SELECT * FROM `heidao-market.mafia1_ods.game_log_status_snapshot` where timestamp >"-02-18 09:30:22" and timestamp < "-02-18 10:40:22" and operation= 'buy_giftbag'
这些年从网上的各位大牛那学到很多，本着开源开放的精神，今天我决定开源我量化交易代码。输入股票代码，和训练的数据时间，自动预测股票未来的走势。。。。。。。。。。。。。。。。。。
#!/usr/bin/env python3# -*- coding: utf-8 -*-"""Created on Tue May 7 17:55:28 @author: lg"""from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY,YEARLY#from matplotlib.finance import quotes_historical_yahoo_ohlc, candlestick_ohlc#import matplotlibimport tushare as tsimport pandas as pdimport matplotlib.pyplot as pltfrom matplotlib.pylab import date2numimport datetimeimport numpy as npfrom pandas import DataFramefrom numpy import row_stack,column_stackfrom mpl_finance import candlestick_ochldf=ts.get_hist_data('601857',start='-01-15',end='-05-07')dd=df[['open','high','low','close']]from mpl_finance import candlestick_ochl,candlestick_ohlc#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_functionimport pandas as pdinputfile1='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 Sequentialfrom keras.layers.core import Dense, Dropout, Activationmodel = 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pile(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)/10 #数据标准化#pre1 = (pre-pre_mean)/pre.std() #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*10+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 SystemExitfig, 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_ochl(ax, c3, width=0.6, colorup='r', colordown='g')candlestick_ohlc(ax, c3, width=0.5, 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()
github 代码
