from jqdatasdk import *
#jqdata的账号密码
auth('我的邮箱' : 'jiaohaibin@ruc.edu.cn',有问题，欢迎与我交流)
df_data_5minute= get_price('AU9999.XSGE',   start_date='2016-01-01', end_date='2018-01-01', frequency='5m')

df_data_5minute.to_csv('黄金主力5分钟数据.csv')

df_data_5minute

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

df.head()

#df.drop('money', axis=1, inplace=True)
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)

# -*- 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)

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

data_train

 from keras.layers import Input, Dense, LSTM
 from keras.models import Model
 output_dim = 1
 batch_size = 256
 epochs = 10
 seq_len = 5
 hidden_size = 128
 X_train = np.array([data_train[i : i + seq_len, 0] for i in range(data_train.shape[0] - seq_len)])[:, :, np.newaxis]
 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, 0] for i in range(data_test.shape[0]- seq_len)])[:, :, np.newaxis]
 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)
 X = Input(shape=[X_train.shape[1], X_train.shape[2],])
 h = LSTM(hidden_size, activation='relu')(X)
 Y = Dense(output_dim, activation='sigmoid')(h)
 model = Model(X, Y)
 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:', model.evaluate(X_train, y_train, batch_size=batch_size))
 print('MSE Test:', 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()

from keras.layers import Input, Dense, LSTM
from keras.models import Model
output_dim = 1
batch_size = 256
epochs = 10
seq_len = 5
hidden_size = 128

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)


X = Input(shape=[X_train.shape[1], X_train.shape[2],])
h = LSTM(hidden_size, activation='relu')(X)
Y = Dense(output_dim, activation='sigmoid')(h)
model = Model(X, Y)


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:', model.evaluate(X_train, y_train, batch_size=batch_size))
print('MSE Test:', 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()