import pandas as pd
import time, datetime
df_data_5minute=pd.read_csv('黄金主力5分钟数据.csv')

df_data_5minute.head()

df_data_5minute

df_data_5minute.drop('Unnamed: 0', axis=1, inplace=True)
df_data_5minute

df=df_data_5minute
close = df['close']
df.drop(labels=['close'], axis=1,inplace = True)
df.insert(0, 'close', close)
df

data_train =df.iloc[:int(df.shape[0] * 0.7), :]
data_test = df.iloc[int(df.shape[0] * 0.7):, :]
print(data_train.shape, data_test.shape)

(37317, 6) (15993, 6)

# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.preprocessing import MinMaxScaler
import time
scaler = MinMaxScaler(feature_range=(-1, 1))
scaler.fit(data_train)

/Users/jiaohaibin/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters

MinMaxScaler(copy=True, feature_range=(-1, 1))

data_train = scaler.transform(data_train)
data_test = scaler.transform(data_test)

data_train

array([[-0.98877193, -0.98736842, -0.98459384, -0.99297259, -0.82504604,
        -0.85978547],
       [-0.99298246, -0.98596491, -0.98739496, -0.99437807, -0.92389948,
        -0.93904608],
       [-0.99438596, -0.99438596, -0.99159664, -0.99437807, -0.95134557,
        -0.96104178],
       ...,
       [ 0.61263158,  0.61824561,  0.61484594,  0.61349262, -0.90916652,
        -0.90885626],
       [ 0.61684211,  0.61403509,  0.61204482,  0.61630358, -0.94754352,
        -0.94737162],
       [ 0.6154386 ,  0.6154386 ,  0.61064426,  0.61349262, -0.94445435,
        -0.9442865 ]])

from keras.layers import Input, Dense, LSTM
from keras.models import Model

from keras.layers import *
from keras.models import *
from keras.optimizers import Adam

output_dim = 1
batch_size = 256 #每轮训练模型时，样本的数量
epochs = 60 #训练60轮次
seq_len = 5
hidden_size = 128


TIME_STEPS = 5
INPUT_DIM = 6

lstm_units = 64
X_train = np.array([data_train[i : i + seq_len, :] for i in range(data_train.shape[0] - seq_len)])
y_train = np.array([data_train[i + seq_len, 0] for i in range(data_train.shape[0]- seq_len)])
X_test = np.array([data_test[i : i + seq_len, :] for i in range(data_test.shape[0]- seq_len)])
y_test = np.array([data_test[i + seq_len, 0] for i in range(data_test.shape[0] - seq_len)])

print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)

(37312, 5, 6) (37312,) (15988, 5, 6) (15988,)

Using TensorFlow backend.

inputs = Input(shape=(TIME_STEPS, INPUT_DIM))
#drop1 = Dropout(0.3)(inputs)

x = Conv1D(filters = 64, kernel_size = 1, activation = 'relu')(inputs)  #, padding = 'same'
#x = Conv1D(filters=128, kernel_size=5, activation='relu')(output1)#embedded_sequences
x = MaxPooling1D(pool_size = 5)(x)
x = Dropout(0.2)(x)
print(x.shape)

WARNING:tensorflow:From /Users/jiaohaibin/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:497: calling conv1d (from tensorflow.python.ops.nn_ops) with data_format=NHWC is deprecated and will be removed in a future version.
Instructions for updating:
`NHWC` for data_format is deprecated, use `NWC` instead
(?, 1, 64)

lstm_out = Bidirectional(LSTM(lstm_units, activation='relu'), name='bilstm')(x)
#lstm_out = LSTM(lstm_units,activation='relu')(x)
print(lstm_out.shape)

