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This article is to share with you about the VNPY parameter optimization function v1 version of the update parameter batch generation method is what, the editor thinks it is very practical, so share it with you to learn, I hope you can get something after reading this article, say no more, follow the editor to have a look.

Parameter optimization function of VNPY is an important function of policy optimization. The main thing is to generate a batch combination of parameters according to the range, and then run in batches to select the best method.

The addParameter method in ctaBaclesting.py provides a way to import parameters in bulk. It is such a parameter to fill in. AddParameter will generate a parameter queue with 1 as the beginning, 5 as the end, and 2 as the step.

Setting.addParameter ('barMins', 1, 5, 2)

However, sometimes there are many parameters, so it is difficult to support them if the parameters have already been maintained in list. I wrote a v2 version. What is passed in is key, and a parameter definition, which can be tuple meta-ancestor, queue list, or a single value.

#-def addParameterV2 (self, key, value): "add optimization parameters"if isinstance (value)" Tuple): if len (value) = 4: if value [3] = = "int": self.paramDict [key] = np.linspace (value [0], value [1], value [2]) .astype (int) .tolist () else: self.paramDict [key] = np.linspace (value [0], value [1]) Value [2]) .tolist () elif len (value) = = 3: l = [] param = value [0] while param

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