578

mix/mix_predict_by_day_578A.py

@@ -0,0 +1,103 @@
+# -*- 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', 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 - 1]])
+            train_x_a = train_x[:,:rows*cols]
+            train_x_a = train_x_a.reshape(train_x.shape[0], rows, cols, 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[:,rows*cols:]
+
+            result = model.predict([train_x_c, train_x_a])
+
+            ratio = 1
+            if train_x_c[0][-1] == 1:
+                ratio = 2
+            elif train_x_c[0][-2] == 1:
+                ratio = 1.6
+            elif train_x_c[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 + 0.4*ratio
+            else:
+                down_num = down_num + ratio
+
+            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
+            if maxx - result[0][3] == 0:
+                x3 = x3 + 1
+
+        # print(key, int(up_num), int(down_num), (down_num*1.2 + 2)/(up_num*1.2 + 2), )
+        print(key, x0, x1, x2,x3, (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\\stock321_28d_5D_20200429.log', model_path='321_28d_mix_5D_ma5_s_seq_2', rows=28, cols=20)
+
+    predict(file_path='D:\\data\\quantization\\stock327_28d_' + today + '.log', model_path='327_28d_mix_5D_ma5_s_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)

mix/mix_predict_by_day_578B.py

@@ -0,0 +1,94 @@
+# -*- 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)

mix/mix_predict_everyday_600.py

@@ -35,16 +35,16 @@ def predict_today(file, day, model='10_18d', log=True):
 
     for line in lines:
         train_x = np.array([line[:size - 1]])
-        train_x_tmp = train_x[:,:30*19]
-        train_x_a = train_x_tmp.reshape(train_x.shape[0], 30, 19, 1)
-        # train_x_b = train_x_tmp.reshape(train_x.shape[0], 18, 24)
-        train_x_c = train_x[:,30*19:]
+        # train_x_tmp = train_x[:,:30*19]
+        # train_x_a = train_x_tmp.reshape(train_x.shape[0], 30, 19, 1)
+        # # train_x_b = train_x_tmp.reshape(train_x.shape[0], 18, 24)
+        # train_x_c = train_x[:,30*19:]
 
-        result = model.predict([train_x_c, train_x_a, ])
+        result = model.predict([train_x, ])
         # print(result, line[-1])
         stock = code_table.find_one({'ts_code':line[-1][0]})
 
-        if result[0][0] > 0.5 and stock['sw_industry'] in industry_list:
+        if result[0][0] > 0.85:
             if line[-1][0].startswith('688'):
                 continue
             # 去掉ST
@@ -66,27 +66,24 @@ def predict_today(file, day, model='10_18d', log=True):
 
             if stock['ts_code'] in zixuan_stock_list:
                 # print(line[-1], stock['name'], stock['sw_industry'], str(concept_detail_list), 'buy', k_table_list[0]['pct_chg'])
-                print(stock['ts_code'], stock['name'], '买入评级', k_table_list[0]['pct_chg'])
-                Z_list.append([stock['name'], stock['sw_industry'], k_table_list[0]['pct_chg']])
-            elif stock['ts_code'] in ROE_stock_list:
-                print(stock['ts_code'], stock['name'], '买入评级', k_table_list[0]['pct_chg'])
-                R_list.append([stock['name'], stock['sw_industry'], k_table_list[0]['pct_chg']])
+                print(stock['ts_code'], stock['name'], '买入评级', result[0][0])
+                Z_list.append([stock['name'], stock['sw_industry'], result[0][0]])
+            elif stock['ts_code'] in ROE_stock_list or stock['ts_code'] in zeng_stock_list:
+                print(stock['ts_code'], stock['name'], '买入评级', result[0][0])
+                R_list.append([stock['name'], stock['sw_industry'], result[0][0]])
             else:
-                O_list.append([stock['name'], stock['sw_industry'], k_table_list[0]['pct_chg']])
+                O_list.append([stock['name'], stock['sw_industry'], result[0][0]])
 
             if log is True:
                 with open('D:\\data\\quantization\\predict\\' + str(day) + '_mix.txt', mode='a', encoding="utf-8") as f:
                     f.write(str(line[-1]) + ' ' + stock['name'] + ' ' + stock['sw_industry'] + ' ' + str(concept_detail_list) + ' ' + str(result[0][0]) + '\n')
 