(?, 128)

from keras import backend as K
from keras.engine.topology import Layer
import numpy as np
from keras import initializers
# Attention GRU network  未用     
class AttLayer(Layer):
    def __init__(self, **kwargs):
        self.init = initializers.get('normal')
        #self.input_spec = [InputSpec(ndim=3)]
        super(AttLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        assert len(input_shape)==128
        #self.W = self.init((input_shape[-1],1))
        self.W = self.init((input_shape[-1],))
        #self.input_spec = [InputSpec(shape=input_shape)]
        self.trainable_weights = [self.W]
        super(AttLayer, self).build(input_shape)  # be sure you call this somewhere!

    def call(self, x, mask=None):
        eij = K.tanh(K.dot(x, self.W))
        
        ai = K.exp(eij)
        weights = ai/K.sum(ai, axis=1).dimshuffle(0,'x')
        
        weighted_input = x*weights.dimshuffle(0,1,'x')
        return weighted_input.sum(axis=1)

    def get_output_shape_for(self, input_shape):
        return (input_shape[0], input_shape[-1])

'''
l_att = AttLayer()(lstm_out)
output = Dense(1, activation='sigmoid')(l_att)
print(output.shape)'''

"\nl_att = AttLayer()(lstm_out)\noutput = Dense(1, activation='sigmoid')(l_att)\nprint(output.shape)"

from keras.layers import Input, Dense, merge
from keras import layers
# ATTENTION PART STARTS HERE
attention_probs = Dense(128, activation='sigmoid', name='attention_vec')(lstm_out)
#attention_mul=layers.merge([stm_out,attention_probs], output_shape],mode='concat',concat_axis=1))
attention_mul =Multiply()([lstm_out, attention_probs])
#attention_mul = merge([lstm_out, attention_probs],output_shape=32, name='attention_mul', mode='mul')

output = Dense(1, activation='sigmoid')(attention_mul)
#output = Dense(10, activation='sigmoid')(drop2)

model = Model(inputs=inputs, outputs=output)
print(model.summary())

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, 5, 6)         0                                            
__________________________________________________________________________________________________
conv1d_1 (Conv1D)               (None, 5, 64)        448         input_1[0][0]                    
__________________________________________________________________________________________________
max_pooling1d_1 (MaxPooling1D)  (None, 1, 64)        0           conv1d_1[0][0]                   
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 1, 64)        0           max_pooling1d_1[0][0]            
__________________________________________________________________________________________________
bilstm (Bidirectional)          (None, 128)          66048       dropout_1[0][0]                  
__________________________________________________________________________________________________
attention_vec (Dense)           (None, 128)          16512       bilstm[0][0]                     
__________________________________________________________________________________________________
multiply_1 (Multiply)           (None, 128)          0           bilstm[0][0]                     
                                                                 attention_vec[0][0]              
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 1)            129         multiply_1[0][0]                 
==================================================================================================
Total params: 83,137
Trainable params: 83,137
Non-trainable params: 0
__________________________________________________________________________________________________
None

model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, shuffle=False)
y_pred = model.predict(X_test)
print('MSE Train loss:', model.evaluate(X_train, y_train, batch_size=batch_size))
print('MSE Test loss:', model.evaluate(X_test, y_test, batch_size=batch_size))
plt.plot(y_test, label='test')
plt.plot(y_pred, label='pred')
plt.legend()
plt.show()

