个股预测

mix/mix_predict_200.py

@@ -0,0 +1,96 @@
+# -*- encoding:utf-8 -*-
+import numpy as np
+from keras.models import load_model
+import joblib
+
+
+def read_data(path):
+    lines = []
+    with open(path) as f:
+        for line in f.readlines()[:]:
+            line = eval(line.strip())
+            lines.append(line)
+
+    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),lines
+
+
+def _score(fact, line):
+    up_right = 0
+    up_error = 0
+
+    if fact[0] == 1:
+        up_right = up_right + 1.04
+    elif fact[1] == 1:
+        up_right = up_right + 1
+    elif fact[1] == 2:
+        up_right = up_right + 0.96
+        up_error = up_error + 0.3
+    else:
+        up_error = up_error + 1
+        up_right = up_right + 0.90
+    return up_right,up_error
+
+
+def predict(file_path='', model_path='15min_dnn_seq.h5', idx=-1, row=18, col=20):
+    test_x,test_y,lines=read_data(file_path)
+
+    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[:,row*col:]
+
+    model=load_model(model_path)
+    score = model.evaluate([test_x_c, test_x_a,], test_y)
+    print('MIX', score)
+
+    up_num = 0
+    up_error = 0
+    up_right = 0
+    down_num = 0
+    down_error = 0
+    down_right = 0
+    i = 0
+    result = model.predict([test_x_c, test_x_a])
+    win_dnn = []
+    for r in result:
+        fact = test_y[i]
+
+        if idx in [-2]:
+            if r[0] > 0.5 or r[1] > 0.5:
+                pass
+        else:
+            if r[0] > 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[2] > 0.6:
+                if fact[0] == 1:
+                    down_error = down_error + 1
+                    down_right = down_right + 1.1
+                elif fact[1] == 1:
+                    down_error = down_error + 0.2
+                    down_right = down_right + 0.99
+                else:
+                    down_right = down_right + 0.88
+                down_num = down_num + 1
+
+        i = i + 1
+    if up_num == 0:
+        up_num = 1
+    if down_num == 0:
+        down_num = 1
+    print('MIX', up_right, up_num, up_right/up_num, up_error/up_num, down_right/down_num, down_error/down_num)
+    return win_dnn,up_right/up_num,down_right/down_num
+
+
+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\\stock6_test.log', model_path='15m_dnn_seq.h5')
+    # multi_predict(model='15_18d')
+    # predict_today(20200229, model='11_18d')

mix/mix_predict_by_day_190.py

@@ -81,5 +81,5 @@ if __name__ == '__main__':
     # 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_20200325.log', model_path='196_18d_mix_6D_ma5_s_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\\stock9_18_4.log', model_path='18d_dnn_seq.h5')

mix/mix_predict_everyday.py

@@ -227,6 +227,6 @@ 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\\stock6_test.log', model_path='15m_dnn_seq.h5')
     # multi_predict()
-    predict_today("D:\\data\\quantization\\stock186_18d_20200325.log", 20200325, model='186_18d_mix_6D_ma5_s_seq.h5', log=True)
+    predict_today("D:\\data\\quantization\\stock186_18d_20200326.log", 20200326, model='186_18d_mix_6D_ma5_s_seq.h5', log=True)
     # join_two_day(20200305, 20200305)
     # check_everyday(20200311, 20200312)

mix/mix_train_190.py

@@ -25,8 +25,10 @@ model_path = '196_18d_mix_6D_ma5_s_seq.h5'
 file_path1='D:\\data\\quantization\\stock196_18d_test.log'
 
 '''
+大盘预测
 结果均用使用ma
-4 ROC         
+6 ROC
+5 after用5日         
 '''
 
 def read_data(path, path1=file_path1):
@@ -36,10 +38,10 @@ def read_data(path, path1=file_path1):
             line = eval(f.readline().strip())
             lines.append(line)
 
