 20 results as signal. One advantage of that method is that you can also get a confidence estimate of your prediction thanks to the standard deviation of your 20 results.


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The first thing you can do to help a neural network learn more rapidly is to normalize all inputs between 0 and 1. The library sklearn has a preprocess.scale() function that does just that -- make sure to do it separately for training and testing data (or training, validation and testing data if you use three separate sets). This alone can make a huge ...


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So I'll start with what I have done recently for my undergraduate thesis before relating it to your question. I trained a SVM on Technical Analysis data to classify the trend for the next hour. Unlike your strategy, I did not train the model with/for visual inspection of price patterns etc, but rather trained the model on the rate of change of several ...


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