都是用dnn

stock/compont_predict.py

@@ -3,8 +3,9 @@ from stock import dnn_predict
 
 
 def and_predict():
-    cnn_result = cnn_predict.predict()
+    # cnn_result = cnn_predict.predict()
     dnn_result = dnn_predict.predict(file_path='D:\\data\\quantization\\stock6_5_test.log', model_path='5d_dnn_seq.h5')
+    cnn_result = dnn_predict.predict(file_path='D:\\data\\quantization\\stock6_test.log', model_path='15m_dnn_seq.h5')
     print('计算完成')
 
     with open('and_predict.txt', 'a') as f:

stock/dnn_predict.py

@@ -6,20 +6,21 @@ from keras.layers import Dense,Dropout
 import random
 from keras.models import load_model
 
-lines = []
+
 def read_data(path):
+    lines = []
     with open(path) as f:
-        for line in f.readlines():
+        for line in f.readlines()[:]:
             lines.append(eval(line.strip()))
 
     size = len(lines[0])
     train_x=[s[:size - 2] for s in lines]
     train_y=[s[size-1] for s in lines]
-    return np.array(train_x),np.array(train_y)
+    return np.array(train_x),np.array(train_y),lines
 
 
 def predict(file_path='', model_path='15min_dnn_seq.h5'):
-    test_x,test_y=read_data(file_path)
+    test_x,test_y,lines=read_data(file_path)
 
     model=load_model(model_path)
     score = model.evaluate(test_x, test_y)
@@ -35,6 +36,7 @@ def predict(file_path='', model_path='15min_dnn_seq.h5'):
             fact = test_y[i]
             if r[0] > 0.5:
                 f.write(str([lines[i][-2], lines[i][-1]]) + "\n")
+                win_dnn.append([lines[i][-2], lines[i][-1]])
                 if fact[0] == 1:
                     up_right = up_right + 1
                 elif fact[1] == 1:
@@ -47,5 +49,5 @@ def predict(file_path='', model_path='15min_dnn_seq.h5'):
 
 
 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\\stock6_test.log', model_path='15m_dnn_seq.h5')
+    predict(file_path='D:\\data\\quantization\\stock6_5_test.log', model_path='5d_dnn_seq.h5')
+    # predict(file_path='D:\\data\\quantization\\stock6_test.log', model_path='15m_dnn_seq.h5')