-    # with open(path1) as f:
-    #     for x in range(50000):
-    #         line = eval(f.readline().strip())
-    #         lines.append(line)
+    with open(path1) as f:
+        for x in range(50000):
+            line = eval(f.readline().strip())
+            lines.append(line)
 
     random.shuffle(lines)
     print('读取数据完毕')

mix/mix_train_200.py

@@ -19,16 +19,29 @@ from keras.callbacks import EarlyStopping
 early_stopping = EarlyStopping(monitor='accuracy', patience=5, verbose=2)
 
 epochs= 68
-size = 400000 #18W 60W
-file_path = 'D:\\data\\quantization\\stock200_18d_train2.log'
-model_path = '200_18d_mix_6D_ma5_s_seq.h5'
-file_path1='D:\\data\\quantization\\stock200_18d_test.log'
+size = 410000 #18W 60W
+file_path = 'D:\\data\\quantization\\stock202_18d_train2.log'
+model_path = '213_18d_mix_6D_ma5_s_seq.h5'
+file_path1='D:\\data\\quantization\\stock202_18d_test.log'
 '''
 ROC
-1大盘涨停 涨停/跌停/创业板涨停跌停   
-2大盘涨停 涨停/跌停
-3beta函数修正
-4用两个kernel
+0大盘涨停 涨停/跌停/创业板涨停跌停     42,97,45    43,97,36 
+1大盘涨停 涨停/跌停                    48,98,38
+2beta函数修正                          46,98,39    47,97,27      <---2
+3用两个kernel                          42,98,42
+4窗口大小修改  
+  3*3                                  45,98,43    45,97,34      <---4
+  6*6                                  46,98,41
+  6*20                                 45,98,38    47,97,28      <---5
+  9*9                                  42,97,45    42,97,36      <---6
+  9*20                                 45,98,39    
+  3*20                                 42,98,40    44,97,30      <---7
+5 修改神经元
+ mlp -> 4+3层                          42,98,41    51,97,31      <---9
+ 外层 -> 3+4层                                     52,97,37      <--11
+beta+4+3                                           49,96,37
+beta+2+5                                           52,96,38
+
 
 '''
 
@@ -40,7 +53,7 @@ def read_data(path, path1=file_path1):
             lines.append(line)
 
     with open(path1) as f:
-        for x in range(30000):
+        for x in range(50000):
             line = eval(f.readline().strip())
             lines.append(line)
 
@@ -77,8 +90,11 @@ train_x_c = train_x[:,18*20:]
 def create_mlp(dim, regress=False):
     # define our MLP network
     model = Sequential()
-    model.add(Dense(96, input_dim=dim, activation="relu"))
-    model.add(Dense(96, activation="relu"))
+    model.add(Dense(128, input_dim=dim, activation="relu"))
+    model.add(Dropout(0.2))
+    model.add(Dense(128, activation="relu"))
+    model.add(Dense(128, activation="relu"))
+    # model.add(Dense(128, activation="relu"))
 
     # check to see if the regression node should be added
     if regress:
@@ -131,30 +147,33 @@ 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, 21, 1, kernel_size=(3, 3), size=64, regress=False, output=128)       # 31 97 46
-# cnn_0 = create_cnn(18, 21, 1, kernel_size=(6, 6), size=64, regress=False, output=128)         # 29 98 47
-# cnn_0 = create_cnn(18, 21, 1, kernel_size=(9, 9), size=64, regress=False, output=128)         # 28 97 53
-cnn_0 = create_cnn(18, 20, 1, kernel_size=(3, 20), size=96, regress=False, output=96)       #A23 99 33 A' 26 99 36 #B 34 98 43
-# cnn_1 = create_cnn(18, 21, 1, kernel_size=(18, 11), size=96, regress=False, output=96)
+# cnn_0 = create_cnn(18, 20, 1, kernel_size=(3, 3), size=90, regress=False, output=96)       # 31 97 46
+cnn_0 = create_cnn(18, 20, 1, kernel_size=(6, 20), 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)
 # cnn_1 = create_cnn(9, 26, 1, kernel_size=(2, 14), size=36, regress=False, output=64)
 
