def __init__(self, predict_range=1): """ 初始化KDJ模型 :param predict_range: K预测未来涨跌时间范围 :return: """ super(KDJModel, self).__init__() self.predict_range = predict_range self.para_k, self.para_d, self.para_j = 0.0, 0.0, 0.0 self.KDJ = KDJ() self.LC = LabelConstructor(predict_range)
class KDJModel(Model):
def __init__(self, predict_range=1):
"""
初始化KDJ模型
:param predict_range: K预测未来涨跌时间范围
:return:
"""
super(KDJModel, self).__init__()
self.predict_range = predict_range
self.para_k, self.para_d, self.para_j = 0.0, 0.0, 0.0
self.KDJ = KDJ()
self.LC = LabelConstructor(predict_range)
def train(self, dataset):
print("Training SVM Model...")
X_train = self.KDJ(dataset)
# 数据缩放
self.min_max_scaler = preprocessing.MinMaxScaler()
X_train = self.min_max_scaler.fit_transform(X_train)
print(self.min_max_scaler.scale_, self.min_max_scaler.min_)
# 得到label Y
Y_train = self.LC.construct(dataset)
#for (i,d) in enumerate(dataset):
# print(d, X_train_minmax[i], Y_train[i])
# 用SVM拟合数据
self.svm = SVC()
self.svm.fit(X_train, Y_train)
print("Linear SVM Model:", self.svm)
def predict(self, dataset):
k, d, j = self.KDJ.current(dataset[-1], True)
y = self.svm.predict(self.min_max_scaler.transform([k,d,j]))[0]
print(dataset[-1], [k,d,j], y)
return y