Epoch 1/60
37312/37312 [==============================] - 3s 92us/step - loss: 0.1865
Epoch 2/60
37312/37312 [==============================] - 2s 46us/step - loss: 0.0514
Epoch 3/60
37312/37312 [==============================] - 2s 42us/step - loss: 0.0442
Epoch 4/60
37312/37312 [==============================] - 2s 44us/step - loss: 0.0439
Epoch 5/60
37312/37312 [==============================] - 1s 39us/step - loss: 0.0436
Epoch 6/60
37312/37312 [==============================] - 1s 35us/step - loss: 0.0432
Epoch 7/60
37312/37312 [==============================] - 1s 35us/step - loss: 0.0429
Epoch 8/60
37312/37312 [==============================] - 1s 39us/step - loss: 0.0426
Epoch 9/60
37312/37312 [==============================] - 1s 37us/step - loss: 0.0424
Epoch 10/60
37312/37312 [==============================] - 1s 34us/step - loss: 0.0422
Epoch 11/60
37312/37312 [==============================] - 1s 36us/step - loss: 0.0420
Epoch 12/60
37312/37312 [==============================] - 1s 40us/step - loss: 0.0419
Epoch 13/60
37312/37312 [==============================] - 1s 40us/step - loss: 0.0418
Epoch 14/60
37312/37312 [==============================] - 1s 38us/step - loss: 0.0417
Epoch 15/60
37312/37312 [==============================] - 2s 42us/step - loss: 0.0417
Epoch 16/60
37312/37312 [==============================] - 2s 45us/step - loss: 0.0416
Epoch 17/60
37312/37312 [==============================] - 1s 39us/step - loss: 0.0416
Epoch 18/60
37312/37312 [==============================] - 2s 42us/step - loss: 0.0416
Epoch 19/60
37312/37312 [==============================] - 2s 44us/step - loss: 0.0416
Epoch 20/60
37312/37312 [==============================] - 1s 35us/step - loss: 0.0416
Epoch 21/60
37312/37312 [==============================] - 1s 40us/step - loss: 0.0416
Epoch 22/60
37312/37312 [==============================] - 2s 41us/step - loss: 0.0416
Epoch 23/60
37312/37312 [==============================] - 1s 37us/step - loss: 0.0415
Epoch 24/60
37312/37312 [==============================] - 1s 35us/step - loss: 0.0416
Epoch 25/60
37312/37312 [==============================] - 1s 40us/step - loss: 0.0415
Epoch 26/60
37312/37312 [==============================] - 2s 41us/step - loss: 0.0415
Epoch 27/60
37312/37312 [==============================] - 2s 46us/step - loss: 0.0415
Epoch 28/60
37312/37312 [==============================] - 2s 47us/step - loss: 0.0415
Epoch 29/60
37312/37312 [==============================] - 2s 43us/step - loss: 0.0414
Epoch 30/60
37312/37312 [==============================] - 1s 39us/step - loss: 0.0414
Epoch 31/60
37312/37312 [==============================] - 2s 41us/step - loss: 0.0414
Epoch 32/60