 # create the input to our final set of layers as the *output* of both
 # the MLP and CNN
-combinedInput = concatenate([mlp.output, cnn_0.output])
+combinedInput = concatenate([mlp.output, cnn_0.output, ])
 
 # our final FC layer head will have two dense layers, the final one
 # being our regression head
 x = Dense(1024, activation="relu", kernel_regularizer=regularizers.l1(0.003))(combinedInput)
 x = Dropout(0.2)(x)
 x = Dense(1024, activation="relu")(x)
+x = Dropout(0.2)(x)
+x = Dense(1024, activation="relu")(x)
 x = Dense(1024, activation="relu")(x)
+# x = Dense(512, activation="relu")(x)
 # 在建设一层
 x = Dense(3, activation="softmax")(x)
 
 # our final model will accept categorical/numerical data on the MLP
 # input and images on the CNN input, outputting a single value (the
 # predicted price of the house)
-model = Model(inputs=[mlp.input, cnn_0.input,], outputs=x)
+model = Model(inputs=[mlp.input, cnn_0.input, ], outputs=x)
 
 
 print("Starting training ")
@@ -169,7 +188,7 @@ model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=['accuracy
 # train the model
 print("[INFO] training model...")
 model.fit(
-    [train_x_c, train_x_a,], train_y,
+    [train_x_c, train_x_a, ], train_y,
     # validation_data=([testAttrX, testImagesX], testY),
     # epochs=int(3*train_x_a.shape[0]/1300),
     epochs=epochs,
@@ -187,7 +206,7 @@ test_x_c = test_x[:,18*20:]
 
 # make predictions on the testing data
 print("[INFO] predicting house prices...")
-score  = model.evaluate([test_x_c, test_x_a], test_y)
+score  = model.evaluate([test_x_c, test_x_a,], test_y)
 
 print(score)
 print('Test score:', score[0])

