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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[-2][-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', rows=18, cols=18):
- day_lines = read_data(file_path)
- print('数据读取完毕')
- model=load_model(model_path + '.h5')
- 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
- size = len(lines[0])
- x0 = 0
- x1 = 0
- x2 = 0
- x3 = 0
- x4 = 0
- for line in lines:
- train_x = np.array([line[:size - 2]])
- result = model.predict([train_x])
- ratio = 1
- if train_x[0][-1] == 1:
- ratio = 2
- elif train_x[0][-2] == 1:
- ratio = 1.6
- elif train_x[0][-3] == 1:
- ratio = 1.3
- if result[0][0]> 0.5:
- up_num = up_num + ratio
- elif result[0][1] > 0.5:
- up_num = up_num + 0.4*ratio
- elif result[0][2] > 0.5:
- down_num = down_num + ratio
- maxx = max(result[0])
- if maxx - result[0][0] == 0:
- x0 = x0 + 1
- elif maxx - result[0][1] == 0:
- x1 = x1 + 1
- else:
- x2 = x2 + 1
- # print(key, int(up_num), int(down_num), (down_num*1.2 + 2)/(up_num*1.2 + 2), )
- print(key, x0, x1, x2, (down_num*1.5 + 2)/(up_num*1.2 + 2))
- import datetime
- if __name__ == '__main__':
- today = datetime.datetime.now()
- today = today
- today = '' #today.strftime('%Y%m%d')
- # 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_20200310.log', model_path='16_18d_mix_seq')
- # predict(file_path='D:\\data\\quantization\\stock196_18d_20200326.log', model_path='196_18d_mix_6D_ma5_s_seq')
- predict(file_path='D:\\data\\quantization\\stock578A_12d_train3.log', model_path='5d_578A_dnn_seq', rows=28, cols=20)
- # predict(file_path='D:\\data\\quantization\\stock578A_12d_' + today + '.log', model_path='5d_578A_dnn_seq', rows=28, cols=20)
- # predict(file_path='D:\\data\\quantization\\stock9_18_4.log', model_path='18d_dnn_seq.h5')
- # predict(file_path='D:\\data\\quantization\\stock324_28d_3D_20200414_A.log', model_path='324_28d_mix_5D_ma5_s_seq', rows=28, cols=18)
- # predict(file_path='D:\\data\\quantization\\stock324_28d_3D_20200414_A.log', model_path='603_30d_mix_5D_ma5_s_seq', rows=30, cols=19)
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