37312/37312 [==============================] - 2s 42us/step - loss: 0.0414
Epoch 33/60
37312/37312 [==============================] - 1s 37us/step - loss: 0.0414
Epoch 34/60
37312/37312 [==============================] - 2s 44us/step - loss: 0.0414
Epoch 35/60
37312/37312 [==============================] - 2s 49us/step - loss: 0.0413
Epoch 36/60
37312/37312 [==============================] - 1s 40us/step - loss: 0.0413
Epoch 37/60
37312/37312 [==============================] - 1s 35us/step - loss: 0.0413
Epoch 38/60
37312/37312 [==============================] - 2s 48us/step - loss: 0.0413
Epoch 39/60
37312/37312 [==============================] - 1s 40us/step - loss: 0.0412
Epoch 40/60
37312/37312 [==============================] - 1s 38us/step - loss: 0.0413
Epoch 41/60
37312/37312 [==============================] - 2s 42us/step - loss: 0.0412
Epoch 42/60
37312/37312 [==============================] - 2s 41us/step - loss: 0.0412
Epoch 43/60
37312/37312 [==============================] - 1s 36us/step - loss: 0.0412
Epoch 44/60
37312/37312 [==============================] - 1s 40us/step - loss: 0.0412
Epoch 45/60
37312/37312 [==============================] - 2s 43us/step - loss: 0.0412
Epoch 46/60
37312/37312 [==============================] - 1s 37us/step - loss: 0.0412
Epoch 47/60
37312/37312 [==============================] - 1s 38us/step - loss: 0.0412
Epoch 48/60
37312/37312 [==============================] - 2s 43us/step - loss: 0.0412
Epoch 49/60
37312/37312 [==============================] - 1s 39us/step - loss: 0.0411
Epoch 50/60
37312/37312 [==============================] - 1s 37us/step - loss: 0.0411
Epoch 51/60
37312/37312 [==============================] - 2s 42us/step - loss: 0.0411
Epoch 52/60
37312/37312 [==============================] - 2s 43us/step - loss: 0.0411
Epoch 53/60
37312/37312 [==============================] - 1s 38us/step - loss: 0.0411
Epoch 54/60
37312/37312 [==============================] - 2s 47us/step - loss: 0.0410
Epoch 55/60
37312/37312 [==============================] - 2s 50us/step - loss: 0.0410
Epoch 56/60
37312/37312 [==============================] - 2s 43us/step - loss: 0.0410
Epoch 57/60
37312/37312 [==============================] - 2s 48us/step - loss: 0.0410
Epoch 58/60
37312/37312 [==============================] - 2s 50us/step - loss: 0.0410
Epoch 59/60
37312/37312 [==============================] - 2s 40us/step - loss: 0.0410
Epoch 60/60
37312/37312 [==============================] - 2s 45us/step - loss: 0.0410
37312/37312 [==============================] - 1s 20us/step
MSE Train loss: 0.04148709757006819
15988/15988 [==============================] - 0s 15us/step
MSE Test loss: 0.0005358342003804944