-        # elif result[0][1] > 0.5:
-        #     if stock['ts_code'] in holder_stock_list:
-        #         print(stock['ts_code'], stock['name'], '震荡评级')
-        # elif result[0][2] > 0.4:
-        #     if stock['ts_code'] in holder_stock_list:
-        #         print(stock['ts_code'], stock['name'], '赶紧卖出')
-        # else:
-        #     if stock['ts_code'] in holder_stock_list or stock['ts_code'] in ROE_stock_list:
-        #         print(stock['ts_code'], stock['name'], result[0],)
+        elif result[0][1] > 0.5:
+            if stock['ts_code'] in holder_stock_list:
+                print(stock['ts_code'], stock['name'], '震荡评级')
+        elif result[0][2] > 0.5:
+            if stock['ts_code'] in holder_stock_list:
+                print(stock['ts_code'], stock['name'], '赶紧卖出')
 
     # print(gainian_map)
     # print(hangye_map)
@@ -121,46 +118,12 @@ def predict_today(file, day, model='10_18d', log=True):
     print(O_list[:3])
 
 
-def _read_pfile_map(path):
-    s_list = []
-    with open(path, encoding='utf-8') as f:
-        for line in f.readlines()[:]:
-            s_list.append(line)
-    return s_list
-
-
-def join_two_day(a, b):
-    a_list = _read_pfile_map('D:\\data\\quantization\\predict\\' + str(a) + '.txt')
-    b_list = _read_pfile_map('D:\\data\\quantization\\predict\\dmi_' + str(b) + '.txt')
-    for a in a_list:
-        for b in b_list:
-            if a[2:11] == b[2:11]:
-                print(a)
-
-
-def check_everyday(day, today):
-    a_list = _read_pfile_map('D:\\data\\quantization\\predict\\' + str(day) + '.txt')
-    x = 0
-    for a in a_list:
-        print(a[:-1])
-        k_day_list = list(k_table.find({'code':a[2:11], 'tradeDate':{'$lte':int(today)}}).sort('tradeDate', pymongo.DESCENDING).limit(5))
-        if k_day_list is not None and len(k_day_list) > 0:
-            k_day = k_day_list[0]
-            k_day_0 = k_day_list[-1]
-            k_day_last = k_day_list[1]
-            if ((k_day_last['close'] - k_day_0['pre_close'])/k_day_0['pre_close']) < 0.2:
-                print(k_day['open'], k_day['close'], 100*(k_day['close'] - k_day_last['close'])/k_day_last['close'])
-                x = x + 100*(k_day['close'] - k_day_last['close'])/k_day_last['close']
-
-    print(x/len(a_list))
-
-
 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')
     # multi_predict()
     # predict_today("D:\\data\\quantization\\stock405_30d_20200413.log", 20200413, model='405_30d_mix_5D_ma5_s_seq.h5', log=True)
-    # 模型A
-    predict_today("D:\\data\\quantization\\stock603_30d_20200415.log", 20200415, model='603_30d_mix_5D_ma5_s_seq.h5', log=True)
+    # 模型 下跌趋势，没大涨的不要买
+    predict_today("D:\\data\\quantization\\stock578A_5d_20200430.log", 20200430, model='5d_578A_dnn_seq.h5', log=True)
     # join_two_day(20200305, 20200305)
     # check_everyday(20200311, 20200312)

stock/dnn_predict.py

@@ -31,7 +31,7 @@ def predict(file_path='', model_path='15min_dnn_seq.h5'):
     with open('dnn_predict_5d.txt', 'a') as f:
         for r in result:
             fact = test_y[i]
-            if r[0] > 0.7:
+            if r[0] > 0.8:
                 # f.write(str([lines[i][-2], lines[i][-1]]) + "\n")
                 # win_dnn.append([lines[i][-2], lines[i][-1]])
                 if fact[0] == 1:
@@ -74,5 +74,5 @@ def multi_predict():
 
