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1

Essentially you are creating a mapping, or a function here. In the first instance you had a maximal score of 10, and you mapped as follows: $$f(volume) = \left \{ \begin{matrix} 10* volume/200, \quad volume < 200 \\ 10 * 1, \quad volume \geq 200 \end{matrix} \right .$$ You have coded this in JS as (where 200 is upper_lim): var test = 10*Math.min(...


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Since it is your model you can do anything. What I would do is use some dynamic outlier exclusion. For example in this case you know the min is zero. One method (of many) might be to evaluate the median (since it might be more robust that the standard deviation or mean) and use 2 x median as your upper limit: >>> arr = np.array([100,200,19,0,200,...


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There does not exist a single metric that encompasses all your criteria; but you could simply construct a (linear or non-linear) combination of the measures you like. For the win/loss streaks you describe, you could either look at the absolute maximum drawdown of the cumulative series (1 in the first case, 3 in the second), or look at streaks and penalise ...


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no, it will likely make also 50% loss. You have to also consider the criteria for closing the positions. They are not the same for closing the position having been a short or long. That is, a loss of 10% due to a stop-loss is not necessarily guaranteed to become a 10% win when the position's direction has changed (I think). In other words, if you just ...


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