	Unnamed: 0	open	close	high	low	volume	money
0	2016-01-04 09:05:00	226.70	226.65	226.85	226.45	5890.0	1.335146e+09
1	2016-01-04 09:10:00	226.75	226.50	226.75	226.40	2562.0	5.804133e+08
2	2016-01-04 09:15:00	226.45	226.45	226.60	226.40	1638.0	3.709666e+08
3	2016-01-04 09:20:00	226.45	226.25	226.50	226.20	3162.0	7.157891e+08
4	2016-01-04 09:25:00	226.25	226.25	226.30	226.20	1684.0	3.809907e+08

	Unnamed: 0	open	close	high	low	volume	money
0	2016-01-04 09:05:00	226.70	226.65	226.85	226.45	5890.0	1.335146e+09
1	2016-01-04 09:10:00	226.75	226.50	226.75	226.40	2562.0	5.804133e+08
2	2016-01-04 09:15:00	226.45	226.45	226.60	226.40	1638.0	3.709666e+08
3	2016-01-04 09:20:00	226.45	226.25	226.50	226.20	3162.0	7.157891e+08
4	2016-01-04 09:25:00	226.25	226.25	226.30	226.20	1684.0	3.809907e+08
5	2016-01-04 09:30:00	226.25	226.30	226.35	226.20	922.0	2.086313e+08
6	2016-01-04 09:35:00	226.30	226.35	226.40	226.20	2476.0	5.603541e+08
7	2016-01-04 09:40:00	226.30	226.45	226.45	226.25	2516.0	5.695246e+08
8	2016-01-04 09:45:00	226.45	226.35	226.45	226.30	1344.0	3.042327e+08
9	2016-01-04 09:50:00	226.30	226.30	226.35	226.20	1414.0	3.199363e+08
10	2016-01-04 09:55:00	226.35	226.45	226.50	226.30	1610.0	3.645328e+08
11	2016-01-04 10:00:00	226.45	226.40	226.50	226.40	972.0	2.200957e+08
12	2016-01-04 10:05:00	226.40	226.50	226.55	226.35	2004.0	4.538166e+08
13	2016-01-04 10:10:00	226.50	226.45	226.55	226.40	780.0	1.766423e+08
14	2016-01-04 10:15:00	226.45	226.45	226.50	226.40	1530.0	3.464690e+08
15	2016-01-04 10:35:00	226.55	226.45	226.65	226.45	2564.0	5.807784e+08
16	2016-01-04 10:40:00	226.45	226.50	226.55	226.45	900.0	2.038475e+08
17	2016-01-04 10:45:00	226.55	226.70	226.80	226.50	3008.0	6.817039e+08
18	2016-01-04 10:50:00	226.70	226.65	226.85	226.60	2510.0	5.691306e+08
19	2016-01-04 10:55:00	226.65	226.60	226.65	226.60	930.0	2.107595e+08
20	2016-01-04 11:00:00	226.65	226.75	226.75	226.60	1184.0	2.683818e+08
21	2016-01-04 11:05:00	226.75	226.65	226.75	226.60	1044.0	2.366603e+08
22	2016-01-04 11:10:00	226.65	226.60	226.70	226.60	342.0	7.751130e+07
23	2016-01-04 11:15:00	226.60	226.60	226.65	226.55	640.0	1.450196e+08
24	2016-01-04 11:20:00	226.60	226.65	226.70	226.60	502.0	1.137778e+08
25	2016-01-04 11:25:00	226.65	226.95	226.95	226.65	3222.0	7.308042e+08
26	2016-01-04 11:30:00	226.90	226.90	226.95	226.80	1472.0	3.339398e+08
27	2016-01-04 13:35:00	227.10	227.25	227.25	227.00	4894.0	1.111496e+09
