cnn

stock/cnn_predict_by_stock.py

@@ -0,0 +1,120 @@
+# -*- encoding:utf-8 -*-
+import numpy as np
+from keras.models import load_model
+import joblib
+
+
+def read_data(path):
+    stock_lines = {}
+    with open(path) as f:
+        for line in f.readlines()[:]:
+            line = eval(line.strip())
+            stock = str(line[-2][0])
+
+            if stock in stock_lines:
+                stock_lines[stock].append(line)
+            else:
+                stock_lines[stock] = [line]
+    # print(len(day_lines['20191230']))
+    return stock_lines
+
+
+import pymongo
+from util.mongodb import get_mongo_table_instance
+code_table = get_mongo_table_instance('tushare_code')
+k_table = get_mongo_table_instance('stock_day_k')
+
+
+def predict(file_path='', model_path='15min_dnn_seq'):
+    stock_lines = read_data(file_path)
+    print('数据读取完毕')
+
+    models = []
+    # for x in range(0, 12):
+    models.append(load_model(model_path + '.h5'))
+    estimator = joblib.load('km_dmi_18.pkl')
+    print('模型加载完毕')
+
+    total_money = 0
+    total_num = 0
+    items = sorted(stock_lines.keys())
+    for key in items:
+        # print(day)
+        lines = stock_lines[key]
+        init_money = 10000
+        last_price = 1
+
+        if lines[0][-2][0].startswith('6'):
+            continue
+
+        buy = 0 # 0空 1买入 2卖出
+        chiyou_0 = 0
+        high_price = 0
+
+        x = 24 # 每条数据项数
+        k = 18 # 周期
+        for line in lines:
+            # v = line[1:x*k + 1]
+            # v = np.array(v)
+            # v = v.reshape(k, x)
+            # v = v[:,6:10]
+            # v = v.reshape(1, 4*k)
+            # print(v)
+
+            train_x = np.array([line[:-2]])
+            train_x = train_x.reshape(train_x.shape[0], 1,6,77)
+            result = models[0].predict(train_x)
+
+            stock_name = line[-2]
+            today_price = list(k_table.find({'code':line[-2][0], 'tradeDate':{'$gt':int(line[-2][1])}}).sort('tradeDate',pymongo.ASCENDING).limit(1))
+            today_price = today_price[0]
+
+            if result[0][0] > 0.6 or result[0][1] > 0.6: #and (r[0] not in [2,6,8,10]):
+                chiyou_0 = 0
+                if buy == 0:
+                    last_price = today_price['open']
+                    high_price = last_price
+                    print('首次买入', stock_name, today_price['open'])
+                    buy = 1
+                else:
+                    init_money = init_money * (today_price['close'] - last_price)/last_price + init_money
+                    last_price = today_price['close']
+                    print('买入+买入', stock_name, today_price['close'])
+                    buy = 1
+                    if last_price > high_price:
+                        high_price = last_price
+            elif buy == 1:
+                chiyou_0 = chiyou_0 + 1
+                last_price = today_price['close']
+                if chiyou_0 > 2 and today_price['close'] < last_price:
+                    print('卖出', stock_name, today_price['close'])
+                    init_money = init_money * (today_price['close'] - last_price)/last_price + init_money
+                    buy = 0
+                    chiyou_0 = 0
+
+                elif init_money < 9000:
+                    print('止损卖出', stock_name, today_price['close'])
+                    init_money = init_money * (today_price['close'] - last_price)/last_price + init_money
+                    buy = 0
+                    chiyou_0 = 0
+
+        print(key, init_money)
+
+        with open('D:\\data\\quantization\\stock_16_18d' + '_' +  'profit.log', 'a') as f:
+            if init_money > 10000:
+                f.write(str(key) + ' ' + str(init_money) + '\n')
+            elif init_money < 10000:
+                f.write(str(key) + ' ' + str(init_money) + '\n')
+
+        if init_money != 10000:
+            total_money = total_money + init_money
+            total_num = total_num + 1
+
+    print(total_money, total_num, total_money/total_num/10000)
+
+
+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\\stock12_18d_test.log', model_path='12_18d_dnn_seq')
+    predict(file_path='D:\\data\\quantization\\stock16_18d_test.log', model_path='16_18d_cnn_seq')
+    # predict(file_path='D:\\data\\quantization\\stock12_18d_20190103_20190604.log', model_path='13_18d_dnn_seq')

