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What is the idea behind using Machine Learning in finance? Let's assume that we have just one instrument given by its prices. At a given moment of time, we can "compress" the available history of prices into a vector of features (for example, momentum, volatility, some other technical indicators). In addition to that we can extend the features vector by some fundamental factors (like company parameters at a given moment of time).

Now we want to have a machine learning model (for example a neural network or random forest) that take the extended vector of features as input and generate the optimal position for the given future period of time.

The problem is that we do not have target to train the model. Yes, we do have the price change for the given period of time but should we try to predict it? Or, in other words, let's assume that we do have a model that predicts the observed price changes with some accuracy, how do we transform these predictions into allocations?

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  • $\begingroup$ You should not be predicting the price change. Instead, try predicting whether the price will go up or down. Of course, I am simplifying the actual process but you get the idea: address a classification problem instead of a regression problem. $\endgroup$
    – stans
    Jan 22 at 19:44
  • $\begingroup$ @stans, but how can we use a classifier. Let's assume that a well trained classifier predicts that the price is going to go up with probability 0.55, what should we do? Yes, it is more likely to go up, but maybe if it goes up it goes just 3 points and if it goes down (with probability 0.45), it will go, on average 5 points! $\endgroup$
    – Roman
    Jan 23 at 14:07
  • $\begingroup$ You have to develop optimal specifications of your strategy. That is what proprietary trading is. $\endgroup$
    – stans
    Jan 23 at 14:19

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