mix/mix_train_300.py

@@ -0,0 +1,196 @@
+import keras
+# -*- encoding:utf-8 -*-
+import numpy as np
+from keras.models import Sequential
+# 优化方法选用Adam(其实可选项有很多，如SGD)
+from keras.optimizers import Adam
+import random
+from keras.models import load_model
+from imblearn.over_sampling import RandomOverSampler
+from keras.utils import np_utils
+# 用于模型初始化，Conv2D模型初始化、Activation激活函数，MaxPooling2D是池化层
+# Flatten作用是将多位输入进行一维化
+# Dense是全连接层
+from keras.layers import Conv2D, Activation, MaxPool2D, Flatten, Dense,Dropout,Input,MaxPooling2D,BatchNormalization,concatenate
+from keras import regularizers
+from keras.models import Model
+from keras.callbacks import EarlyStopping
+
+early_stopping = EarlyStopping(monitor='accuracy', patience=5, verbose=2)
+
+epochs= 8
+size = 10000 #18W 60W
+file_path = 'D:\\data\\quantization\\stock300_18d_train2.log'
+model_path = '300_18d_mix_6D_ma5_s_seq.h5'
+file_path1='D:\\data\\quantization\\stock300_18d_test.log'
+col = 18
+'''
+ROC     30*18
+DMI     30*20
+MACD    30*19
+RSI     30*17
+
+'''
+
+def read_data(path, path1=file_path1):
+    lines = []
+    with open(path) as f:
+        for x in range(size): #610000
+            line = eval(f.readline().strip())
+            lines.append(line)
+
+    with open(path1) as f:
+        for x in range(20000):
+            line = eval(f.readline().strip())
+            lines.append(line)
+
+    random.shuffle(lines)
+    print('读取数据完毕')
+
+    d=int(0.85*len(lines))
+    length = len(lines[0])
+
+    train_x=[s[:length - 2] for s in lines[0:d]]
+    train_y=[s[-1] for s in lines[0:d]]
+    test_x=[s[:length - 2] for s in lines[d:]]
+    test_y=[s[-1] for s in lines[d:]]
+
+    print('转换数据完毕')
+
+    ros = RandomOverSampler(random_state=0)
+    X_resampled, y_resampled = ros.fit_sample(np.array(train_x), np.array(train_y))
+
+    print('数据重采样完毕')
+
+    return X_resampled,y_resampled,np.array(test_x),np.array(test_y)
+
+
+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_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:]
+
+
+def create_mlp(dim, regress=False):
+    # define our MLP network
+    model = Sequential()
+    model.add(Dense(128, input_dim=dim, activation="relu"))
+    model.add(Dropout(0.2))
+    model.add(Dense(128, activation="relu"))
+
+    # check to see if the regression node should be added
+    if regress:
+        model.add(Dense(1, activation="linear"))
+
+    # return our model
+    return model
+
+
+def create_cnn(width, height, depth, size=48, kernel_size=(5, 6), regress=False, output=24):
+    # initialize the input shape and channel dimension, assuming
+    # TensorFlow/channels-last ordering
+    inputShape = (width, height, 1)
+    chanDim = -1
+
+    # define the model input
+    inputs = Input(shape=inputShape)
+    # x = inputs
+    # CONV => RELU => BN => POOL
+    x = Conv2D(size, kernel_size, strides=2, padding="same")(inputs)
+    x = Activation("relu")(x)
+    x = BatchNormalization(axis=chanDim)(x)
+
+    # y = Conv2D(24, (2, 8), strides=2, padding="same")(inputs)
+    # y = Activation("relu")(y)
+    # y = BatchNormalization(axis=chanDim)(y)
+
+    # flatten the volume, then FC => RELU => BN => DROPOUT
+    x = Flatten()(x)
+    x = Dense(output)(x)
+    x = Activation("relu")(x)
+    x = BatchNormalization(axis=chanDim)(x)
+    x = Dropout(0.2)(x)
+
+    # apply another FC layer, this one to match the number of nodes
+    # coming out of the MLP
+    x = Dense(output)(x)
+    x = Activation("relu")(x)
+
+    # check to see if the regression node should be added
+    if regress:
+        x = Dense(1, activation="linear")(x)
+
+    # construct the CNN
+    model = Model(inputs, x)
+
+    # return the CNN
+    return model
+
+
+# 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(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)
+# cnn_1 = create_cnn(9, 26, 1, kernel_size=(2, 14), size=36, regress=False, output=64)
+
+# create the input to our final set of layers as the *output* of both
+# the MLP and CNN
+combinedInput = concatenate([mlp.output, cnn_0.output, ])
+
+# our final FC layer head will have two dense layers, the final one
+# being our regression head
+x = Dense(1024, activation="relu", kernel_regularizer=regularizers.l1(0.003))(combinedInput)
+x = Dropout(0.2)(x)
+x = Dense(1024, activation="relu")(x)
+x = Dense(1024, activation="relu")(x)
+x = Dense(1024, activation="relu")(x)
+# 在建设一层
+x = Dense(3, activation="softmax")(x)
+
+# our final model will accept categorical/numerical data on the MLP
+# input and images on the CNN input, outputting a single value (the
+# predicted price of the house)
+model = Model(inputs=[mlp.input, cnn_0.input, ], outputs=x)
+
+
+print("Starting training ")
+# h = model.fit(train_x, train_y, batch_size=4096*2, epochs=500, shuffle=True)
+
+# compile the model using mean absolute percentage error as our loss,
+# implying that we seek to minimize the absolute percentage difference
+# between our price *predictions* and the *actual prices*
+opt = Adam(lr=1e-3, decay=1e-3 / 200)
+model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=['accuracy'])
+
+# train the model
+print("[INFO] training model...")
+model.fit(
+    [train_x_c, train_x_a, ], train_y,
+    # validation_data=([testAttrX, testImagesX], testY),
+    # epochs=int(3*train_x_a.shape[0]/1300),
+    epochs=epochs,
+    batch_size=2048, shuffle=True,
+    callbacks=[early_stopping]
+)
+
+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_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:]
+
+# make predictions on the testing data
+print("[INFO] predicting house prices...")
+score  = model.evaluate([test_x_c, test_x_a,], test_y)
+
+print(score)
+print('Test score:', score[0])
+print('Test accuracy:', score[1])