def __init__(self): input_file_path = "input/PriceVolume/bench_mark.xlsx" bench_mark_list,self.bench_mark_name_list = self.load_bench_mark_data(input_file_path) #bench_mark_list = ["000016.SH","000001.SH","000300.SH","399006.SZ","399905.SZ"] #self.bench_mark_name_list = [u"上证50",u"上证综指",u"沪深300",u"创业板指",u"中证500"] #bench_mark_list = ["0806.HK","QIHU.N"] #self.bench_mark_name_list = [u"惠理",u"奇虎"] stock_data_list = LoadData.get_daily_stock_data(bench_mark_list,"20110101") self.price_index = 4 self.volume_index = 5 self.trade_cost = 1-0.001 self.range_day = 1 self.regression_day = [2,5] # short flag = 1 表示可以做空 short_flag = 1 self.price_list = [] self.volume_list = [] for ii in range(len(bench_mark_list)): self.price_list.append(list(zip(*stock_data_list[ii])[self.price_index])) self.volume_list.append(list(zip(*stock_data_list[ii])[self.volume_index])) self.plot(self.price_list,bench_mark_list,self.range_day,short_flag) #self.run(bench_mark_list) print "done\n"
def __init__(self): file_path = "input/HighLowPoint/user_data.xlsx" stock_code_list,stock_name_list,stock_buy_price_list,stock_sell_price_list = LoadUserData.load_user_data(file_path) stock_daily_data_list = LoadData.get_daily_stock_data(stock_code_list) self.fall_end_flag = [] self.rise_end_flag = [] print "begin running\n" self.run(stock_code_list,stock_buy_price_list,stock_sell_price_list)
def __init__(self): file_path = "input/HighLowPoint/user_data.xlsx" stock_code_list, stock_name_list, stock_buy_price_list, stock_sell_price_list = LoadUserData.load_user_data( file_path) stock_daily_data_list = LoadData.get_daily_stock_data(stock_code_list) self.fall_end_flag = [] self.rise_end_flag = [] print "begin running\n" self.run(stock_code_list, stock_buy_price_list, stock_sell_price_list)
def get_high_low_points(self, stock_code_list): msg = '''<html> <tr>Trend inverse reminder from H&L Model</tr> <table width="300" border="1" bordercolor="black" cellspacing="1">''' bar_size = [5, 30, 60] column_num = [5, 9, 13] threshold = [0.03, 0.05, 0.15] send_email_flag = False for ii in range(len(bar_size)): if bar_size[ii] == 60: stock_intraday_data = LoadData.get_daily_stock_data( stock_code_list, "20100101") msg = msg + ''' <tr><td rowspan="2">''' + ''' daily </td>''' else: stock_intraday_data = LoadData.get_intraday_stock_data( stock_code_list, bar_size=bar_size[ii], delta_days=365) msg = msg + ''' <tr><td rowspan="2">''' + str( bar_size[ii]) + ''' min </td>''' high_point_index_list, high_point_list, high_point_time_list, low_point_index_list, low_point_list, low_point_time_list = HighLowPoint.get_high_low_points_list( stock_intraday_data, threshold[ii]) fall_end_list = [] rise_end_lsit = [] fall_end_msg = "" rise_end_msg = "" for jj in range(len(stock_code_list)): self.write_data_to_file(column_num[ii], jj, high_point_list[jj][-1], high_point_time_list[jj][-1], low_point_list[jj][-1], low_point_time_list[jj][-1]) temp_msg_fall_end, temp_msg_rise_end = self.judge_trend_inverse( high_point_list[jj], low_point_list[jj], self.current_price[jj]) if temp_msg_fall_end == True: fall_end_list.append(stock_code_list[jj]) send_email_flag = True if temp_msg_rise_end == True: rise_end_lsit.append(stock_code_list[jj]) send_email_flag = True for item in fall_end_list: fall_end_msg = fall_end_msg + item + " " for item in rise_end_lsit: rise_end_msg = rise_end_msg + item + " " msg = msg + "<td>" + "Falling End" + "</td><td>" + fall_end_msg + "</td></tr><tr><td> Rising End </td><td>" + rise_end_msg + "</td></tr> \n" msg = msg + " </table></html>" print msg if send_email_flag == True: a = SendEmail.Send_Email("Trend inverse reminder from H&L Model", msg) print a.isSend
