大盘指数

stock/dnn_predict_by_day.py

@@ -18,13 +18,13 @@ def read_data(path):
     return day_lines
 
 
-def predict(file_path='', model_path='15min_dnn_seq.h5'):
+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('10_18d_dnn_seq_' + str(x) + '.h5'))
+        models.append(load_model(model_path + '_' + str(x) + '.h5'))
     estimator = joblib.load('km_dmi_18.pkl')
     print('模型加载完毕')
 
@@ -49,27 +49,25 @@ def predict(file_path='', model_path='15min_dnn_seq.h5'):
             train_x = np.array([line[:-1]])
             result = models[r[0]].predict(train_x)
 
-            # if r[0] in [2,4,7,11]:
-            #     if result[0][0] > 0.5 or result[0][1] > 0.5:
-            #         up_num = up_num + 1
-            #     elif result[0][2] > 0.5:
-            #         up_num = up_num + 0.12
-            # elif r[0] in [1,6]:
-            #     if result[0][3] > 0.5 or result[0][4] > 0.5:
-            #         down_num = down_num + 1
-            #     elif result[0][2] > 0.5:
-            #         down_num = down_num + 0.12
-            # elif r[0] in [10]: #
-            #     if result[0][0] > 0.5 or result[0][1] > 0.5:
+            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][2] > 0.5:
+                up_num = up_num + 0.5 # 悲观调大 乐观调小
+
+            # if result[0][0] > 0.5 or result[0][1] > 0.5:
+            #     if r[0] in [0,2,3,4,5,9,10,11]:
             #         up_num = up_num + 1
-            #     elif result[0][3] > 0.5 or result[0][4] > 0.5:
+            #     elif r[0] in [8]:
+            #         up_num = up_num + 0.6
+            #     else:
+            #         up_num = up_num + 0.4
+            # if result[0][3] > 0.5 or result[0][4] > 0.5:
+            #     if r[0] in [4,6,]:
             #         down_num = down_num + 1
-            # else:
-                # pass
-            if result[0][0] > 0.5 or result[0][1] > 0.5:
-                up_num = up_num + 0.5
-            elif result[0][3] > 0.5 or result[0][4] > 0.5:
-                down_num = down_num + 0.5
+            #     elif r[0] in [0,1,3,7,8,]:
+            #         down_num = down_num + 0.6
+            #     else:
+            #         down_num = down_num + 0.4
 
         print(key, int(up_num), int(down_num), (down_num*1.2 + 2)/(up_num*1.2 + 2))
 
@@ -78,5 +76,6 @@ 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\\stock10_18d_20190103_20190604.log', model_path='18d_dnn_seq.h5')
+    predict(file_path='D:\\data\\quantization\\stock11_18d_20200221.log', model_path='11_18d_dnn_seq')
+    # predict(file_path='D:\\data\\quantization\\stock11_18d_20190103_20190604.log', model_path='11_18d_dnn_seq')
     # predict(file_path='D:\\data\\quantization\\stock9_18_4.log', model_path='18d_dnn_seq.h5')