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- # -*- encoding:utf-8 -*-
- import numpy as np
- from keras.models import load_model
- import joblib
- def read_data(path):
- day_lines = {}
- with open(path) as f:
- for line in f.readlines()[:]:
- line = eval(line.strip())
- date = str(line[-1][-1])
- if date in day_lines:
- day_lines[date].append(line)
- else:
- day_lines[date] = [line]
- # print(len(day_lines['20191230']))
- return day_lines
- def predict(file_path='', model_path='15min_dnn_seq'):
- day_lines = read_data(file_path)
- print('数据读取完毕')
- models = []
- for x in range(0, 12):
- models.append(load_model(model_path + '_' + str(x) + '.h5'))
- estimator = joblib.load('km_dmi_18.pkl')
- print('模型加载完毕')
- items = sorted(day_lines.keys())
- for key in items:
- # print(day)
- lines = day_lines[key]
- up_num = 0
- down_num = 0
- x = 24 # 每条数据项数
- k = 18 # 周期
- for line in lines:
- v = line[1:x*k+1]
- v = np.array(v)
- v = v.reshape(k, x)
- v = v[:,4:8]
- v = v.reshape(1, 4*k)
- # print(v)
- r = estimator.predict(v)
- train_x = np.array([line[:-1]])
- result = models[r[0]].predict(train_x)
- if result[0][3] > 0.5 or result[0][4] > 0.5:
- down_num = down_num + 1
- elif result[0][1] > 0.5 or result[0][0] > 0.5:
- up_num = up_num + 0.6 # 乐观调大 悲观调小
- print(key, int(up_num), int(down_num), (down_num*1.2 + 2)/(up_num*1.2 + 2))
- if __name__ == '__main__':
- # predict(file_path='D:\\data\\quantization\\stock6_5_test.log', model_path='5d_dnn_seq.h5')
- # predict(file_path='D:\\data\\quantization\\stock9_18_20200220.log', model_path='18d_dnn_seq.h5')
- # predict(file_path='D:\\data\\quantization\\stock9_18_2.log', model_path='18d_dnn_seq.h5')
- predict(file_path='D:\\data\\quantization\\stock16_18d_20200311.log', model_path='16_18d_dnn_seq')
- # predict(file_path='D:\\data\\quantization\\stock11_18d_20190103_20190604.log', model_path='14_18d_dnn_seq')
- # predict(file_path='D:\\data\\quantization\\stock9_18_4.log', model_path='18d_dnn_seq.h5')
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