def __init__(self, ma_days=200, start_date="19950101", init_days=60, short_flag=0): input_file_path = "../input/DeviationFromMA/user_data.xlsx" self.user_stock_code_list, self.user_stock_name_list = self.load_excel( input_file_path) self.user_stock_data_list = LoadData.get_daily_stock_data( self.user_stock_code_list, start_date) self.short_flag = short_flag self.trade_cost = 1 - 0.001 self.ma_days = ma_days self.user_stock_price_data_list = [] self.user_stock_uniform_price_list = [] self.user_stock_datetime_list = [] self.user_stock_price_data_old_list = [] for ii in range(len(self.user_stock_code_list)): self.user_stock_price_data_list.append( (list(zip(*self.user_stock_data_list[ii])[4]))[init_days:]) self.user_stock_price_data_old_list.append( list(zip(*self.user_stock_data_list[ii])[4])) self.user_stock_datetime_list.append( (list(zip(*self.user_stock_data_list[ii])[0]))[init_days:]) self.user_stock_uniform_price_list.append( CalcIndex.Uniform_Price(self.user_stock_price_data_list[ii])) self.price_ma_list = CalcIndex.Calc_MA( self.user_stock_price_data_old_list, int(ma_days), int(init_days)) self.diviation_from_ma_list, self.diviation_from_ma_std_list = self.calc_diviation_from_ma( ma_days) self.ma_dict = {60: 10, 120: 20, 200: 60, 250: 60} self.diviation_ma_list = CalcIndex.Calc_MA(self.diviation_from_ma_list, self.ma_dict[self.ma_days]) #self.plot(ma_days) #self.eveluate_index(ma_days) long_stand_deviation = {60: -0.5, 120: -0.1, 200: -0.15, 250: -0.2} short_stand_deviation = {60: 0.5, 120: 0.1, 200: 0.15, 250: 0.2} self.back_test_info = self.back_test(long_stand_deviation[ma_days], short_stand_deviation[ma_days]) self.plot_back_test(ma_days) print "done."
def __init__(self): file_path = "input/user_data.xlsx" self.user_stock_code_list,self.user_stock_name_list,user_stock_buy_point_list,user_stock_sell_point_list = LoadUserData.load_user_data(file_path) self.daily_stock_data_all = LoadData.get_daily_stock_data(self.user_stock_code_list,"20150101") #存放收盘价 self.daily_stock_data = [] for ii in range(len(self.daily_stock_data_all)): temp = [] for jj in range(len(self.daily_stock_data_all[ii])): temp.append(self.daily_stock_data_all[ii][jj][4]) self.daily_stock_data.append(temp) #print self.daily_stock_data[0][-1],self.daily_stock_data[1][-1] self.reminder_info= [] self.run()
def __init__(self): file_path = "input/user_data.xlsx" self.user_stock_code_list, self.user_stock_name_list, user_stock_buy_point_list, user_stock_sell_point_list = LoadUserData.load_user_data( file_path) self.daily_stock_data_all = LoadData.get_daily_stock_data( self.user_stock_code_list, "20150101") #存放收盘价 self.daily_stock_data = [] for ii in range(len(self.daily_stock_data_all)): temp = [] for jj in range(len(self.daily_stock_data_all[ii])): temp.append(self.daily_stock_data_all[ii][jj][4]) self.daily_stock_data.append(temp) #print self.daily_stock_data[0][-1],self.daily_stock_data[1][-1] self.reminder_info = [] self.run()
def get_high_low_points(self,stock_code_list): msg = '''<html> <tr>Trend inverse reminder from H&L Model</tr> <table width="300" border="1" bordercolor="black" cellspacing="1">''' bar_size = [5,30,60] column_num = [5,9,13] threshold = [0.03,0.05,0.15] send_email_flag = False for ii in range(len(bar_size)): if bar_size[ii]==60: stock_intraday_data = LoadData.get_daily_stock_data(stock_code_list,"20100101") msg = msg +''' <tr><td rowspan="2">''' + ''' daily </td>''' else: stock_intraday_data = LoadData.get_intraday_stock_data(stock_code_list,bar_size=bar_size[ii],delta_days = 365) msg = msg +''' <tr><td rowspan="2">''' +str(bar_size[ii]) + ''' min </td>''' high_point_index_list,high_point_list,high_point_time_list,low_point_index_list,low_point_list,low_point_time_list = HighLowPoint.get_high_low_points_list(stock_intraday_data,threshold[ii]) fall_end_list = [] rise_end_lsit = [] fall_end_msg = "" rise_end_msg = "" for jj in range(len(stock_code_list)): self.write_data_to_file(column_num[ii],jj,high_point_list[jj][-1],high_point_time_list[jj][-1],low_point_list[jj][-1],low_point_time_list[jj][-1]) temp_msg_fall_end, temp_msg_rise_end = self.judge_trend_inverse(high_point_list[jj],low_point_list[jj],self.current_price[jj]) if temp_msg_fall_end ==True: fall_end_list.append(stock_code_list[jj]) send_email_flag = True if temp_msg_rise_end == True: rise_end_lsit.append(stock_code_list[jj]) send_email_flag = True for item in fall_end_list: fall_end_msg = fall_end_msg + item + " " for item in rise_end_lsit: rise_end_msg = rise_end_msg + item +" " msg = msg +"<td>" + "Falling End" + "</td><td>"+fall_end_msg +"</td></tr><tr><td> Rising End </td><td>"+rise_end_msg+"</td></tr> \n" msg = msg +" </table></html>" print msg if send_email_flag == True: a = SendEmail.Send_Email("Trend inverse reminder from H&L Model",msg) print a.isSend
def __init__(self): input_file_path = "input/PriceVolume/bench_mark.xlsx" bench_mark_list, self.bench_mark_name_list = self.load_bench_mark_data( input_file_path) #bench_mark_list = ["000016.SH","000001.SH","000300.SH","399006.SZ","399905.SZ"] #self.bench_mark_name_list = [u"上证50",u"上证综指",u"沪深300",u"创业板指",u"中证500"] #bench_mark_list = ["0806.HK","QIHU.N"] #self.bench_mark_name_list = [u"惠理",u"奇虎"] stock_data_list = LoadData.get_daily_stock_data( bench_mark_list, "20110101") self.price_index = 4 self.volume_index = 5 self.trade_cost = 1 - 0.001 self.range_day = 1 self.regression_day = [2, 5] # short flag = 1 表示可以做空 short_flag = 1 self.price_list = [] self.volume_list = [] for ii in range(len(bench_mark_list)): self.price_list.append( list(zip(*stock_data_list[ii])[self.price_index])) self.volume_list.append( list(zip(*stock_data_list[ii])[self.volume_index])) self.plot(self.price_list, bench_mark_list, self.range_day, short_flag) #self.run(bench_mark_list) print "done\n"
def __init__(self,ma_days=200,start_date="19950101",init_days = 60,short_flag =0): input_file_path = "../input/DeviationFromMA/user_data.xlsx" self.user_stock_code_list,self.user_stock_name_list = self.load_excel(input_file_path) self.user_stock_data_list = LoadData.get_daily_stock_data(self.user_stock_code_list,start_date) self.short_flag = short_flag self.trade_cost = 1-0.001 self.ma_days = ma_days self.user_stock_price_data_list = [] self.user_stock_uniform_price_list =[] self.user_stock_datetime_list = [] self.user_stock_price_data_old_list =[] for ii in range(len(self.user_stock_code_list)): self.user_stock_price_data_list.append((list(zip(*self.user_stock_data_list[ii])[4]))[init_days:]) self.user_stock_price_data_old_list.append(list(zip(*self.user_stock_data_list[ii])[4])) self.user_stock_datetime_list.append((list(zip(*self.user_stock_data_list[ii])[0]))[init_days:]) self.user_stock_uniform_price_list.append(CalcIndex.Uniform_Price(self.user_stock_price_data_list[ii])) self.price_ma_list = CalcIndex.Calc_MA(self.user_stock_price_data_old_list,int(ma_days),int(init_days)) self.diviation_from_ma_list,self.diviation_from_ma_std_list = self.calc_diviation_from_ma(ma_days) self.ma_dict ={60:10,120:20,200:60,250:60} self.diviation_ma_list = CalcIndex.Calc_MA(self.diviation_from_ma_list,self.ma_dict[self.ma_days]) #self.plot(ma_days) #self.eveluate_index(ma_days) long_stand_deviation = {60:-0.5,120:-0.1,200:-0.15,250:-0.2} short_stand_deviation = {60:0.5,120:0.1,200:0.15,250:0.2} self.back_test_info = self.back_test(long_stand_deviation[ma_days],short_stand_deviation[ma_days]) self.plot_back_test(ma_days) print "done."
