300基本费了

mix/mix_predict_200.py

@@ -91,7 +91,7 @@ def predict(file_path='', model_path='15min_dnn_seq.h5', idx=-1, row=18, col=20)
 if __name__ == '__main__':
     # predict(file_path='D:\\data\\quantization\\stock181_18d_test.log', model_path='181_18d_mix_6D_ma5_s_seq.h5')
     # predict(file_path='D:\\data\\quantization\\stock201_18d_train1.log', model_path='213_18d_mix_6D_ma5_s_seq.h5', row=18, col=20)
-    predict(file_path='D:\\data\\quantization\\stock301_18d_train1.log', model_path='301_18d_mix_6D_ma5_s_seq.h5', row=30, col=20)
+    predict(file_path='D:\\data\\quantization\\stock314_24d_train1.log', model_path='314_24d_mix_6D_ma5_s_seq.h5', row=24, col=18)
     # predict(file_path='D:\\data\\quantization\\stock6_test.log', model_path='15m_dnn_seq.h5')
     # multi_predict(model='15_18d')
     # predict_today(20200229, model='11_18d')

mix/mix_train_300.py

@@ -20,15 +20,24 @@ early_stopping = EarlyStopping(monitor='accuracy', patience=5, verbose=2)
 
 epochs= 88
 size = 400000 #18W 60W
-file_path = 'D:\\data\\quantization\\stock302_18d_train2.log'
-model_path = '302_18d_mix_6D_ma5_s_seq.h5'
-file_path1='D:\\data\\quantization\\stock302_18d_test.log'
-col = 20
+file_path = 'D:\\data\\quantization\\stock314_24d_train2.log'
+model_path = '314_24d_mix_6D_ma5_s_seq.h5'
+file_path1='D:\\data\\quantization\\stock314_24d_test.log'
+col = 18
+row = 24
 '''
-ROC     30*18           38,100,17
-DMI     30*20           39,101,13
-MACD    30*19           
-RSI     30*17
+30d+ma5
+0 ROC     30*18           38,100,17
+1 DMI     30*20           39,101,13
+2 MACD    30*19           
+3 RSI     30*17           
+30d+close
+4 ROC     30*18           
+5 DMI     30*20           
+6 MACD    30*19           32,96,44
+7 RSI     30*17           31,96,42
+24d+close
+14 ROC    24*18           31,95,52
 
 '''
 
@@ -67,11 +76,11 @@ def read_data(path, path1=file_path1):
 
 train_x,train_y,test_x,test_y=read_data(file_path)
 
-train_x_a = train_x[:,:30*col]
-train_x_a = train_x_a.reshape(train_x.shape[0], 30, col, 1)
+train_x_a = train_x[:,:row*col]
+train_x_a = train_x_a.reshape(train_x.shape[0], row, col, 1)
 # train_x_b = train_x[:, 9*26:18*26]
 # train_x_b = train_x_b.reshape(train_x.shape[0], 9, 26, 1)
-train_x_c = train_x[:,30*col:]
+train_x_c = train_x[:,row*col:]
 
 
 def create_mlp(dim, regress=False):
@@ -135,7 +144,7 @@ def create_cnn(width, height, depth, size=48, kernel_size=(5, 6), regress=False,
 # create the MLP and CNN models
 mlp = create_mlp(train_x_c.shape[1], regress=False)
 # cnn_0 = create_cnn(18, 20, 1, kernel_size=(3, 3), size=90, regress=False, output=96)       # 31 97 46
-cnn_0 = create_cnn(30, col, 1, kernel_size=(6, col), size=96, regress=False, output=96)         # 29 98 47
+cnn_0 = create_cnn(row, col, 1, kernel_size=(6, col), size=96, regress=False, output=96)         # 29 98 47
 # cnn_0 = create_cnn(18, 20, 1, kernel_size=(9, 9), size=90, regress=False, output=96)         # 28 97 53
 # cnn_0 = create_cnn(18, 20, 1, kernel_size=(3, 20), size=90, regress=False, output=96)
 # cnn_1 = create_cnn(18, 20, 1, kernel_size=(18, 10), size=80, regress=False, output=96)
@@ -182,11 +191,11 @@ model.fit(
 
 model.save(model_path)
 
-test_x_a = test_x[:,:30*col]
-test_x_a = test_x_a.reshape(test_x.shape[0], 30, col, 1)
+test_x_a = test_x[:,:row*col]
+test_x_a = test_x_a.reshape(test_x.shape[0], row, col, 1)
 # test_x_b = test_x[:, 9*26:9*26+9*26]
 # test_x_b = test_x_b.reshape(test_x.shape[0], 9, 26, 1)
-test_x_c = test_x[:,30*col:]
+test_x_c = test_x[:,row*col:]
 
 # make predictions on the testing data
 print("[INFO] predicting house prices...")