28	2016-01-04 13:40:00	227.25	227.55	227.60	227.20	5338.0	1.214103e+09
29	2016-01-04 13:45:00	227.60	227.75	228.00	227.50	8612.0	1.961599e+09
...	...	...	...	...	...	...	...
53280	2017-12-29 10:35:00	278.05	277.95	278.05	277.90	448.0	1.245318e+08
53281	2017-12-29 10:40:00	277.90	277.95	278.00	277.90	506.0	1.406423e+08
53282	2017-12-29 10:45:00	277.95	277.95	278.00	277.95	180.0	5.003790e+07
53283	2017-12-29 10:50:00	277.95	278.00	278.05	277.95	936.0	2.602273e+08
53284	2017-12-29 10:55:00	278.05	277.90	278.05	277.90	942.0	2.618281e+08
53285	2017-12-29 11:00:00	277.85	277.90	277.95	277.85	518.0	1.439454e+08
53286	2017-12-29 11:05:00	277.95	277.95	277.95	277.90	614.0	1.706443e+08
53287	2017-12-29 11:10:00	277.90	277.90	277.95	277.85	1046.0	2.906776e+08
53288	2017-12-29 11:15:00	277.95	277.90	277.95	277.90	206.0	5.725350e+07
53289	2017-12-29 11:20:00	277.90	277.90	277.95	277.85	740.0	2.056435e+08
53290	2017-12-29 11:25:00	277.90	277.85	277.90	277.85	200.0	5.557570e+07
53291	2017-12-29 11:30:00	277.90	277.90	277.95	277.85	756.0	2.100840e+08
53292	2017-12-29 13:35:00	277.90	278.00	278.00	277.90	490.0	1.362097e+08
53293	2017-12-29 13:40:00	278.00	278.05	278.15	278.00	768.0	2.135675e+08
53294	2017-12-29 13:45:00	278.10	278.15	278.15	278.05	252.0	7.008070e+07
53295	2017-12-29 13:50:00	278.10	278.05	278.10	278.00	800.0	2.224430e+08
53296	2017-12-29 13:55:00	278.00	278.00	278.05	277.95	184.0	5.115390e+07
53297	2017-12-29 14:00:00	278.00	277.95	278.00	277.90	474.0	1.317464e+08
53298	2017-12-29 14:05:00	277.95	277.95	277.95	277.90	334.0	9.282880e+07
53299	2017-12-29 14:10:00	277.95	277.90	277.95	277.90	332.0	9.226560e+07
53300	2017-12-29 14:15:00	277.90	277.95	277.95	277.90	672.0	1.867720e+08
53301	2017-12-29 14:20:00	277.90	277.85	277.95	277.85	994.0	2.762458e+08
53302	2017-12-29 14:25:00	277.90	277.90	277.95	277.85	352.0	9.781830e+07
53303	2017-12-29 14:30:00	277.90	277.80	277.95	277.80	784.0	2.178426e+08
53304	2017-12-29 14:35:00	277.85	277.80	277.85	277.75	920.0	2.555711e+08
53305	2017-12-29 14:40:00	277.80	277.80	277.85	277.75	606.0	1.683349e+08
53306	2017-12-29 14:45:00	277.80	277.85	277.85	277.80	560.0	1.555840e+08
53307	2017-12-29 14:50:00	277.85	277.85	277.90	277.80	802.0	2.228271e+08
53308	2017-12-29 14:55:00	277.85	277.75	277.90	277.75	1236.0	3.433855e+08
53309	2017-12-29 15:00:00	277.80	277.80	277.90	277.70	1790.0	4.972797e+08