def creepy_data_from_wind_to_local(self): for ii in range(len(self.user_stock_name_list)): self.path_is_exist(self.user_stock_name_list[ii]) data_base_path = sys.path[0] + "\\" + "data" + "\\" + "data_base" stock_fold_path = data_base_path + "\\" + self.user_stock_name_list[ii] for data_type in self.data_type_list: data_path = stock_fold_path + "\\" + data_type latest_date = self.get_latest_date_in_fold(data_path) print latest_date temp_stock_name = [] temp_stock_name.append(self.user_stock_name_list[ii]) delta_day = ( datetime.now() - datetime(int(latest_date[0:4]), int(latest_date[4:6]), int(latest_date[6:8])) ).days print latest_date, delta_day, self.user_stock_name_list[ii] pos = self.user_stock_name_list[ii].split(".")[1] print pos if data_type == "daily": stock_daily_data = LoadData.get_daily_stock_data(temp_stock_name, latest_date) for jj in range(len(stock_daily_data[0])): output_data_path = data_path + "\\" + stock_daily_data[0][jj][0] + ".txt" WriteToFile.write_to_file(output_data_path, stock_daily_data[0][jj]) if pos != "SZ" and pos != "SH": break if float(delta_day) > 3 * 365: delta_day = 1095 if data_type == "60min": print "aaa" stock_60_min_data = LoadData.get_intraday_stock_data( temp_stock_name, bar_size=60, delta_days=delta_day ) date_60_min_index = self.get_day_sperate_index(stock_60_min_data) print stock_60_min_data print date_60_min_index for jj in range(len(date_60_min_index) - 1): file_path = data_path + "\\" + stock_60_min_data[0][date_60_min_index[jj]][0][0:8] + ".txt" WriteToFile.write_list_to_file( file_path, stock_60_min_data[0][date_60_min_index[jj] : date_60_min_index[jj + 1]] ) elif data_type == "30min": stock_30_min_data = LoadData.get_intraday_stock_data( temp_stock_name, bar_size=30, delta_days=delta_day ) date_30_min_index = self.get_day_sperate_index(stock_30_min_data) for jj in range(len(date_30_min_index) - 1): file_path = data_path + "\\" + stock_30_min_data[0][date_30_min_index[jj]][0][0:8] + ".txt" WriteToFile.write_list_to_file( file_path, stock_30_min_data[0][date_30_min_index[jj] : date_30_min_index[jj + 1]] ) elif data_type == "15min": stock_15_min_data = LoadData.get_intraday_stock_data( temp_stock_name, bar_size=15, delta_days=delta_day ) date_15_min_index = self.get_day_sperate_index(stock_15_min_data) for jj in range(len(date_15_min_index) - 1): file_path = data_path + "\\" + stock_15_min_data[0][date_15_min_index[jj]][0][0:8] + ".txt" WriteToFile.write_list_to_file( file_path, stock_15_min_data[0][date_15_min_index[jj] : date_15_min_index[jj + 1]] ) elif data_type == "5min": stock_5_min_data = LoadData.get_intraday_stock_data( temp_stock_name, bar_size=5, delta_days=delta_day ) date_5_min_index = self.get_day_sperate_index(stock_5_min_data) for jj in range(len(date_5_min_index) - 1): file_path = data_path + "\\" + stock_5_min_data[0][date_5_min_index[jj]][0][0:8] + ".txt" WriteToFile.write_list_to_file( file_path, stock_5_min_data[0][date_5_min_index[jj] : date_5_min_index[jj + 1]] )
