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test: new calibration method for leap
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@ -230,14 +230,14 @@ class LEAP_C(object):
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self.maxlist.append(d)
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if d <= np.percentile(
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self.openlist, 10
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self.openlist, 2
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): # an aditional approach could be using the place where on average it is most stable, denoting what distance is the most stable "open"
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self.minlist.append(d)
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if len(self.maxlist) > 2000:
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self.maxlist.pop(0)
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if len(self.maxlist) > 2000:
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if len(self.minlist) > 2000:
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self.minlist.pop(0)
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# this should be the average most open value, the average of top 200 values in rolling calibration
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# with this we can use it as the "openstate" (0.7, for expanded squeeze)
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@ -247,8 +247,8 @@ class LEAP_C(object):
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normal_open = ((sum(self.maxlist) / len(self.maxlist)) * 0.90 + max(self.openlist) * 0.10) / (
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0.95 + 0.15
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)
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normal_close = ((sum(self.minlist) / len(self.minlist)) * 0.70 + min(self.openlist) * 0.40) / (
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0.7 + 0.4
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normal_close = ((sum(self.minlist) / len(self.minlist)) * 0.05 + min(self.openlist) * 0.95) / (
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0.05 + 0.95
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)
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except:
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normal_open = 0.8
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@ -266,11 +266,13 @@ class LEAP_C(object):
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self.openlist.pop(0)
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self.openlist.append(d)
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# print(normal_close, normal_open)
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# print(normal_close, normal_open, min(self.openlist))
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try:
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per = (d - normal_open) / (min(self.openlist) - normal_open)
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fullper = (d - normal_open) / (normal_close - normal_open)
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fullper = (d - normal_open) / (
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normal_close - normal_open
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) # this should not be so different to per, idk why
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oldper = (d - max(self.openlist)) / (min(self.openlist) - max(self.openlist))
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