SELECT * FROM `heidao-market.mafia1_ods.game_log_status_snapshot` where timestamp >"2020-02-18 09:30:22" and timestamp < "2020-02-18 10:40:22" and operation= 'buy_giftbag'

? ? ? ?? ? ? ?這些年從網上的各位大牛那學到很多，本著開源開放的精神，今天我決定開源我量化交易代碼。輸入股票代碼，和訓練的數據時間，自動預測股票未來的走勢。。。。。。。。。。。。。。。。。。

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue May 7 17:55:28 2019@author: lg """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 from mpl_finance import candlestick_ochl df=ts.get_hist_data('601857',start='2019-01-15',end='2019-05-07') 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、訓練多層神經網絡，并完成模型的檢驗 #from __future__ import print_function import pandas as pdinputfile1='fg.xls' #訓練數據 testoutputfile = 'test_output_data.xls' #測試數據模型輸出文件 data_train = pd.read_excel(inputfile1) #讀入訓練數據(由日志標記事件是否為洗浴) data_mean = data_train.mean() data_std = data_train.std() data_train1 = (data_train-data_mean)/5 #數據標準化y_train = data_train1.iloc[:,120:140].as_matrix() #訓練樣本標簽列 x_train = data_train1.iloc[:,0:120].as_matrix() #訓練樣本特征 #y_test = data_test.iloc[:,4].as_matrix() #測試樣本標簽列from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activationmodel = Sequential() #建立模型 model.add(Dense(input_dim = 120, output_dim = 240)) #添加輸入層、隱藏層的連接 model.add(Activation('relu')) #以Relu函數為激活函數 model.add(Dense(input_dim = 240, output_dim = 120)) #添加隱藏層、隱藏層的連接 model.add(Activation('relu')) #以Relu函數為激活函數 model.add(Dense(input_dim = 120, output_dim = 120)) #添加隱藏層、隱藏層的連接 model.add(Activation('relu')) #以Relu函數為激活函數 model.add(Dense(input_dim = 120, output_dim = 20)) #添加隱藏層、輸出層的連接 model.add(Activation('sigmoid')) #以sigmoid函數為激活函數 #編譯模型，損失函數為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) #訓練模型 model.save_weights('net.model') #保存模型參數inputfile2='gg.xls' #預測數據 pre = pd.read_excel(inputfile2) pre_mean = data_mean[0:120] pre_std = pre.std() pre1 = (pre-pre_mean)/10 #數據標準化 #pre1 = (pre-pre_mean)/pre.std() #數據標準化 pre2 = pre1.iloc[:,0:120].as_matrix() #預測樣本特征 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 代碼

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