def creepy_data_from_wind_to_local(self): for ii in range(len(self.user_stock_name_list)): self.path_is_exist(self.user_stock_name_list[ii]) data_base_path = sys.path[0] + "\\" + "data" + "\\" + "data_base" stock_fold_path = data_base_path + "\\" + self.user_stock_name_list[ ii] for data_type in self.data_type_list: data_path = stock_fold_path + "\\" + data_type latest_date = self.get_latest_date_in_fold(data_path) print latest_date temp_stock_name = [] temp_stock_name.append(self.user_stock_name_list[ii]) delta_day = ( datetime.now() - datetime(int(latest_date[0:4]), int(latest_date[4:6]), int(latest_date[6:8]))).days print latest_date, delta_day, self.user_stock_name_list[ii] pos = self.user_stock_name_list[ii].split(".")[1] print pos if data_type == "daily": stock_daily_data = LoadData.get_daily_stock_data( temp_stock_name, latest_date) for jj in range(len(stock_daily_data[0])): output_data_path = data_path + "\\" + stock_daily_data[ 0][jj][0] + ".txt" WriteToFile.write_to_file(output_data_path, stock_daily_data[0][jj]) if pos != "SZ" and pos != "SH": break if float(delta_day) > 3 * 365: delta_day = 1095 if data_type == "60min": print "aaa" stock_60_min_data = LoadData.get_intraday_stock_data( temp_stock_name, bar_size=60, delta_days=delta_day) date_60_min_index = self.get_day_sperate_index( stock_60_min_data) print stock_60_min_data print date_60_min_index for jj in range(len(date_60_min_index) - 1): file_path = data_path + "\\" + stock_60_min_data[0][ date_60_min_index[jj]][0][0:8] + ".txt" WriteToFile.write_list_to_file( file_path, stock_60_min_data[0] [date_60_min_index[jj]:date_60_min_index[jj + 1]]) elif data_type == "30min": stock_30_min_data = LoadData.get_intraday_stock_data( temp_stock_name, bar_size=30, delta_days=delta_day) date_30_min_index = self.get_day_sperate_index( stock_30_min_data) for jj in range(len(date_30_min_index) - 1): file_path = data_path + "\\" + stock_30_min_data[0][ date_30_min_index[jj]][0][0:8] + ".txt" WriteToFile.write_list_to_file( file_path, stock_30_min_data[0] [date_30_min_index[jj]:date_30_min_index[jj + 1]]) elif data_type == "15min": stock_15_min_data = LoadData.get_intraday_stock_data( temp_stock_name, bar_size=15, delta_days=delta_day) date_15_min_index = self.get_day_sperate_index( stock_15_min_data) for jj in range(len(date_15_min_index) - 1): file_path = data_path + "\\" + stock_15_min_data[0][ date_15_min_index[jj]][0][0:8] + ".txt" WriteToFile.write_list_to_file( file_path, stock_15_min_data[0] [date_15_min_index[jj]:date_15_min_index[jj + 1]]) elif data_type == "5min": stock_5_min_data = LoadData.get_intraday_stock_data( temp_stock_name, bar_size=5, delta_days=delta_day) date_5_min_index = self.get_day_sperate_index( stock_5_min_data) for jj in range(len(date_5_min_index) - 1): file_path = data_path + "\\" + stock_5_min_data[0][ date_5_min_index[jj]][0][0:8] + ".txt" WriteToFile.write_list_to_file( file_path, stock_5_min_data[0] [date_5_min_index[jj]:date_5_min_index[jj + 1]])
# -*- coding: utf-8 -*- # File Name: main.py # Author: Changsheng Zhang # mail: [email protected] # Created Time: Tue Jul 7 10:09:08 2015 ######################################################################### import os import sys import strategy.test as test import data.LoadData as LoadData if __name__ == '__main__': b = LoadData.get_daily_stock_data(["600000.SH"]) print b[0][0]