利用 多层神经网络预测未来价格

from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY,YEARLY
from matplotlib.finance import quotes_historical_yahoo_ohlc, candlestick_ohlc
#import matplotlib
import tushare as ts
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.pylab import date2num
import datetime
import numpy as np
from pandas import DataFrame
from numpy import row_stack,column_stack

df=ts.get_hist_data('601857',start='2016-06-15',end='2017-11-06')
dd=df[['open','high','low','close']]

#print(dd.values.shape[0])

dd1=dd .sort_index()

dd2=dd1.values.flatten()

g1=dd2[::-1]

g2=g1[0:120]

g3=g2[::-1]

gg=DataFrame(g3)

gg.T.to_excel('gg.xls') 



#dd3=pd.DataFrame(dd2)
#dd3.T.to_excel('d8.xls') 

g=dd2[0:140]
for i in range(dd.values.shape[0]-34):

    s=dd2[i*4:i*4+140]
    g=row_stack((g,s))

fg=DataFrame(g)

print(fg)    
fg.to_excel('fg.xls') 


#-*- coding: utf-8 -*-
#建立、训练多层神经网络,并完成模型的检验
#from __future__ import print_function
import pandas as pd


inputfile1='fg.xls' #训练数据
testoutputfile = 'test_output_data.xls' #测试数据模型输出文件
data_train = pd.read_excel(inputfile1) #读入训练数据(由日志标记事件是否为洗浴)
data_mean = data_train.mean()
data_std = data_train.std()
data_train1 = (data_train-data_mean)/5  #数据标准化

y_train = data_train1.iloc[:,120:140].as_matrix() #训练样本标签列
x_train = data_train1.iloc[:,0:120].as_matrix() #训练样本特征
#y_test = data_test.iloc[:,4].as_matrix() #测试样本标签列

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation

model = Sequential() #建立模型
model.add(Dense(input_dim = 120, output_dim = 240)) #添加输入层、隐藏层的连接
model.add(Activation('relu')) #以Relu函数为激活函数
model.add(Dense(input_dim = 240, output_dim = 120)) #添加隐藏层、隐藏层的连接
model.add(Activation('relu')) #以Relu函数为激活函数
model.add(Dense(input_dim = 120, output_dim = 120)) #添加隐藏层、隐藏层的连接
model.add(Activation('relu')) #以Relu函数为激活函数
model.add(Dense(input_dim = 120, output_dim = 20)) #添加隐藏层、输出层的连接
model.add(Activation('sigmoid')) #以sigmoid函数为激活函数
#编译模型,损失函数为binary_crossentropy,用adam法求解
model.compile(loss='mean_squared_error', optimizer='adam')

model.fit(x_train, y_train, nb_epoch = 100, batch_size = 8) #训练模型
model.save_weights('net.model') #保存模型参数

inputfile2='gg.xls' #预测数据
pre = pd.read_excel(inputfile2)                  

pre_mean = data_mean[0:120]
pre_std = pre.std()
pre1 = (pre-pre_mean)/5  #数据标准化

pre2 = pre1.iloc[:,0:120].as_matrix() #预测样本特征                 
r = pd.DataFrame(model.predict(pre2))
rt=r*5+data_mean[120:140].as_matrix()
print(rt.round(2))



rt.to_excel('rt.xls') 

#print(r.values@data_train.iloc[:,116:120].std().values+data_mean[116:120].as_matrix())



a=list(df.index[0:-1])

b=a[0]

c= datetime.datetime.strptime(b,'%Y-%m-%d')

d = date2num(c)


c1=[d+i+1 for i in range(5)]
c2=np.array([c1])

r1=rt.values.flatten()
r2=r1[0:4]
for i in range(4):

    r3=r1[i*4+4:i*4+8]
    r2=row_stack((r2,r3))

c3=column_stack((c2.T,r2))
r5=DataFrame(c3)

if len(c3) == 0:
    raise SystemExit

fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)

#ax.xaxis.set_major_locator(mondays)
#ax.xaxis.set_minor_locator(alldays)
#ax.xaxis.set_major_formatter(mondayFormatter)
#ax.xaxis.set_minor_formatter(dayFormatter)

#plot_day_summary(ax, quotes, ticksize=3)
candlestick_ohlc(ax, c3, width=0.6, colorup='r', colordown='g')

ax.xaxis_date()
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')

ax.grid(True)
#plt.title('000002')
plt.show()

这里写图片描述

posted @   luoganttcc  阅读(4)  评论(0编辑  收藏  举报
相关博文:
阅读排行:
· DeepSeek “源神”启动!「GitHub 热点速览」
· 我与微信审核的“相爱相杀”看个人小程序副业
· 微软正式发布.NET 10 Preview 1:开启下一代开发框架新篇章
· 如何使用 Uni-app 实现视频聊天(源码,支持安卓、iOS)
· C# 集成 DeepSeek 模型实现 AI 私有化(本地部署与 API 调用教程)
点击右上角即可分享
微信分享提示