123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081 |
- # -*- 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')
- 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 - 1]])
- train_x_a = train_x[:,:18*18]
- train_x_a = train_x_a.reshape(train_x.shape[0], 18, 18, 1)
- # train_x_b = train_x[:, 18*18:18*18+2*18]
- # train_x_b = train_x_b.reshape(train_x.shape[0], 18, 2, 1)
- train_x_c = train_x[:,18*18:]
- result = model.predict([train_x_c, train_x_a])
- if result[0][2]> 0.5:
- down_num = down_num + 1
- elif result[0][0] > 0.5:
- up_num = up_num + 1
- elif result[0][1] > 0.5:
- up_num = up_num + 0.2
- # else:
- # up_num = up_num + 0.4 # 乐观调大 悲观调小
- # down_num = down_num + 0.6
- maxx = max(result[0])
- if maxx - result[0][0] == 0:
- x0 = x0 + 1
- if maxx - result[0][1] == 0:
- x1 = x1 + 1
- if maxx - result[0][2] == 0:
- 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*1.2 + 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_20200310.log', model_path='16_18d_mix_seq')
- predict(file_path='D:\\data\\quantization\\stock186_18d_20191211.log', model_path='186_18d_mix_6D_ma5_s_seq')
- # predict(file_path='D:\\data\\quantization\\stock9_18_4.log', model_path='18d_dnn_seq.h5')
|