 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\\stock571_12d_train1.log', model_path='5d_571_dnn_seq.h5')
+    predict(file_path='D:\\data\\quantization\\stock578A_12d_train3.log', model_path='5d_578A_dnn_seq.h5')
     # multi_predict()

stock/dnn_train.py

@@ -15,11 +15,13 @@ early_stopping = EarlyStopping(monitor='accuracy', patience=5, verbose=2)
 def read_data(path):
     lines = []
     with open(path) as f:
-        # for x in range(30000):
-        # lines.append(eval(f.readline().strip()))
         for line in f.readlines()[:]:
             lines.append(eval(line.strip()))
 
+    # with open("D:\\data\\quantization\\stock578A_12d_train1.log") as f:
+    #     for line in f.readlines()[:]:
+    #         lines.append(eval(line.strip()))
+
     random.shuffle(lines)
     print('读取数据完毕')
 
@@ -73,15 +75,15 @@ def train(result_class=3, file_path="D:\\data\\quantization\\stock6.log", model_
 
     input_dim = train_x.shape[1]
     model = Sequential()
-    model.add(Dense(units=220+input_dim, input_dim=input_dim,  activation='relu'))
-    model.add(Dense(units=220+input_dim, activation='relu',kernel_regularizer=regularizers.l1(0.001)))
-    model.add(Dense(units=220+input_dim, activation='relu'))
+    model.add(Dense(units=320+input_dim, input_dim=input_dim,  activation='relu'))
+    model.add(Dense(units=320+input_dim, activation='relu',kernel_regularizer=regularizers.l1(0.001)))
+    model.add(Dense(units=320+input_dim, activation='relu'))
     model.add(Dropout(0.1))
     # model.add(Dense(units=220 + input_dim, activation='relu'))
     # model.add(Dropout(0.1))
-    model.add(Dense(units=220+input_dim, activation='selu'))
+    model.add(Dense(units=320+input_dim, activation='selu'))
     model.add(Dropout(0.1))
-    # model.add(Dense(units=220+input_dim, activation='selu'))
+    model.add(Dense(units=320+input_dim, activation='selu'))
     # model.add(Dropout(0.1))
     model.add(Dense(units=512, activation='relu'))
 
@@ -90,7 +92,7 @@ def train(result_class=3, file_path="D:\\data\\quantization\\stock6.log", model_
 
     print("Starting training ")
     # model.fit(train_x, train_y, batch_size=1024, epochs=400 + 4*int(len(train_x)/1000), shuffle=True)
-    model.fit(train_x, train_y, batch_size=4096, epochs=128, shuffle=True, callbacks=[early_stopping])
+    model.fit(train_x, train_y, batch_size=4096*2, epochs=128, shuffle=True, callbacks=[early_stopping])
     score = model.evaluate(test_x, test_y)
     print(score)
     print('Test score:', score[0])
@@ -114,13 +116,24 @@ def train(result_class=3, file_path="D:\\data\\quantization\\stock6.log", model_
 565 6d input_dim=26  
 566 6d input_dim=26  42,98,57  随机44,97,60
 567 2d input_dim=70  >0.9 30,100,51  随机44,97,60
-568 3d-3 input_dim=  >0.7 35,97,64
+
+568 3d-3 input_dim=  >0.7 35,97,64   
 569 4d-3 input_dim=  >0.7 33,100,52   >0.9 33,100.3,50
-570 5d-3 input_dim=  >0.9 32,102,42   
+570 5d-3 input_dim=  >0.9 32,102,42   随机45,99,56
 571 6d-3 >0.7 37,100,48
+572 5d-3 + macd+roc 37,101,46
+573 +index  32,101,44
+574 modify  35,101.9,43
+575 modify  34,101,45
+576 去掉index 35,101,47
+577 加上r0_ratio 34,100,48
+578 570修正 30,101,47 
+578A 38,101.7 44
+578B 4class 38,101.7 44
+574A >0.8 34,98,59
 '''
 if __name__ == '__main__':
     # train(input_dim=176, result_class=5, file_path="D:\\data\\quantization\\stock6_5.log", model_name='5d_dnn_seq.h5')
-    train(result_class=3, file_path="D:\\data\\quantization\\stock571_12d_train2.log", model_name='5d_571_dnn_seq.h5')
+    train(result_class=4, file_path="D:\\data\\quantization\\stock578B_12d_train2.log", model_name='5d_578B_dnn_seq.h5')
     # resample('D:\\data\\quantization\\stock8_14.log')
     # mul_train()