stock/cnn_predict_dmi.py

@@ -33,6 +33,7 @@ def _score(fact, line):
         up_right = up_right + 1
     elif fact[3] == 1:
         up_right = up_right + 0.94
+        up_error = up_error + 1
     else:
         up_error = up_error + 1
         up_right = up_right + 0.88
@@ -62,29 +63,18 @@ def predict(file_path='', model_path='15min_dnn_seq.h5', idx=-1):
         if idx in [-2]:
             if r[0] > 0.5 or r[1] > 0.5:
                 pass
-                # if fact[0] == 1:
-                #     up_right = up_right + 1.12
-                # elif fact[1] == 1:
-                #     up_right = up_right + 1.06
-                # elif fact[2] == 1:
-                #     up_right = up_right + 1
-                # elif fact[3] == 1:
-                #     up_right = up_right + 0.94
-                # else:
-                #     up_error = up_error + 1
-                #     up_right = up_right + 0.88
-                # up_num = up_num + 1
         else:
-            if r[0] > 0.6 or r[1] > 0.6:
+            if r[0] > 0.7 or r[1] > 0.7:
                 tmp_right,tmp_error = _score(fact, lines[i])
                 up_right = tmp_right + up_right
                 up_error = tmp_error + up_error
                 up_num = up_num + 1
-            elif r[3] > 0.5 or r[4] > 0.5:
+            elif r[3] > 0.6 or r[4] > 0.6:
                 if fact[0] == 1:
                     down_error = down_error + 1
                     down_right = down_right + 1.12
                 elif fact[1] == 1:
+                    down_error = down_error + 1
                     down_right = down_right + 1.06
                 elif fact[2] == 1:
                     down_right = down_right + 1

stock/cnn_train_dmi.py

@@ -18,7 +18,7 @@ from keras import regularizers
 def read_data(path):
     lines = []
     with open(path) as f:
-        for x in range(200000):
+        for x in range(20000):
             lines.append(eval(f.readline().strip()))
 
     random.shuffle(lines)
@@ -42,8 +42,8 @@ def read_data(path):
 
 
 train_x,train_y,test_x,test_y=read_data("D:\\data\\quantization\\stock16_18d_train.log")
-train_x = train_x.reshape(train_x.shape[0], 1,6,77)
-test_x = test_x.reshape(test_x.shape[0], 1,6, 77)
+train_x = train_x.reshape(train_x.shape[0], 1,77,6)
+test_x = test_x.reshape(test_x.shape[0], 1,77, 6)
 
 
 
@@ -51,22 +51,20 @@ model = Sequential()
 
 # 模型卷积层设计
 model.add(Conv2D(
-    nb_filter=32,  # 第一层设置32个滤波器
-    nb_row=10,
-    nb_col=6,  # 设置滤波器的大小为5*5
+    kernel_size=(5, 6), filters=64,
     padding='same',  # 选择滤波器的扫描方式，即是否考虑边缘
-    input_shape=(1,6,77),  # 设置输入的形状
+    input_shape=(1,77,6),  # 设置输入的形状
     # batch_input_shape=(64, 1, 28, 28),
 ))
 # 选择激活函数
 model.add(Activation('relu'))
 
-# 设置下采样(池化层）
-model.add(MaxPool2D(
-    pool_size=(4,1),  # 下采样格为2*2
-    strides=(2,2),  # 向右向下的步长
-    padding='same', # padding mode is 'same'
-))
+# # 设置下采样(池化层）
+# model.add(MaxPool2D(
+#     pool_size=(4,1),  # 下采样格为2*2
+#     strides=(2,2),  # 向右向下的步长
+#     padding='same', # padding mode is 'same'
+# ))
 
 # 使用Flatten函数，将输入数据扁平化（因为输入数据是一个多维的形式，需要将其扁平化）
 model.add(Flatten())  # 将多维的输入一维化
@@ -89,7 +87,7 @@ model.compile(optimizer=adam,
     metrics=['accuracy'])
 
 print("Starting training ")
-h=model.fit(train_x, train_y, batch_size=4096*2, epochs=150, shuffle=True)
+h=model.fit(train_x, train_y, batch_size=4096*2, epochs=50, shuffle=True)
 score = model.evaluate(test_x, test_y)
 print(score)
 print('Test score:', score[0])