	open	close	high	low	volume	money
0	226.70	226.65	226.85	226.45	5890.0	1.335146e+09
1	226.75	226.50	226.75	226.40	2562.0	5.804133e+08
2	226.45	226.45	226.60	226.40	1638.0	3.709666e+08
3	226.45	226.25	226.50	226.20	3162.0	7.157891e+08
4	226.25	226.25	226.30	226.20	1684.0	3.809907e+08
5	226.25	226.30	226.35	226.20	922.0	2.086313e+08
6	226.30	226.35	226.40	226.20	2476.0	5.603541e+08
7	226.30	226.45	226.45	226.25	2516.0	5.695246e+08
8	226.45	226.35	226.45	226.30	1344.0	3.042327e+08
9	226.30	226.30	226.35	226.20	1414.0	3.199363e+08
10	226.35	226.45	226.50	226.30	1610.0	3.645328e+08
11	226.45	226.40	226.50	226.40	972.0	2.200957e+08
12	226.40	226.50	226.55	226.35	2004.0	4.538166e+08
13	226.50	226.45	226.55	226.40	780.0	1.766423e+08
14	226.45	226.45	226.50	226.40	1530.0	3.464690e+08
15	226.55	226.45	226.65	226.45	2564.0	5.807784e+08
16	226.45	226.50	226.55	226.45	900.0	2.038475e+08
17	226.55	226.70	226.80	226.50	3008.0	6.817039e+08
18	226.70	226.65	226.85	226.60	2510.0	5.691306e+08
19	226.65	226.60	226.65	226.60	930.0	2.107595e+08
20	226.65	226.75	226.75	226.60	1184.0	2.683818e+08
21	226.75	226.65	226.75	226.60	1044.0	2.366603e+08
22	226.65	226.60	226.70	226.60	342.0	7.751130e+07
23	226.60	226.60	226.65	226.55	640.0	1.450196e+08
24	226.60	226.65	226.70	226.60	502.0	1.137778e+08
25	226.65	226.95	226.95	226.65	3222.0	7.308042e+08
26	226.90	226.90	226.95	226.80	1472.0	3.339398e+08
27	227.10	227.25	227.25	227.00	4894.0	1.111496e+09
28	227.25	227.55	227.60	227.20	5338.0	1.214103e+09
29	227.60	227.75	228.00	227.50	8612.0	1.961599e+09
...	...	...	...	...	...	...
53280	278.05	277.95	278.05	277.90	448.0	1.245318e+08
53281	277.90	277.95	278.00	277.90	506.0	1.406423e+08
53282	277.95	277.95	278.00	277.95	180.0	5.003790e+07
53283	277.95	278.00	278.05	277.95	936.0	2.602273e+08
53284	278.05	277.90	278.05	277.90	942.0	2.618281e+08
53285	277.85	277.90	277.95	277.85	518.0	1.439454e+08
53286	277.95	277.95	277.95	277.90	614.0	1.706443e+08
53287	277.90	277.90	277.95	277.85	1046.0	2.906776e+08
53288	277.95	277.90	277.95	277.90	206.0	5.725350e+07
53289	277.90	277.90	277.95	277.85	740.0	2.056435e+08
53290	277.90	277.85	277.90	277.85	200.0	5.557570e+07
53291	277.90	277.90	277.95	277.85	756.0	2.100840e+08
53292	277.90	278.00	278.00	277.90	490.0	1.362097e+08
53293	278.00	278.05	278.15	278.00	768.0	2.135675e+08
53294	278.10	278.15	278.15	278.05	252.0	7.008070e+07
53295	278.10	278.05	278.10	278.00	800.0	2.224430e+08
53296	278.00	278.00	278.05	277.95	184.0	5.115390e+07
53297	278.00	277.95	278.00	277.90	474.0	1.317464e+08
53298	277.95	277.95	277.95	277.90	334.0	9.282880e+07
53299	277.95	277.90	277.95	277.90	332.0	9.226560e+07
53300	277.90	277.95	277.95	277.90	672.0	1.867720e+08
53301	277.90	277.85	277.95	277.85	994.0	2.762458e+08
53302	277.90	277.90	277.95	277.85	352.0	9.781830e+07
53303	277.90	277.80	277.95	277.80	784.0	2.178426e+08
53304	277.85	277.80	277.85	277.75	920.0	2.555711e+08
53305	277.80	277.80	277.85	277.75	606.0	1.683349e+08
53306	277.80	277.85	277.85	277.80	560.0	1.555840e+08
53307	277.85	277.85	277.90	277.80	802.0	2.228271e+08
53308	277.85	277.75	277.90	277.75	1236.0	3.433855e+08
53309	277.80	277.80	277.90	277.70	1790.0	4.972797e+08

	close	open	high	low	volume	money
0	226.65	226.70	226.85	226.45	5890.0	1.335146e+09
1	226.50	226.75	226.75	226.40	2562.0	5.804133e+08
2	226.45	226.45	226.60	226.40	1638.0	3.709666e+08
3	226.25	226.45	226.50	226.20	3162.0	7.157891e+08
4	226.25	226.25	226.30	226.20	1684.0	3.809907e+08
5	226.30	226.25	226.35	226.20	922.0	2.086313e+08
6	226.35	226.30	226.40	226.20	2476.0	5.603541e+08
7	226.45	226.30	226.45	226.25	2516.0	5.695246e+08
8	226.35	226.45	226.45	226.30	1344.0	3.042327e+08
9	226.30	226.30	226.35	226.20	1414.0	3.199363e+08
10	226.45	226.35	226.50	226.30	1610.0	3.645328e+08
11	226.40	226.45	226.50	226.40	972.0	2.200957e+08
12	226.50	226.40	226.55	226.35	2004.0	4.538166e+08
13	226.45	226.50	226.55	226.40	780.0	1.766423e+08
14	226.45	226.45	226.50	226.40	1530.0	3.464690e+08
15	226.45	226.55	226.65	226.45	2564.0	5.807784e+08
16	226.50	226.45	226.55	226.45	900.0	2.038475e+08
17	226.70	226.55	226.80	226.50	3008.0	6.817039e+08
18	226.65	226.70	226.85	226.60	2510.0	5.691306e+08
19	226.60	226.65	226.65	226.60	930.0	2.107595e+08
20	226.75	226.65	226.75	226.60	1184.0	2.683818e+08
21	226.65	226.75	226.75	226.60	1044.0	2.366603e+08
22	226.60	226.65	226.70	226.60	342.0	7.751130e+07
23	226.60	226.60	226.65	226.55	640.0	1.450196e+08
24	226.65	226.60	226.70	226.60	502.0	1.137778e+08
25	226.95	226.65	226.95	226.65	3222.0	7.308042e+08
26	226.90	226.90	226.95	226.80	1472.0	3.339398e+08
27	227.25	227.10	227.25	227.00	4894.0	1.111496e+09
28	227.55	227.25	227.60	227.20	5338.0	1.214103e+09
29	227.75	227.60	228.00	227.50	8612.0	1.961599e+09
...	...	...	...	...	...	...
53280	277.95	278.05	278.05	277.90	448.0	1.245318e+08
53281	277.95	277.90	278.00	277.90	506.0	1.406423e+08
53282	277.95	277.95	278.00	277.95	180.0	5.003790e+07
53283	278.00	277.95	278.05	277.95	936.0	2.602273e+08
53284	277.90	278.05	278.05	277.90	942.0	2.618281e+08
53285	277.90	277.85	277.95	277.85	518.0	1.439454e+08
53286	277.95	277.95	277.95	277.90	614.0	1.706443e+08
53287	277.90	277.90	277.95	277.85	1046.0	2.906776e+08
53288	277.90	277.95	277.95	277.90	206.0	5.725350e+07
53289	277.90	277.90	277.95	277.85	740.0	2.056435e+08
53290	277.85	277.90	277.90	277.85	200.0	5.557570e+07
53291	277.90	277.90	277.95	277.85	756.0	2.100840e+08
53292	278.00	277.90	278.00	277.90	490.0	1.362097e+08
53293	278.05	278.00	278.15	278.00	768.0	2.135675e+08
53294	278.15	278.10	278.15	278.05	252.0	7.008070e+07
53295	278.05	278.10	278.10	278.00	800.0	2.224430e+08
53296	278.00	278.00	278.05	277.95	184.0	5.115390e+07
53297	277.95	278.00	278.00	277.90	474.0	1.317464e+08
53298	277.95	277.95	277.95	277.90	334.0	9.282880e+07
53299	277.90	277.95	277.95	277.90	332.0	9.226560e+07
53300	277.95	277.90	277.95	277.90	672.0	1.867720e+08
53301	277.85	277.90	277.95	277.85	994.0	2.762458e+08
53302	277.90	277.90	277.95	277.85	352.0	9.781830e+07
53303	277.80	277.90	277.95	277.80	784.0	2.178426e+08
53304	277.80	277.85	277.85	277.75	920.0	2.555711e+08
53305	277.80	277.80	277.85	277.75	606.0	1.683349e+08
53306	277.85	277.80	277.85	277.80	560.0	1.555840e+08
53307	277.85	277.85	277.90	277.80	802.0	2.228271e+08
53308	277.75	277.85	277.90	277.75	1236.0	3.433855e+08
53309	277.80	277.80	277.90	277.70	1790.0	4.972797e+08

量化交易吧 / 数理科学 帖子：3364714 新帖：1

Keras框架 深度学习模型CNN+LSTM+Attention机制 预测黄金主力收盘价

SCSDV_d发表于：6 月 20 日 22：37回复(1)

全部回复

0/140

粉丝:555

帖子数:0

粉丝:675

帖子数:391

粉丝:708

帖子数:7

量化课程

热门标签

删除回复

确认要删除这篇文章么？

举报用户

信息提示

该文章已删除

设置置顶

完成设置【置顶】！

设置置顶

已取消设置【置顶】！

设置精华

完成设置【精华】！

设置精华

已取消设置【精华】！

审核信息

该文章已审核通过

审核信息

您已设置该文章审核不通过

举报成功

您已举报成功

用户登录

移动帖子

创建私信

屏蔽提示

确认要屏蔽该用户么？

屏蔽回复

您已对该用户实现屏蔽

信息回复

已发送成功

量化交易吧 / 数理科学帖子：3364714 新帖：1

Keras框架深度学习模型CNN+LSTM+Attention机制预测黄金主力收盘价